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A Neural Weather Model for Eight-Hour Precipitation Forecasting

Wednesday, March 25, 2020

Posted by Nal Kalchbrenner and Casper Sønderby, Research Scientists, Google Research, Amsterdamdeep neural networksTPUsprevious researchnowcastingMetNet: A Neural Weather Model for Precipitation ForecastingNOAA

MetNet model predictions compared to the ground truth as measured by the NOAA multi-radar/multi-sensor system (MRMS). The MetNet model (top) displays the probabilities for 1 mm/hr precipitation predicted from 2 minutes to 480 minutes ahead, whereas the MRMS data (bottom) shows the areas receiving at least 1 mm/hr of precipitation over that same time period.

Neural Weather Modelmulti-radar/multi-sensor systemGeostationary Operational Environmental Satellite

Size of the input patch containing satellite and radar images (large, 1024 x 1024 km square) and of the output predicted radar image (small, 64 x 64 km square).

convolutional LSTMaxial self-attention

The architecture of the neural weather model, MetNet. The input satellite and radar images first pass through a spatial downsampler to reduce memory consumption. They are then processed by a convolutional LSTM at 15 minute intervals over the 90 minutes of input data. Then axial attention layers are used to make the network see the entirety of the input images.

Resultsprecipitation rate forecasting benchmarkHigh Resolution Rapid Refreshbaseline modeloptical flowTPUsF1-score

Performance evaluated in terms of F1-score at 1.0 mm/h precipitation rate (higher is better). The neural weather model (MetNet) outperforms the physics-based model (HRRR) currently operational in the US for timescales up to 8 hours ahead.

single

Comparison between the output from MetNet (top) and HRRR (bottom) to ground-truth (middle) as retrieved from the NOAA MRMS system. Notice that while the HRRR model predicts structure that appears to be more similar to that of the ground-truth, the structure predicted may be grossly incorrect.

HRRR (left) predicts a single potential future outcome (in red) out of the many possible outcomes, whereas MetNet (right) directly accounts for uncertainty by assigning probabilities over the future outcomes.

Future DirectionsAcknowledgementsThis project was done in collaboration with Lasse Espeholt, Jonathan Heek, Mostafa Dehghani, Avital Oliver, Tim Salimans, Shreya Agrawal and Jason Hickey. We would also like to thank Manoj Kumar, Wendy Shang, Dick Weissenborn, Cenk Gazen, John Burge, Stephen Hoyer, Lak Lakshmanan, Rob Carver, Carla Bromberg and Aaron Bell for useful discussions and Tom Small for help with the visualizations.