Paper on Ozone Forecast with Deep Learning published

Lukas H. Leufen, Felix Kleinert (both FZ Jülich and University of Bonn) and Martin G. Schultz (FZ Jülich) have published their latest research results of the study “Exploring decomposition of temporal patterns to facilitate learning of neural networks for ground-level daily maximum 8-hour average ozone prediction” in the Journal Environmental Data Science.  The study shows how the accuracy of deep neural networks for forecasting ground-level ozone can be improved by splitting long-term and short-term weather patterns. The article is available at .