Deep learning accurately forecasts heat waves, cold spells

EurekAlert  February 4, 2020 Researchers at Rice University have created a deep learning computer system called “capsule neural network”. During training, it examines hundreds of pairs of maps. Each map shows surface temperatures and air pressures at five-kilometers height, and each pair shows those conditions several days apart. The training includes scenarios that produced extreme weather — extended hot and cold spells that can lead to deadly heat waves and winter storms. Once trained, the system was able to examine maps it had not previously seen and make five-day forecasts of extreme weather with 85% accuracy…read more. Open Access TECHNICAL ARTICLE

Closing critical gap in weather forecasting

Science Daily  December 7, 2019 An international team of researchers (USA – NOAA, NASA, George Mason University, University of Florida, SUNY Stony Brook, Canada) reports that the Subseasonal Experiment (SubX) is a multimodel subseasonal prediction [weather conditions 3-to-4 weeks out] experiment designed around operational requirements with the goal of improving subseasonal forecasts. Seven global models have produced 17 years of retrospective (re)forecasts and more than a year of weekly real-time forecasts. The reforecasts and forecasts are archived at the Data Library of the International Research Institute for Climate and Society, Columbia University, providing a comprehensive database for research on subseasonal […]