diff --git a/.renku/datasets/a31a4922-5cbd-4d8e-bee3-435b1fd85282/metadata.yml b/.renku/datasets/a31a4922-5cbd-4d8e-bee3-435b1fd85282/metadata.yml
index ddded2356135fc22bcad70d9b01fa28c2643f447..6de0cd4571a96cffade1eda1768e530a01be41fb 100644
--- a/.renku/datasets/a31a4922-5cbd-4d8e-bee3-435b1fd85282/metadata.yml
+++ b/.renku/datasets/a31a4922-5cbd-4d8e-bee3-435b1fd85282/metadata.yml
@@ -37,410 +37,297 @@
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-- '@id': https://localhost/urls/%2F%2Frenkulab.io%2Fdatasets%2Fc2b8a1b6-dfc3-4f53-894f-1ef658d7fb22
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@@ -3492,7 +3316,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:33:20.263473+00:00'
+ - '@value': '2021-07-23T20:48:27.428695+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3513,7 +3337,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:33:18.165848+00:00'
+ - '@value': '2021-07-23T20:48:26.527044+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3534,7 +3358,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:33:18.016127+00:00'
+ - '@value': '2021-07-23T20:48:26.470385+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3555,7 +3379,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:33:18.529876+00:00'
+ - '@value': '2021-07-23T20:48:26.698816+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3576,7 +3400,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:33:18.402606+00:00'
+ - '@value': '2021-07-23T20:48:26.640300+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3597,7 +3421,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:33:19.607994+00:00'
+ - '@value': '2021-07-23T20:48:27.177258+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3618,7 +3442,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:33:19.371184+00:00'
+ - '@value': '2021-07-23T20:48:27.062421+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3660,7 +3484,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:32:04.901350+00:00'
+ - '@value': '2021-07-23T20:46:08.467926+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3681,7 +3505,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:32:06.122328+00:00'
+ - '@value': '2021-07-23T20:46:09.052969+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3702,7 +3526,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:32:05.976012+00:00'
+ - '@value': '2021-07-23T20:46:09.003595+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3723,7 +3547,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:32:04.209267+00:00'
+ - '@value': '2021-07-23T20:46:08.166072+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3744,7 +3568,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:32:04.064784+00:00'
+ - '@value': '2021-07-23T20:46:08.114547+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3765,7 +3589,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:32:04.597262+00:00'
+ - '@value': '2021-07-23T20:46:08.319587+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3786,7 +3610,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:32:04.434072+00:00'
+ - '@value': '2021-07-23T20:46:08.268280+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3807,7 +3631,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:32:05.365509+00:00'
+ - '@value': '2021-07-23T20:46:08.771274+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3828,7 +3652,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:36:50.823288+00:00'
+ - '@value': '2021-07-27T06:59:54.746723+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3849,7 +3673,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:36:50.655269+00:00'
+ - '@value': '2021-07-27T06:59:54.836856+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3870,7 +3694,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:36:49.783626+00:00'
+ - '@value': '2021-07-23T20:53:11.561155+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3891,7 +3715,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:36:49.615718+00:00'
+ - '@value': '2021-07-23T20:53:11.482055+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3912,7 +3736,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:36:48.962132+00:00'
+ - '@value': '2021-07-23T20:53:10.965269+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3933,7 +3757,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:36:51.259416+00:00'
+ - '@value': '2021-07-23T20:53:12.398608+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3954,7 +3778,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:36:51.093550+00:00'
+ - '@value': '2021-07-23T20:53:12.320500+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3975,7 +3799,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:36:48.169175+00:00'
+ - '@value': '2021-07-23T20:53:10.476927+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -3996,7 +3820,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:36:47.998956+00:00'
+ - '@value': '2021-07-23T20:53:10.401581+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -4017,7 +3841,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:36:48.612708+00:00'
+ - '@value': '2021-07-23T20:53:10.711405+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -4038,7 +3862,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:36:48.441773+00:00'
+ - '@value': '2021-07-23T20:53:10.630283+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -4059,7 +3883,7 @@
- http://schema.org/DigitalDocument
- http://www.w3.org/ns/prov#Entity
http://schema.org/dateCreated:
- - '@value': '2021-06-19T16:36:50.106176+00:00'
+ - '@value': '2021-07-23T20:53:11.788777+00:00'
http://schema.org/isPartOf:
- '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
http://schema.org/name:
@@ -4074,7 +3898,196 @@
- '@value': false
https://swissdatasciencecenter.github.io/renku-ontology#source:
- '@value': data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/valid_time/.zarray
-- '@id': https://renkulab.io/datasets/e5c7d143-70c7-41a5-86e4-6f86ea2d17f3
+- '@id': https://renkulab.io/blob/f9e5eb0c2e00829924d843b3135d4ef15f2b5ac4/data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr/forecast_time/0
+ '@type':
+ - http://purl.org/wf4ever/wfprov#Artifact
+ - http://schema.org/DigitalDocument
+ - http://www.w3.org/ns/prov#Entity
+ http://schema.org/dateCreated:
+ - '@value': '2021-07-23T20:46:08.419134+00:00'
+ http://schema.org/isPartOf:
+ - '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
+ http://schema.org/name:
+ - '@value': '0'
+ http://schema.org/url:
+ - '@value': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template/files/blob/data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr/forecast_time/0
+ http://www.w3.org/2000/01/rdf-schema#label:
+ - '@value': data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr/forecast_time/0@f9e5eb0c2e00829924d843b3135d4ef15f2b5ac4
+ http://www.w3.org/ns/prov#atLocation:
+ - '@value': data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr/forecast_time/0
+ https://swissdatasciencecenter.github.io/renku-ontology#external:
+ - '@value': false
+ https://swissdatasciencecenter.github.io/renku-ontology#source:
+ - '@value': data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr/forecast_time/0
+- '@id': https://renkulab.io/blob/f9e5eb0c2e00829924d843b3135d4ef15f2b5ac4/data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr/t2m/.zarray
+ '@type':
+ - http://purl.org/wf4ever/wfprov#Artifact
+ - http://schema.org/DigitalDocument
+ - http://www.w3.org/ns/prov#Entity
+ http://schema.org/dateCreated:
+ - '@value': '2021-07-23T20:46:08.924797+00:00'
+ http://schema.org/isPartOf:
+ - '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
+ http://schema.org/name:
+ - '@value': .zarray
+ http://schema.org/url:
+ - '@value': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template/files/blob/data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr/t2m/.zarray
+ http://www.w3.org/2000/01/rdf-schema#label:
+ - '@value': data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr/t2m/.zarray@f9e5eb0c2e00829924d843b3135d4ef15f2b5ac4
+ http://www.w3.org/ns/prov#atLocation:
+ - '@value': data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr/t2m/.zarray
+ https://swissdatasciencecenter.github.io/renku-ontology#external:
+ - '@value': false
+ https://swissdatasciencecenter.github.io/renku-ontology#source:
+ - '@value': data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr/t2m/.zarray
+- '@id': https://renkulab.io/blob/f9e5eb0c2e00829924d843b3135d4ef15f2b5ac4/data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr/tp/.zarray
+ '@type':
+ - http://purl.org/wf4ever/wfprov#Artifact
+ - http://schema.org/DigitalDocument
+ - http://www.w3.org/ns/prov#Entity
+ http://schema.org/dateCreated:
+ - '@value': '2021-07-23T20:46:08.624964+00:00'
+ http://schema.org/isPartOf:
+ - '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
+ http://schema.org/name:
+ - '@value': .zarray
+ http://schema.org/url:
+ - '@value': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template/files/blob/data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr/tp/.zarray
+ http://www.w3.org/2000/01/rdf-schema#label:
+ - '@value': data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr/tp/.zarray@f9e5eb0c2e00829924d843b3135d4ef15f2b5ac4
+ http://www.w3.org/ns/prov#atLocation:
+ - '@value': data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr/tp/.zarray
+ https://swissdatasciencecenter.github.io/renku-ontology#external:
+ - '@value': false
+ https://swissdatasciencecenter.github.io/renku-ontology#source:
+ - '@value': data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr/tp/.zarray
+- '@id': https://renkulab.io/blob/f9e5eb0c2e00829924d843b3135d4ef15f2b5ac4/data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr/valid_time/0.0
+ '@type':
+ - http://purl.org/wf4ever/wfprov#Artifact
+ - http://schema.org/DigitalDocument
+ - http://www.w3.org/ns/prov#Entity
+ http://schema.org/dateCreated:
+ - '@value': '2021-07-23T20:46:08.674540+00:00'
+ http://schema.org/isPartOf:
+ - '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
+ http://schema.org/name:
+ - '@value': '0.0'
+ http://schema.org/url:
+ - '@value': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template/files/blob/data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr/valid_time/0.0
+ http://www.w3.org/2000/01/rdf-schema#label:
