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diff --git a/.renku/datasets/a31a4922-5cbd-4d8e-bee3-435b1fd85282/metadata.yml b/.renku/datasets/a31a4922-5cbd-4d8e-bee3-435b1fd85282/metadata.yml
new file mode 100644
index 0000000000000000000000000000000000000000..6d0eafa7df1a783e69c7af7df1185b99ae6c7a9a
--- /dev/null
+++ b/.renku/datasets/a31a4922-5cbd-4d8e-bee3-435b1fd85282/metadata.yml
@@ -0,0 +1,11861 @@
+- '@id': https://localhost/datasettags/0.1%40e6ef313e5633d2cdb97455fcbfdafbd8d38e2605
+ '@type':
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+ http://schema.org/description:
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+ http://schema.org/location:
+ - '@value': e6ef313e5633d2cdb97455fcbfdafbd8d38e2605
+ http://schema.org/name:
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+ http://schema.org/startDate:
+ - '@value': '2021-05-24T15:34:09.986028+00:00'
+- '@id': https://localhost/urls/%2F%2Frenkulab.io%2Fdatasets%2F1b6ed739-a52f-44fd-bcf5-21b7a1996709
+ '@type':
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+ http://schema.org/url:
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+ '@type':
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+ - http://schema.org/DigitalDocument
+ - http://www.w3.org/ns/prov#Entity
+ http://schema.org/dateCreated:
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+ http://schema.org/isPartOf:
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+ http://schema.org/name:
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+ http://schema.org/url:
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+ - '@id': https://renkulab.io/blob/66bda0a213c6ebdd2194d06a06df304841cc543e/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/.zattrs
+ - '@id': https://renkulab.io/blob/66bda0a213c6ebdd2194d06a06df304841cc543e/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/weekofyear/.zattrs
+ - '@id': https://renkulab.io/blob/66bda0a213c6ebdd2194d06a06df304841cc543e/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/weekofyear/0
+ - '@id': https://renkulab.io/blob/66bda0a213c6ebdd2194d06a06df304841cc543e/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/weekofyear/.zarray
+ - '@id': https://renkulab.io/blob/66bda0a213c6ebdd2194d06a06df304841cc543e/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/latitude/.zattrs
+ - '@id': https://renkulab.io/blob/66bda0a213c6ebdd2194d06a06df304841cc543e/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/latitude/0
+ - '@id': https://renkulab.io/blob/66bda0a213c6ebdd2194d06a06df304841cc543e/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/latitude/.zarray
+ - '@id': https://renkulab.io/blob/228e5dd8e9f1be564a464e1f7aaf2c610e4310e4/data/ecmwf_recalibrated_benchmark_2020_biweekly_terciled.nc
+ http://schema.org/identifier:
+ - '@value': 3a4ebb8b-9258-4ed3-bfac-25a85ed282a4
+ http://schema.org/isPartOf:
+ - '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
+ http://schema.org/keywords: []
+ http://schema.org/name:
+ - '@value': s2s-ai-challenge
+ http://schema.org/subjectOf:
+ - '@id': https://localhost/datasettags/0.1%40e6ef313e5633d2cdb97455fcbfdafbd8d38e2605
+ http://schema.org/url:
+ - '@value': https://renkulab.io/datasets/3a4ebb8b-9258-4ed3-bfac-25a85ed282a4
+ http://www.w3.org/2000/01/rdf-schema#label:
+ - '@value': 3a4ebb8b-9258-4ed3-bfac-25a85ed282a4
+ http://www.w3.org/ns/prov#atLocation:
+ - '@value': .renku/datasets/a31a4922-5cbd-4d8e-bee3-435b1fd85282
+ http://www.w3.org/ns/prov#wasDerivedFrom:
+ - '@id': https://localhost/urls/%2F%2Frenkulab.io%2Fdatasets%2F1b6ed739-a52f-44fd-bcf5-21b7a1996709
+- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
+ '@type':
+ - http://schema.org/Project
+ - http://www.w3.org/ns/prov#Location
+ http://schema.org/agent:
+ - '@value': 0.14.2
+ http://schema.org/creator:
+ - '@id': mailto:aaron.spring@mpimet.mpg.de
+ http://schema.org/dateCreated:
+ - '@value': '2021-04-28T12:48:48.162011+00:00'
+ http://schema.org/name:
+ - '@value': s2s-ai-challenge-template
+ http://schema.org/schemaVersion:
+ - '@value': '8'
+ https://swissdatasciencecenter.github.io/renku-ontology#automatedTemplateUpdate:
+ - '@value': false
+ https://swissdatasciencecenter.github.io/renku-ontology#immutableTemplateFiles: []
+ https://swissdatasciencecenter.github.io/renku-ontology#templateId:
+ - '@value': python-minimal
+ https://swissdatasciencecenter.github.io/renku-ontology#templateMetadata:
+ - '@value': '{"__template_source__": "https://github.com/SwissDataScienceCenter/renku-project-template",
+ "__template_ref__": "0.1.19", "__template_id__": "python-minimal", "__namespace__":
+ "aaron.spring", "__automated_update__": true, "__repository__": "https://renkulab.io/gitlab",
+ "__sanitized_project_name__": "s2s-ai-challenge-template", "__project_slug__":
+ "aaron.spring/s2s-ai-challenge-template", "description": "template to contribute
+ to s2s-ai-challenge", "name": "s2s-ai-challenge-template"}'
+ https://swissdatasciencecenter.github.io/renku-ontology#templateReference:
+ - '@value': 0.1.19
+ https://swissdatasciencecenter.github.io/renku-ontology#templateSource:
+ - '@value': https://github.com/SwissDataScienceCenter/renku-project-template
+ https://swissdatasciencecenter.github.io/renku-ontology#templateVersion:
+ - '@value': 817b03c0f434a79eb54473688c1f897a66228b3d
+- '@id': mailto:aaron.spring@mpimet.mpg.de
+ '@type':
+ - http://schema.org/Person
+ - http://www.w3.org/ns/prov#Person
+ http://schema.org/email:
+ - '@value': aaron.spring@mpimet.mpg.de
+ http://schema.org/name:
+ - '@value': aaronspring
+ - '@value': Aaron Spring
+ http://www.w3.org/2000/01/rdf-schema#label:
+ - '@value': aaronspring
+ - '@value': Aaron Spring
diff --git a/.renku/refs/datasets/s2s-ai-challenge b/.renku/refs/datasets/s2s-ai-challenge
new file mode 120000
index 0000000000000000000000000000000000000000..40334ecb856cd0369c8e242897623f2c2a5d504e
--- /dev/null
+++ b/.renku/refs/datasets/s2s-ai-challenge
@@ -0,0 +1 @@
+../../datasets/a31a4922-5cbd-4d8e-bee3-435b1fd85282/metadata.yml
\ No newline at end of file
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 46024fb564cfba87d952c3fb709e2116287fb7db..c534ed582237b7ace43e633f936c49d7160ad897 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,31 @@
# CHANGELOG
+### 2021-05-26 !4
+
+- update `README` how to join competition
+- git lfs track zarr: `git lfs track "**/*.zarr/**"`
+- add notebooks:
+ - create renku datasets: `renku_datasets_biweekly.ipynb`
+ - RPSS verification: `RPSS_verification.ipynb`
+ - ML train and predict based on weatherbench: `ML_train_and_predict.ipynb`
+ - mean bias reduction: `mean_bias_reduction.ipynb`
+ - template for training and predictions: `ML_forecast_template.ipynb`
+- add renku dataset `s2s-ai-challenge` with files:
+ - `hindcast-like-observations_2000-2019_biweekly_deterministic.zarr`
+ - `forecast-like-observations_2020_biweekly_deterministic.zarr`
+ - `hindcast-like-observations_2000-2019_biweekly_tercile-edges.nc`
+ - `hindcast-like-observations_2000-2019_biweekly_terciled.zarr`
+ - `forecast-like-observations_2020_biweekly_terciled.nc`
+ - `ecmwf_forecast-input_2020_biweekly_deterministic.zarr`
+ - `ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr`
+ - `ecmwf_recalibrated_benchmark_2020_biweekly_terciled.nc`
+- add reproducibility section below in training
+- how to deal with this dry mask? provide as renku dataset? now implicitly masked in categorized observations `obs_p`
+- justify if training takes more than a week
+- show RPS for all years ToDo: take RPSS
+
+
+
### 2021-05-10
- `git lfs track “submissions/*.ncâ€`, so submission netcdf files are not carried in `git` but `git lfs`
diff --git a/README.md b/README.md
index 63cd0e0d93d581ac45326e34493178604c83c548..c2b607f181c3d7422bda7a7abc6ee086b8500eda 100644
--- a/README.md
+++ b/README.md
@@ -13,15 +13,18 @@ Find an overview of [repositories and websites](https://renkulab.io/gitlab/aaron
## Introduction
-This is a Renku project - basically a git repository with some
-bells and whistles. You'll find we have already created some
-useful things like `data` and `notebooks` directories and
+This is a Renku project. Renku is a platform for reproducible and collaborative data analysis.
+At its simplest a Renku project is a gitlab repository with added functionality.
+So you can use this project just as a gitlab repository if you wish. However, you may be surprised
+by what Renku has to offer and if you are curious the best place to start is the
+[Renku documentation](https://renku.readthedocs.io/en/latest/).
+You'll find we have already created some useful things like `data` and `notebooks` directories and
a `Dockerfile`.
## Join the challenge
### 1. The simplest way to join the S2S AI Challenge is forking this renku project.
-Ensure that you do not fork the gitlab repository, but the renku project.
+Ensure you fork the renku project and the underlying gitlab repository through the renkulab.io page.
Fork this template renku project from https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template/settings.
@@ -37,22 +40,20 @@ Registrations are not required before October 31st 2021, but highly [appreciated
### 3. Make the project private
-Now check out the gitlab repository by clicking on "View in gitlab".
+Now navigate to the gitlab page by clicking on "View in gitlab" in the upper right corner.
Under "Settings" - "General" - "Visibility" you can set your project private.
<img src="docs/screenshots/gitlab_visibility.png" width="300">
Now other people cannot steal your idea/code.
-Now please modify the README in your fork with team details and a
+Please modify the `README` in your fork with your team's details and a
description of your method.
Please use different branches if you try out different methods. The scorer finds branches from all branches.
### 4. Add the `scorer` user to your repo with Reporter permissions
-The scorer is not yet ready, but will follow this [verification notebook](https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge-template/-/blob/master/notebooks/verification_RPSS.ipynb).
-
-Todo: How to add scorer to repo
+The scorer follows the code shown in the [verification notebook](https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge-template/-/blob/master/notebooks/verification_RPSS.ipynb). The scorer's username on gitlab is `s2saichallengescorer`. You should add it to your project with `Reporter` permissions. Under "Members" - "Invite Members" - "GitLab member or Email address", add `s2saichallengescorer`. The scorer will only ever clone your repository and evaluate your submission. It will never make any changes to your code.
## Make Predictions
@@ -83,13 +84,21 @@ Get corresponding observations/ground truth:
### 7. Let the Machine Learning model perform subseasonal 2020 predictions
and save them as `netcdf` files.
-The submissions have to placed in the `submissions` folder with filename `submission_your_choice.nc`,
-see [example](https://renkulab.io/gitlab/aaron.spring/s2s-ai-competition-bootstrap/-/blob/master/submissions/submission_rb_prediction_2020.nc).
-
-### 8. `git commit` training pipeline and netcdf submission
-For later verification of the organizers and reviewers, reproducibility and scoring of submissions,
-the training notebook/pipeline and submission file `ML_prediction_2020.nc` must be committed with `git lfs`:
-
+The submissions have to placed in the `submissions` folder with filename `ML_prediction_2020.nc`,
+see [example](https://renkulab.io/gitlab/aaron.spring/s2s-ai-competition-bootstrap/-/blob/master/submissions/ML_prediction_2020.nc).
+
+### 9. `git commit` training pipeline and netcdf submission
+For later verification by the organizers, reproducibility and scoring of submissions,
+commit the training notebook/pipeline and submission file
+`submissions/ML_prediction_2020.nc` with `git lfs`.
+After committing, `git tag submission-method_name-number`. The automated scorer will
+evaulate any tag (regardless of which branch it is on) that starts with the word `submission`
+followed by any other combination of characters. In other words, any tags that satisfy the
+regex `^submission.*` will be evaluated by the scorer. In addition, the scorer will only look for the
+results in a file named `ML_prediction_2020.nc` located in the `submissions` folder
+at the root of each competitor's repository.
+
+Here is an example of a set of commands that would commit the results and add the scorer tag.
