Affiliation:
1. Department of Mathematics University of Hamburg Hamburg Germany
2. Department of Mathematics and Statistics Memorial University of Newfoundland St. John's NL Canada
Abstract
AbstractEnsemble prediction systems are an invaluable tool for weather forecasting. Practically, ensemble predictions are obtained by running several perturbations of the deterministic control forecast. However, ensemble prediction is associated with a high computational cost and often involves statistical post‐processing steps to improve its quality. Here we propose to use deep‐learning‐based algorithms to learn the statistical properties of an ensemble prediction system, the ensemble spread, given only the deterministic control forecast. Thus, once trained, the costly ensemble prediction system will not be needed anymore to obtain future ensemble forecasts, and the statistical properties of the ensemble can be derived from a single deterministic forecast. We adapt the classical pix2pix architecture to a three‐dimensional model and train them against several years of operational (ensemble) weather forecasts for the 500 hPa geopotential height. The results demonstrate that the trained models indeed allow obtaining a highly accurate ensemble spread from the control forecast only.
Funder
Deutsche Forschungsgemeinschaft
Natural Sciences and Engineering Research Council of Canada
Publisher
American Geophysical Union (AGU)
Subject
General Earth and Planetary Sciences,Geophysics
Cited by
11 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献