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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": [
"## 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",
"\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 RPSS scores for the training period with script `skill_by_year`, see in section 6.3 `predict`.\n",
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" - [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"
]
},
{
"Here, we just remove the mean bias from the ensemble mean forecast."
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},
{
"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)` RPSS for all years as calculated by `skill_by_year`. For now RPS, todo: change to RPSS."
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},
{
"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 skill_by_year"
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},
{
"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",
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" vertical-align: middle;\n",
" }\n",
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"</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": [
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},
{
"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": [
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Submission"
]
},
{
"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 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"
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},
{
"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",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# packages in environment at /opt/conda:\n",
"#\n",
"# Name Version Build Channel\n",
"_libgcc_mutex 0.1 conda_forge conda-forge\n",
"_openmp_mutex 4.5 1_gnu conda-forge\n",
"_tflow_select 2.3.0 mkl defaults\n",
"absl-py 0.12.0 py38h06a4308_0 defaults\n",
"aiobotocore 1.2.2 pyhd3eb1b0_0 defaults\n",
"aiohttp 3.7.4.post0 pypi_0 pypi\n",
"aioitertools 0.7.1 pyhd3eb1b0_0 defaults\n",
"alembic 1.4.3 pyh9f0ad1d_0 conda-forge\n",
"ansiwrap 0.8.4 pypi_0 pypi\n",
"appdirs 1.4.4 pypi_0 pypi\n",
"argcomplete 1.12.2 pypi_0 pypi\n",
"argon2-cffi 20.1.0 py38h497a2fe_2 conda-forge\n",
"argparse 1.4.0 pypi_0 pypi\n",
"asciitree 0.3.3 py_2 defaults\n",
"astunparse 1.6.3 py_0 defaults\n",
"async-timeout 3.0.1 pypi_0 pypi\n",
"async_generator 1.10 py_0 conda-forge\n",
"attrs 20.3.0 pyhd3deb0d_0 conda-forge\n",
"backcall 0.2.0 pyh9f0ad1d_0 conda-forge\n",
"backports 1.0 py_2 conda-forge\n",
"backports.functools_lru_cache 1.6.1 py_0 conda-forge\n",
"binutils_impl_linux-64 2.35.1 h193b22a_1 conda-forge\n",
"binutils_linux-64 2.35 h67ddf6f_30 conda-forge\n",
"black 20.8b1 pypi_0 pypi\n",
"blas 1.0 mkl defaults\n",
"bleach 3.2.1 pyh9f0ad1d_0 conda-forge\n",
"blinker 1.4 py_1 conda-forge\n",
"bokeh 2.3.2 py38h06a4308_0 defaults\n",
"botocore 1.20.78 pyhd3eb1b0_1 defaults\n",
"bottleneck 1.3.2 py38heb32a55_1 defaults\n",
"branca 0.3.1 pypi_0 pypi\n",
"brotlipy 0.7.0 py38h497a2fe_1001 conda-forge\n",