```bash
# run your training and create file ../submissions/ML_prediction_2020.nc
git lfs track "*.nc" # do once, already done in template
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-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Train ML model to correct predictions of week 3-4 & 5-6\n",
- "\n",
- "This notebook create a Machine Learning `ML_model` to predict weeks 3-4 & 5-6 based on `S2S` weeks 3-4 & 5-6 forecasts and is compared to `CPC` observations for the [`s2s-ai-challenge`](https://s2s-ai-challenge.github.io/)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Synopsis"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Data used\n",
- "\n",
- "Training-input for Machine Learning model:\n",
- "- hindcasts of models: ECMWF\n",
- "\n",
- "Forecast-input for Machine Learning model:\n",
- "- real-time 2020 forecasts of the same models\n",
- "\n",
- "Compare Machine Learning model forecast against:\n",
- "- `CPC` observations 2020"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Method: (`name`) mean bias reduction\n",
- "\n",
- "- calculate bias from 2000-2019\n",
- "- remove bias from 2020 forecast"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Resources used\n",
- "for training\n",
- "\n",
- "- platform: renku\n",
- "- memory: 8 GB\n",
- "- processors: 2 CPU\n",
- "- storage required: 10 GB"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Safeguards\n",
- "\n",
- "All points have to be [x] checked. If not, your submission is invalid.\n",
- "\n",
- "Changes to the code after submissions are not possible, as the `commit` before the `tag` will be reviewed.\n",
- "(Only in exceptions and if previous effort in reproducibility can be found, it may be allowed to improve readability and reproducibility after November 1st 2021.)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Safeguards to prevent [overfitting](https://en.wikipedia.org/wiki/Overfitting?wprov=sfti1) \n",
- "\n",
- "If the organizers suspect overfitting, your contribution can be disqualified.\n",
- "\n",
- " - [ ] We didnt use 2020 observations in training (explicit overfitting and cheating)\n",
- " - [ ] We didnt repeatedly verify my model on 2020 observations and incrementally improved my RPSS (implicit overfitting)\n",
- " - [ ] We tried our best to prevent [data leakage](https://en.wikipedia.org/wiki/Leakage_(machine_learning)?wprov=sfti1).\n",
- " - [ ] We separate honor the `train-validate-test` [split principle](https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets). This means that the hindcast data is split into `train` and `validate`, whereas `test` is withheld.\n",
- " - [ ] We did use `test` explicitly in training or implicitly in incrementally adjusting parameters.\n",
- " - [ ] We considered [cross-validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics))."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Safeguards for Reproducibility\n",
- "Notebook/code must be independently reproducible from scratch by the organizers (after the competition), if not possible: no prize\n",
- " - [ ] All training data is publicly available (no pre-trained private neural networks, as they are not reproducible for us)\n",
- " - [ ] Code is well documented, readable and reproducible.\n",
- " - [ ] Code to reproduce runs within a day."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Todos to improve template\n",
- "\n",
- "This is just a demo.\n",
- "\n",
- "- [ ] for both variables\n",
- "- [ ] for both `lead_time`s\n",
- "- [ ] ensure probabilistic prediction outcome with `category` dim"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Imports"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "from tensorflow.keras.layers import Input, Dense, Flatten\n",
- "from tensorflow.keras.models import Sequential\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "import xarray as xr\n",
- "xr.set_options(display_style='text')\n",
- "\n",
- "from dask.utils import format_bytes\n",
- "import xskillscore as xs"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Get training data\n",
- "\n",
- "preprocessing of input data may be done in separate notebook/script"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Hindcast\n",
- "\n",
- "get weekly initialized hindcasts"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "v='tp'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
- "\u001b[0m\n"
- ]
- }
- ],
- "source": [
- "# preprocessed as renku dataset\n",
- "!renku storage pull ../data/ECMWF_hc_tp_weekly_2000_2019.zarr/"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "hc_weekly = xr.open_zarr('../data/ECMWF_hc_tp_weekly_2000_2019.zarr')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "def add_time_from_forecast_reference_time_and_step(benchmark, init_dim='time'):\n",
- " \"\"\"Creates time(forecast_reference_time, step).\n",
- " \n",
- " step: pd.Timedelta\n",
- " forecast_reference_time: datetime\n",
- " \"\"\"\n",
- " times = xr.concat(\n",
- " [\n",
- " xr.DataArray(\n",
- " benchmark[init_dim] + step,\n",
- " dims=init_dim,\n",
- " coords={init_dim: benchmark[init_dim]},\n",
- " )\n",
- " for step in benchmark.step\n",
- " ],\n",
- " dim=\"step\",\n",
- " join=\"inner\",\n",
- " compat=\"broadcast_equals\",\n",
- " )\n",
- " benchmark = benchmark.assign_coords(valid_time=times)\n",
- " return benchmark"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "hc_weekly = add_time_from_forecast_reference_time_and_step(hc_weekly)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Observations\n",
- "corresponding to hindcasts"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
- "\u001b[0m\n"
- ]
- }
- ],
- "source": [
- "# as prepared renku datasets FIXME\n",
- "!renku storage pull ../data/cpc-rain-1998-2020-weekly-averaged-1.5-deg/rain_verification_1998_2020.nc"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "obs = xr.open_dataset(f'../data/cpc-rain-1998-2020-weekly-averaged-1.5-deg/rain_verification_1998_2020.nc', chunks={}).rename({'rain':v,'time':'valid_time'})"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# ML model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "based on [Weatherbench](https://github.com/pangeo-data/WeatherBench/blob/master/quickstart.ipynb)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "import sys\n",
- "sys.path.insert(1, '/work/s2s-ai-competition-bootstrap/WeatherBench')\n",
- "from src.train_nn import DataGenerator, PeriodicConv2D, create_predictions\n",
- "import tensorflow.keras as keras"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "bs=32\n",
- "\n",
- "import numpy as np\n",
- "class DataGenerator(keras.utils.Sequence):\n",
- " def __init__(self, ds, verif, step, batch_size=bs, shuffle=True, load=True, mean=None, std=None):\n",
- " \"\"\"\n",
- " Data generator for WeatherBench data.\n",
- " Template from https://stanford.edu/~shervine/blog/keras-how-to-generate-data-on-the-fly\n",
- " Args:\n",
- " ds: model\n",
- " verif: obs\n",
- " step: Lead_time/step as in model\n",
- " batch_size: Batch size\n",
- " shuffle: bool. If True, data is shuffled.\n",
- " load: bool. If True, datadet is loaded into RAM.\n",
- " mean: If None, compute mean from data.\n",
- " std: If None, compute standard deviation from data.\n",
- " \n",
- " Todo:\n",
- " - use number\n",
- " - dont use .sel(step=step) to train over all steps at once\n",
- " \"\"\"\n",
- "\n",
- " self.ds = ds\n",
- " self.batch_size = batch_size\n",
- " self.shuffle = shuffle\n",
- " self.lead_time = step\n",
- "\n",
- "\n",
- " self.data = ds.transpose('time', ...).sel(step=step)\n",
- " self.mean = self.data.mean('time').compute() if mean is None else mean\n",
- " self.std = self.data.std('time').compute() if std is None else std\n",
- " \n",
- " self.verif_data = verif.transpose('time', ...).sel(step=step)\n",
- " self.verif_mean = self.verif_data.mean('time').compute() if mean is None else mean\n",
- " self.verif_std = self.verif_data.std('time').compute() if std is None else std\n",
- "\n",
- " # Normalize\n",
- " self.data = (self.data - self.mean) / self.std\n",
- " self.verif_data = (self.verif_data - self.verif_mean) / self.verif_std\n",
- " \n",
- " self.n_samples = self.data.time.size\n",
- " self.time = ds.time\n",
- "\n",
- " self.on_epoch_end()\n",
- "\n",
- " # For some weird reason calling .load() earlier messes up the mean and std computations\n",
- " if load: print('Loading data into RAM'); self.data.load()\n",
- "\n",
- " def __len__(self):\n",
- " 'Denotes the number of batches per epoch'\n",
- " return int(np.ceil(self.n_samples / self.batch_size))\n",
- "\n",
- " def __getitem__(self, i):\n",
- " 'Generate one batch of data'\n",
- " idxs = self.idxs[i * self.batch_size:(i + 1) * self.batch_size]\n",
- " # got all nan if nans not masked\n",
- " X = self.data.isel(time=idxs).fillna(0.).values\n",
- " y = self.verif_data.isel(time=idxs).fillna(0.).values\n",
- " return X, y\n",
- "\n",
- " def on_epoch_end(self):\n",
- " 'Updates indexes after each epoch'\n",
- " self.idxs = np.arange(self.n_samples)\n",
- " if self.shuffle == True:\n",
- " np.random.shuffle(self.idxs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# just train model for week 5: FIXME: finally it should be 2 bi-weekly `lead_time`\n",
- "step = hc_weekly.isel(step=2).step\n",
- "\n",
- "step"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "# mask\n",
- "hc_weekly = hc_weekly.where(obs.isel(valid_time=0,drop=True).notnull())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## data prep: train, valid, test"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "# time is the forecast_reference_time\n",
- "time_train_start,time_train_end='2000','2017'\n",
- "time_valid_start,time_valid_end='2018','2019'\n",
- "time_test = '2020'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/opt/conda/lib/python3.7/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
- " x = np.divide(x1, x2, out)\n",
- "/opt/conda/lib/python3.7/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
- " x = np.divide(x1, x2, out)\n",
- "/opt/conda/lib/python3.7/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
- " x = np.divide(x1, x2, out)\n",
- "/opt/conda/lib/python3.7/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
- " x = np.divide(x1, x2, out)\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loading data into RAM\n"
- ]
- }
- ],
- "source": [
- "dg_train = DataGenerator(\n",
- " hc_weekly.isel(number=0).sel(time=slice(time_train_start,time_train_end))[v],\n",
- " obs.sel(valid_time=hc_weekly.valid_time, method='nearest')[v].sel(time=slice(time_train_start,time_train_end)),\n",
- " step=step, batch_size=bs, load=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loading data into RAM\n"
- ]
- }
- ],
- "source": [
- "dg_valid = DataGenerator(\n",
- " hc_weekly.isel(number=0).sel(time=slice(time_valid_start,time_valid_end))[v],\n",
- " obs.sel(valid_time=hc_weekly.valid_time, method='nearest')[v].sel(time=slice(time_valid_start,time_valid_end)),\n",
- " step=step, batch_size=bs, mean=dg_train.mean, std=dg_train.std, shuffle=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loading data into RAM\n"
- ]
- }
- ],
- "source": [
- "dg_test = DataGenerator(hc_weekly.isel(number=0).sel(time=time_test)[v],\n",
- " obs.sel(valid_time=hc_weekly.valid_time, method='nearest')[v].sel(time=time_test),\n",
- " step, batch_size=bs, mean=dg_train.mean, std=dg_train.std, shuffle=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "((32, 121, 240), (32, 121, 240))"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "X, y = dg_valid[0]\n",
- "X.shape, y.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# short look into training data: large biases\n",
- "# any problem from normalizing?\n",
- "i=4\n",
- "xr.DataArray(np.vstack([X[i],y[i]])).plot(yincrease=False, robust=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## `fit`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [],
- "source": [
- "cnn = keras.models.Sequential([\n",
- " PeriodicConv2D(filters=32, kernel_size=5, conv_kwargs={'activation':'relu'}, input_shape=(32, 64, 1)),\n",
- " PeriodicConv2D(filters=1, kernel_size=5)\n",
- "])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "periodic_conv2d (PeriodicCon (None, 32, 64, 32) 832 \n",
- "_________________________________________________________________\n",
- "periodic_conv2d_1 (PeriodicC (None, 32, 64, 1) 801 \n",
- "=================================================================\n",
- "Total params: 1,633\n",
- "Trainable params: 1,633\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "cnn.summary()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [],
- "source": [
- "cnn.compile(keras.optimizers.Adam(1e-4), 'mse')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [],
- "source": [
- "import warnings\n",
- "warnings.simplefilter(\"ignore\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "30/30 [==============================] - 70s 2s/step - loss: 0.3419 - val_loss: 0.6589\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<tensorflow.python.keras.callbacks.History at 0x7f094c44efd0>"
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "cnn.fit(dg_train, epochs=1, validation_data=dg_valid)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## `predict`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def create_predictions(model, dg, step):\n",
- " \"\"\"Create non-iterative predictions\"\"\"\n",
- " preds = model.predict(dg).squeeze()\n",
- " # Unnormalize\n",
- " preds = preds * dg.std.values + dg.mean.values\n",
- " da = xr.DataArray(\n",
- " preds,\n",
- " dims=['time', 'latitude', 'longitude'],\n",
- " coords={'time': dg.ds.time, 'latitude': dg.ds.latitude, 'longitude': dg.ds.longitude},\n",
- " )\n",
- " da=da.assign_coords(step=step)\n",
- " return da"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [],
- "source": [
- "preds = create_predictions(cnn, dg_test, step)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Coordinates:\n",
- " * time (time) datetime64[ns] 2000-01-02 2000-01-09 ... 2000-12-31\n",
- " * latitude (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
- " * longitude (longitude) float64 0.0 1.5 3.0 4.5 ... 354.0 355.5 357.0 358.5\n",
- " surface int64 ...\n",
- " step timedelta64[ns] 35 days"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "preds.coords"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Validate predictions"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [],
- "source": [
- "obs_test = obs.sel(valid_time=hc_weekly.valid_time, method='nearest')[v].sel(time=time_test)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "rmse_ML = xs.rmse(preds, obs_test.sel(step=step), dim='time')\n",
- "rmse_ML.plot(robust=True)\n",
- "plt.title('RMSE ML predictions 2020')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### predict over all steps"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loading data into RAM\n",
- "Loading data into RAM\n",
- "Loading data into RAM\n",
- "Loading data into RAM\n"
- ]
- }
- ],
- "source": [
- "# this is not useful but results have expected dimensions\n",
- "# actually train for each step and use all members for training and validation\n",
- "\n",
- "preds=[]\n",
- "for step in hc_weekly.step:\n",
- " dg_test = DataGenerator(hc_weekly.isel(number=0).sel(time=slice(time_test))[v], obs_test,\n",
- " step=step, batch_size=bs, mean=dg_train.mean, std=dg_train.std, shuffle=False)\n",
- " preds.append(create_predictions(cnn, dg_test, step))\n",
- "preds = xr.concat(preds, 'step')\n",
- "preds['step']=hc_weekly.step\n",
- "preds=preds.to_dataset(name=v)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {},
- "outputs": [],
- "source": [
- "preds = preds.expand_dims('number').rename({'time':'forecast_reference_time'})"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Coordinates:\n",
- " * forecast_reference_time (forecast_reference_time) datetime64[ns] 2000-01...\n",
- " * latitude (latitude) float64 90.0 88.5 87.0 ... -88.5 -90.0\n",
- " * longitude (longitude) float64 0.0 1.5 3.0 ... 357.0 358.5\n",
- " surface int64 ...\n",
- " * step (step) timedelta64[ns] 21 days 28 days ... 42 days"
- ]
- },
- "execution_count": 33,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "preds.coords"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# todo: convert preds to preds_as_terciles"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Submission"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "preds.sizes # expect: category(3), longitude, latitude, step(2), forecast_time (53)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "format_bytes(preds.nbytes)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [],
- "source": [
- "preds.to_netcdf('submissions/ML_prediction_2020.nc')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "!git commit -m 'method name'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "!git tag \"predefined-tag-0.0.1\" # if this is to be checked by scorer\n",
- "git push --tags"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Misc"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## reforecasts for `ML_model`\n",
- "- required for terciles\n",
- "- dependent of `lead_time/step`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {},
- "outputs": [],
- "source": [
- "# running a reforecast based on training data\n",
- "# todo: require cnn todo bi-weekly predictions, otherwise average step/lead_time bi-weekly\n",
- "preds = create_predictions(cnn, dg_train, step)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {},
- "outputs": [],
- "source": [
- "quantile_kwargs={'q':[.33,.66], 'skipna':False}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Coordinates:\n",
- " * latitude (latitude) float64 90.0 88.5 87.0 ... -88.5 -90.0\n",
- " * longitude (longitude) float64 0.0 1.5 3.0 ... 357.0 358.5\n",
- " * category_edge (category_edge) float64 0.33 0.66\n",
- " * forecast_reference_time (forecast_reference_time) int64 1 2 3 ... 51 52 53"
- ]
- },
- "execution_count": 39,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "ML_terciles = preds.groupby('time.weekofyear').quantile(dim=['time'], **quantile_kwargs).rename({'quantile':'category_edge','weekofyear':'forecast_reference_time'}).compute()\n",
- "ML_terciles.coords"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [],
- "source": [
- "#ML_terciles.isel(forecast_reference_time=[0,24]).plot(col='category_edge',row='forecast_reference_time')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'24.63 MB'"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "format_bytes(ML_terciles.nbytes) # *2 for variable; *2 for steps"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {},
- "outputs": [],
- "source": [
- "ML_terciles.to_netcdf('ML_terciles.nc')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.6"
- },
- "toc-autonumbering": true
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/ML_forecast_template.ipynb b/notebooks/ML_forecast_template.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..05b455a26b6bcb0d54381b9ce5315db17e116690
--- /dev/null
+++ b/notebooks/ML_forecast_template.ipynb
@@ -0,0 +1,617 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Train ML model for predictions of week 3-4 & 5-6\n",
+ "\n",
+ "This notebook create a Machine Learning `ML_model` to predict weeks 3-4 & 5-6 based on `S2S` weeks 3-4 & 5-6 forecasts and is compared to `CPC` observations for the [`s2s-ai-challenge`](https://s2s-ai-challenge.github.io/)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Synopsis"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Method: (`name`) mean bias reduction\n",
+ "\n",
+ "- calculate bias from 2000-2019\n",
+ "- remove bias from 2020 forecast"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Data used\n",
+ "\n",
+ "Training-input for Machine Learning model:\n",
+ "- renku datasets, climetlab, IRIDL\n",
+ "\n",
+ "Forecast-input for Machine Learning model:\n",
+ "- renku datasets, climetlab, IRIDL\n",
+ "\n",
+ "Compare Machine Learning model forecast against ground truth:\n",
+ "- renku datasets, climetlab, IRIDL"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Resources used\n",
+ "for training\n",
+ "\n",
+ "- platform: renku\n",
+ "- memory: 8 GB\n",
+ "- processors: 2 CPU\n",
+ "- storage required: 10 GB"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Safeguards\n",
+ "\n",
+ "All points have to be [x] checked. If not, your submission is invalid.\n",
+ "\n",
+ "Changes to the code after submissions are not possible, as the `commit` before the `tag` will be reviewed.\n",
+ "(Only in exceptions and if previous effort in reproducibility can be found, it may be allowed to improve readability and reproducibility after November 1st 2021.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Safeguards to prevent [overfitting](https://en.wikipedia.org/wiki/Overfitting?wprov=sfti1) \n",
