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MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather. (arXiv:2111.09954v2 [cs.LG] UPDATED)
May 25, 2022, 1:11 a.m. | Sylwester Klocek, Haiyu Dong, Matthew Dixon, Panashe Kanengoni, Najeeb Kazmi, Pete Luferenko, Zhongjian Lv, Shikhar Sharma, Jonathan Weyn, Siqi Xiang
cs.LG updates on arXiv.org arxiv.org
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 …
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