Skill Evaluation of Probabilistic Forecasts by the Atmospheric Seasonal Predictability Experiments

Abstract

Probabilistic forecast skill of the atmospheric seasonal predictability experiments is evaluated using the Japan Meteorological Agency (JMA) Atmospheric General Circulation Model (AGCM), which is a global spectral model of T63 resolution. Four-month ensemble integrations were carried out with nine consecutive days of initial condition preceding the target season. All four seasons in a 15-year period from 1979 to 1993 were chosen as target seasons. The model was forced with observed sea surface temperature (SST) during the time integrations. Probabilistic forecasts of 500 hPa height, 850 hPa temperature and precipitation are verified by four skill measures; the Brier skill score and its decomposition to reliability and resolution, relative operating characteristics (ROC), ranked probability score (RPS) and rank histogram. It is revealed that probabilistic forecast bears some similarity in the seasonality and regionality of skill, with deterministic forecast such as relatively higher skill in winter of the Northern Hemisphere, and over East Asia and North America. Skill of precipitation is found generally lower than that of 500 hPa height and 850 hPa temperature, as is also recognized for deterministic forecast skill.