+ - '@value': data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr/valid_time/0.0@f9e5eb0c2e00829924d843b3135d4ef15f2b5ac4
+ http://www.w3.org/ns/prov#atLocation:
+ - '@value': data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr/valid_time/0.0
+ https://swissdatasciencecenter.github.io/renku-ontology#external:
+ - '@value': false
+ https://swissdatasciencecenter.github.io/renku-ontology#source:
+ - '@value': data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr/valid_time/0.0
+- '@id': https://renkulab.io/blob/f9e5eb0c2e00829924d843b3135d4ef15f2b5ac4/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/.zmetadata
+ '@type':
+ - http://purl.org/wf4ever/wfprov#Artifact
+ - http://schema.org/DigitalDocument
+ - http://www.w3.org/ns/prov#Entity
+ http://schema.org/dateCreated:
+ - '@value': '2021-07-27T06:59:54.925794+00:00'
+ http://schema.org/isPartOf:
+ - '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
+ http://schema.org/name:
+ - '@value': .zmetadata
+ http://schema.org/url:
+ - '@value': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template/files/blob/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/.zmetadata
+ http://www.w3.org/2000/01/rdf-schema#label:
+ - '@value': data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/.zmetadata@f9e5eb0c2e00829924d843b3135d4ef15f2b5ac4
+ http://www.w3.org/ns/prov#atLocation:
+ - '@value': data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/.zmetadata
+ https://swissdatasciencecenter.github.io/renku-ontology#external:
+ - '@value': false
+ https://swissdatasciencecenter.github.io/renku-ontology#source:
+ - '@value': data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/.zmetadata
+- '@id': https://renkulab.io/blob/f9e5eb0c2e00829924d843b3135d4ef15f2b5ac4/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/forecast_time/0
+ '@type':
+ - http://purl.org/wf4ever/wfprov#Artifact
+ - http://schema.org/DigitalDocument
+ - http://www.w3.org/ns/prov#Entity
+ http://schema.org/dateCreated:
+ - '@value': '2021-07-23T20:53:10.888125+00:00'
+ http://schema.org/isPartOf:
+ - '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
+ http://schema.org/name:
+ - '@value': '0'
+ http://schema.org/url:
+ - '@value': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template/files/blob/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/forecast_time/0
+ http://www.w3.org/2000/01/rdf-schema#label:
+ - '@value': data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/forecast_time/0@f9e5eb0c2e00829924d843b3135d4ef15f2b5ac4
+ http://www.w3.org/ns/prov#atLocation:
+ - '@value': data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/forecast_time/0
+ https://swissdatasciencecenter.github.io/renku-ontology#external:
+ - '@value': false
+ https://swissdatasciencecenter.github.io/renku-ontology#source:
+ - '@value': data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/forecast_time/0
+- '@id': https://renkulab.io/blob/f9e5eb0c2e00829924d843b3135d4ef15f2b5ac4/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/t2m/.zarray
+ '@type':
+ - http://purl.org/wf4ever/wfprov#Artifact
+ - http://schema.org/DigitalDocument
+ - http://www.w3.org/ns/prov#Entity
+ http://schema.org/dateCreated:
+ - '@value': '2021-07-23T20:53:12.154580+00:00'
+ http://schema.org/isPartOf:
+ - '@id': https://renkulab.io/projects/aaron.spring/s2s-ai-challenge-template
+ http://schema.org/name:
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http://schema.org/agent:
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diff --git a/.renku/metadata.yml b/.renku/metadata.yml
index fd95a06a3aa50826f720201a38fee67fb9cd898a..6abdf5d8ba87f9f04eeac32dc98f175fda5512ab 100644
--- a/.renku/metadata.yml
+++ b/.renku/metadata.yml
@@ -2,7 +2,7 @@
'@type':
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http://schema.org/creator:
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diff --git a/CHANGELOG.md b/CHANGELOG.md
index a84a1dff1c72208522391c3611c5f337e195f5be..b9e1c29e636daae53f9cb7ce32d3a776e76f0189 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -3,6 +3,10 @@
### *unreleased*
- add `eccodes` to [`environment.yml`](https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge-template/-/blob/master/environment.yml) to resolve `cfgrib` `ImportError` (https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge/-/issues/26, !15, [Aaron Spring](https://renkulab.io/gitlab/aaron.spring))
+- Create categories by `groupby('week')`. (https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge/-/issues/29, !18 [Aaron Spring](https://renkulab.io/gitlab/aaron.spring) with help from [Sergi Bech Sala](https://renkulab.io/gitlab/sbechsal7) and [Llorenç Lledó](https://renkulab.io/gitlab/llledo))
+- Regrid observations conservatively with [xesmf](https://pangeo-xesmf.readthedocs.io/) (https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge/-/issues/32, !18, [Aaron Spring](https://renkulab.io/gitlab/aaron.spring) with help from [Arlan Dirkson](https://renkulab.io/gitlab/arlan.dirkson))
+- Observations now have data at longitude=0 (https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge/-/issues/35, !18, [Aaron Spring](https://renkulab.io/gitlab/aaron.spring) with help from [Sergi Bech Sala](https://renkulab.io/gitlab/sbechsal7))
+- Missing `tp` observations after leap days added (https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge/-/issues/31, !18, [Aaron Spring](https://renkulab.io/gitlab/aaron.spring) with help from [David Landry](https://renkulab.io/gitlab/david.landry])
### 2021-06-19: `v0.3.1` *release*
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diff --git a/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/tp/2.0.0.0.0 b/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/tp/2.0.0.0.0
index 8b4622390337b9b37bd82c4fd846272ee7b2545c..b789c7b12f4cc731538f525a93f13d38bd238ff1 100644
--- a/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/tp/2.0.0.0.0
+++ b/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/tp/2.0.0.0.0
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
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-size 11559549
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+size 10191280
diff --git a/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/tp/2.0.1.0.0 b/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/tp/2.0.1.0.0
index 90bb267c7d3e33af3fbeb7a80adaba99b811b4e7..d23c6100809ae06cb919f0f84463959cd7d8eef0 100644
--- a/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/tp/2.0.1.0.0
+++ b/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/tp/2.0.1.0.0
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
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+size 10136293
diff --git a/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/weekofyear/.zarray b/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/weekofyear/.zarray
deleted file mode 100644
index b096b389e850892aeb036343c9359986c6c2f433..0000000000000000000000000000000000000000
--- a/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/weekofyear/.zarray
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/weekofyear/.zattrs b/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/weekofyear/.zattrs
deleted file mode 100644
index ebc6e1319e3a982178a02d3a0bb5bf1630880885..0000000000000000000000000000000000000000
--- a/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/weekofyear/.zattrs
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/weekofyear/0 b/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/weekofyear/0
deleted file mode 100644
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--- a/data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr/weekofyear/0
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/notebooks/ML_train_and_predict.ipynb b/notebooks/ML_train_and_predict.ipynb
index 87c5a8d2b0e2bc7cf3c3841c04287ff8d9d62149..6f875f430451cef974f827b5f8605e980695e886 100644
--- a/notebooks/ML_train_and_predict.ipynb
+++ b/notebooks/ML_train_and_predict.ipynb
@@ -126,16 +126,7 @@
"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"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from tensorflow.keras.layers import Input, Dense, Flatten\n",
"from tensorflow.keras.models import Sequential\n",
@@ -198,19 +189,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 3,
"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"
- ]
- }
- ],
+ "outputs": [],
"source": [
"hind_2000_2019 = xr.open_zarr(\"../data/ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr\", consolidated=True)"
]
@@ -236,7 +217,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -272,7 +253,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -300,7 +281,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@@ -323,23 +304,9 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Cloning into 'WeatherBench'...\n",
- "remote: Enumerating objects: 718, done.\u001b[K\n",
- "remote: Counting objects: 100% (3/3), done.\u001b[K\n",
- "remote: Compressing objects: 100% (3/3), done.\u001b[K\n",
- "remote: Total 718 (delta 0), reused 0 (delta 0), pack-reused 715\u001b[K\n",
- "Receiving objects: 100% (718/718), 17.77 MiB | 14.96 MiB/s, done.\n",
- "Resolving deltas: 100% (424/424), done.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# run once only and dont commit\n",
"!git clone https://github.com/pangeo-data/WeatherBench/"
@@ -347,7 +314,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -359,7 +326,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -453,7 +420,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -464,7 +431,14 @@
"Coordinates:\n",
" lead_time timedelta64[ns] 14 days\n",
"Attributes:\n",
- " comment: lead_time describes bi-weekly aggregates. The pd.Timedelta corr...</pre>"
+ " aggregate: The pd.Timedelta corresponds to the first day of a biweek...\n",
+ " description: Forecast period is the time interval between the forecast...\n",
+ " long_name: lead time\n",
+ " standard_name: forecast_period\n",
+ " week34_t2m: mean[14 days, 27 days]\n",
+ " week34_tp: 28 days minus 14 days\n",
+ " week56_t2m: mean[28 days, 41 days]\n",
+ " week56_tp: 42 days minus 28 days</pre>"
],
"text/plain": [
"<xarray.DataArray 'lead_time' ()>\n",
@@ -472,10 +446,17 @@
"Coordinates:\n",
" lead_time timedelta64[ns] 14 days\n",
"Attributes:\n",
- " comment: lead_time describes bi-weekly aggregates. The pd.Timedelta corr..."
