  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.
