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Machine Learning for Precipitation Nowcasting from Radar Images

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arxiv 1912.12132 v1 pith:TYCPZAM7 submitted 2019-12-11 cs.CV cs.LGstat.ML

Shreya Agrawal , Luke Barrington , Carla Bromberg , John Burge , Cenk Gazen , Jason Hickey This is my paper · ORCID
classification cs.CVcs.LGstat.ML
keywords nowcastingprecipitationhigh-resolutionlearningproblemadaptationapplicationchange
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High-resolution nowcasting is an essential tool needed for effective adaptation to climate change, particularly for extreme weather. As Deep Learning (DL) techniques have shown dramatic promise in many domains, including the geosciences, we present an application of DL to the problem of precipitation nowcasting, i.e., high-resolution (1 km x 1 km) short-term (1 hour) predictions of precipitation. We treat forecasting as an image-to-image translation problem and leverage the power of the ubiquitous UNET convolutional neural network. We find this performs favorably when compared to three commonly used models: optical flow, persistence and NOAA's numerical one-hour HRRR nowcasting prediction.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 240 citations worldwide. Full citation record

  1. FuXi-Nowcast: Environment-conditioned deep learning for severe convection nowcasting

    physics.ao-ph 2025-12 conditional novelty 6.0 of 10

    An environment-conditioned deep-learning system with a convective-signal enhancement module reports higher CSI than CMA-MESO for reflectivity, rainfall, and wind gusts up to 12 h over East China.

  2. GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations

    physics.ao-ph 2024-12 conditional novelty 6.0 of 10

    A graph-neural-network weather model trained only on raw observations produces skillful global forecasts out to five days, with tropical 2-meter temperature forecasts competitive with the operational IFS.

  3. Stop using root-mean-square error as a precipitation target!

    physics.ao-ph 2025-09 conditional novelty 5.0 of 10

    Training precipitation models with Tweedie deviance instead of root-mean-square error improves extreme-rainfall recall in downscaling and improves nowcasting skill, with gains compounding over autoregressive lead times.

  4. Self-supervised Spatial-Temporal Learner for Precipitation Nowcasting

    cs.LG 2024-12 conditional novelty 5.0 of 10

    SpaT-SparK, a SparK-based masked-image-modeling pretrainer with a translation network, reduces pMSE for short-term precipitation nowcasting but sacrifices recall and skill scores versus the smaller SmaAt-UNet.

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