+ "\n",
+ "If the organizers suspect overfitting, your contribution can be disqualified.\n",
+ "\n",
+ " - [ ] We didnt use 2020 observations in training (explicit overfitting and cheating)\n",
+ " - [ ] We didnt repeatedly verify my model on 2020 observations and incrementally improved my RPSS (implicit overfitting)\n",
+ " - [ ] We provide RPS scores for the training period with script `print_RPS_per_year`, see in section 6.3 `predict`.\n",
+ " - [ ] We tried our best to prevent [data leakage](https://en.wikipedia.org/wiki/Leakage_(machine_learning)?wprov=sfti1).\n",
+ " - [ ] We honor the `train-validate-test` [split principle](https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets). This means that the hindcast data is split into `train` and `validate`, whereas `test` is withheld.\n",
+ " - [ ] We did use `test` explicitly in training or implicitly in incrementally adjusting parameters.\n",
+ " - [ ] We considered [cross-validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics))."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Safeguards for Reproducibility\n",
+ "Notebook/code must be independently reproducible from scratch by the organizers (after the competition), if not possible: no prize\n",
+ " - [ ] All training data is publicly available (no pre-trained private neural networks, as they are not reproducible for us)\n",
+ " - [ ] Code is well documented, readable and reproducible.\n",
+ " - [ ] Code to reproduce training and predictions should run within a day on the described architecture. If the training takes longer than a day, please justify why this is needed. Please do not submit training piplelines, which take weeks to train."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Todos to improve template\n",
+ "\n",
+ "This is just a demo.\n",
+ "\n",
+ "- [ ] for both variables\n",
+ "- [ ] for both `lead_time`s\n",
+ "- [ ] ensure probabilistic prediction outcome with `category` dim"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from tensorflow.keras.layers import Input, Dense, Flatten\n",
+ "from tensorflow.keras.models import Sequential\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "import xarray as xr\n",
+ "xr.set_options(display_style='text')\n",
+ "\n",
+ "from dask.utils import format_bytes\n",
+ "import xskillscore as xs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Get training data\n",
+ "\n",
+ "preprocessing of input data may be done in separate notebook/script"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Hindcast\n",
+ "\n",
+ "get weekly initialized hindcasts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# consider renku datasets\n",
+ "#! renku storage pull path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Observations\n",
+ "corresponding to hindcasts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# consider renku datasets\n",
+ "#! renku storage pull path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# ML model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bs=32\n",
+ "\n",
+ "import numpy as np\n",
+ "class DataGenerator(keras.utils.Sequence):\n",
+ " def __init__(self):\n",
+ " \"\"\"\n",
+ " Data generator\n",
+ " \n",
+ " Template from https://stanford.edu/~shervine/blog/keras-how-to-generate-data-on-the-fly\n",
+ " \n",
+ " Args:\n",
+ " \n",
+ " \"\"\"\n",
+ "\n",
+ " self.on_epoch_end()\n",
+ "\n",
+ " # For some weird reason calling .load() earlier messes up the mean and std computations\n",
+ " if load: print('Loading data into RAM'); self.data.load()\n",
+ "\n",
+ " def __len__(self):\n",
+ " 'Denotes the number of batches per epoch'\n",
+ " return int(np.ceil(self.n_samples / self.batch_size))\n",
+ "\n",
+ " def __getitem__(self, i):\n",
+ " 'Generate one batch of data'\n",
+ " idxs = self.idxs[i * self.batch_size:(i + 1) * self.batch_size]\n",
+ " # got all nan if nans not masked\n",
+ " X = self.data.isel(time=idxs).fillna(0.).values\n",
+ " y = self.verif_data.isel(time=idxs).fillna(0.).values\n",
+ " return X, y\n",
+ "\n",
+ " def on_epoch_end(self):\n",
+ " 'Updates indexes after each epoch'\n",
+ " self.idxs = np.arange(self.n_samples)\n",
+ " if self.shuffle == True:\n",
+ " np.random.shuffle(self.idxs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## data prep: train, valid, test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# time is the forecast_reference_time\n",
+ "time_train_start,time_train_end='2000','2017'\n",
+ "time_valid_start,time_valid_end='2018','2019'\n",
+ "time_test = '2020'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dg_train = DataGenerator()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dg_valid = DataGenerator()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dg_test = DataGenerator()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## `fit`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cnn = keras.models.Sequential([])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cnn.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cnn.compile(keras.optimizers.Adam(1e-4), 'mse')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import warnings\n",
+ "warnings.simplefilter(\"ignore\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cnn.fit(dg_train, epochs=1, validation_data=dg_valid)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## `predict`\n",
+ "\n",
+ "Create predictions and print `mean(variable, lead_time, longitude, weighted latitude)` RPS for all years as calculated by `print_RPS_per_year`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scripts import print_RPS_per_year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_predictions(model, dg):\n",
+ " \"\"\"Create non-iterative predictions\"\"\"\n",
+ " preds = model.predict(dg).squeeze()\n",
+ " # transform\n",
+ " \n",
+ " return preds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### `predict` training period in-sample"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "preds_is = create_predictions(cnn, dg_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print_RPS_per_year(preds_is)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### `predict` valid out-of-sample"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "preds_os = create_predictions(cnn, dg_valid)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print_RPS_per_year(preds_os)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### `predict` test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "preds_test = create_predictions(cnn, dg_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print_RPS_per_year(preds_test)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Submission"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "preds_test.sizes # expect: category(3), longitude, latitude, lead_time(2), forecast_time (53)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "format_bytes(preds_test.nbytes) # should around 150 MB"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scripts import assert_predictions_2020\n",
+ "assert_predictions_2020(preds_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "preds_test.to_netcdf('../submissions/ML_prediction_2020.nc')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "git commit -m \"commit submission for my_method_name\" # whatever message you want"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "git tag \"submission-my_method_name-0.0.1\" # if this is to be checked by scorer, only the last submitted==tagged version will be considered"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "git push --tags"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Reproducibility"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## memory"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# https://phoenixnap.com/kb/linux-commands-check-memory-usage\n",
+ "!free -g"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## CPU"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!lscpu"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## software"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!conda list"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:s2s-ai]",
+ "language": "python",
+ "name": "conda-env-s2s-ai-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.10"
+ },
+ "toc-autonumbering": true
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/ML_train_and_predict.ipynb b/notebooks/ML_train_and_predict.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..37c93b14035a9f441652c11e8b20f284a6eeb378
--- /dev/null
+++ b/notebooks/ML_train_and_predict.ipynb
@@ -0,0 +1,1316 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Train ML model to correct predictions of week 3-4 & 5-6\n",
+ "\n",
+ "This notebook create a Machine Learning `ML_model` to predict weeks 3-4 & 5-6 based on `S2S` weeks 3-4 & 5-6 forecasts and is compared to `CPC` observations for the [`s2s-ai-challenge`](https://s2s-ai-challenge.github.io/)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Synopsis"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Method: `ML-based mean bias reduction`\n",
+ "\n",
+ "- calculate the ML-based bias from 2000-2019 deterministic ensemble mean forecast\n",
+ "- remove that the ML-based bias from 2020 forecast deterministic ensemble mean forecast"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Data used\n",
+ "\n",
+ "type: renku datasets\n",
+ "\n",
+ "Training-input for Machine Learning model:\n",
+ "- hindcasts of models:\n",
+ " - ECMWF: `ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr`\n",
+ "\n",
+ "Forecast-input for Machine Learning model:\n",
+ "- real-time 2020 forecasts of models:\n",
+ " - ECMWF: `ecmwf_forecast-input_2020_biweekly_deterministic.zarr`\n",
+ "\n",
+ "Compare Machine Learning model forecast against against ground truth:\n",
+ "- `CPC` observations:\n",
+ " - `hindcast-like-observations_biweekly_deterministic.zarr`\n",
+ " - `forecast-like-observations_2020_biweekly_deterministic.zarr`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Resources used\n",
+ "for training\n",
+ "\n",
+ "- platform: MPI-M supercompute 1 Node\n",
+ "- memory: 64 GB\n",
+ "- processors: 36 CPU\n",
+ "- storage required: 10 GB"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Safeguards\n",
+ "\n",
+ "All points have to be [x] checked. If not, your submission is invalid.\n",
+ "\n",
+ "Changes to the code after submissions are not possible, as the `commit` before the `tag` will be reviewed.\n",
+ "(Only in exceptions and if previous effort in reproducibility can be found, it may be allowed to improve readability and reproducibility after November 1st 2021.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Safeguards to prevent [overfitting](https://en.wikipedia.org/wiki/Overfitting?wprov=sfti1) \n",
+ "\n",
+ "If the organizers suspect overfitting, your contribution can be disqualified.\n",
+ "\n",
+ " - [x] We didnt use 2020 observations in training (explicit overfitting and cheating)\n",
+ " - [x] We didnt repeatedly verify my model on 2020 observations and incrementally improved my RPSS (implicit overfitting)\n",
+ " - [x] We provide RPS scores for the training period with script `print_RPS_per_year`, see in section 6.3 `predict`.\n",
+ " - [x] We tried our best to prevent [data leakage](https://en.wikipedia.org/wiki/Leakage_(machine_learning)?wprov=sfti1).\n",
+ " - [x] We honor the `train-validate-test` [split principle](https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets). This means that the hindcast data is split into `train` and `validate`, whereas `test` is withheld.\n",
+ " - [x] We did use `test` explicitly in training or implicitly in incrementally adjusting parameters.\n",
+ " - [x] We considered [cross-validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics))."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Safeguards for Reproducibility\n",
+ "Notebook/code must be independently reproducible from scratch by the organizers (after the competition), if not possible: no prize\n",
+ " - [x] All training data is publicly available (no pre-trained private neural networks, as they are not reproducible for us)\n",
+ " - [x] Code is well documented, readable and reproducible.\n",
+ " - [x] Code to reproduce training and predictions should run within a day on the described architecture. If the training takes longer than a day, please justify why this is needed. Please do not submit training piplelines, which take weeks to train."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Todos to improve template\n",
+ "\n",
+ "This is just a demo.\n",
+ "\n",
+ "- [ ] use multiple predictor variables and two predicted variables\n",
+ "- [ ] for both `lead_time`s in one go\n",
+ "- [ ] consider seasonality, for now all `forecast_time` months are mixed\n",
+ "- [ ] make probabilistic predictions with `category` dim, for now works deterministic"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/conda/lib/python3.8/site-packages/xarray/backends/cfgrib_.py:27: UserWarning: Failed to load cfgrib - most likely there is a problem accessing the ecCodes library. Try `import cfgrib` to get the full error message\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ],
+ "source": [
+ "from tensorflow.keras.layers import Input, Dense, Flatten\n",
+ "from tensorflow.keras.models import Sequential\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "import xarray as xr\n",
+ "xr.set_options(display_style='text')\n",
+ "import numpy as np\n",
+ "\n",
+ "from dask.utils import format_bytes\n",
+ "import xskillscore as xs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Get training data\n",
+ "\n",
+ "preprocessing of input data may be done in separate notebook/script"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Hindcast\n",
+ "\n",
+ "get weekly initialized hindcasts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "v='t2m'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
+ "\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "# preprocessed as renku dataset\n",
+ "!renku storage pull ../data/ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/conda/lib/python3.8/site-packages/xarray/backends/plugins.py:61: RuntimeWarning: Engine 'cfgrib' loading failed:\n",
+ "/opt/conda/lib/python3.8/site-packages/gribapi/_bindings.cpython-38-x86_64-linux-gnu.so: undefined symbol: codes_bufr_key_is_header\n",
+ " warnings.warn(f\"Engine {name!r} loading failed:\\n{ex}\", RuntimeWarning)\n"
+ ]
+ }
+ ],
+ "source": [
+ "hind_2000_2019 = xr.open_zarr(\"../data/ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr\", consolidated=True)#[v]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
+ "\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "# preprocessed as renku dataset\n",
+ "!renku storage pull ../data/ecmwf_forecast-input_2020_biweekly_deterministic.zarr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fct_2020 = xr.open_zarr(\"../data/ecmwf_forecast-input_2020_biweekly_deterministic.zarr\", consolidated=True)#[v]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Observations\n",
+ "corresponding to hindcasts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
+ "\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "# preprocessed as renku dataset\n",
+ "!renku storage pull ../data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "obs_2000_2019 = xr.open_zarr(\"../data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr\", consolidated=True)#[v]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
+ "\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "# preprocessed as renku dataset\n",
+ "!renku storage pull ../data/forecast-like-observations_2020_biweekly_deterministic.zarr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "obs_2020 = xr.open_zarr(\"../data/forecast-like-observations_2020_biweekly_deterministic.zarr\", consolidated=True)#[v]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# ML model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "based on [Weatherbench](https://github.com/pangeo-data/WeatherBench/blob/master/quickstart.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "fatal: destination path 'WeatherBench' already exists and is not an empty directory.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# run once only and dont commit\n",
+ "!git clone https://github.com/pangeo-data/WeatherBench/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "sys.path.insert(1, 'WeatherBench')\n",
+ "from WeatherBench.src.train_nn import DataGenerator, PeriodicConv2D, create_predictions\n",
+ "import tensorflow.keras as keras"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bs=32\n",
+ "\n",
+ "import numpy as np\n",
+ "class DataGenerator(keras.utils.Sequence):\n",
+ " def __init__(self, fct, verif, lead_time, batch_size=bs, shuffle=True, load=True,\n",
+ " mean=None, std=None):\n",
+ " \"\"\"\n",
+ " Data generator for WeatherBench data.\n",
+ " Template from https://stanford.edu/~shervine/blog/keras-how-to-generate-data-on-the-fly\n",
+ "\n",
+ " Args:\n",
+ " fct: forecasts from S2S models: xr.DataArray (xr.Dataset doesnt work properly)\n",
+ " verif: observations with same dimensionality (xr.Dataset doesnt work properly)\n",
+ " lead_time: Lead_time as in model\n",
+ " batch_size: Batch size\n",
+ " shuffle: bool. If True, data is shuffled.\n",
+ " load: bool. If True, datadet is loaded into RAM.\n",
+ " mean: If None, compute mean from data.\n",
+ " std: If None, compute standard deviation from data.\n",
+ " \n",
+ " Todo:\n",
+ " - use number in a better way, now uses only ensemble mean forecast\n",
+ " - dont use .sel(lead_time=lead_time) to train over all lead_time at once\n",
+ " - be sensitive with forecast_time, pool a few around the weekofyear given\n",
+ " - use more variables as predictors\n",
+ " - predict more variables\n",
+ " \"\"\"\n",
+ "\n",
+ " if isinstance(fct, xr.Dataset):\n",
+ " print('convert fct to array')\n",
+ " fct = fct.to_array().transpose(...,'variable')\n",
+ " self.fct_dataset=True\n",
+ " else:\n",
+ " self.fct_dataset=False\n",
+ " \n",
+ " if isinstance(verif, xr.Dataset):\n",
+ " print('convert verif to array')\n",
+ " verif = verif.to_array().transpose(...,'variable')\n",
+ " self.verif_dataset=True\n",
+ " else:\n",
+ " self.verif_dataset=False\n",
+ " \n",
+ " #self.fct = fct\n",
+ " self.batch_size = batch_size\n",
+ " self.shuffle = shuffle\n",
+ " self.lead_time = lead_time\n",
+ "\n",
+ " self.fct_data = fct.transpose('forecast_time', ...).sel(lead_time=lead_time)\n",
+ " self.fct_mean = self.fct_data.mean('forecast_time').compute() if mean is None else mean\n",
+ " self.fct_std = self.fct_data.std('forecast_time').compute() if std is None else std\n",
+ " \n",
+ " self.verif_data = verif.transpose('forecast_time', ...).sel(lead_time=lead_time)\n",
+ " self.verif_mean = self.verif_data.mean('forecast_time').compute() if mean is None else mean\n",
+ " self.verif_std = self.verif_data.std('forecast_time').compute() if std is None else std\n",
+ "\n",
+ " # Normalize\n",
+ " self.fct_data = (self.fct_data - self.fct_mean) / self.fct_std\n",
+ " self.verif_data = (self.verif_data - self.verif_mean) / self.verif_std\n",
+ " \n",
+ " self.n_samples = self.fct_data.forecast_time.size\n",
+ " self.forecast_time = self.fct_data.forecast_time\n",
+ "\n",
+ " self.on_epoch_end()\n",
+ "\n",
+ " # For some weird reason calling .load() earlier messes up the mean and std computations\n",
+ " if load:\n",
+ " # print('Loading data into RAM')\n",
+ " self.fct_data.load()\n",
+ "\n",
+ " def __len__(self):\n",
+ " 'Denotes the number of batches per epoch'\n",
+ " return int(np.ceil(self.n_samples / self.batch_size))\n",
+ "\n",
+ " def __getitem__(self, i):\n",
+ " 'Generate one batch of data'\n",
+ " idxs = self.idxs[i * self.batch_size:(i + 1) * self.batch_size]\n",
+ " # got all nan if nans not masked\n",
+ " X = self.fct_data.isel(forecast_time=idxs).fillna(0.).values\n",
+ " y = self.verif_data.isel(forecast_time=idxs).fillna(0.).values\n",
+ " return X, y\n",
+ "\n",
+ " def on_epoch_end(self):\n",
+ " 'Updates indexes after each epoch'\n",
+ " self.idxs = np.arange(self.n_samples)\n",
+ " if self.shuffle == True:\n",
+ " np.random.shuffle(self.idxs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<pre><xarray.DataArray 'lead_time' ()>\n",
+ "array(1209600000000000, dtype='timedelta64[ns]')\n",
+ "Coordinates:\n",
+ " lead_time timedelta64[ns] 14 days\n",
+ "Attributes:\n",
+ " comment: lead_time describes bi-weekly aggregates. The pd.Timedelta corr...</pre>"
+ ],
+ "text/plain": [
+ "<xarray.DataArray 'lead_time' ()>\n",
+ "array(1209600000000000, dtype='timedelta64[ns]')\n",
+ "Coordinates:\n",
+ " lead_time timedelta64[ns] 14 days\n",
+ "Attributes:\n",
+ " comment: lead_time describes bi-weekly aggregates. The pd.Timedelta corr..."
