@@ -53,7 +53,7 @@ The experiments described in the GMD paper rely on the ERA5 dataset from which 1
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@@ -53,7 +53,7 @@ The experiments described in the GMD paper rely on the ERA5 dataset from which 1
We recommend the users to store the data following the directory structure for the input data described [below](#Input-and-Output-folder-structure-and-naming-convention).
We recommend the users to store the data following the directory structure for the input data described [below](#Input-and-Output-folder-structure-and-naming-convention).
#### Dry run with small samples (~15 GB)
#### Dry run with small samples (~ 5 - ~ 15 GB)
In our application, the typical use-case is to work on a large dataset. Nevertheless, we also prepared an example dataset (1 month data in 2007, 2008, 2009 respectively data with few variables) to help users to run tests on their own machine or to do some quick tests. The data can be downloaded by requesting from Bing Gong <b.gong@fz-juelich.de>. Users of the deepacf-project at JSC can also access the files from `/p/project/deepacf/deeprain/video_prediction_shared_folder/GMD_samples`.
In our application, the typical use-case is to work on a large dataset. Nevertheless, we also prepared an example dataset (1 month data in 2007, 2008, 2009 respectively data with few variables) to help users to run tests on their own machine or to do some quick tests. The data can be downloaded by requesting from Bing Gong <b.gong@fz-juelich.de>. Users of the deepacf-project at JSC can also access the files from `/p/project/deepacf/deeprain/video_prediction_shared_folder/GMD_samples`.
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@@ -61,7 +61,7 @@ In our application, the typical use-case is to work on a large dataset. Neverthe
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@@ -61,7 +61,7 @@ In our application, the typical use-case is to work on a large dataset. Neverthe
#### Climatological mean data
#### Climatological mean data
To compute anomaly correlations in the postprocessing step (see below), climatological mean data is required. This data constitutes the climatological mean for each daytime hour and for each month for the period 1990-2019.
To compute anomaly correlations in the postprocessing step (see below), climatological mean data is required. This data constitutes the climatological mean for each daytime hour and for each month for the period 1990-2019.
For convenince, the data is also provided with our frozon version of code and can be downloaded from [zenodo-link!!]().
For convenince, the data is also provided with our frozon version of code and can be downloaded from the (link)[https://b2share.eudat.eu/records/744bbb4e6ee84a09ad368e8d16713118].