"bzip2 1.0.8 h7f98852_4 conda-forge\n",
"c-ares 1.17.1 h36c2ea0_0 conda-forge\n",
"ca-certificates 2021.4.13 h06a4308_1 defaults\n",
"cachetools 4.2.2 pyhd3eb1b0_0 defaults\n",
"cdsapi 0.5.1 pypi_0 pypi\n",
"certifi 2020.12.5 py38h06a4308_0 defaults\n",
"certipy 0.1.3 py_0 conda-forge\n",
"cffi 1.14.4 py38ha65f79e_1 conda-forge\n",
"cfgrib 0.9.9.0 pyhd8ed1ab_1 conda-forge\n",
"cftime 1.5.0 py38h6323ea4_0 defaults\n",
"chardet 4.0.0 py38h578d9bd_1 conda-forge\n",
"click 7.1.2 pypi_0 pypi\n",
"climetlab 0.7.0 pypi_0 pypi\n",
"climetlab-s2s-ai-challenge 0.6.2 pypi_0 pypi\n",
"cloudpickle 1.6.0 py_0 defaults\n",
"colorama 0.4.4 pypi_0 pypi\n",
"conda 4.9.2 py38h578d9bd_0 conda-forge\n",
"conda-package-handling 1.7.2 py38h8df0ef7_0 conda-forge\n",
"configargparse 1.4.1 pypi_0 pypi\n",
"configurable-http-proxy 1.3.0 0 conda-forge\n",
"coverage 5.5 py38h27cfd23_2 defaults\n",
"cryptography 3.3.1 py38h2b97feb_1 conda-forge\n",
"curl 7.71.1 he644dc0_8 conda-forge\n",
"cycler 0.10.0 py38_0 defaults\n",
"cython 0.29.23 py38h2531618_0 defaults\n",
"cytoolz 0.11.0 py38h7b6447c_0 defaults\n",
"dask 2021.4.0 pyhd3eb1b0_0 defaults\n",
"dask-core 2021.4.0 pyhd3eb1b0_0 defaults\n",
"decorator 4.4.2 py_0 conda-forge\n",
"defusedxml 0.6.0 py_0 conda-forge\n",
"distributed 2021.5.0 py38h06a4308_0 defaults\n",
"distro 1.5.0 pypi_0 pypi\n",
"eccodes 2.18.0 hf05d9b7_0 conda-forge\n",
"ecmwf-api-client 1.6.1 pypi_0 pypi\n",
"ecmwflibs 0.3.7 pypi_0 pypi\n",
"entrypoints 0.3 pyhd8ed1ab_1003 conda-forge\n",
"fasteners 0.16 pyhd3eb1b0_0 defaults\n",
"findlibs 0.0.2 pypi_0 pypi\n",
"folium 0.12.1 pypi_0 pypi\n",
"freetype 2.10.4 h5ab3b9f_0 defaults\n",
"fsspec 0.9.0 pyhd3eb1b0_0 defaults\n",
"gast 0.4.0 py_0 defaults\n",
"gcc_impl_linux-64 9.3.0 h70c0ae5_18 conda-forge\n",
"gcc_linux-64 9.3.0 hf25ea35_30 conda-forge\n",
"gitdb 4.0.7 pypi_0 pypi\n",
"gitpython 3.1.14 pypi_0 pypi\n",
"google-auth 1.30.1 pyhd3eb1b0_0 defaults\n",
"google-auth-oauthlib 0.4.4 pyhd3eb1b0_0 defaults\n",
"google-pasta 0.2.0 py_0 defaults\n",
"grpcio 1.36.1 py38h2157cd5_1 defaults\n",
"gxx_impl_linux-64 9.3.0 hd87eabc_18 conda-forge\n",
"gxx_linux-64 9.3.0 h3fbe746_30 conda-forge\n",
"h5py 2.10.0 py38hd6299e0_1 defaults\n",
"hdf4 4.2.13 h3ca952b_2 defaults\n",
"hdf5 1.10.6 nompi_h3c11f04_101 conda-forge\n",
"heapdict 1.0.1 py_0 defaults\n",
"icu 68.1 h58526e2_0 conda-forge\n",
"idna 2.10 pyh9f0ad1d_0 conda-forge\n",
"importlib-metadata 3.4.0 py38h578d9bd_0 conda-forge\n",
"importlib_metadata 3.4.0 hd8ed1ab_0 conda-forge\n",
"intel-openmp 2021.2.0 h06a4308_610 defaults\n",
"ipykernel 5.4.2 py38h81c977d_0 conda-forge\n",
"ipython 7.19.0 py38h81c977d_2 conda-forge\n",
"ipython_genutils 0.2.0 py_1 conda-forge\n",
"jasper 1.900.1 hd497a04_4 defaults\n",
"jedi 0.17.2 py38h578d9bd_1 conda-forge\n",
"jinja2 2.11.2 pyh9f0ad1d_0 conda-forge\n",
"jmespath 0.10.0 py_0 defaults\n",
"joblib 1.0.1 pyhd3eb1b0_0 defaults\n",
"jpeg 9d h36c2ea0_0 conda-forge\n",
"json5 0.9.5 pyh9f0ad1d_0 conda-forge\n",
"jsonschema 3.2.0 py_2 conda-forge\n",