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 2 bi-weekly `lead_time`: week 3-4\n",
+ "lead = hind_2000_2019.isel(lead_time=0).lead_time\n",
+ "\n",
+ "lead"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# mask, needed?\n",
+ "hind_2000_2019 = hind_2000_2019.where(obs_2000_2019.isel(forecast_time=0, lead_time=0,drop=True).notnull())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## data prep: train, valid, test\n",
+ "\n",
+ "[Use the hindcast period to split train and valid.](https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets) Do not use the 2020 data for testing!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# time is the forecast_time\n",
+ "time_train_start,time_train_end='2000','2017' # train\n",
+ "time_valid_start,time_valid_end='2018','2019' # valid\n",
+ "time_test = '2020' # test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/conda/lib/python3.8/site-packages/dask/array/numpy_compat.py:39: RuntimeWarning: invalid value encountered in true_divide\n",
+ " x = np.divide(x1, x2, out)\n",
+ "/opt/conda/lib/python3.8/site-packages/dask/array/numpy_compat.py:39: RuntimeWarning: invalid value encountered in true_divide\n",
+ " x = np.divide(x1, x2, out)\n",
+ "/opt/conda/lib/python3.8/site-packages/dask/array/numpy_compat.py:39: RuntimeWarning: invalid value encountered in true_divide\n",
+ " x = np.divide(x1, x2, out)\n",
+ "/opt/conda/lib/python3.8/site-packages/dask/array/numpy_compat.py:39: RuntimeWarning: invalid value encountered in true_divide\n",
+ " x = np.divide(x1, x2, out)\n",
+ "/opt/conda/lib/python3.8/site-packages/dask/array/numpy_compat.py:39: RuntimeWarning: invalid value encountered in true_divide\n",
+ " x = np.divide(x1, x2, out)\n"
+ ]
+ }
+ ],
+ "source": [
+ "dg_train = DataGenerator(\n",
+ " hind_2000_2019.mean('realization').sel(forecast_time=slice(time_train_start,time_train_end))[v],\n",
+ " obs_2000_2019.sel(forecast_time=slice(time_train_start,time_train_end))[v],\n",
+ " lead_time=lead, batch_size=bs, load=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/conda/lib/python3.8/site-packages/dask/array/numpy_compat.py:39: RuntimeWarning: invalid value encountered in true_divide\n",
+ " x = np.divide(x1, x2, out)\n",
+ "/opt/conda/lib/python3.8/site-packages/dask/array/numpy_compat.py:39: RuntimeWarning: invalid value encountered in true_divide\n",
+ " x = np.divide(x1, x2, out)\n",
+ "/opt/conda/lib/python3.8/site-packages/dask/array/numpy_compat.py:39: RuntimeWarning: invalid value encountered in true_divide\n",
+ " x = np.divide(x1, x2, out)\n",
+ "/opt/conda/lib/python3.8/site-packages/dask/array/numpy_compat.py:39: RuntimeWarning: invalid value encountered in true_divide\n",
+ " x = np.divide(x1, x2, out)\n",
+ "/opt/conda/lib/python3.8/site-packages/dask/array/numpy_compat.py:39: RuntimeWarning: invalid value encountered in true_divide\n",
+ " x = np.divide(x1, x2, out)\n"
+ ]
+ }
+ ],
+ "source": [
+ "dg_valid = DataGenerator(\n",
+ " hind_2000_2019.mean('realization').sel(forecast_time=slice(time_valid_start,time_valid_end))[v],\n",
+ " obs_2000_2019.sel(forecast_time=slice(time_valid_start,time_valid_end))[v],\n",
+ " lead_time=lead, batch_size=bs, shuffle=False, load=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# do not use, delete?\n",
+ "dg_test = DataGenerator(\n",
+ " fct_2020.mean('realization').sel(forecast_time=time_test)[v],\n",
+ " obs_2020.sel(forecast_time=time_test)[v],\n",
+ " lead_time=lead, batch_size=bs, load=True, mean=dg_train.fct_mean, std=dg_train.fct_std, shuffle=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "((32, 121, 240), (32, 121, 240))"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X, y = dg_valid[0]\n",
+ "X.shape, y.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.collections.QuadMesh at 0x7f3a7e44b730>"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# short look into training data: large biases\n",
+ "# any problem from normalizing?\n",
+ "i=4\n",
+ "xr.DataArray(np.vstack([X[i],y[i]])).plot(yincrease=False, robust=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## `fit`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:AutoGraph could not transform <bound method PeriodicPadding2D.call of <WeatherBench.src.train_nn.PeriodicPadding2D object at 0x7f3a7c21bfd0>> and will run it as-is.\n",
+ "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
+ "Cause: module 'gast' has no attribute 'Index'\n",
+ "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
+ "WARNING: AutoGraph could not transform <bound method PeriodicPadding2D.call of <WeatherBench.src.train_nn.PeriodicPadding2D object at 0x7f3a7c21bfd0>> and will run it as-is.\n",
+ "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
+ "Cause: module 'gast' has no attribute 'Index'\n",
+ "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
+ ]
+ }
+ ],
+ "source": [
+ "cnn = keras.models.Sequential([\n",
+ " PeriodicConv2D(filters=32, kernel_size=5, conv_kwargs={'activation':'relu'}, input_shape=(32, 64, 1)),\n",
+ " PeriodicConv2D(filters=1, kernel_size=5)\n",
+ "])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "periodic_conv2d (PeriodicCon (None, 32, 64, 32) 832 \n",
+ "_________________________________________________________________\n",
+ "periodic_conv2d_1 (PeriodicC (None, 32, 64, 1) 801 \n",
+ "=================================================================\n",
+ "Total params: 1,633\n",
+ "Trainable params: 1,633\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "cnn.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cnn.compile(keras.optimizers.Adam(1e-4), 'mse')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import warnings\n",
+ "warnings.simplefilter(\"ignore\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/3\n",
+ "30/30 [==============================] - 24s 744ms/step - loss: 0.2325 - val_loss: 0.1270\n",
+ "Epoch 2/3\n",
+ "30/30 [==============================] - 22s 717ms/step - loss: 0.1188 - val_loss: 0.0791\n",
+ "Epoch 3/3\n",
+ "30/30 [==============================] - 22s 733ms/step - loss: 0.0766 - val_loss: 0.0620\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<tensorflow.python.keras.callbacks.History at 0x7f3a880d2700>"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cnn.fit(dg_train, epochs=3, validation_data=dg_valid)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## `predict`\n",
+ "\n",
+ "Create predictions and print `mean(variable, lead_time, longitude, weighted latitude)` RPS for all years as calculated by `print_RPS_per_year`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scripts import add_valid_time_from_forecast_reference_time_and_lead_time\n",
+ "\n",
+ "def _create_predictions(model, dg, lead):\n",
+ " \"\"\"Create non-iterative predictions\"\"\"\n",
+ " preds = model.predict(dg).squeeze()\n",
+ " # Unnormalize\n",
+ " preds = preds * dg.fct_std.values + dg.fct_mean.values\n",
+ " if dg.verif_dataset:\n",
+ " da = xr.DataArray(\n",
+ " preds,\n",
+ " dims=['forecast_time', 'latitude', 'longitude','variable'],\n",
+ " coords={'forecast_time': dg.fct_data.forecast_time, 'latitude': dg.fct_data.latitude,\n",
+ " 'longitude': dg.fct_data.longitude},\n",
+ " ).to_dataset() # doesnt work yet\n",
+ " else:\n",
+ " da = xr.DataArray(\n",
+ " preds,\n",
+ " dims=['forecast_time', 'latitude', 'longitude'],\n",
+ " coords={'forecast_time': dg.fct_data.forecast_time, 'latitude': dg.fct_data.latitude,\n",
+ " 'longitude': dg.fct_data.longitude},\n",
+ " )\n",
+ " da = da.assign_coords(lead_time=lead)\n",
+ " # da = add_valid_time_from_forecast_reference_time_and_lead_time(da)\n",
+ " return da"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# optionally masking the ocean when making probabilistic\n",
+ "mask = obs_2020.std(['lead_time','forecast_time']).notnull()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scripts import make_probabilistic"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
+ "\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "!renku storage pull ../data/hindcast-like-observations_2000-2019_biweekly_tercile-edges.nc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cache_path='../data'\n",
+ "tercile_file = f'{cache_path}/hindcast-like-observations_2000-2019_biweekly_tercile-edges.nc'\n",
+ "tercile_edges = xr.open_dataset(tercile_file)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# this is not useful but results have expected dimensions\n",
+ "# actually train for each lead_time\n",
+ "\n",
+ "def create_predictions(cnn, fct, obs, time):\n",
+ " preds_test=[]\n",
+ " for lead in fct.lead_time:\n",
+ " dg = DataGenerator(fct.mean('realization').sel(forecast_time=time)[v],\n",
+ " obs.sel(forecast_time=time)[v],\n",
+ " lead_time=lead, batch_size=bs, mean=dg_train.fct_mean, std=dg_train.fct_std, shuffle=False)\n",
+ " preds_test.append(_create_predictions(cnn, dg, lead))\n",
+ " preds_test = xr.concat(preds_test, 'lead_time')\n",
+ " preds_test['lead_time'] = fct.lead_time\n",
+ " # add valid_time coord\n",
+ " preds_test = add_valid_time_from_forecast_reference_time_and_lead_time(preds_test)\n",
+ " preds_test = preds_test.to_dataset(name=v)\n",
+ " # add fake var\n",
+ " preds_test['tp'] = preds_test['t2m']\n",
+ " # make probabilistic\n",
+ " preds_test = make_probabilistic(preds_test.expand_dims('realization'), tercile_edges, mask=mask)\n",
+ " return preds_test"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### `predict` training period in-sample"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
+ "\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "!renku storage pull ../data/forecast-like-observations_2020_biweekly_terciled.nc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
+ "\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "!renku storage pull ../data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scripts import print_RPS_per_year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " RPS\n",
+ "year \n",
+ "2000 0.864946\n",
+ "2001 0.944157\n",
+ "2002 0.975950\n",
+ " RPS\n",
+ "year \n",
+ "2003 0.940808\n",
+ "2004 0.946251\n",
+ "2005 1.008755\n",
+ " RPS\n",
+ "year \n",
+ "2006 0.962032\n",
+ "2007 1.009629\n",
+ "2008 0.975747\n",
+ " RPS\n",
+ "year \n",
+ "2009 1.009750\n",
+ "2010 1.005306\n",
+ "2011 0.955670\n",
+ " RPS\n",
+ "year \n",
+ "2012 0.991964\n",
+ "2013 1.010457\n",
+ "2014 1.014053\n",
+ " RPS\n",
+ "year \n",
+ "2015 1.020807\n",
+ "2016 1.084565\n",
+ "2017 1.054406\n"
+ ]
+ }
+ ],
+ "source": [
+ "step = 3\n",
+ "for year in np.arange(int(time_train_start), int(time_train_end) -1, step): # loop over years to consume less memory on renku\n",
+ " preds_is = create_predictions(cnn, hind_2000_2019, obs_2000_2019, time=slice(str(year), str(year+step-1))).compute()\n",
+ " print(print_RPS_per_year(preds_is))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# not on renkulab, simply do\n",
+ "#preds_is = create_predictions(cnn, hind_2000_2019, obs_2000_2019, time=slice(time_train_start, time_train_end))\n",
+ "#print_RPS_per_year(preds_is)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### `predict` validation period out-of-sample"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>RPS</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>year</th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2018</th>\n",
+ " <td>1.045750</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2019</th>\n",
+ " <td>1.097249</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " RPS\n",
+ "year \n",
+ "2018 1.045750\n",
+ "2019 1.097249"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "preds_os = create_predictions(cnn, hind_2000_2019, obs_2000_2019, time=slice(time_valid_start, time_valid_end))\n",
+ "\n",
+ "print_RPS_per_year(preds_os)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### `predict` test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>RPS</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>year</th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2020</th>\n",
+ " <td>1.052517</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " RPS\n",
+ "year \n",
+ "2020 1.052517"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "preds_test = create_predictions(cnn, fct_2020, obs_2020, time=time_test)\n",
+ "\n",
+ "print_RPS_per_year(preds_test)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Submission"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Frozen(SortedKeysDict({'category': 3, 'lead_time': 2, 'forecast_time': 53, 'latitude': 121, 'longitude': 240}))"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "preds_test.sizes # expect: category(3), longitude, latitude, lead_time(2), forecast_time (53)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'140.92 MiB'"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "format_bytes(preds_test.nbytes)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scripts import assert_predictions_2020\n",
+ "assert_predictions_2020(preds_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "preds_test.to_netcdf('../submissions/ML_prediction_2020.nc')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!git add ../submissions/ML_prediction_2020.nc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!git commit -m \"template_test commit message\" # whatever message you want"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!git tag \"submission-template_test-0.0.1\" # if this is to be checked by scorer, only the last submitted==tagged version will be considered"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!git push --tags"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Reproducibility"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## memory"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# https://phoenixnap.com/kb/linux-commands-check-memory-usage\n",
+ "!free -g"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## CPU"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!lscpu"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## software"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!conda list"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:s2s-ai]",
+ "language": "python",
+ "name": "conda-env-s2s-ai-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.10"
+ },
+ "toc-autonumbering": true
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/RPSS_verification.ipynb b/notebooks/RPSS_verification.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a70d5307762dd27dfda3594ee9892705c9824cbb
--- /dev/null
+++ b/notebooks/RPSS_verification.ipynb
@@ -0,0 +1,462 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Score biweekly ML forecasts against re-calibrated ECWMF forecast with RPSS"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Goal:\n",
+ "\n",
+ "- Score biweekly ML forecasts against re-calibrated ECWMF forecast with RPSS\n",
+ "\n",
+ "Requirements:\n",
+ "\n",
+ "- [`xskillscore`](https://github.com/xarray-contrib/xskillscore)\n",
+ "- [renku datasets](https://renku-python.readthedocs.io/en/latest/commands.html#module-renku.cli.dataset) / file\n",
+ " - observations\n",
+ " - probabilistic:\n",
+ " - renku dataset: `forecast-like-observations_2020_biweekly_terciled.nc`\n",
+ " - ML forecasts\n",
+ " - probabilistic:\n",
+ " - file: `../submissions/ML_prediction_2020.nc`\n",
+ " - benchmark:\n",
+ " - probabilistic:\n",
+ " - renku dataset: `ecmwf_recalibrated_benchmark_2020_biweekly_terciled.nc` \n",
+ " \n",
+ "Output:\n",
+ "- RPSS score spatially averaged from [90N-60S] and averaged over both `lead_time`s and variables"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/conda/lib/python3.8/site-packages/xarray/backends/cfgrib_.py:27: UserWarning: Failed to load cfgrib - most likely there is a problem accessing the ecCodes library. Try `import cfgrib` to get the full error message\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ],
+ "source": [
+ "import xarray as xr\n",
+ "import xskillscore as xs\n",
+ "import numpy as np\n",
+ "\n",
+ "xr.set_options(keep_attrs=True)\n",
+ "xr.set_options(display_style='text')\n",
+ "\n",
+ "cache_path = \"../data\"\n",
+ "\n",
