Temperature Forecasting using Tower Networks

Abstract

In this paper, we present the tower network, a novel, computationally lightweight deep neural network for multimodal data analytics and video prediction. The tower network is especially useful when it comes to combining different types of input data, a problem not greatly explored within deep learning. The architecture is further applied to a real-world example, where information from historic meteorological observations and numerical weather predictions are combined to produce high-quality forecasts of temperature for 1 to 6 hours into the future. The performance of the proposed model is assessed in terms of root mean squared error (RMSE), and the tower network outperforms even state-of-the-art forecasts from the Norwegian weather forecasting app yr.no from 3 hours into the future. On average, the RMSE of the tower network is approximately 6% smaller than that of yr.no, and approximately 27% smaller than that of the raw numerical weather predictions.