Weather Radar Echo Extrapolation with Dynamic Weight Loss
نویسندگان
چکیده
Precipitation nowcasting is an important tool for economic and social services, especially forecasting severe weather. The crucial challenging part of radar echo image prediction the focus radar-based precipitation nowcasting. Recently, a number deep learning models have been designed to solve problem extrapolating images. Although these methods can generate better results than traditional extrapolation methods, issue error accumulation in exacerbated by using only mean square (MSE) absolute (MAE) as loss functions. In this paper, we approach from perspective function propose dynamic weight (DWL), simple but effective extrapolation. method adds model self-adjusted weights weighted structural similarity index measures. Radar experiments are performed on four models, ConvLSTM, ConvGRU, PredRNN, PredRNN++. reflectivity predicted Nanjing University C-band Polarimetric (NJU-CPOL) weather data. quantitative statistics show that DWL reduces MAE up 10.61%, 5.31%, 14.8%, 13.63%, respectively, over 1 h period. reducing errors time, improving predictive performance currently popular models.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2023
ISSN: ['2072-4292']
DOI: https://doi.org/10.3390/rs15123138