+ "plot = False"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Get categorized"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2020 observations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
+ "\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "# to use retrieve from git lfs\n",
+ "!renku storage pull ../data/forecast-like-observations_2020_biweekly_terciled.nc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/conda/lib/python3.8/site-packages/xarray/backends/plugins.py:61: RuntimeWarning: Engine 'cfgrib' loading failed:\n",
+ "/opt/conda/lib/python3.8/site-packages/gribapi/_bindings.cpython-38-x86_64-linux-gnu.so: undefined symbol: codes_bufr_key_is_header\n",
+ " warnings.warn(f\"Engine {name!r} loading failed:\\n{ex}\", RuntimeWarning)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Frozen(SortedKeysDict({'category': 3, 'lead_time': 2, 'forecast_time': 53, 'latitude': 121, 'longitude': 240}))"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "obs_p = xr.open_dataset(f'{cache_path}/forecast-like-observations_2020_biweekly_terciled.nc')\n",
+ "\n",
+ "obs_p.sizes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2020 ML-based forecasts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
+ "\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "!renku storage pull ../submissions/ML_prediction_2020.nc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Frozen(SortedKeysDict({'forecast_time': 53, 'latitude': 121, 'lead_time': 2, 'longitude': 240, 'category': 3}))"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# submission for your ML model\n",
+ "fct_p = xr.open_dataset(f\"../submissions/ML_prediction_2020.nc\")\n",
+ "fct_p.sizes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2020 ECMWF recalibrated benchmark"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
+ "\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "!renku storage pull ../data/ecmwf_recalibrated_benchmark_2020_biweekly_terciled.nc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Frozen(SortedKeysDict({'forecast_time': 53, 'category': 3, 'lead_time': 2, 'latitude': 121, 'longitude': 240}))"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "bench_p = xr.open_dataset('../data/ecmwf_recalibrated_benchmark_2020_biweekly_terciled.nc')\n",
+ "bench_p.sizes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### checks"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scripts import assert_predictions_2020"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "assert_predictions_2020(fct_p)\n",
+ "assert_predictions_2020(bench_p)\n",
+ "assert_predictions_2020(obs_p)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# RPS"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## RPS ML model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rps_ML = xs.rps(obs_p, fct_p, category_edges=None, dim='forecast_time', input_distributions='p').compute()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if plot:\n",
+ " for v in rps_ML.data_vars:\n",
+ " rps_ML[v].plot(robust=True, col='lead_time')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## RPS re-calibrated ECMWF benchmark"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rps_bench = xs.rps(obs_p, bench_p, category_edges=None, dim='forecast_time', input_distributions='p').compute()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if plot:\n",
+ " for v in rps_bench.data_vars:\n",
+ " rps_bench[v].plot(robust=True, col='lead_time')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## RPSS"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rpss = (1 - rps_ML / rps_bench)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if plot:\n",
+ " for v in rpss.data_vars:\n",
+ " rpss[v].plot(robust=True, col='lead_time')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# scoring for RPS leaderboard"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# check for -inf grid cells\n",
+ "if (rpss==-np.inf).to_array().any():\n",
+ " (rpss == rpss.min()).sum()\n",
+ "\n",
+ " # dirty fix\n",
+ " rpss = rpss.clip(-1, 1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# what to do with requested grid cells where NaN is submitted? also penalize"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mask = xr.ones_like(rpss.isel(lead_time=0, drop=True)).reset_coords(drop=True).t2m\n",
+ "boundary_tropics = 30\n",
+ "mask = xr.concat([mask.where(mask.latitude > boundary_tropics),\n",
+ " mask.where(np.abs(mask.latitude) <= boundary_tropics),\n",
+ " mask.where((mask.latitude < -boundary_tropics) & (mask.latitude > -60))],'area')\n",
+ "mask = mask.assign_coords(area=['northern_extratropics', 'tropics', 'southern_extratropics'])\n",
+ "mask.name = 'area'\n",
+ "\n",
+ "mask = mask.where(rpss.t2m.isel(lead_time=0, drop=True).notnull())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# weighted area mean\n",
+ "weights = np.cos(np.deg2rad(np.abs(mask.latitude)))\n",
+ "scores = (rpss*mask).weighted(weights).mean('latitude').mean('longitude')\n",
+ "pd_scores = scores.reset_coords(drop=True).to_dataframe().unstack(0).round(2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# final score\n",
+ "scores = rpss.weighted(weights).mean('latitude').mean('longitude')\n",
+ "# spatially weighted score averaged over lead_times and variables to one single value"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<xarray.DataArray ()>\n",
+ "array(-0.00725325)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# score transfered to leaderboard\n",
+ "scores = scores.to_array().mean().reset_coords(drop=True)\n",
+ "print(scores)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.6"
+ },
+ "toc-autonumbering": true
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/mean_bias_reduction.ipynb b/notebooks/mean_bias_reduction.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..304db87f1659e3348673a5f4571b125c30492713
--- /dev/null
+++ b/notebooks/mean_bias_reduction.ipynb
@@ -0,0 +1,887 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Train ML model to correct predictions of week 3-4 & 5-6\n",
+ "\n",
+ "This notebook create a Machine Learning `ML_model` to predict weeks 3-4 & 5-6 based on `S2S` weeks 3-4 & 5-6 forecasts and is compared to `CPC` observations for the [`s2s-ai-challenge`](https://s2s-ai-challenge.github.io/)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Synopsis"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Method: `mean bias reduction`\n",
+ "\n",
+ "- calculate the mean bias from 2000-2019 deterministic ensemble mean forecast\n",
+ "- remove that mean bias from 2020 forecast deterministic ensemble mean forecast\n",
+ "- no Machine Learning used here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Data used\n",
+ "\n",
+ "type: renku datasets\n",
+ "\n",
+ "Training-input for Machine Learning model:\n",
+ "- hindcasts of models:\n",
+ " - ECMWF: `ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr`\n",
+ "\n",
+ "Forecast-input for Machine Learning model:\n",
+ "- real-time 2020 forecasts of models:\n",
+ " - ECMWF: `ecmwf_forecast-input_2020_biweekly_deterministic.zarr`\n",
+ "\n",
+ "Compare Machine Learning model forecast against against ground truth:\n",
+ "- `CPC` observations:\n",
+ " - `hindcast-like-observations_biweekly_deterministic.zarr`\n",
+ " - `forecast-like-observations_2020_biweekly_deterministic.zarr`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Resources used\n",
+ "for training\n",
+ "\n",
+ "- platform: MPI-M supercompute 1 Node\n",
+ "- memory: 64 GB\n",
+ "- processors: 36 CPU\n",
+ "- storage required: 10 GB"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Safeguards\n",
+ "\n",
+ "All points have to be [x] checked. If not, your submission is invalid.\n",
+ "\n",
+ "Changes to the code after submissions are not possible, as the `commit` before the `tag` will be reviewed.\n",
+ "(Only in exceptions and if previous effort in reproducibility can be found, it may be allowed to improve readability and reproducibility after November 1st 2021.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Safeguards to prevent [overfitting](https://en.wikipedia.org/wiki/Overfitting?wprov=sfti1) \n",
+ "\n",
+ "If the organizers suspect overfitting, your contribution can be disqualified.\n",
+ "\n",
+ " - [x] We didnt use 2020 observations in training (explicit overfitting and cheating)\n",
+ " - [x] We didnt repeatedly verify my model on 2020 observations and incrementally improved my RPSS (implicit overfitting)\n",
+ " - [x] We provide RPS scores for the training period with script `print_RPS_per_year`, see in section 6.3 `predict`.\n",
+ " - [x] We tried our best to prevent [data leakage](https://en.wikipedia.org/wiki/Leakage_(machine_learning)?wprov=sfti1).\n",
+ " - [x] We honor the `train-validate-test` [split principle](https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets). This means that the hindcast data is split into `train` and `validate`, whereas `test` is withheld.\n",
+ " - [x] We did use `test` explicitly in training or implicitly in incrementally adjusting parameters.\n",
+ " - [x] We considered [cross-validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics))."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Safeguards for Reproducibility\n",
+ "Notebook/code must be independently reproducible from scratch by the organizers (after the competition), if not possible: no prize\n",
+ " - [x] All training data is publicly available (no pre-trained private neural networks, as they are not reproducible for us)\n",
+ " - [x] Code is well documented, readable and reproducible.\n",
+ " - [x] Code to reproduce training and predictions should run within a day on the described architecture. If the training takes longer than a day, please justify why this is needed. Please do not submit training piplelines, which take weeks to train."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import xarray as xr\n",
+ "xr.set_options(display_style='text')\n",
+ "import numpy as np\n",
+ "\n",
+ "from dask.utils import format_bytes\n",
+ "import xskillscore as xs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Get training data\n",
+ "\n",
+ "preprocessing of input data may be done in separate notebook/script"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Hindcast\n",
+ "\n",
+ "get weekly initialized hindcasts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
+ "\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "# preprocessed as renku dataset\n",
+ "!renku storage pull ../data/ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "hind_2000_2019 = xr.open_zarr(\"../data/ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr\", consolidated=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
+ "\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "# preprocessed as renku dataset\n",
+ "!renku storage pull ../data/ecmwf_forecast-input_2020_biweekly_deterministic.zarr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fct_2020 = xr.open_zarr(\"../data/ecmwf_forecast-input_2020_biweekly_deterministic.zarr\", consolidated=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Observations\n",
+ "corresponding to hindcasts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
+ "\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "# preprocessed as renku dataset\n",
+ "!renku storage pull ../data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "obs_2000_2019 = xr.open_zarr(\"../data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr\", consolidated=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
+ "\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "# preprocessed as renku dataset\n",
+ "!renku storage pull ../data/forecast-like-observations_2020_biweekly_deterministic.zarr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "obs_2020 = xr.open_zarr(\"../data/forecast-like-observations_2020_biweekly_deterministic.zarr\", consolidated=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# no ML model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# mask, needed?\n",
+ "hind_2000_2019 = hind_2000_2019.where(obs_2000_2019.isel(forecast_time=0, lead_time=0,drop=True).notnull())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/accessor_dt.py:381: FutureWarning: dt.weekofyear and dt.week have been deprecated. Please use dt.isocalendar().week instead.\n",
+ " FutureWarning,\n",
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
+ " x = np.divide(x1, x2, out)\n"
+ ]
+ }
+ ],
+ "source": [
+ "bias_2000_2019 = (hind_2000_2019.mean('realization') - obs_2000_2019).groupby('forecast_time.weekofyear').mean().compute()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## `predict`\n",
+ "\n",
+ "Create predictions and print `mean(variable, lead_time, longitude, weighted latitude)` RPS for all years as calculated by `print_RPS_per_year`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scripts import make_probabilistic"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33m\u001b[1mWarning: \u001b[0mRun CLI commands only from project's root directory.\n",
+ "\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "!renku storage pull ../data/hindcast-like-observations_2000-2019_biweekly_tercile-edges.nc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cache_path='../data'\n",
+ "tercile_file = f'{cache_path}/hindcast-like-observations_2000-2019_biweekly_tercile-edges.nc'\n",
+ "tercile_edges = xr.open_dataset(tercile_file)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# this is not useful but results have expected dimensions\n",
+ "# actually train for each lead_time\n",
+ "\n",
+ "def create_predictions(fct, bias):\n",
+ " preds = fct - bias.sel(weekofyear=fct.forecast_time.dt.weekofyear)\n",
+ " preds = make_probabilistic(preds, tercile_edges)\n",
+ " return preds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### `predict` training period in-sample"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!renku storage pull ../data/forecast-like-observations_2020_biweekly_terciled.nc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!renku storage pull ../data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scripts import print_RPS_per_year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/accessor_dt.py:381: FutureWarning: dt.weekofyear and dt.week have been deprecated. Please use dt.isocalendar().week instead.\n",
+ " FutureWarning,\n",
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/accessor_dt.py:381: FutureWarning: dt.weekofyear and dt.week have been deprecated. Please use dt.isocalendar().week instead.\n",
+ " FutureWarning,\n"
+ ]
+ }
+ ],
+ "source": [
+ "preds_is = create_predictions(hind_2000_2019, bias_2000_2019).compute()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>RPS</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>year</th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2000</th>\n",
+ " <td>0.463290</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2001</th>\n",
+ " <td>0.501615</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2002</th>\n",
+ " <td>0.498100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2003</th>\n",
+ " <td>0.499914</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2004</th>\n",
+ " <td>0.533146</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2005</th>\n",
+ " <td>0.486682</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2006</th>\n",
+ " <td>0.492787</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2007</th>\n",
+ " <td>0.555934</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2008</th>\n",
+ " <td>0.507756</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2009</th>\n",
+ " <td>0.515228</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2010</th>\n",
+ " <td>0.498032</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2011</th>\n",
+ " <td>0.548217</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2012</th>\n",
+ " <td>0.556501</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2013</th>\n",
+ " <td>0.519008</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2014</th>\n",
+ " <td>0.521487</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2015</th>\n",
+ " <td>0.507068</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2016</th>\n",
