  Numerical weather forecasting using high-resolution physical models often
requires extensive computational resources on supercomputers, which diminishes
their wide usage in most real-life applications. As a remedy, applying deep
learning methods has revealed innovative solutions within this field. To this
end, we introduce a novel deep learning architecture for forecasting
high-resolution spatio-temporal weather data. Our approach extends the
conventional encoder-decoder structure by integrating Convolutional Long-short
Term Memory and Convolutional Neural Networks. In addition, we incorporate
attention and context matcher mechanisms into the model architecture. Our
Weather Model achieves significant performance improvements compared to
baseline deep learning models, including ConvLSTM, TrajGRU, and U-Net. Our
experimental evaluation involves high-scale, real-world benchmark numerical
weather datasets, namely the ERA5 hourly dataset on pressure levels and
WeatherBench. Our results demonstrate substantial improvements in identifying
spatial and temporal correlations with attention matrices focusing on distinct
parts of the input series to model atmospheric circulations. We also compare
our model with high-resolution physical models using the benchmark metrics and
show that our Weather Model is accurate and easy to interpret.