+ " aggregate: The pd.Timedelta corresponds to the first day of a biweek...\n",
+ " description: Forecast period is the time interval between the forecast...\n",
+ " long_name: lead time\n",
+ " standard_name: forecast_period\n",
+ " week34_t2m: mean[14 days, 27 days]\n",
+ " week34_tp: 28 days minus 14 days\n",
+ " week56_t2m: mean[28 days, 41 days]\n",
+ " week56_tp: 42 days minus 28 days"
]
},
- "execution_count": 14,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -489,7 +470,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -508,7 +489,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -520,22 +501,22 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "/opt/conda/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\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",
- "/opt/conda/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\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",
- "/opt/conda/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\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",
- "/opt/conda/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\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",
- "/opt/conda/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\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"
]
}
@@ -549,22 +530,22 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "/opt/conda/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\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",
- "/opt/conda/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\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",
- "/opt/conda/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\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",
- "/opt/conda/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\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",
- "/opt/conda/lib/python3.8/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\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"
]
}
@@ -578,7 +559,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@@ -591,7 +572,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
@@ -600,7 +581,7 @@
"((32, 121, 240), (32, 121, 240))"
]
},
- "execution_count": 20,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -612,37 +593,14 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 16,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.collections.QuadMesh at 0x7fd217042850>"
- ]
- },
- "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"
- }
- ],
+ "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)"
+ "# i=4\n",
+ "# xr.DataArray(np.vstack([X[i],y[i]])).plot(yincrease=False, robust=True)"
]
},
{
@@ -654,18 +612,18 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 17,
"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 0x7fd217b127c0>> and will run it as-is.\n",
+ "WARNING:tensorflow:AutoGraph could not transform <bound method PeriodicPadding2D.call of <WeatherBench.src.train_nn.PeriodicPadding2D object at 0x2ac3698c8350>> 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 0x7fd217b127c0>> and will run it as-is.\n",
+ "WARNING: AutoGraph could not transform <bound method PeriodicPadding2D.call of <WeatherBench.src.train_nn.PeriodicPadding2D object at 0x2ac3698c8350>> 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"
@@ -681,7 +639,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -709,7 +667,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
@@ -718,7 +676,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
@@ -728,7 +686,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 21,
"metadata": {},
"outputs": [
{
@@ -736,18 +694,18 @@
"output_type": "stream",
"text": [
"Epoch 1/2\n",
- "30/30 [==============================] - 24s 719ms/step - loss: 0.3028 - val_loss: 0.1696\n",
+ "30/30 [==============================] - 20s 638ms/step - loss: 0.2237 - val_loss: 0.1192\n",
"Epoch 2/2\n",
- "30/30 [==============================] - 21s 697ms/step - loss: 0.1617 - val_loss: 0.0993\n"
+ "30/30 [==============================] - 18s 594ms/step - loss: 0.1046 - val_loss: 0.0647\n"
]
},
{
"data": {
"text/plain": [
- "<tensorflow.python.keras.callbacks.History at 0x7fd2166d8d90>"
+ "<tensorflow.python.keras.callbacks.History at 0x2ac369a0a310>"
]
},
- "execution_count": 26,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
@@ -767,9 +725,18 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 22,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING: ecmwflibs universal: cannot find a library called MagPlus\n",
+ "Magics library could not be found\n"
+ ]
+ }
+ ],
"source": [
"from scripts import add_valid_time_from_forecast_reference_time_and_lead_time\n",
"\n",
@@ -799,7 +766,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
@@ -809,7 +776,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
@@ -836,7 +803,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
@@ -847,7 +814,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
@@ -918,7 +885,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
@@ -952,67 +919,75 @@
" <tbody>\n",
" <tr>\n",
" <th>2000</th>\n",
- " <td>-1.293103</td>\n",
+ " <td>-1.233510</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001</th>\n",
- " <td>-1.446606</td>\n",
+ " <td>-1.388854</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002</th>\n",
- " <td>-1.494487</td>\n",
+ " <td>-1.465850</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003</th>\n",
- " <td>-1.484899</td>\n",
+ " <td>-1.413647</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004</th>\n",
- " <td>-1.421862</td>\n",
+ " <td>-1.414394</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005</th>\n",
- " <td>-1.549783</td>\n",
+ " <td>-1.543710</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006</th>\n",
- " <td>-1.508035</td>\n",
+ " <td>-1.440741</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2007</th>\n",
- " <td>-1.502208</td>\n",
+ " <td>-1.544764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2008</th>\n",
- " <td>-1.493371</td>\n",
+ " <td>-1.432690</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009</th>\n",
- " <td>-1.568156</td>\n",
+ " <td>-1.525836</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010</th>\n",
- " <td>-1.519528</td>\n",
+ " <td>-1.504275</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011</th>\n",
- " <td>-1.389702</td>\n",
+ " <td>-1.457459</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012</th>\n",
- " <td>-1.499871</td>\n",
+ " <td>-1.533434</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013</th>\n",
- " <td>-1.549204</td>\n",
+ " <td>-1.509833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014</th>\n",
- " <td>-1.500869</td>\n",
+ " <td>-1.476839</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015</th>\n",
- " <td>-1.506727</td>\n",
+ " <td>-1.512971</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2016</th>\n",
+ " <td>-1.558262</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017</th>\n",
+ " <td>-1.538098</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -1021,25 +996,27 @@
"text/plain": [
" RPSS\n",
"year \n",
- "2000 -1.293103\n",
- "2001 -1.446606\n",
- "2002 -1.494487\n",
- "2003 -1.484899\n",
- "2004 -1.421862\n",
- "2005 -1.549783\n",
- "2006 -1.508035\n",
- "2007 -1.502208\n",
- "2008 -1.493371\n",
- "2009 -1.568156\n",
- "2010 -1.519528\n",
- "2011 -1.389702\n",
- "2012 -1.499871\n",
- "2013 -1.549204\n",
- "2014 -1.500869\n",
- "2015 -1.506727"
+ "2000 -1.233510\n",
+ "2001 -1.388854\n",
+ "2002 -1.465850\n",
