Abstract
Numerical weather forecasting on high-resolution physical models consume hours of computations on supercomputers. Application of deep learning and machine learning methods in forecasting revealed new solutions in this area. In this paper, we forecast high-resolution numeric weather data using both input weather data and observations by providing a novel deep learning architecture. We formulate the problem as spatio-temporal prediction. Our model is composed of Convolutional Long-short Term Memory, and Convolutional Neural Network units with encoder-decoder structure. We enhance the short-long term performance and interpretability with an attention and a context matcher mechanism. We perform experiments on high-scale, real-life, benchmark numerical weather dataset, ERA5 hourly data on pressure levels, and forecast the temperature. The results show significant improvements in capturing both spatial and temporal correlations with attention matrices focusing on different parts of the input series. Our model obtains the best validation and the best test score among the baseline models, including ConvLSTM forecasting network and U-Net. We provide qualitative and quantitative results and show that our model forecasts 10 time steps with 3 hour frequency with an average of 2 degrees error. Our code and the data are publicly available.
Abstract (translated)
URL
https://arxiv.org/abs/2102.00696
