An evaluation of ECMWF SEAS5 seasonal climate forecasts for Australia using a new forecast calibration algorithm. (December 2019)

• Record Type: Journal Article
• Authors: Wang, Q.J.; Shao, Yawen; Song, Yong; Schepen, Andrew; Robertson, David E.; Ryu, Dongryeol; Pappenberger, Florian
• Is Part Of: Environmental modelling & software. Volume 122(2019)
• Publication Date: 2019-12
• Subjects: Forecast calibration and evaluation -- Forecast skill -- Forecast reliability -- Precipitation -- Temperature; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2019.104550
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The commencement of SEAS5 model for operational seasonal climate forecasting by the European Centre for Medium-Range Weather Forecasts (ECMWF) is a new development. It replaces the ECMWF System 4, which had a large international community of seasonal climate forecast users. To assist potential users of SEAS5 forecasts, a systematic and detailed evaluation of forecast skill and reliability of climate variables over land areas is valuable. In this regional study, we evaluate SEAS5 performance in forecasting precipitation and daily minimum temperature (Tmin) and daily maximum temperature (Tmax) for the Australian continent, based on 36 years of re-forecast data. We evaluate forecasts after simple mean-corrections, and statistically calibrated forecasts using the Bayesian joint probability (BJP) modelling approach. We also provide a comparison with System 4. A new simpler and more efficient BJP algorithm is introduced to facilitate this study and support wider use of the algorithm in other applications. Highlights: Seasonal climate forecasts of the new ECMWF SEAS5 model are evaluated for Australia. SEAS5 offers considerable skill for forecasting the coming month. The skill drops significantly beyond one month forecast horizon. A simpler and more efficient Bayesian joint probability (BJP) algorithm is introduced. Forecasts are more skillful and reliable after calibration using the BJP algorithm.