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- W4291316400 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." @default.
- W4291316400 created "2022-08-14" @default.
- W4291316400 creator A5002841764 @default.
- W4291316400 date "2022-08-14" @default.
- W4291316400 modified "2023-09-30" @default.
- W4291316400 title "Reply on RC1" @default.
- W4291316400 doi "https://doi.org/10.5194/egusphere-2022-440-ac1" @default.
- W4291316400 hasPublicationYear "2022" @default.
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