Preprints
https://doi.org/10.5194/egusphere-2022-440
https://doi.org/10.5194/egusphere-2022-440
 
21 Jun 2022
21 Jun 2022
Status: this preprint is open for discussion.

Deep Learning Approach Towards Precipitation Nowcasting: Evaluating Regional Extrapolation Capabilities

Tarek Beutler1, Annette Rudolph2, Daniel Goehring1, and Nikki Vercauteren3 Tarek Beutler et al.
  • 1Department of Computer Science, Freie Universität Berlin, Germany
  • 2Department of Geosciences, Freie Universität Berlin, Germany
  • 3Department of Geosciences, University of Oslo, Norway

Abstract. Precipitation nowcasting refers to the prediction of precipitation intensity in a local region and in a short timeframe up to 6 hours. The evaluation of spatial and temporal information still challenges todays numerical weather prediction models. The increasing possibilities to store and evaluate data combined with the advancements in the developments of artificial intelligence algorithms make it natural to use these methods to improve precipitation nowcasting. In this work a Convolutional Long Short-Term Memory network (ConvLSTM) is applied to Radar data of the GermanWeather Service. The positive effectiveness of finetuning a network pretrained at a different location and for different precipitation intensity thresholds is demonstrated. Furthermore, in the framework of two case studies the skill scores for the different thresholds are shown for a prediction time up to 100 minutes. The results highlight promising regional extrapolation capabilities for such neural networks for precipitation nowcasting.

Tarek Beutler et al.

Status: open (until 16 Aug 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Tarek Beutler et al.

Tarek Beutler et al.

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Short summary
Precipitation nowcasting refers to the prediction of precipitation intensity in a local region and in a short timeframe up to 6 hours. The increasing possibilities to store and evaluate data combined with the advancements in the developments of artificial intelligence algorithms make it natural to use these methods to improve precipitation nowcasting. The positive effectiveness of finetuning and promising skill scores for a prediction time up to 100 minutes are shown.