A curated UK rain radar data set for training and benchmarking nowcasting models
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Abstract (arXiv)
This paper documents a data set of UK rain radar image sequences for use in statistical modeling and machine learning methods for nowcasting. The main dataset contains 1,000 randomly sampled sequences of length 20 steps (15-minute increments) of 2D radar intensity fields of dimension 40x40 (at 5km spatial resolution). Spatially stratified sampling ensures spatial homogeneity despite removal of clear-sky cases by threshold-based truncation. For each radar sequence, additional atmospheric and geographic features are made available, including date, location, mean elevation, mean wind direction and speed and prevailing storm type. New R functions to extract data from the binary "Nimrod" radar data format are provided. A case study is presented to train and evaluate a simple convolutional neural network for radar nowcasting, including self-contained R code.
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References · 5
- 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,436
- 2021Skillful Precipitation Nowcasting using Deep Generative Models of Radarcited by 311
- 2020MetNet: A Neural Weather Model for Precipitation Forecastingcited by 131
- 2023Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscalingcited by 110
- 2019Machine Learning for Precipitation Nowcasting from Radar Imagescited by 56
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