![]() The main publicly available products are (a) RADOLAN and RADKLIM 15, by the German Weather Service (b) NEXRAD Level II 16, by the US National Oceanic and Atmospheric Service (NOAA) and (c) the dataset by the Royal Netherlands Meteorological Institute (KNMI) 17. Open datasets are collected and maintained by international Weather Data institutions across the US and Europe. Significant efforts have been undertaken to share open source weather radar resources, including software for analysis and visualization 9, 10, 11, 12, 13, 14 and open data repositories (see ). State of the art solutions for precipitation nowcasting are solidly based on weather radar data 7, due to a known direct relationship between radar reflectivity and rain rate 8. 0 to 6 hours), in particular for extreme and fast evolving precipitation events 6. forecasting within a short time interval (e.g. In this work, we focus on precipitation nowcasting, i.e. Software methods for data pre-processing, model training and inference, and a pre-trained model are publicly available on GitHub ( ) for study replication and reproducibility.Įffects of climate change on the increased frequency and magnitude of extreme weather events have been consistently described 1, 2, and a shift towards a more extreme precipitation climate (the so-called “tropicalisation”) has been predicted by models 3 and observed across Europe 4, 5. We validate TAASRAD19 as a benchmark for nowcasting methods by introducing a TrajGRU deep learning model to forecast reflectivity, and a procedure based on the UMAP dimensionality reduction algorithm for interactive exploration. The TAASRAD19 distribution also includes a curated set of 1,732 sequences, for a total of 362,233 radar images, labeled with precipitation type tags assigned by expert meteorologists. Data are expressed as 2D images, considering the maximum reflectivity on the vertical section at 5 min sampling rate, covering an area of 240 km of diameter at 500 m horizontal resolution. The dataset includes 894,916 timesteps of precipitation from more than 9 years of data, offering a novel resource to develop and benchmark analog ensemble models and machine learning solutions for precipitation nowcasting. We introduce TAASRAD19, a high-resolution radar reflectivity dataset collected by the Civil Protection weather radar of the Trentino South Tyrol Region, in the Italian Alps.
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