+ "2003 -1.413647\n",
+ "2004 -1.414394\n",
+ "2005 -1.543710\n",
+ "2006 -1.440741\n",
+ "2007 -1.544764\n",
+ "2008 -1.432690\n",
+ "2009 -1.525836\n",
+ "2010 -1.504275\n",
+ "2011 -1.457459\n",
+ "2012 -1.533434\n",
+ "2013 -1.509833\n",
+ "2014 -1.476839\n",
+ "2015 -1.512971\n",
+ "2016 -1.558262\n",
+ "2017 -1.538098"
]
},
- "execution_count": 35,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
@@ -1071,7 +1048,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 28,
"metadata": {},
"outputs": [
{
@@ -1105,11 +1082,11 @@
" <tbody>\n",
" <tr>\n",
" <th>2018</th>\n",
- " <td>-1.432631</td>\n",
+ " <td>-1.476327</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019</th>\n",
- " <td>-1.544451</td>\n",
+ " <td>-1.529335</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -1118,11 +1095,11 @@
"text/plain": [
" RPSS\n",
"year \n",
- "2018 -1.432631\n",
- "2019 -1.544451"
+ "2018 -1.476327\n",
+ "2019 -1.529335"
]
},
- "execution_count": 36,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
@@ -1142,7 +1119,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 29,
"metadata": {},
"outputs": [
{
@@ -1176,19 +1153,19 @@
" <tbody>\n",
" <tr>\n",
" <th>2020</th>\n",
- " <td>-1.4709</td>\n",
+ " <td>-1.443918</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " RPSS\n",
- "year \n",
- "2020 -1.4709"
+ " RPSS\n",
+ "year \n",
+ "2020 -1.443918"
]
},
- "execution_count": 37,
+ "execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
@@ -1208,7 +1185,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
@@ -1218,7 +1195,7 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
@@ -1227,12 +1204,12 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "#!git add ../submissions/ML_prediction_2020.nc\n",
- "#!git add ML_train_and_prediction.ipynb"
+ "# !git add ../submissions/ML_prediction_2020.nc\n",
+ "# !git add ML_train_and_prediction.ipynb"
]
},
{
@@ -1241,7 +1218,7 @@
"metadata": {},
"outputs": [],
"source": [
- "#!git commit -m \"template_test commit message\" # whatever message you want"
+ "# !git commit -m \"template_test commit message\" # whatever message you want"
]
},
{
@@ -1250,7 +1227,7 @@
"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"
+ "# !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"
]
},
{
@@ -1259,7 +1236,7 @@
"metadata": {},
"outputs": [],
"source": [
- "#!git push --tags"
+ "# !git push --tags"
]
},
{
@@ -1705,9 +1682,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python [conda env:s2s-ai]",
"language": "python",
- "name": "python3"
+ "name": "conda-env-s2s-ai-py"
},
"language_info": {
"codemirror_mode": {
@@ -1719,7 +1696,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.6"
+ "version": "3.7.10"
},
"toc-autonumbering": true
},
diff --git a/notebooks/mean_bias_reduction.ipynb b/notebooks/mean_bias_reduction.ipynb
index ebce68410e3e68a864ba290ef69666579086eb4a..7e1a033189dcf82d53a0e62af7a6f3b2fa9394c2 100644
--- a/notebooks/mean_bias_reduction.ipynb
+++ b/notebooks/mean_bias_reduction.ipynb
@@ -117,7 +117,7 @@
{
"data": {
"text/plain": [
- "<xarray.core.options.set_options at 0x2b37fc26ec50>"
+ "<xarray.core.options.set_options at 0x2b858800a050>"
]
},
"execution_count": 1,
@@ -150,7 +150,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -255,15 +255,33 @@
"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",
+ "WARNING: ecmwflibs universal: cannot find a library called MagPlus\n",
+ "Magics library could not be found\n"
+ ]
+ }
+ ],
+ "source": [
+ "from scripts import add_year_week_coords\n",
+ "obs_2000_2019 = add_year_week_coords(obs_2000_2019)\n",
+ "hind_2000_2019 = add_year_week_coords(hind_2000_2019)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "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": [
- "bias_2000_2019 = (hind_2000_2019.mean('realization') - obs_2000_2019).groupby('forecast_time.weekofyear').mean().compute()"
+ "bias_2000_2019 = (hind_2000_2019.mean('realization') - obs_2000_2019).groupby('week').mean().compute()"
]
},
{
@@ -277,19 +295,9 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"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"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from scripts import make_probabilistic"
]
@@ -305,7 +313,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -315,12 +323,14 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def create_predictions(fct, bias):\n",
- " preds = fct - bias.sel(weekofyear=fct.forecast_time.dt.weekofyear)\n",
+ " if 'week' not in fct.coords:\n",
+ " fct = add_year_week_coords(fct)\n",
+ " preds = fct - bias.sel(week=fct.week)\n",
" preds = make_probabilistic(preds, tercile_edges)\n",
" return preds.astype('float32')"
]
@@ -352,24 +362,22 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 11,
"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"
- ]
- }
- ],
+ "outputs": [],
"source": [
"preds_is = create_predictions(hind_2000_2019, bias_2000_2019).compute()"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scripts import skill_by_year"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 13,
@@ -406,83 +414,83 @@
" <tbody>\n",
" <tr>\n",
" <th>2000</th>\n",
- " <td>-0.141857</td>\n",
+ " <td>-0.147543</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001</th>\n",
- " <td>-0.203405</td>\n",
+ " <td>-0.190513</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002</th>\n",
- " <td>-0.202549</td>\n",
+ " <td>-0.186966</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003</th>\n",
- " <td>-0.206234</td>\n",
+ " <td>-0.197091</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004</th>\n",
- " <td>-0.549463</td>\n",
+ " <td>-0.194299</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005</th>\n",
- " <td>-0.168421</td>\n",
+ " <td>-0.163684</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006</th>\n",
- " <td>-0.184515</td>\n",
+ " <td>-0.180648</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2007</th>\n",
- " <td>-0.616939</td>\n",
+ " <td>-0.182294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2008</th>\n",
- " <td>-0.195251</td>\n",
+ " <td>-0.173836</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009</th>\n",
- " <td>-0.202809</td>\n",
+ " <td>-0.208382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010</th>\n",
- " <td>-0.189126</td>\n",
+ " <td>-0.150463</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011</th>\n",
- " <td>-0.678302</td>\n",
+ " <td>-0.177190</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012</th>\n",
- " <td>-0.620137</td>\n",
+ " <td>-0.176229</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013</th>\n",
- " <td>-0.202285</td>\n",
+ " <td>-0.186028</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014</th>\n",
- " <td>-0.206982</td>\n",
+ " <td>-0.192715</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015</th>\n",
- " <td>-0.172498</td>\n",
+ " <td>-0.165891</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016</th>\n",
- " <td>-0.136464</td>\n",
+ " <td>-0.159139</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
- " <td>-0.638293</td>\n",
+ " <td>-0.193667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018</th>\n",
- " <td>-0.667205</td>\n",
+ " <td>-0.202936</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019</th>\n",
- " <td>-0.180896</td>\n",
+ " <td>-0.177937</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -491,26 +499,26 @@
"text/plain": [
" RPSS\n",
"year \n",
- "2000 -0.141857\n",
- "2001 -0.203405\n",
- "2002 -0.202549\n",
- "2003 -0.206234\n",
- "2004 -0.549463\n",
- "2005 -0.168421\n",