+ " <td>0.520476</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017</th>\n",
+ " <td>0.590591</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2018</th>\n",
+ " <td>0.604847</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2019</th>\n",
+ " <td>0.546725</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " RPS\n",
+ "year \n",
+ "2000 0.463290\n",
+ "2001 0.501615\n",
+ "2002 0.498100\n",
+ "2003 0.499914\n",
+ "2004 0.533146\n",
+ "2005 0.486682\n",
+ "2006 0.492787\n",
+ "2007 0.555934\n",
+ "2008 0.507756\n",
+ "2009 0.515228\n",
+ "2010 0.498032\n",
+ "2011 0.548217\n",
+ "2012 0.556501\n",
+ "2013 0.519008\n",
+ "2014 0.521487\n",
+ "2015 0.507068\n",
+ "2016 0.520476\n",
+ "2017 0.590591\n",
+ "2018 0.604847\n",
+ "2019 0.546725"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "print_RPS_per_year(preds_is)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### `predict` test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/accessor_dt.py:381: FutureWarning: dt.weekofyear and dt.week have been deprecated. Please use dt.isocalendar().week instead.\n",
+ " FutureWarning,\n",
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/accessor_dt.py:381: FutureWarning: dt.weekofyear and dt.week have been deprecated. Please use dt.isocalendar().week instead.\n",
+ " FutureWarning,\n"
+ ]
+ }
+ ],
+ "source": [
+ "preds_test = create_predictions(fct_2020, bias_2000_2019)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>RPS</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>year</th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2020</th>\n",
+ " <td>0.520714</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " RPS\n",
+ "year \n",
+ "2020 0.520714"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "print_RPS_per_year(preds_test)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Submission"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'147.76 MB'"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "format_bytes(preds_test.nbytes)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scripts import assert_predictions_2020\n",
+ "assert_predictions_2020(preds_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "del preds_test['weekofyear']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "preds_test.to_netcdf('../submissions/ML_prediction_2020.nc')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!git add ../submissions/ML_prediction_2020.nc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[AS_initial 62f4de3] template_test no ML mean bias reduction\n",
+ " 1 file changed, 2 insertions(+), 2 deletions(-)\n"
+ ]
+ }
+ ],
+ "source": [
+ "!git commit -m \"template_test no ML mean bias reduction\" # whatever message you want"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!git tag \"submission-no_ML_mean_bias_reduction-0.0.1\" # if this is to be checked by scorer, only the last submitted==tagged version will be considered"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!git push --tags"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Reproducibility"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## memory"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " total used free shared buffers cached\n",
+ "Mem: 62 21 41 0 0 5\n",
+ "-/+ buffers/cache: 15 47\n",
+ "Swap: 0 0 0\n"
+ ]
+ }
+ ],
+ "source": [
+ "# https://phoenixnap.com/kb/linux-commands-check-memory-usage\n",
+ "!free -g"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## CPU"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Architecture: x86_64\n",
+ "CPU op-mode(s): 32-bit, 64-bit\n",
+ "Byte Order: Little Endian\n",
+ "CPU(s): 72\n",
+ "On-line CPU(s) list: 0-71\n",
+ "Thread(s) per core: 2\n",
+ "Core(s) per socket: 18\n",
+ "Socket(s): 2\n",
+ "NUMA node(s): 2\n",
+ "Vendor ID: GenuineIntel\n",
+ "CPU family: 6\n",
+ "Model: 79\n",
+ "Model name: Intel(R) Xeon(R) CPU E5-2695 v4 @ 2.10GHz\n",
+ "Stepping: 1\n",
+ "CPU MHz: 1200.000\n",
+ "BogoMIPS: 4190.00\n",
+ "Virtualization: VT-x\n",
+ "L1d cache: 32K\n",
+ "L1i cache: 32K\n",
+ "L2 cache: 256K\n",
+ "L3 cache: 46080K\n",
+ "NUMA node0 CPU(s): 0-17,36-53\n",
+ "NUMA node1 CPU(s): 18-35,54-71\n"
+ ]
+ }
+ ],
+ "source": [
+ "!lscpu"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## software"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!conda list"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:s2s-ai]",
+ "language": "python",
+ "name": "conda-env-s2s-ai-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.10"
+ },
+ "toc-autonumbering": true
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/renku_datasets_biweekly.ipynb b/notebooks/renku_datasets_biweekly.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..538cc801df03cc546146a600ea24755a59a606ab
--- /dev/null
+++ b/notebooks/renku_datasets_biweekly.ipynb
@@ -0,0 +1,1445 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Create biweekly renku datasets from `climetlab-s2s-ai-challenge`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Goal:\n",
+ "\n",
+ "- Create biweekly renku datasets from [`climatelab-s2s-ai-challenge`](https://github.com/ecmwf-lab/climetlab-s2s-ai-challenge).\n",
+ "- These renku datasets are then used in notebooks:\n",
+ " - `ML_train_and_predict.ipynb` to train the ML model and do ML-based predictions\n",
+ " - `RPSS_verification.ipynb` to calculate RPSS of the ML model\n",
+ "\n",
+ "Requirements:\n",
+ "- [`climetlab`](https://github.com/ecmwf/climetlab)\n",
+ "- [`climatelab-s2s-ai-challenge`](https://github.com/ecmwf-lab/climetlab-s2s-ai-challenge)\n",
+ "- S2S and CPC observations uploaded on [European Weather Cloud (EWC)](https://storage.ecmwf.europeanweather.cloud/s2s-ai-challenge/data/training-input/0.3.0/netcdf/index.html)\n",
+ "\n",
+ "Output: [renku dataset](https://renku-python.readthedocs.io/en/latest/commands.html#module-renku.cli.dataset) `s2s-ai-challenge`\n",
+ "- observations\n",
+ " - deterministic:\n",
+ " - `hindcast-like-observations_2000-2019_biweekly_deterministic.zarr`\n",
+ " - `forecast-like-observations_2020_biweekly_deterministic.zarr`\n",
+ " - edges:\n",
+ " - `hindcast-like-observations_2000-2019_biweekly_tercile-edges.nc`\n",
+ " - probabilistic:\n",
+ " - `hindcast-like-observations_2000-2019_biweekly_terciled.zarr`\n",
+ " - `forecast-like-observations_2020_biweekly_terciled.nc`\n",
+ "- forecasts/hindcasts\n",
+ " - deterministic:\n",
+ " - `ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr`\n",
+ " - `ecmwf_forecast-input_2020_biweekly_deterministic.zarr`\n",
+ " - more models could be added\n",
+ "- benchmark:\n",
+ " - probabilistic:\n",
+ " - `ecmwf_recalibrated_benchmark_2020_biweekly_terciled.nc`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!renku dataset create s2s-ai-challenge"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING: ecmwflibs universal: cannot find a library called MagPlus\n",
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/climetlab/plotting/drivers/magics/actions.py:36: UserWarning: Magics library could not be found\n",
+ " warnings.warn(str(e))\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<xarray.core.options.set_options at 0x2b2b3825c090>"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import climetlab as cml\n",
+ "import matplotlib.pyplot as plt\n",
+ "import xarray as xr\n",
+ "import xskillscore as xs\n",
+ "import pandas as pd\n",
+ "\n",
+ "xr.set_options(keep_attrs=True)\n",
+ "xr.set_options(display_style='text')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# caching path for climetlab\n",
+ "cache_path = \"/work/mh0727/m300524/S2S_AI/cache\" # set your own path\n",
+ "cml.settings.set(\"cache-directory\", cache_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cache_path = \"../data\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Climetlab version : 0.7.0\n",
+ "Climetlab-s2s-ai-challenge plugin version : 0.6.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "import climetlab_s2s_ai_challenge\n",
+ "print(f'Climetlab version : {cml.__version__}')\n",
+ "print(f'Climetlab-s2s-ai-challenge plugin version : {climetlab_s2s_ai_challenge.__version__}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Download and cache\n",
+ "\n",
+ "Download all files for the observations, forecast and hindcast."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['20200102', '20200109', '20200116', '20200123', '20200130']"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dates = xr.cftime_range(start='20200102',freq='7D', periods=53).strftime('%Y%m%d').to_list()\n",
+ "dates[:5]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## observations `output-reference`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "obs_dataset_labels = ['training-output-reference','test-output-reference'] # ML community\n",
+ "# equiv to\n",
+ "obs_dataset_labels = ['hindcast-like-observations','forecast-like-observations'] # NWP community"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "varlist_obs = ['tp','t2m']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%time\n",
+ "# takes 5h for one model\n",
+ "for v in varlist_obs:\n",
+ " for d in dates:\n",
+ " for ds in obs_dataset_labels:\n",
+ " print(ds,v,d)\n",
+ " # only netcdf, no choice\n",
+ " cml.load_dataset(f\"s2s-ai-challenge-{ds}\", date=d, parameter=v).to_xarray()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## hindcast and forecast `input`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "forecast_dataset_labels = ['training-input','test-input'] # ML community\n",
+ "# equiv to\n",
+ "forecast_dataset_labels = ['hindcast-input','forecast-input'] # NWP community"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "varlist_forecast = ['tp','t2m']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model_list = ['ecmwf']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%time\n",
+ "# takes 4-5h for one model\n",
+ "for model in model_list:\n",
+ " for v in varlist_forecast:\n",
+ " for d in dates:\n",
+ " for ds in forecast_dataset_labels:\n",
+ " print(ds,v,d)\n",
+ " # take netcdf format\n",
+ " cml.load_dataset(f\"s2s-ai-challenge-{ds}\", origin=model, date=d, parameter=v, format='netcdf').to_xarray()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# create bi-weekly aggregates"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scripts import add_valid_time_from_forecast_reference_time_and_lead_time"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<pre><xarray.DataArray (lead_time: 14)>\n",
+ "array([1209600000000000, 1296000000000000, 1382400000000000,\n",
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+ " 1728000000000000, 1814400000000000, 1900800000000000,\n",
+ " 1987200000000000, 2073600000000000, 2160000000000000,\n",
+ " 2246400000000000, 2332800000000000], dtype='timedelta64[ns]')\n",
+ "Coordinates:\n",
+ " * lead_time (lead_time) timedelta64[ns] 14 days 15 days ... 26 days 27 days</pre>"
+ ],
+ "text/plain": [
+ "<xarray.DataArray (lead_time: 14)>\n",
+ "array([1209600000000000, 1296000000000000, 1382400000000000,\n",
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+ " 2246400000000000, 2332800000000000], dtype='timedelta64[ns]')\n",
+ "Coordinates:\n",
+ " * lead_time (lead_time) timedelta64[ns] 14 days 15 days ... 26 days 27 days"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "w34 = [pd.Timedelta(f'{i} d') for i in range(14,28)]\n",
+ "w34 = xr.DataArray(w34,dims='lead_time', coords={'lead_time':w34})\n",
+ "w34"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<pre><xarray.DataArray (lead_time: 14)>\n",
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+ "Coordinates:\n",
+ " * lead_time (lead_time) timedelta64[ns] 28 days 29 days ... 40 days 41 days</pre>"
+ ],
+ "text/plain": [
+ "<xarray.DataArray (lead_time: 14)>\n",
+ "array([2419200000000000, 2505600000000000, 2592000000000000,\n",
+ " 2678400000000000, 2764800000000000, 2851200000000000,\n",
+ " 2937600000000000, 3024000000000000, 3110400000000000,\n",
+ " 3196800000000000, 3283200000000000, 3369600000000000,\n",
+ " 3456000000000000, 3542400000000000], dtype='timedelta64[ns]')\n",
+ "Coordinates:\n",
+ " * lead_time (lead_time) timedelta64[ns] 28 days 29 days ... 40 days 41 days"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "w56 = [pd.Timedelta(f'{i} d') for i in range(28,42)]\n",
+ "w56 = xr.DataArray(w56,dims='lead_time', coords={'lead_time':w56})\n",
+ "w56"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# starting dates forecast_time in 2020\n",
+ "dates = xr.cftime_range(start='20200102',freq='7D', periods=53).strftime('%Y%m%d').to_list()\n",
+ "\n",
+ "biweekly_lead = [pd.Timedelta(f\"{i} d\") for i in [14, 28]] # take first day of biweekly average \n",
+ "# alternative: new lead_time coordinate: use 21 35 as mid points of week 3-4 and week 5-6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "hindcast-input tp\n",
+ "By downloading data from this dataset, you agree to the terms and conditions defined at https://apps.ecmwf.int/datasets/data/s2s/licence/. If you do not agree with such terms, do not download the data. \n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING: ecmwflibs universal: found eccodes at /work/mh0727/m300524/conda-envs/s2s-ai/lib/libeccodes.so\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Warning: ecCodes 2.21.0 or higher is recommended. You are running version 2.12.3\n",
+ "hindcast-input t2m\n",
+ "save to: ../data/ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr\n",
+ "forecast-input tp\n",
+ "By downloading data from this dataset, you agree to the terms and conditions defined at https://apps.ecmwf.int/datasets/data/s2s/licence/. If you do not agree with such terms, do not download the data. \n",
+ "forecast-input t2m\n",
+ "save to: ../data/ecmwf_forecast-input_2020_biweekly_deterministic.zarr\n",
+ "hindcast-like-observations tp\n",
+ "By downloading data from this dataset, you agree to the terms and conditions defined at https://apps.ecmwf.int/datasets/data/s2s/licence/. If you do not agree with such terms, do not download the data. This dataset has been dowloaded from IRIDL. By downloading this data you also agree to the terms and conditions defined at https://iridl.ldeo.columbia.edu.\n",
+ "hindcast-like-observations t2m\n",
+ "save to: ../data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
+ " x = np.divide(x1, x2, out)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "forecast-like-observations tp\n",
+ "By downloading data from this dataset, you agree to the terms and conditions defined at https://apps.ecmwf.int/datasets/data/s2s/licence/. If you do not agree with such terms, do not download the data. This dataset has been dowloaded from IRIDL. By downloading this data you also agree to the terms and conditions defined at https://iridl.ldeo.columbia.edu.\n",
+ "forecast-like-observations t2m\n",
+ "save to: ../data/forecast-like-observations_2020_biweekly_deterministic.zarr\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
+ " x = np.divide(x1, x2, out)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# refactor after closed: https://github.com/ecmwf/climetlab/issues/16 https://github.com/ecmwf-lab/climetlab-s2s-ai-challenge/issues/9\n",
+ "# then, no need to loop over dates\n",
+ "model = 'ecmwf'\n",
+ "for dsl in forecast_dataset_labels + obs_dataset_labels: # dataset labels\n",
+ " for v in varlist_forecast:\n",
+ " print(dsl,v)\n",
+ " ds = []\n",
+ " for d in dates:\n",
+ " if 'input' in dsl:\n",
+ " # just ecmwf model for now\n",
+ " ds2 = cml.load_dataset(f\"s2s-ai-challenge-{dsl}\", origin=model, date=d, parameter=v, format='netcdf').to_xarray()\n",
+ " elif 'observation' in dsl: # obs only netcdf, no choice\n",
+ " ds2 = cml.load_dataset(f\"s2s-ai-challenge-{dsl}\", date=d, parameter=v).to_xarray().chunk()\n",
+ " if v == 't2m': # biweekly mean\n",
+ " d34 = ds2.sel(lead_time=w34).mean('lead_time')\n",
+ " d56 = ds2.sel(lead_time=w56).mean('lead_time')\n",
+ " ds2 = xr.concat([d34,d56],'lead_time').assign_coords(lead_time=biweekly_lead)\n",
+ " elif v=='tp': # biweekly sum\n",
+ " d34 = ds2.sel(lead_time=pd.Timedelta(\"27 d\")) - ds2.sel(lead_time=pd.Timedelta(\"13 d\")) # tp from day 14 to day 27\n",
