Enhancing Weather Model: A Meteorological Incremental Learning Method with Long-term and Short-term Asynchronous Updating Strategy
Junjiao Liu, Cheng Zhang, Guoshuai Zhao, Xueming Qian · 2024
Improving model prediction capability based on continuously collected data is one of the biggest challenges in a mature, intelligent weather forecasting system. To tackle this problem, we propose an online incremental learning strategy for meteorological models with asynchronous updates. In short, we divide two different meteorological incremental learning settings according to the characteristics of meteorological data, distinguishing between increments within short-term time windows and increments for long-term data. For short-term data, a structure-based incremental learning method with a fast iteration rate is used, and a Residual-Net (R-Net) is proposed to improve the performance of the model; for long-term data, a replay-based incremental learning method called Gradient-based Core-set Selection and Weighting method (GCSW) is proposed, by performing cosine distance and normalization calculations to obtain sample weights and weighting the coreset samples to avoid catastrophic forgetting. The comparative experiments on temperature prediction incremental datasets in Beijing and Xi’an show that our proposed method achieves the best results in both short-term and long-term incremental experimental settings than all other baseline methods, demonstrating the advantages of the algorithm we proposed on meteorological datasets. The code is available as an open source repository on GitHub https://github.com/liujunjiao1/Enhancing-Weather-Model.