- "2006 -0.184515\n",
- "2007 -0.616939\n",
- "2008 -0.195251\n",
- "2009 -0.202809\n",
- "2010 -0.189126\n",
- "2011 -0.678302\n",
- "2012 -0.620137\n",
- "2013 -0.202285\n",
- "2014 -0.206982\n",
- "2015 -0.172498\n",
- "2016 -0.136464\n",
- "2017 -0.638293\n",
- "2018 -0.667205\n",
- "2019 -0.180896"
+ "2000 -0.147543\n",
+ "2001 -0.190513\n",
+ "2002 -0.186966\n",
+ "2003 -0.197091\n",
+ "2004 -0.194299\n",
+ "2005 -0.163684\n",
+ "2006 -0.180648\n",
+ "2007 -0.182294\n",
+ "2008 -0.173836\n",
+ "2009 -0.208382\n",
+ "2010 -0.150463\n",
+ "2011 -0.177190\n",
+ "2012 -0.176229\n",
+ "2013 -0.186028\n",
+ "2014 -0.192715\n",
+ "2015 -0.165891\n",
+ "2016 -0.159139\n",
+ "2017 -0.193667\n",
+ "2018 -0.202936\n",
+ "2019 -0.177937"
]
},
"execution_count": 13,
@@ -519,8 +527,6 @@
}
],
"source": [
- "from scripts import skill_by_year\n",
- "\n",
"skill_by_year(preds_is)"
]
},
@@ -535,18 +541,7 @@
"cell_type": "code",
"execution_count": 14,
"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"
- ]
- }
- ],
+ "outputs": [],
"source": [
"preds_test = create_predictions(fct_2020, bias_2000_2019)"
]
@@ -595,7 +590,7 @@
" <tbody>\n",
" <tr>\n",
" <th>2020</th>\n",
- " <td>-0.093422</td>\n",
+ " <td>-0.096323</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -604,7 +599,7 @@
"text/plain": [
" RPSS\n",
"year \n",
- "2020 -0.093422"
+ "2020 -0.096323"
]
},
"execution_count": 15,
@@ -665,8 +660,8 @@
"metadata": {},
"outputs": [],
"source": [
- "#!git add ../submissions/ML_prediction_2020.nc\n",
- "#!git add mean_bias_reduction.ipynb"
+ "# !git add ../submissions/ML_prediction_2020.nc\n",
+ "# !git add mean_bias_reduction.ipynb"
]
},
{
@@ -684,7 +679,7 @@
"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"
+ "#!git tag \"submission-no_ML_mean_bias_reduction-0.0.2\" # if this is to be checked by scorer, only the last submitted==tagged version will be considered"
]
},
{
@@ -712,7 +707,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
@@ -720,8 +715,8 @@
"output_type": "stream",
"text": [
" total used free shared buffers cached\n",
- "Mem: 62 15 46 0 0 5\n",
- "-/+ buffers/cache: 10 52\n",
+ "Mem: 62 18 43 0 0 5\n",
+ "-/+ buffers/cache: 12 49\n",
"Swap: 0 0 0\n"
]
}
@@ -740,7 +735,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -761,8 +756,8 @@
"Model: 79\n",
"Model name: Intel(R) Xeon(R) CPU E5-2695 v4 @ 2.10GHz\n",
"Stepping: 1\n",
- "CPU MHz: 2100.000\n",
- "BogoMIPS: 4190.01\n",
+ "CPU MHz: 2101.000\n",
+ "BogoMIPS: 4190.00\n",
"Virtualization: VT-x\n",
"L1d cache: 32K\n",
"L1i cache: 32K\n",
@@ -786,7 +781,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 21,
"metadata": {},
"outputs": [
{
@@ -821,29 +816,29 @@
"bleach 3.3.0 pyhd3eb1b0_0 \n",
"blinker 1.4 py37h06a4308_0 \n",
"bokeh 2.3.0 py37h06a4308_0 \n",
- "botocore 1.20.33 pyhd3eb1b0_1 \n",
+ "botocore 1.19.52 pyhd3eb1b0_0 \n",
"bottleneck 1.3.2 py37heb32a55_1 \n",
"bracex 2.1.1 pypi_0 pypi\n",
"branca 0.3.1 pypi_0 pypi\n",
"brotlipy 0.7.0 py37h27cfd23_1003 \n",
"bzip2 1.0.8 h7b6447c_0 \n",
"c-ares 1.17.1 h27cfd23_0 \n",
- "ca-certificates 2021.1.19 h06a4308_1 \n",
+ "ca-certificates 2021.5.30 ha878542_0 conda-forge\n",
"cachecontrol 0.11.7 pypi_0 pypi\n",
"cachetools 4.2.1 pyhd3eb1b0_0 \n",
"calamus 0.3.7 pypi_0 pypi\n",
"cdsapi 0.5.1 pypi_0 pypi\n",
- "certifi 2020.12.5 py37h06a4308_0 \n",
+ "certifi 2021.5.30 py37h89c1867_0 conda-forge\n",
"cffi 1.14.5 py37h261ae71_0 \n",
"cfgrib 0.9.8.5 pyhd8ed1ab_0 conda-forge\n",
- "cftime 1.4.1 py37h6323ea4_0 \n",
+ "cftime 1.5.0 pypi_0 pypi\n",
"chardet 3.0.4 py37h06a4308_1003 \n",
"click 7.1.2 pyhd3eb1b0_0 \n",
"click-completion 0.5.2 pypi_0 pypi\n",
"click-plugins 1.1.1 pypi_0 pypi\n",
- "climetlab 0.8.0 pypi_0 pypi\n",
- "climetlab-s2s-ai-challenge 0.6.7 pypi_0 pypi\n",
- "climetlab-s2s-ai-competition 0.3.7 pypi_0 pypi\n",
+ "climetlab 0.8.6 pypi_0 pypi\n",
+ "climetlab-s2s-ai-challenge 0.8.0 pypi_0 pypi\n",
+ "climpred 2.1.4 pypi_0 pypi\n",
"cloudpickle 1.6.0 py_0 \n",
"colorama 0.4.4 pypi_0 pypi\n",
"coloredlogs 15.0 pypi_0 pypi\n",
@@ -858,12 +853,14 @@
"cython 0.29.22 py37h2531618_0 \n",
"cytoolz 0.11.0 py37h7b6447c_0 \n",
"dask 2021.3.0 pypi_0 pypi\n",
+ "dask-core 2021.6.2 pyhd8ed1ab_0 conda-forge\n",
"dask-labextension 5.0.1 pypi_0 pypi\n",
"dbus 1.13.18 hb2f20db_0 \n",
"decorator 4.4.2 pyhd3eb1b0_0 \n",
"defusedxml 0.7.1 pyhd3eb1b0_0 \n",
- "distributed 2021.3.0 py37h06a4308_0 \n",
+ "distributed 2021.6.2 py37h89c1867_0 conda-forge\n",
"docopt 0.6.2 py37h06a4308_0 \n",
+ "docutils 0.15.2 py37h89c1867_2 conda-forge\n",
"eccodes 1.2.0 pypi_0 pypi\n",
"ecmwf-api-client 1.6.1 pypi_0 pypi\n",
"ecmwflibs 0.2.3 pypi_0 pypi\n",
@@ -889,9 +886,9 @@
"gst-plugins-base 1.14.0 h8213a91_2 \n",
"gstreamer 1.14.0 h28cd5cc_2 \n",
"h5netcdf 0.10.0 pyhd8ed1ab_0 conda-forge\n",
- "h5py 2.10.0 py37h7918eee_0 \n",
+ "h5py 2.10.0 nompi_py37h1e651dc_105 conda-forge\n",
"hdf4 4.2.13 h3ca952b_2 \n",
- "hdf5 1.10.4 hb1b8bf9_0 \n",
+ "hdf5 1.10.6 nompi_h3c11f04_101 conda-forge\n",
"heapdict 1.0.1 py_0 \n",
"humanfriendly 9.1 pypi_0 pypi\n",
"humanize 2.6.0 pypi_0 pypi\n",
@@ -936,7 +933,7 @@
"libgcc-ng 9.1.0 hdf63c60_0 \n",
"libgfortran-ng 7.3.0 hdf63c60_0 \n",
"libllvm10 10.0.1 hbcb73fb_5 \n",
- "libnetcdf 4.6.2 hbdf4f91_1001 conda-forge\n",
+ "libnetcdf 4.7.4 nompi_h56d31a8_107 conda-forge\n",
"libpng 1.6.37 hbc83047_0 \n",
"libprotobuf 3.14.0 h8c45485_0 \n",
"libsodium 1.0.18 h36c2ea0_1 conda-forge\n",
@@ -973,10 +970,11 @@
"nbclient 0.5.3 pyhd3eb1b0_0 \n",
"nbconvert 6.0.7 py37_0 \n",
"nbformat 5.1.2 pyhd3eb1b0_1 \n",
+ "nc-time-axis 1.3.1 pypi_0 pypi\n",
"ncurses 6.2 he6710b0_1 \n",
"ndg-httpsclient 0.5.1 pypi_0 pypi\n",
"nest-asyncio 1.5.1 pyhd3eb1b0_0 \n",
- "netcdf4 1.5.1 py37had58050_0 conda-forge\n",
+ "netcdf4 1.5.4 nompi_py37hcbfd489_103 conda-forge\n",
"networkx 2.5 pypi_0 pypi\n",
"notebook 6.3.0 py37h06a4308_0 \n",
"numba 0.53.0 py37ha9443f7_0 \n",
@@ -1059,6 +1057,7 @@
"simpervisor 0.4 pypi_0 pypi\n",
"sip 4.19.8 py37hf484d3e_0 \n",
"six 1.15.0 py37h06a4308_0 \n",
+ "sklearn-xarray 0.4.0 pypi_0 pypi\n",
"smmap 3.0.5 pypi_0 pypi\n",
"sniffio 1.2.0 pypi_0 pypi\n",
"sortedcontainers 2.3.0 pyhd3eb1b0_0 \n",
@@ -1085,7 +1084,7 @@
"typed-ast 1.4.2 pypi_0 pypi\n",
"typing-extensions 3.7.4.3 hd3eb1b0_0 \n",
"typing_extensions 3.7.4.3 pyh06a4308_0 \n",
- "urllib3 1.25.11 pypi_0 pypi\n",
+ "urllib3 1.25.11 py_0 conda-forge\n",
"wcmatch 6.1 pypi_0 pypi\n",
"wcwidth 0.2.5 py_0 \n",
"webencodings 0.5.1 py37_1 \n",
@@ -1093,7 +1092,7 @@
"werkzeug 1.0.1 pyhd3eb1b0_0 \n",
"wheel 0.36.2 pyhd3eb1b0_0 \n",
"wrapt 1.12.1 py37h7b6447c_1 \n",
- "xarray 0.17.0 pyhd3eb1b0_0 \n",
+ "xarray 0.18.2 pypi_0 pypi\n",
"xhistogram 0.1.2 pyhd8ed1ab_0 conda-forge\n",
"xskillscore 0.0.20 pypi_0 pypi\n",
"xz 5.2.5 h7b6447c_0 \n",
diff --git a/notebooks/renku_datasets_biweekly.ipynb b/notebooks/renku_datasets_biweekly.ipynb
index e9c0573b4dc193de9a9c89444f35259f4c557d94..9b252725730fd9b53fc458da367a0b49adc2f0e7 100644
--- a/notebooks/renku_datasets_biweekly.ipynb
+++ b/notebooks/renku_datasets_biweekly.ipynb
@@ -46,9 +46,36 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING: ecmwflibs universal: cannot find a library called MagPlus\n",
+ "Magics library could not be found\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Climetlab version : 0.8.6\n",
+ "Climetlab-s2s-ai-challenge plugin version : 0.8.0\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<xarray.core.options.set_options at 0x2b51c148f590>"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"import matplotlib.pyplot as plt\n",
"import xarray as xr\n",