+ " d56 = ds2.sel(lead_time=pd.Timedelta(\"41 d\")) - ds2.sel(lead_time=pd.Timedelta(\"27 d\")) # tp from day 28 to day 42\n",
+ " ds2 = xr.concat([d34,d56],'lead_time').assign_coords(lead_time=biweekly_lead)\n",
+ " ds.append(ds2.chunk({'forecast_time':1}))\n",
+ " \n",
+ " # sort forecast_time\n",
+ " ds = xr.concat(ds, 'forecast_time').sortby('forecast_time')\n",
+ " ds = add_valid_time_from_forecast_reference_time_and_lead_time(ds)\n",
+ " ds.lead_time.attrs['comment'] = 'lead_time describes bi-weekly aggregates. The pd.Timedelta corresponds to the first day of the '\n",
+ "\n",
+ " # first run on tp and add t2m to that dataset\n",
+ " if v == 'tp':\n",
+ " ds_ = ds.copy()\n",
+ " elif v == 't2m':\n",
+ " ds_[v] = ds[v]\n",
+ " ds = ds_\n",
+ "\n",
+ " if 'test' in dsl:\n",
+ " ds = ds.chunk('auto')\n",
+ " else:\n",
+ " ds = ds.chunk({'forecast_time':12,'lead_time':-1,'longitude':'auto','latitude':'auto'}) # 3 month init chunk\n",
+ " \n",
+ " if 'hindcast' in dsl:\n",
+ " time = '2000-2019'\n",
+ " if 'input' in dsl:\n",
+ " name = f'{model}_{dsl}'\n",
+ " elif 'observations':\n",
+ " name = dsl\n",
+ " \n",
+ " elif 'forecast' in dsl:\n",
+ " time = '2020'\n",
+ " if 'input' in dsl:\n",
+ " name = f'{model}_{dsl}'\n",
+ " elif 'observations':\n",
+ " name = dsl\n",
+ " else:\n",
+ " assert False\n",
+ " \n",
+ " # pattern: {model if applies _}{observations/forecast/hindcast}_{time}_biweekly_deterministic.zarr\n",
+ " zp = f'{cache_path}/{name}_{time}_biweekly_deterministic.zarr'\n",
+ " print(f'save to: {zp}')\n",
+ " ds.to_zarr(zp, consolidated=True, mode='w')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## add to `renku` dataset `s2s-ai-challenge`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# observations as hindcast\n",
+ "# run renku commands from projects root directory only\n",
+ "# !renku dataset add s2s-ai-challenge data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# for further use retrieve from git lfs\n",
+ "# !renku storage pull ../data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Frozen(SortedKeysDict({'forecast_time': 1060, 'latitude': 121, 'lead_time': 2, 'longitude': 240})) \n",
+ " Coordinates:\n",
+ " * forecast_time (forecast_time) datetime64[ns] 2000-01-02 ... 2019-12-31\n",
+ " * latitude (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+ " * lead_time (lead_time) timedelta64[ns] 14 days 28 days\n",
+ " * longitude (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+ " valid_time (lead_time, forecast_time) datetime64[ns] dask.array<chunksize=(2, 1060), meta=np.ndarray> \n",
+ " 985.065144 MB\n"
+ ]
+ }
+ ],
+ "source": [
+ "obs_2000_2019 = xr.open_zarr(f\"{cache_path}/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr\", consolidated=True)\n",
+ "print(obs_2000_2019.sizes,'\\n',obs_2000_2019.coords,'\\n', obs_2000_2019.nbytes/1e6,'MB')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# observations as forecast\n",
+ "# run renku commands from projects root directory only\n",
+ "# !renku dataset add s2s-ai-challenge data/forecast-like-observations_2020_biweekly_deterministic.zarr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Frozen(SortedKeysDict({'forecast_time': 53, 'latitude': 121, 'lead_time': 2, 'longitude': 240})) \n",
+ " Coordinates:\n",
+ " * forecast_time (forecast_time) datetime64[ns] 2020-01-02 ... 2020-12-31\n",
+ " * latitude (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+ " * lead_time (lead_time) timedelta64[ns] 14 days 28 days\n",
+ " * longitude (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+ " valid_time (lead_time, forecast_time) datetime64[ns] dask.array<chunksize=(2, 53), meta=np.ndarray> \n",
+ " 49.256016 MB\n"
+ ]
+ }
+ ],
+ "source": [
+ "obs_2020 = xr.open_zarr(f\"{cache_path}/forecast-like-observations_2020_biweekly_deterministic.zarr\", consolidated=True)\n",
+ "print(obs_2020.sizes,'\\n',obs_2020.coords,'\\n', obs_2020.nbytes/1e6,'MB')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# ecmwf hindcast-input\n",
+ "# run renku commands from projects root directory only\n",
+ "# !renku dataset add s2s-ai-challenge data/ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Frozen(SortedKeysDict({'forecast_time': 1060, 'latitude': 121, 'lead_time': 2, 'longitude': 240, 'realization': 11})) \n",
+ " Coordinates:\n",
+ " * forecast_time (forecast_time) datetime64[ns] 2000-01-02 ... 2019-12-31\n",
+ " * latitude (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+ " * lead_time (lead_time) timedelta64[ns] 14 days 28 days\n",
+ " * longitude (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+ " * realization (realization) int64 0 1 2 3 4 5 6 7 8 9 10\n",
+ " valid_time (lead_time, forecast_time) datetime64[ns] dask.array<chunksize=(2, 1060), meta=np.ndarray> \n",
+ " 5417.730832 MB\n"
+ ]
+ }
+ ],
+ "source": [
+ "hind_2000_2019 = xr.open_zarr(f\"{cache_path}/ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr\", consolidated=True)\n",
+ "print(hind_2000_2019.sizes,'\\n',hind_2000_2019.coords,'\\n', hind_2000_2019.nbytes/1e6,'MB')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# ecmwf forecast-input\n",
+ "# run renku commands from projects root directory only\n",
+ "# !renku dataset add s2s-ai-challenge data/ecmwf_forecast-input_2020_biweekly_deterministic.zarr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Frozen(SortedKeysDict({'forecast_time': 53, 'latitude': 121, 'lead_time': 2, 'longitude': 240, 'realization': 51})) \n",
+ " Coordinates:\n",
+ " * forecast_time (forecast_time) datetime64[ns] 2020-01-02 ... 2020-12-31\n",
+ " * latitude (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+ " * lead_time (lead_time) timedelta64[ns] 14 days 28 days\n",
+ " * longitude (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+ " * realization (realization) int64 0 1 2 3 4 5 6 7 ... 44 45 46 47 48 49 50\n",
+ " valid_time (lead_time, forecast_time) datetime64[ns] dask.array<chunksize=(2, 53), meta=np.ndarray> \n",
+ " 1255.926504 MB\n"
+ ]
+ }
+ ],
+ "source": [
+ "fct_2020 = xr.open_zarr(f\"{cache_path}/ecmwf_forecast-input_2020_biweekly_deterministic.zarr\", consolidated=True)\n",
+ "print(fct_2020.sizes,'\\n',fct_2020.coords,'\\n', fct_2020.nbytes/1e6,'MB')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# tercile edges\n",
+ "\n",
+ "Create 2 tercile edges at 1/3 and 2/3 quantiles of the 2000-2019 biweekly distrbution for each week of the year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tercile_file = f'{cache_path}/hindcast-like-observations_2000-2019_biweekly_tercile-edges.nc'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/accessor_dt.py:381: FutureWarning: dt.weekofyear and dt.week have been deprecated. Please use dt.isocalendar().week instead.\n",
+ " FutureWarning,\n",
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1390: RuntimeWarning: All-NaN slice encountered\n",
+ " overwrite_input, interpolation)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 15min 49s, sys: 6min 16s, total: 22min 6s\n",
+ "Wall time: 13min 14s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "xr.open_zarr(f'{cache_path}/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr',\n",
+ " consolidated=True).chunk({'forecast_time':-1,'longitude':'auto'}).groupby('forecast_time.weekofyear').quantile(q=[1./3.,2./3.], dim=['forecast_time']).rename({'quantile':'category_edge'}).to_netcdf(tercile_file)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<pre><xarray.Dataset>\n",
+ "Dimensions: (category_edge: 2, latitude: 121, lead_time: 2, longitude: 240, weekofyear: 53)\n",
+ "Coordinates:\n",
+ " * latitude (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+ " * lead_time (lead_time) timedelta64[ns] 14 days 28 days\n",
+ " * longitude (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+ " * category_edge (category_edge) float64 0.3333 0.6667\n",
+ " * weekofyear (weekofyear) int64 1 2 3 4 5 6 7 8 ... 47 48 49 50 51 52 53\n",
+ "Data variables:\n",
+ " t2m (weekofyear, category_edge, lead_time, latitude, longitude) float64 ...\n",
+ " tp (weekofyear, category_edge, lead_time, latitude, longitude) float64 ...</pre>"
+ ],
+ "text/plain": [
+ "<xarray.Dataset>\n",
+ "Dimensions: (category_edge: 2, latitude: 121, lead_time: 2, longitude: 240, weekofyear: 53)\n",
+ "Coordinates:\n",
+ " * latitude (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+ " * lead_time (lead_time) timedelta64[ns] 14 days 28 days\n",
+ " * longitude (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+ " * category_edge (category_edge) float64 0.3333 0.6667\n",
+ " * weekofyear (weekofyear) int64 1 2 3 4 5 6 7 8 ... 47 48 49 50 51 52 53\n",
+ "Data variables:\n",
+ " t2m (weekofyear, category_edge, lead_time, latitude, longitude) float64 ...\n",
+ " tp (weekofyear, category_edge, lead_time, latitude, longitude) float64 ..."
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tercile_edges = xr.open_dataset(tercile_file)\n",
+ "\n",
+ "tercile_edges"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(98.507024, 'MB')"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tercile_edges.nbytes*1e-6,'MB'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# run renku commands from projects root directory only\n",
+ "# tercile edges\n",
+ "#!renku dataset add s2s-ai-challenge data/hindcast-like-observations_2000-2019_biweekly_tercile-edges.nc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# to use retrieve from git lfs\n",
+ "#!renku storage pull ../data/hindcast-like-observations_2000-2019_biweekly_tercile-edges.nc\n",
+ "#xr.open_dataset(\"../data/hindcast-like-observations_2000-2019_biweekly_tercile-edges.nc\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# observations in categories\n",
+ "\n",
+ "- counting how many deterministic forecasts realizations fall into each category, like counting rps\n",
+ "- categorize forecast-like-observations 2020 into categories"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Frozen(SortedKeysDict({'forecast_time': 53, 'latitude': 121, 'lead_time': 2, 'longitude': 240}))"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "obs_2020 = xr.open_zarr(f'{cache_path}/forecast-like-observations_2020_biweekly_deterministic.zarr', consolidated=True)\n",
+ "obs_2020.sizes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a mask for land grid\n",
+ "mask = obs_2020.std(['lead_time','forecast_time']).notnull()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# mask.to_array().plot(col='variable')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tercile_edges = xr.open_dataset(tercile_file)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<pre><xarray.DataArray 'tp' (weekofyear: 53, category_edge: 2, lead_time: 2, latitude: 121, longitude: 240)>\n",
+ "[6156480 values with dtype=float64]\n",
+ "Coordinates:\n",
+ " * latitude (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+ " * lead_time (lead_time) timedelta64[ns] 14 days 28 days\n",
+ " * longitude (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+ " * category_edge (category_edge) float64 0.3333 0.6667\n",
+ " * weekofyear (weekofyear) int64 1 2 3 4 5 6 7 8 ... 47 48 49 50 51 52 53\n",
+ "Attributes:\n",
+ " comment: precipitation accumulated since lead_time including 0 days\n",
+ " long_name: total precipitation\n",
+ " pointwidth: 0\n",
+ " standard_name: precipitation_amount\n",
+ " units: kg m-2</pre>"
+ ],
+ "text/plain": [
+ "<xarray.DataArray 'tp' (weekofyear: 53, category_edge: 2, lead_time: 2, latitude: 121, longitude: 240)>\n",
+ "[6156480 values with dtype=float64]\n",
+ "Coordinates:\n",
+ " * latitude (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+ " * lead_time (lead_time) timedelta64[ns] 14 days 28 days\n",
+ " * longitude (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+ " * category_edge (category_edge) float64 0.3333 0.6667\n",
+ " * weekofyear (weekofyear) int64 1 2 3 4 5 6 7 8 ... 47 48 49 50 51 52 53\n",
+ "Attributes:\n",
+ " comment: precipitation accumulated since lead_time including 0 days\n",
+ " long_name: total precipitation\n",
+ " pointwidth: 0\n",
+ " standard_name: precipitation_amount\n",
+ " units: kg m-2"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tercile_edges.tp"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# total precipitation in arid regions are masked\n",
+ "# Frederic Vitart suggested by email: \"Based on your map we could mask all the areas where the lower tercile boundary is lower than 0.1 mm\"\n",
+ "# we are using a dry mask as in https://doi.org/10.1175/MWR-D-17-0092.1\n",
+ "th = 0.01\n",
+ "tp_arid_mask = tercile_edges.tp.isel(category_edge=0, lead_time=0, drop=True) > th\n",
+ "#tp_arid_mask.where(mask.tp).plot(col='forecast_time', col_wrap=4)\n",
+ "#plt.suptitle(f'dry mask: week 3-4 tp 1/3 category_edge > {th} kg m-2',y=1., x=.4)\n",
+ "#plt.savefig('dry_mask.png')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# look into tercile edges"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#tercile_edges.isel(forecast_time=0)['tp'].plot(col='lead_time',row='category_edge', robust=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#tercile_edges.isel(forecast_time=[0,20],category_edge=1)['tp'].plot(col='lead_time', row='forecast_time', robust=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# tercile_edges.tp.mean(['forecast_time']).plot(col='lead_time',row='category_edge',vmax=.5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## categorize observations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scripts import make_probabilistic"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/accessor_dt.py:381: FutureWarning: dt.weekofyear and dt.week have been deprecated. Please use dt.isocalendar().week instead.\n",
+ " FutureWarning,\n"
+ ]
+ }
+ ],
+ "source": [
+ "obs_2020_p = make_probabilistic(obs_2020, tercile_edges, mask=mask)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(147.760264, 'MB')"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "obs_2020_p.nbytes/1e6, 'MB'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
+ " x = np.divide(x1, x2, out)\n"
+ ]
+ }
+ ],
+ "source": [
+ "obs_2020_p.to_netcdf(f'{cache_path}/forecast-like-observations_2020_biweekly_terciled.nc')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# forecast-like-observations terciled\n",
+ "# run renku commands from projects root directory only\n",
+ "# !renku dataset add s2s-ai-challenge data/forecast-like-observations_2020_biweekly_terciled.nc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<pre><xarray.Dataset>\n",
+ "Dimensions: (category: 3, forecast_time: 53, latitude: 121, lead_time: 2, longitude: 240)\n",
+ "Coordinates:\n",
+ " * forecast_time (forecast_time) datetime64[ns] 2020-01-02 ... 2020-12-31\n",
+ " * latitude (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+ " * lead_time (lead_time) timedelta64[ns] 14 days 28 days\n",
+ " * longitude (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+ " valid_time (lead_time, forecast_time) datetime64[ns] ...\n",
+ " weekofyear (forecast_time) int64 ...\n",
+ " * category (category) object 'below normal' 'near normal' 'above normal'\n",
+ "Data variables:\n",
+ " t2m (category, lead_time, forecast_time, latitude, longitude) float64 ...\n",
+ " tp (category, lead_time, forecast_time, latitude, longitude) float64 ...</pre>"
+ ],
+ "text/plain": [
+ "<xarray.Dataset>\n",
+ "Dimensions: (category: 3, forecast_time: 53, latitude: 121, lead_time: 2, longitude: 240)\n",
+ "Coordinates:\n",
+ " * forecast_time (forecast_time) datetime64[ns] 2020-01-02 ... 2020-12-31\n",
+ " * latitude (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+ " * lead_time (lead_time) timedelta64[ns] 14 days 28 days\n",
+ " * longitude (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+ " valid_time (lead_time, forecast_time) datetime64[ns] ...\n",
+ " weekofyear (forecast_time) int64 ...\n",
+ " * category (category) object 'below normal' 'near normal' 'above normal'\n",
+ "Data variables:\n",
+ " t2m (category, lead_time, forecast_time, latitude, longitude) float64 ...\n",
+ " tp (category, lead_time, forecast_time, latitude, longitude) float64 ..."
