Deep Learning Approach Towards Precipitation Nowcasting: Evaluating Regional Extrapolation Capabilities
In one sentence. ConvLSTM precipitation nowcasting on German radar data shows that finetuning across locations and thresholds extends skill to 100 minutes.
Abstract
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.
Labels
- Topic
- Nowcasting
- Type
- New model or method
- Method
- RNN / LSTM / ConvLSTM, CNN / U-Net
- Time scale
- 0–6 h
- Domain
- Regional
- Data
- Radar
- Status
- Research
- Identifiers
- DOI:10.5194/egusphere-2022-440 OpenAlex:W4283214584
- Added
- 2026-10-22
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