@@ -71,9 +98,16 @@
"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)\n",
- "\n",
+ "cache_path = \"/work/mh0727/m300524/S2S_AI/cache4\" # set your own path\n",
+ "cml.settings.set(\"cache-directory\", cache_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
"cache_path = \"../data\""
]
},
@@ -88,7 +122,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -106,7 +140,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -124,7 +158,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -145,7 +179,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -201,12 +235,123 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "datasetlabel: hindcast-like-observations, center: ecmwf, parameter: 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"
+ ]
+ },
+ {
+ "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 2.18.0\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": [
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/indexing.py:1379: PerformanceWarning: Slicing with an out-of-order index is generating 20 times more chunks\n",
+ " return self.array[key]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "datasetlabel: hindcast-like-observations, center: ecmwf, parameter: t2m\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/indexing.py:1379: PerformanceWarning: Slicing is producing a large chunk. To accept the large\n",
+ "chunk and silence this warning, set the option\n",
+ " >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n",
+ " ... array[indexer]\n",
+ "\n",
+ "To avoid creating the large chunks, set the option\n",
+ " >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
+ " ... array[indexer]\n",
+ " return self.array[key]\n",
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/indexing.py:1379: PerformanceWarning: Slicing is producing a large chunk. To accept the large\n",
+ "chunk and silence this warning, set the option\n",
+ " >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n",
+ " ... array[indexer]\n",
+ "\n",
+ "To avoid creating the large chunks, set the option\n",
+ " >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
+ " ... array[indexer]\n",
+ " return self.array[key]\n",
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/indexing.py:1379: PerformanceWarning: Slicing with an out-of-order index is generating 20 times more chunks\n",
+ " return self.array[key]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "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": [
+ "datasetlabel: forecast-like-observations, center: ecmwf, parameter: 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",
+ "datasetlabel: forecast-like-observations, center: ecmwf, parameter: t2m\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "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": [
"for c, center in enumerate(center_list): # forecast centers (could also take models)\n",
- " for dsl in obs_dataset_labels + forecast_dataset_labels: # climetlab dataset labels\n",
+ " for dsl in obs_dataset_labels:# + forecast_dataset_labels: # climetlab dataset labels\n",
" for p, parameter in enumerate(varlist_forecast): # variables\n",
" if c != 0 and 'observation' in dsl: # only do once for observations \n",
" continue\n",
@@ -285,7 +430,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -310,7 +455,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -319,6 +464,16 @@
"# !renku dataset add s2s-ai-challenge data/forecast-like-observations_2020_biweekly_deterministic.zarr"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# for further use retrieve from git lfs\n",
+ "# !renku storage pull ../data/forecast-like-observations_2020_biweekly_deterministic.zarr"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 9,
@@ -359,23 +514,17 @@
"cell_type": "code",
"execution_count": 11,
"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"
- ]
- }
- ],
+ "outputs": [],
+ "source": [
+ "# for further use retrieve from git lfs\n",
+ "# !renku storage pull ../data/ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"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')"
@@ -383,7 +532,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -392,44 +541,140 @@
"# !renku dataset add s2s-ai-challenge data/ecmwf_forecast-input_2020_biweekly_deterministic.zarr"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# for further use retrieve from git lfs\n",
+ "# !renku storage pull ../data/ecmwf_forecast-input_2020_biweekly_deterministic.zarr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "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": 13,
"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": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scripts import add_year_week_coords"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
"outputs": [
{
- "name": "stdout",
+ "name": "stderr",
"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"
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/accessor_dt.py:383: FutureWarning: dt.weekofyear and dt.week have been deprecated. Please use dt.isocalendar().week instead.\n",
+ " FutureWarning,\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')"
+ "# add week for groupby, see https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge/-/issues/29\n",
+ "obs_2000_2019 = add_year_week_coords(obs_2000_2019)"
]
},
{
- "cell_type": "markdown",
+ "cell_type": "code",
+ "execution_count": 16,
"metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<pre><xarray.Dataset>\n",
+ "Dimensions: (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",
+ " week (forecast_time) int64 1 2 3 4 5 6 7 ... 47 48 49 50 51 52 53\n",
+ " year (forecast_time) int64 2000 2000 2000 2000 ... 2019 2019 2019\n",
+ "Data variables:\n",
+ " t2m (lead_time, forecast_time, latitude, longitude) float32 dask.array<chunksize=(2, 530, 121, 240), meta=np.ndarray>\n",
+ " tp (lead_time, forecast_time, latitude, longitude) float32 dask.array<chunksize=(2, 530, 121, 240), meta=np.ndarray>\n",
+ "Attributes:\n",
+ " created_by_script: tools/observations/makefile\n",
+ " created_by_software: climetlab-s2s-ai-challenge\n",
+ " function: climetlab_s2s_ai_challenge.extra.forecast_like_obse...\n",
+ " postprocessed_by: https://renkulab.io/gitlab/aaron.spring/s2s-ai-chal...\n",
+ " regrid_method: conservative\n",
+ " source_dataset_name: NOAA NCEP CPC UNIFIED_PRCP GAUGE_BASED GLOBAL v1p0 ...\n",
+ " source_hosting: IRIDL\n",
+ " source_url: http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/...</pre>"
+ ],
+ "text/plain": [
+ "<xarray.Dataset>\n",
+ "Dimensions: (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",
+ " week (forecast_time) int64 1 2 3 4 5 6 7 ... 47 48 49 50 51 52 53\n",
+ " year (forecast_time) int64 2000 2000 2000 2000 ... 2019 2019 2019\n",
+ "Data variables:\n",
+ " t2m (lead_time, forecast_time, latitude, longitude) float32 dask.array<chunksize=(2, 530, 121, 240), meta=np.ndarray>\n",
+ " tp (lead_time, forecast_time, latitude, longitude) float32 dask.array<chunksize=(2, 530, 121, 240), meta=np.ndarray>\n",
+ "Attributes:\n",
+ " created_by_script: tools/observations/makefile\n",
+ " created_by_software: climetlab-s2s-ai-challenge\n",
+ " function: climetlab_s2s_ai_challenge.extra.forecast_like_obse...\n",
+ " postprocessed_by: https://renkulab.io/gitlab/aaron.spring/s2s-ai-chal...\n",
+ " regrid_method: conservative\n",
+ " source_dataset_name: NOAA NCEP CPC UNIFIED_PRCP GAUGE_BASED GLOBAL v1p0 ...\n",
+ " source_hosting: IRIDL\n",
+ " source_url: http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/..."
