Multi-step Spatio-Temporal Temperature Forecasting

Atmospheric analyses and simulations requires high resolution physical models on super computers that consume many hours of computations. Deep learning and machine learning methods used in forecasting revealed new solutions in this area. Main goal of this paper to solve high-resolution numeric forecasting problem. We present a model architecture for spatiotemporal prediction. Model is composed of Convolutional Long-short Term Memory (Conv-LSTM) units with encoder-decoder structure. Model takes sequence of inputs and outputs the next time sequence. Model is trained in a supervised manner. Experiments are made on high-scale benchmark numerical weather dataset. All selected baseline models are surpassed by 3 degrees C mean square error (MSE) with statistically significant results. Both spatial and temporal changes in the temperature is captured. Model forecasted 5 time steps with 1, 3 and 24 time difference successfully.