HR-Extreme: A High-Resolution Dataset for Extreme Weather Forecasting
Nian Ran Peng Xiao Yue Wang Wesley Shi Jianxin Lin Qi Meng Richard Allmendinger
The application of large deep learning models in weather forecasting has led to
significant advancements in the field, including higher-resolution forecasting and
extended prediction periods exemplified by models such as Pangu and Fuxi. Despite
these successes, previous research has largely been characterized by the neglect
of extreme weather events, and the availability of datasets specifically curated for
such events remains limited. Given the critical importance of accurately forecasting
extreme weather, this study introduces a comprehensive dataset that incorporates
high-resolution extreme weather cases derived from the High-Resolution Rapid
Refresh (HRRR) data, a 3-km real-time dataset provided by NOAA. We also
evaluate the current state-of-the-art deep learning models and Numerical Weather
Prediction (NWP) systems on HR-Extreme, and provide a improved baseline
deep learning model called HR-Heim which has superior performance on both
general loss and HR-Extreme compared to others. Our results reveal that the
errors of extreme weather cases are significantly larger than overall forecast error,
highlighting them as an crucial source of loss in weather prediction. These findings
underscore the necessity for future research to focus on improving the accuracy of
extreme weather forecasts to enhance their practical utility
Weather Forecast Dataset, Extreme Weather, Deep Learning, Numerical Weather Prediction
We present HR-Extreme, a high-resolution dataset for evaluating extreme weather forecasting accuracy, enhancing the practical utility of SOTA models.