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"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"
+ "obs_2000_2019"
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
@@ -438,15 +683,13 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 18,
"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"
]
@@ -455,68 +698,69 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "CPU times: user 21min 25s, sys: 9min 19s, total: 30min 45s\n",
- "Wall time: 18min 4s\n"
+ "CPU times: user 19min 35s, sys: 8min 33s, total: 28min 9s\n",
+ "Wall time: 16min 44s\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'}).astype('float32').to_netcdf(tercile_file)"
+ "obs_2000_2019.chunk({'forecast_time':-1,'longitude':'auto'}).groupby('week').quantile(q=[1./3.,2./3.], dim='forecast_time').rename({'quantile':'category_edge'}).astype('float32').to_netcdf(tercile_file)"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<pre><xarray.Dataset>\n",
- "Dimensions: (category_edge: 2, latitude: 121, lead_time: 2, longitude: 240, weekofyear: 53)\n",
+ "Dimensions: (category_edge: 2, latitude: 121, lead_time: 2, longitude: 240, week: 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",
+ " * week (week) int64 1 2 3 4 5 6 7 8 9 ... 45 46 47 48 49 50 51 52 53\n",
"Data variables:\n",
- " t2m (weekofyear, category_edge, lead_time, latitude, longitude) float32 ...\n",
- " tp (weekofyear, category_edge, lead_time, latitude, longitude) float32 ...\n",
+ " t2m (week, category_edge, lead_time, latitude, longitude) float32 ...\n",
+ " tp (week, category_edge, lead_time, latitude, longitude) float32 ...\n",
"Attributes:\n",
" created_by_script: tools/observations/makefile\n",
" created_by_software: climetlab-s2s-ai-challenge\n",
" function: climetlab_s2s_ai_challenge.extra.forecast_like_obse...\n",
- " postprocessed: by https://renkulab.io/gitlab/aaron.spring/s2s-ai-c...\n",
+ " postprocessed_by: https://renkulab.io/gitlab/aaron.spring/s2s-ai-chal...\n",
+ " regrid_method: conservative\n",
" source_dataset_name: NOAA NCEP CPC UNIFIED_PRCP GAUGE_BASED GLOBAL v1p0 ...\n",
" source_hosting: IRIDL\n",
" source_url: http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/...</pre>"
],
"text/plain": [
"<xarray.Dataset>\n",
- "Dimensions: (category_edge: 2, latitude: 121, lead_time: 2, longitude: 240, weekofyear: 53)\n",
+ "Dimensions: (category_edge: 2, latitude: 121, lead_time: 2, longitude: 240, week: 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",
+ " * week (week) int64 1 2 3 4 5 6 7 8 9 ... 45 46 47 48 49 50 51 52 53\n",
"Data variables:\n",
- " t2m (weekofyear, category_edge, lead_time, latitude, longitude) float32 ...\n",
- " tp (weekofyear, category_edge, lead_time, latitude, longitude) float32 ...\n",
+ " t2m (week, category_edge, lead_time, latitude, longitude) float32 ...\n",
+ " tp (week, category_edge, lead_time, latitude, longitude) float32 ...\n",
"Attributes:\n",
" created_by_script: tools/observations/makefile\n",
" created_by_software: climetlab-s2s-ai-challenge\n",
" function: climetlab_s2s_ai_challenge.extra.forecast_like_obse...\n",
- " postprocessed: by https://renkulab.io/gitlab/aaron.spring/s2s-ai-c...\n",
+ " postprocessed_by: https://renkulab.io/gitlab/aaron.spring/s2s-ai-chal...\n",
+ " regrid_method: conservative\n",
" source_dataset_name: NOAA NCEP CPC UNIFIED_PRCP GAUGE_BASED GLOBAL v1p0 ...\n",
" source_hosting: IRIDL\n",
" source_url: http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/..."
]
},
- "execution_count": 16,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
@@ -529,7 +773,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -538,7 +782,7 @@
"(49.255184, 'MB')"
]
},
- "execution_count": 17,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
@@ -549,7 +793,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
@@ -560,7 +804,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
@@ -581,7 +825,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
@@ -590,7 +834,7 @@
"Frozen(SortedKeysDict({'forecast_time': 53, 'latitude': 121, 'lead_time': 2, 'longitude': 240}))"
]
},
- "execution_count": 20,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
@@ -602,7 +846,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
@@ -612,7 +856,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
@@ -621,7 +865,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
@@ -635,39 +879,16 @@
"#plt.savefig('dry_mask.png')"
]
},
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [],
- "source": [
- "# look into tercile edges"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [],
- "source": [
- "#tercile_edges.isel(forecast_time=0)['tp'].plot(col='lead_time',row='category_edge', robust=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "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": 27,
"metadata": {},
"outputs": [],
"source": [
+ "# look into tercile edges\n",
+ "\n",
+ "# tercile_edges.isel(forecast_time=0)['tp'].plot(col='lead_time',row='category_edge', robust=True)\n",
+ "# tercile_edges.isel(forecast_time=[0,20],category_edge=1)['tp'].plot(col='lead_time', row='forecast_time', robust=True)\n",
"# tercile_edges.tp.mean(['forecast_time']).plot(col='lead_time',row='category_edge',vmax=.5)"
]
},
@@ -698,12 +919,21 @@
"cell_type": "code",
"execution_count": 29,
"metadata": {},
+ "outputs": [],
+ "source": [
+ "# tp_arid_mask.isel(week=[0,10,20,30,40]).plot(col='week')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "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",
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/accessor_dt.py:383: FutureWarning: dt.weekofyear and dt.week have been deprecated. Please use dt.isocalendar().week instead.\n",
" FutureWarning,\n"
]
}
@@ -714,7 +944,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 31,
"metadata": {},
"outputs": [
{
@@ -723,7 +953,7 @@
"(147.75984, 'MB')"
]
},
- "execution_count": 30,
+ "execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
@@ -734,7 +964,52 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 32,
+ "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] dask.array<chunksize=(2, 53), meta=np.ndarray>\n",
+ " * category (category) <U12 'below normal' 'near normal' 'above normal'\n",
+ "Data variables:\n",
+ " t2m (category, lead_time, forecast_time, latitude, longitude) float64 dask.array<chunksize=(1, 2, 53, 121, 240), meta=np.ndarray>\n",
+ " tp (category, lead_time, forecast_time, latitude, longitude) float64 dask.array<chunksize=(1, 2, 53, 121, 240), meta=np.ndarray></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] dask.array<chunksize=(2, 53), meta=np.ndarray>\n",
+ " * category (category) <U12 'below normal' 'near normal' 'above normal'\n",
+ "Data variables:\n",
+ " t2m (category, lead_time, forecast_time, latitude, longitude) float64 dask.array<chunksize=(1, 2, 53, 121, 240), meta=np.ndarray>\n",
+ " tp (category, lead_time, forecast_time, latitude, longitude) float64 dask.array<chunksize=(1, 2, 53, 121, 240), meta=np.ndarray>"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "obs_2020_p"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
"metadata": {},
"outputs": [
{
@@ -752,7 +1027,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
@@ -763,7 +1038,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 35,
"metadata": {},
"outputs": [
{
@@ -797,7 +1072,7 @@
" tp (category, lead_time, forecast_time, latitude, longitude) float32 ..."
