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arXiv · 2111.09954

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

Sylwester Klocek·Haiyu Dong·Matthew Dixon·Panashe Kanengoni·Najeeb Kazmi·Pete Luferenko·Zhongjian Lv·Shikhar Sharma·Jonathan Weyn·Siqi Xiang
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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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Sylwester Klocek, Haiyu Dong, Matthew Dixon, Panashe Kanengoni, Najeeb Kazmi, Pete Luferenko, Zhongjian Lv, Shikhar Sharma, Jonathan Weyn, Siqi Xiang. 2021-11-18. MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather. https://arxiv.org/abs/2111.09954

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