Design and Construction of Daily-updating Objective Climate Prediction System Based on the Real-time Forecast of CFSv2

Abstract

— The CFSv2 forecast products have been widely used in climate prediction operation all over the world. Although the real-time forecast is able to basically capture large pattern of climate anomaly, there still exists obvious bias, which may have enormous impacts on predicted result and thus cannot be neglected. Presently, how to smartly use the massive modeling outputs to improve forecast skill is very important for objective prediction. In this paper, a statistical downscaling strategy for correcting systematic bias through recovering modeling-climatology to its observational counterpart is introduced, and with such methodology, an operational platform conducting real-time 1-30d and 10-30d temperature and precipitation objective prediction is constructed for Zhejiang province. Various verification schemes of the Ps score, Pc score, ACC, SCC, RMSE, the absolute bias, relative bias, and sign coherence are applied on long-term temperature and rainfall assessment. Given the behavior of 335 independent forecast ensembles from January 1 st to November 30 th in 2019, predictive ability of the downscaling model is forecast. In general, forecast presentation demonstrates this system is practically useful and valuable.