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# to use retrieve from git lfs\n",
+ "#!renku storage pull ../data/forecast-like-observations_2020_biweekly_terciled.nc\n",
+ "xr.open_dataset(\"../data/forecast-like-observations_2020_biweekly_terciled.nc\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "obs_2000_2019 = xr.open_zarr(f'{cache_path}/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr', consolidated=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/accessor_dt.py:381: FutureWarning: dt.weekofyear and dt.week have been deprecated. Please use dt.isocalendar().week instead.\n",
+ " FutureWarning,\n"
+ ]
+ }
+ ],
+ "source": [
+ "obs_2000_2019_p = make_probabilistic(obs_2000_2019, tercile_edges, mask=mask)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(2955.147368, 'MB')"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "obs_2000_2019_p.nbytes/1e6, 'MB'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
+ " x = np.divide(x1, x2, out)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<xarray.backends.zarr.ZarrStore at 0x2b2b614e84b0>"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "obs_2000_2019_p.chunk('auto').to_zarr(f'{cache_path}/hindcast-like-observations_2000-2019_biweekly_terciled.zarr', consolidated=True, mode='w')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# forecast-like-observations terciled\n",
+ "# run renku commands from projects root directory only\n",
+ "# !renku dataset add s2s-ai-challenge data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<pre><xarray.Dataset>\n",
+ "Dimensions: (category: 3, forecast_time: 1060, latitude: 121, lead_time: 2, longitude: 240)\n",
+ "Coordinates:\n",
+ " * category (category) <U12 'below normal' 'near normal' 'above normal'\n",
+ " * forecast_time (forecast_time) datetime64[ns] 2000-01-02 ... 2019-12-31\n",
+ " * latitude (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+ " * lead_time (lead_time) timedelta64[ns] 14 days 28 days\n",
+ " * longitude (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+ " valid_time (lead_time, forecast_time) datetime64[ns] dask.array<chunksize=(2, 1060), meta=np.ndarray>\n",
+ " weekofyear (forecast_time) int64 dask.array<chunksize=(1060,), meta=np.ndarray>\n",
+ "Data variables:\n",
+ " t2m (category, lead_time, forecast_time, latitude, longitude) float64 dask.array<chunksize=(3, 2, 96, 121, 240), meta=np.ndarray>\n",
+ " tp (category, lead_time, forecast_time, latitude, longitude) float64 dask.array<chunksize=(3, 2, 96, 121, 240), meta=np.ndarray></pre>"
+ ],
+ "text/plain": [
+ "<xarray.Dataset>\n",
+ "Dimensions: (category: 3, forecast_time: 1060, latitude: 121, lead_time: 2, longitude: 240)\n",
+ "Coordinates:\n",
+ " * category (category) <U12 'below normal' 'near normal' 'above normal'\n",
+ " * forecast_time (forecast_time) datetime64[ns] 2000-01-02 ... 2019-12-31\n",
+ " * latitude (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+ " * lead_time (lead_time) timedelta64[ns] 14 days 28 days\n",
+ " * longitude (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+ " valid_time (lead_time, forecast_time) datetime64[ns] dask.array<chunksize=(2, 1060), meta=np.ndarray>\n",
+ " weekofyear (forecast_time) int64 dask.array<chunksize=(1060,), meta=np.ndarray>\n",
+ "Data variables:\n",
+ " t2m (category, lead_time, forecast_time, latitude, longitude) float64 dask.array<chunksize=(3, 2, 96, 121, 240), meta=np.ndarray>\n",
+ " tp (category, lead_time, forecast_time, latitude, longitude) float64 dask.array<chunksize=(3, 2, 96, 121, 240), meta=np.ndarray>"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# to use retrieve from git lfs\n",
+ "#!renku storage pull ../data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr\n",
+ "xr.open_zarr(\"../data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# benchmark"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "By downloading data from this dataset, you agree to the terms and conditions defined at https://apps.ecmwf.int/datasets/data/s2s/licence/. If you do not agree with such terms, do not download the data. \n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING: ecmwflibs universal: found eccodes at /work/mh0727/m300524/conda-envs/s2s-ai/lib/libeccodes.so\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Warning: ecCodes 2.21.0 or higher is recommended. You are running version 2.12.3\n"
+ ]
+ }
+ ],
+ "source": [
+ "bench_p = cml.load_dataset(\"s2s-ai-challenge-test-output-benchmark\",\n",
+ " parameter='t2m', \n",
+ " weeks=['34','56']\n",
+ " ).to_xarray()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bench_p['tp'] = cml.load_dataset(\"s2s-ai-challenge-test-output-benchmark\",\n",
+ " parameter='tp', \n",
+ " weeks=['34','56']\n",
+ " ).to_xarray().tp"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bench_p['category'] = ['below normal','near normal','above normal']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bench_p['lead_time'] = biweekly_lead"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bench_p = bench_p / 100\n",
+ "\n",
+ "bench_p.tp.attrs['units']=''\n",
+ "bench_p.t2m.attrs['units']=''"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# bench_p.isel(forecast_time=2).t2m.plot(row='lead_time', col='category')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<pre><xarray.Dataset>\n",
+ "Dimensions: (category: 3, forecast_time: 53, latitude: 121, lead_time: 2, longitude: 240)\n",
+ "Coordinates:\n",
+ " * forecast_time (forecast_time) datetime64[ns] 2020-01-02 ... 2020-12-31\n",
+ " * category (category) <U12 'below normal' 'near normal' 'above normal'\n",
+ " * lead_time (lead_time) timedelta64[ns] 14 days 28 days\n",
+ " * latitude (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+ " * longitude (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+ " valid_time (forecast_time, lead_time) datetime64[ns] dask.array<chunksize=(53, 1), meta=np.ndarray>\n",
+ "Data variables:\n",
+ " t2m (category, forecast_time, lead_time, latitude, longitude) float32 dask.array<chunksize=(3, 53, 1, 121, 240), meta=np.ndarray>\n",
+ " tp (category, forecast_time, lead_time, latitude, longitude) float32 dask.array<chunksize=(3, 53, 1, 121, 240), meta=np.ndarray>\n",
+ "Attributes:\n",
+ " GRIB_edition: 1\n",
+ " GRIB_centre: ecmf\n",
+ " GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n",
+ " GRIB_subCentre: 0\n",
+ " Conventions: CF-1.7\n",
+ " institution: European Centre for Medium-Range Weather Forecasts\n",
+ " history: 2021-05-25T12:55 GRIB to CDM+CF via cfgrib-0.9.9...</pre>"
+ ],
+ "text/plain": [
+ "<xarray.Dataset>\n",
+ "Dimensions: (category: 3, forecast_time: 53, latitude: 121, lead_time: 2, longitude: 240)\n",
+ "Coordinates:\n",
+ " * forecast_time (forecast_time) datetime64[ns] 2020-01-02 ... 2020-12-31\n",
+ " * category (category) <U12 'below normal' 'near normal' 'above normal'\n",
+ " * lead_time (lead_time) timedelta64[ns] 14 days 28 days\n",
+ " * latitude (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0\n",
+ " * longitude (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5\n",
+ " valid_time (forecast_time, lead_time) datetime64[ns] dask.array<chunksize=(53, 1), meta=np.ndarray>\n",
+ "Data variables:\n",
+ " t2m (category, forecast_time, lead_time, latitude, longitude) float32 dask.array<chunksize=(3, 53, 1, 121, 240), meta=np.ndarray>\n",
+ " tp (category, forecast_time, lead_time, latitude, longitude) float32 dask.array<chunksize=(3, 53, 1, 121, 240), meta=np.ndarray>\n",
+ "Attributes:\n",
+ " GRIB_edition: 1\n",
+ " GRIB_centre: ecmf\n",
+ " GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n",
+ " GRIB_subCentre: 0\n",
+ " Conventions: CF-1.7\n",
+ " institution: European Centre for Medium-Range Weather Forecasts\n",
+ " history: 2021-05-25T12:55 GRIB to CDM+CF via cfgrib-0.9.9..."
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "bench_p"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bench_p.to_netcdf('../data/ecmwf_recalibrated_benchmark_2020_biweekly_terciled.nc')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!renku dataset add s2s-ai-challenge data/ecmwf_recalibrated_benchmark_2020_biweekly_terciled.nc"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:s2s-ai]",
+ "language": "python",
+ "name": "conda-env-s2s-ai-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.10"
+ },
+ "toc-autonumbering": true
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/scripts.py b/notebooks/scripts.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bf2cddf21f15d5c921bd9fa9c0737c3954be5b4
--- /dev/null
+++ b/notebooks/scripts.py
@@ -0,0 +1,157 @@
+import xarray as xr
+import pandas as pd
+import numpy as np
+
+cache_path = '../data'
+
+
+def add_valid_time_from_forecast_reference_time_and_lead_time(forecast, init_dim='forecast_time'):
+ """Creates valid_time(forecast_time, lead_time).
+
+ lead_time: pd.Timedelta
+ forecast_time: datetime
+ """
+ times = xr.concat(
+ [
+ xr.DataArray(
+ forecast[init_dim] + lead,
+ dims=init_dim,
+ coords={init_dim: forecast[init_dim]},
+ )
+ for lead in forecast.lead_time
+ ],
+ dim="lead_time",
+ join="inner",
+ compat="broadcast_equals",
+ )
+ forecast = forecast.assign_coords(valid_time=times)
+ return forecast
+
+
+def make_probabilistic(ds, tercile_edges, member_dim='realization', mask=None):
+ """Compute probabilities from ds (observations or forecasts) based on tercile_edges."""
+ # broadcast
+ if 'forecast_time' not in tercile_edges.dims and 'weekofyear' in tercile_edges.dims:
+ tercile_edges = tercile_edges.sel(weekofyear=ds.forecast_time.dt.weekofyear)
+ bn = ds < tercile_edges.isel(category_edge=0, drop=True) # below normal
+ n = (ds >= tercile_edges.isel(category_edge=0, drop=True)) & (ds < tercile_edges.isel(category_edge=1, drop=True)) # normal
+ an = ds >= tercile_edges.isel(category_edge=1, drop=True) # above normal
+ if member_dim in ds.dims:
+ bn = bn.mean(member_dim)
+ an = an.mean(member_dim)
+ n = n.mean(member_dim)
+ ds_p = xr.concat([bn, n, an],'category').assign_coords(category=['below normal', 'near normal', 'above normal'])
+ if mask is not None:
+ ds_p = ds_p.where(mask)
+ if 'tp' in ds_p.data_vars:
+ # mask arid grid cells where category_edge are too close to 0
+ # we are using a dry mask as in https://doi.org/10.1175/MWR-D-17-0092.1
+ tp_arid_mask = tercile_edges.tp.isel(category_edge=0, lead_time=0, drop=True) > 0.01
+ ds_p['tp'] = ds_p['tp'].where(tp_arid_mask)
+ # ds_p = ds_p * 100 # in percent %
+ return ds_p
+
+
+def print_RPS_per_year(preds):
+ """Returns pd.Dataframe of RPS per year."""
+ # similar verification_RPSS.ipynb
+ # as scorer bot but returns a score for each year
+ import xarray as xr
+ import xskillscore as xs
+ import pandas as pd
+ import numpy as np
+ xr.set_options(keep_attrs=True)
+
+ # from root
+ #renku storage pull data/forecast-like-observations_2020_biweekly_terciled.nc
+ #renku storage pull data/hindcast-like-observations_2000-2019_biweekly_terciled.nc
+
+ if 2020 in preds.forecast_time.dt.year:
+ obs_p = xr.open_dataset(f'{cache_path}/forecast-like-observations_2020_biweekly_terciled.nc').sel(forecast_time=preds.forecast_time)
+ else:
+ obs_p = xr.open_dataset(f'{cache_path}/hindcast-like-observations_2000-2019_biweekly_terciled.zarr', engine='zarr').sel(forecast_time=preds.forecast_time)
+
+ # ML probabilities
+ fct_p = preds
+
+ ## RPSS
+ # rps_ML
+ rps_ML = xs.rps(obs_p, fct_p, category_edges=None, dim=[], input_distributions='p').compute()
+ rps_ML = rps_ML.groupby('forecast_time.year').mean()
+
+ rpss = rps_ML
+
+ # cleaning
+ # check for -inf grid cells
+ if (rpss==-np.inf).to_array().any():
+ (rpss == rpss.min()).sum()
+
+ # dirty fix
+ rpss = rpss.clip(-1, 1)
+
+ mask = xr.ones_like(rpss.isel(lead_time=0, drop=True)).reset_coords(drop=True).t2m
+ boundary_tropics = 30
+ mask = xr.concat([mask.where(mask.latitude > boundary_tropics),
+ mask.where(np.abs(mask.latitude) <= boundary_tropics),
+ mask.where((mask.latitude < -boundary_tropics) & (mask.latitude > -60))],'area')
+ mask = mask.assign_coords(area=['northern_extratropics', 'tropics', 'southern_extratropics'])
+ mask.name = 'area'
+
+ mask = mask.where(rpss.t2m.isel(lead_time=0, drop=True).notnull())
+
+ # weighted area mean
+ weights = np.cos(np.deg2rad(np.abs(mask.latitude)))
+ scores = (rpss*mask).weighted(weights).mean('latitude').mean('longitude')
+ pd_scores = scores.reset_coords(drop=True).to_dataframe().unstack(0).round(2)
+
+ # final score
+ scores = rpss.weighted(weights).mean('latitude').mean('longitude')
+ # spatially weighted score averaged over lead_times and variables to one single value
+ scores = scores.to_array().mean(['lead_time', 'variable'])
+ return scores.to_dataframe('RPS')
+
+
+def assert_predictions_2020(preds_test):
+ """Check the variables, coordinates and dimensions of 2020 predictions."""
+ # is dataset
+ assert isinstance(preds_test, xr.Dataset)
+
+ # has both vars: tp and t2m
+ assert 'tp' in preds_test.data_vars
+ assert 't2m' in preds_test.data_vars
+
+ ## coords
+ # forecast_time
+ d = pd.date_range(start='2020-01-02', freq='7D', periods=53)
+ forecast_time = xr.DataArray(d, dims='forecast_time', coords={'forecast_time':d})
+ assert (forecast_time == preds_test['forecast_time']).all()
+
+ # longitude
+ lon = np.arange(0., 360., 1.5)
+ longitude = xr.DataArray(lon, dims='longitude', coords={'longitude': lon})
+ assert (longitude == preds_test['longitude']).all()
+
+ # latitude
+ lat = np.arange(90., -90.1, 1.5)
+ latitude = xr.DataArray(lat, dims='latitude', coords={'latitude': lat})
+ assert (latitude == preds_test['latitude']).all()
+
+ # lead_time
+ lead = [pd.Timedelta(f'{i} d') for i in [14, 28]]
+ lead_time = xr.DataArray(lead, dims='lead_time', coords={'lead_time': lead})
+ assert (lead_time == preds_test['lead_time']).all()
+
+ # category
+ cat = np.array(['below normal', 'near normal', 'above normal'], dtype='<U12')
+ category = xr.DataArray(cat, dims='category', coords={'category': cat})
+ assert (category == preds_test['category']).all()
+
+ # size
+ from dask.utils import format_bytes
+ size_in_MB = float(format_bytes(preds_test.nbytes).split(' ')[0])
+ assert size_in_MB > 50
+ assert size_in_MB < 250
+
+ # no other dims
+ assert set(preds_test.dims) - {'category', 'forecast_time', 'latitude', 'lead_time', 'longitude'} == set()
+
\ No newline at end of file
diff --git a/submissions/ML_prediction_2020.nc b/submissions/ML_prediction_2020.nc
new file mode 100644
index 0000000000000000000000000000000000000000..b853ca40721feb56ab58d82fafe58bc610473f15
--- /dev/null
+++ b/submissions/ML_prediction_2020.nc
@@ -0,0 +1,3 @@
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