"jupyter-server-proxy 1.6.0 pypi_0 pypi\n",
"jupyter_client 6.1.11 pyhd8ed1ab_1 conda-forge\n",
"jupyter_core 4.7.0 py38h578d9bd_0 conda-forge\n",
"jupyter_telemetry 0.1.0 pyhd8ed1ab_1 conda-forge\n",
"jupyterhub 1.2.2 pypi_0 pypi\n",
"jupyterlab 2.2.9 py_0 conda-forge\n",
"jupyterlab-git 0.23.3 pypi_0 pypi\n",
"jupyterlab_pygments 0.1.2 pyh9f0ad1d_0 conda-forge\n",
"jupyterlab_server 1.2.0 py_0 conda-forge\n",
"keras-preprocessing 1.1.2 pyhd3eb1b0_0 defaults\n",
"kernel-headers_linux-64 2.6.32 h77966d4_13 conda-forge\n",
"kiwisolver 1.3.1 py38h2531618_0 defaults\n",
"krb5 1.17.2 h926e7f8_0 conda-forge\n",
"lcms2 2.12 h3be6417_0 defaults\n",
"ld_impl_linux-64 2.35.1 hea4e1c9_1 conda-forge\n",
"libaec 1.0.4 he6710b0_1 defaults\n",
"libcurl 7.71.1 hcdd3856_8 conda-forge\n",
"libedit 3.1.20191231 he28a2e2_2 conda-forge\n",
"libev 4.33 h516909a_1 conda-forge\n",
"libffi 3.3 h58526e2_2 conda-forge\n",
"libgcc-devel_linux-64 9.3.0 h7864c58_18 conda-forge\n",
"libgcc-ng 9.3.0 h2828fa1_18 conda-forge\n",
"libgfortran-ng 7.3.0 hdf63c60_0 defaults\n",
"libgomp 9.3.0 h2828fa1_18 conda-forge\n",
"libllvm10 10.0.1 hbcb73fb_5 defaults\n",
"libnetcdf 4.7.4 nompi_h56d31a8_107 conda-forge\n",
"libnghttp2 1.41.0 h8cfc5f6_2 conda-forge\n",
"libpng 1.6.37 hbc83047_0 defaults\n",
"libprotobuf 3.14.0 h8c45485_0 defaults\n",
"libsodium 1.0.18 h36c2ea0_1 conda-forge\n",
"libssh2 1.9.0 hab1572f_5 conda-forge\n",
"libstdcxx-devel_linux-64 9.3.0 hb016644_18 conda-forge\n",
"libstdcxx-ng 9.3.0 h6de172a_18 conda-forge\n",
"libtiff 4.1.0 h2733197_1 defaults\n",
"libuv 1.40.0 h7f98852_0 conda-forge\n",
"llvmlite 0.36.0 py38h612dafd_4 defaults\n",
"locket 0.2.1 py38h06a4308_1 defaults\n",
"lz4-c 1.9.3 h2531618_0 defaults\n",
"magics 1.5.6 pypi_0 pypi\n",
"mako 1.1.4 pyh44b312d_0 conda-forge\n",
"markdown 3.3.4 py38h06a4308_0 defaults\n",
"markupsafe 1.1.1 py38h497a2fe_3 conda-forge\n",
"matplotlib-base 3.3.4 py38h62a2d02_0 defaults\n",
"mistune 0.8.4 py38h497a2fe_1003 conda-forge\n",
"mkl 2021.2.0 h06a4308_296 defaults\n",
"mkl-service 2.3.0 py38h27cfd23_1 defaults\n",
"mkl_fft 1.3.0 py38h42c9631_2 defaults\n",
"mkl_random 1.2.1 py38ha9443f7_2 defaults\n",
"monotonic 1.5 py_0 defaults\n",
"msgpack-python 1.0.2 py38hff7bd54_1 defaults\n",
"multidict 5.1.0 py38h27cfd23_2 defaults\n",
"mypy-extensions 0.4.3 pypi_0 pypi\n",
"nbclient 0.5.0 pypi_0 pypi\n",
"nbconvert 6.0.7 py38h578d9bd_3 conda-forge\n",
"nbdime 2.1.0 pypi_0 pypi\n",
"nbformat 5.1.2 pyhd8ed1ab_1 conda-forge\n",
"nbresuse 0.4.0 pypi_0 pypi\n",
"ncurses 6.2 h58526e2_4 conda-forge\n",
"nest-asyncio 1.4.3 pyhd8ed1ab_0 conda-forge\n",
"netcdf4 1.5.6 pypi_0 pypi\n",
"nodejs 15.3.0 h25f6087_0 conda-forge\n",
"notebook 6.2.0 py38h578d9bd_0 conda-forge\n",
"numba 0.53.1 py38ha9443f7_0 defaults\n",
"numcodecs 0.7.3 py38h2531618_0 defaults\n",
"numpy 1.20.2 py38h2d18471_0 defaults\n",
"numpy-base 1.20.2 py38hfae3a4d_0 defaults\n",
"oauthlib 3.0.1 py_0 conda-forge\n",
"olefile 0.46 py_0 defaults\n",
"openssl 1.1.1k h27cfd23_0 defaults\n",