MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather

• URL: http://arxiv.org/abs/2111.09954v1
• Date: Thu, 18 Nov 2021 21:55:49 GMT
• Title: MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather
• Abstract summary: We present the encoder-forecaster convolutional long short-term memory (LSTM) deep-learning model that powers Microsoft Weather's operational precipitation nowcasting product. This model takes as input a sequence of weather radar mosaics and deterministically predicts future radar reflectivity at lead times up to 6 hours.
• Score: 1.7780828990105975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present the encoder-forecaster convolutional long short-term memory (LSTM) deep-learning model that powers Microsoft Weather's operational precipitation nowcasting product. This model takes as input a sequence of weather radar mosaics and deterministically predicts future radar reflectivity at lead times up to 6 hours. By stacking a large input receptive field along the feature dimension and conditioning the model's forecaster with predictions from the physics-based High Resolution Rapid Refresh (HRRR) model, we are able to outperform optical flow and HRRR baselines by 20-25% on multiple metrics averaged over all lead times.

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