]
},
- "execution_count": 33,
+ "execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
@@ -824,7 +1099,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
@@ -833,14 +1108,14 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 37,
"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",
+ "/work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/accessor_dt.py:383: FutureWarning: dt.weekofyear and dt.week have been deprecated. Please use dt.isocalendar().week instead.\n",
" FutureWarning,\n"
]
}
@@ -851,7 +1126,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 38,
"metadata": {},
"outputs": [
{
@@ -860,7 +1135,7 @@
"(2955.138888, 'MB')"
]
},
- "execution_count": 36,
+ "execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
@@ -871,7 +1146,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 39,
"metadata": {},
"outputs": [
{
@@ -885,10 +1160,10 @@
{
"data": {
"text/plain": [
- "<xarray.backends.zarr.ZarrStore at 0x2b34e40d80c0>"
+ "<xarray.backends.zarr.ZarrStore at 0x2b51c1233360>"
]
},
- "execution_count": 37,
+ "execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
@@ -899,7 +1174,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
@@ -910,7 +1185,7 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 41,
"metadata": {},
"outputs": [
{
@@ -926,8 +1201,8 @@
" * 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",
"Data variables:\n",
- " t2m (category, lead_time, forecast_time, latitude, longitude) float32 dask.array<chunksize=(1, 2, 280, 121, 240), meta=np.ndarray>\n",
- " tp (category, lead_time, forecast_time, latitude, longitude) float32 dask.array<chunksize=(1, 2, 280, 121, 240), meta=np.ndarray></pre>"
+ " t2m (category, lead_time, forecast_time, latitude, longitude) float32 dask.array<chunksize=(1, 2, 530, 121, 240), meta=np.ndarray>\n",
+ " tp (category, lead_time, forecast_time, latitude, longitude) float32 dask.array<chunksize=(1, 2, 530, 121, 240), meta=np.ndarray></pre>"
],
"text/plain": [
"<xarray.Dataset>\n",
@@ -940,11 +1215,11 @@
" * 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",
"Data variables:\n",
- " t2m (category, lead_time, forecast_time, latitude, longitude) float32 dask.array<chunksize=(1, 2, 280, 121, 240), meta=np.ndarray>\n",
- " tp (category, lead_time, forecast_time, latitude, longitude) float32 dask.array<chunksize=(1, 2, 280, 121, 240), meta=np.ndarray>"
+ " t2m (category, lead_time, forecast_time, latitude, longitude) float32 dask.array<chunksize=(1, 2, 530, 121, 240), meta=np.ndarray>\n",
+ " tp (category, lead_time, forecast_time, latitude, longitude) float32 dask.array<chunksize=(1, 2, 530, 121, 240), meta=np.ndarray>"
]
},
- "execution_count": 39,
+ "execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
@@ -957,10 +1232,17 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 47,
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "# checking category frequencies\n",
+ "# o = xr.open_zarr(\"../data/hindcast-like-observations_2000-2019_biweekly_terciled.zarr\")\n",
+ "# w=0\n",
+ "# v='tp'\n",
+ "# o.sel(forecast_time=o.forecast_time.dt.dayofyear==2+7*w).sum('forecast_time', skipna=False)[v].plot(row='lead_time',col='category', levels=[5.5,6.5,7.5])\n",
+ "# o.sel(forecast_time=o.forecast_time.dt.dayofyear==2+7*w).sum('forecast_time', skipna=False).sum('category', skipna=False)[v].plot(row='lead_time', levels=[16.5,17.5,18.5,19.5,20.5])"
+ ]
},
{
"cell_type": "markdown",
diff --git a/notebooks/scripts.py b/notebooks/scripts.py
index fc6fb80ecc4ed0ecf849533a4374db98fca4ccab..d0a0e0ac695480793a7c35459ec4ca5480224bff 100644
--- a/notebooks/scripts.py
+++ b/notebooks/scripts.py
@@ -146,11 +146,25 @@ def ensure_attributes(da, biweekly=False):
return da
-def make_probabilistic(ds, tercile_edges, member_dim='realization', mask=None):
+def add_year_week_coords(ds):
+ import numpy as np
+ if 'week' not in ds.coords and 'year' not in ds.coords:
+ year = ds.forecast_time.dt.year.to_index().unique()
+ week = (list(np.arange(1,54)))
+ weeks = week * len(year)
+ years = np.repeat(year,len(week))
+ ds.coords["week"] = ("forecast_time", weeks)
+ ds.coords['week'].attrs['description'] = "This week represents the number of forecast_time starting from 1 to 53. Note: This week is different from the ISO week from groupby('forecast_time.weekofyear'), see https://en.wikipedia.org/wiki/ISO_week_date and https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge/-/issues/29"
+ ds.coords["year"] = ("forecast_time", years)
+ ds.coords['year'].attrs['long_name'] = "calendar year"
+ return ds
+
+
+def make_probabilistic(ds, tercile_edges, member_dim='realization', mask=None, groupby_coord='week'):
"""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)
+ ds = add_year_week_coords(ds)
+ tercile_edges = tercile_edges.sel({groupby_coord: ds.coords[groupby_coord]})
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
@@ -176,8 +190,10 @@ def make_probabilistic(ds, tercile_edges, member_dim='realization', mask=None):
'comment': 'All three tercile category probabilities must add up to 1.',
'variable_before_categorization': 'https://confluence.ecmwf.int/display/S2S/S2S+Surface+Air+Temperature'
}
- if 'weekofyear' in ds_p.coords:
- ds_p = ds_p.drop('weekofyear')
+ if 'year' in ds_p.coords:
+ del ds_p.coords['year']
+ if groupby_coord in ds_p.coords:
+ ds_p = ds_p.drop(groupby_coord)
return ds_p
diff --git a/submissions/ML_prediction_2020.nc b/submissions/ML_prediction_2020.nc
index ce3b9bea6e7dab19771f22deef58314eb6f860ba..4ee741efc322484daabb3e10589bdcdf2d3b7f12 100644
--- a/submissions/ML_prediction_2020.nc
+++ b/submissions/ML_prediction_2020.nc
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
-oid sha256:3199d9ab2d260810eb4ac2defdbcc462a7d8ecba7a4a4aa009f8f9cfb8d2c359
-size 73902776
+oid sha256:de052442cd8ed26807c40ee545645609562687b3c9a894f219de8035bf77c2d0
+size 73909016