There is a high demand and expectation for sub-seasonal to seasonal (S2S) prediction which provides forecasts beyond 2 weeks, but less than 3 months ahead. To assess the potential benefit of artificial intelligence (AI) methods for S2S prediction through better postprocessing of ensemble prediction system outputs, the World Meteorological Organization (WMO) coordinated a prize challenge in 2021 to improve sub-seasonal prediction. The goal of this competition was to produce the most skillful forecasts of precipitation and two-meter temperature globally averaged over forecast weeks 3 and 4, and weeks 5 and 6 for the year 2020 using artificial intelligence techniques. The top three submissions, described in this article, succeeded in producing S2S forecasts significantly more skillful than the bias-corrected ECMWF operational reference forecasts, particularly for precipitation, through improved calibration of the ECMWF raw forecast outputs or multi-model combination. These forecast improvements should benefit the use of S2S forecasts in applications.
<p>As machine learning algorithms are being used more and more prominently in the meteorology and climate domains, the need for reference datasets has been identified as a priority. Moreover, boilerplate code for data handling is ubiquitous in scientific experiments. In order to focus on science, climate/meteorology/data scientists need generic and reusable domain-specific tools. To achieve these goals, we used the plugin based CliMetLab python package along with many packages listed by Pangeo. &#160;</p><p><br>Our use case consists in providing data for machine learning algorithms in the context of the sub-seasonal to seasonal (S2S) prediction challenge 2021. The data size is about 2 Terabytes of model predictions from three different models. We experimented with providing data in multiple formats: Grib, NetCDF, and Zarr. A Pangeo recipe (using the python package pangeo_forge_recipes) was used to generate Zarr data (relying heavily on xarray and dask for parallelisation). All three versions of the S2S data have been stored on an S3 bucket located on the ECMWF European Weather Cloud (ECMWF-EWC).&#160;</p><p><br>CliMetLab aims at providing a simple interface to access climate and meteorological datasets, seamlessly downloading and caching data, converting to xarray datasets or panda dataframes, plotting data, feed them into machine learning frameworks such as tensorflow or pytorch. CliMetLab is open-source and still a Beta version (https://climetlab.readthedocs.io). The main target platform of CliMetLab is Jupyter notebooks. Additionally, a CliMetLab plugin allows shipping dataset-specific code along with a well-defined published dataset. Taking advantage of the CliMetLab tools to minimize the boilerplate code, a plugin has been developed for S2S data as a companion python package of the dataset.</p>
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