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. CCS CONCEPTS• Applied computing → Earth and atmospheric sciences; • Computing methodologies → Machine learning; Computer vision.

Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources

“…In [ 20 ] we started exploring the possibilities of a new core architecture for multi-modal data analysis with the use case of weather prediction. This article builds upon this preliminary work.…”