Abstract:Subseasonal predictions are gaining more and more attention and importance in many applications, e.g. agriculture or energy&consumption predictions. To bridge the gap between those two temporal horizons and their drivers is, however, a challenge. Several attempts have been made in recent years to improve the numerical weather predictions but they to come at a high computation cost resulting in coarse spatial resolutions.  In the past decade, significant advances were made in improving the S2S … Show more
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