Skill Assessment of Copernicus Climate Change Service Seasonal Ensemble Precipitation Forecasts over Iran  

作　　者：Masoud NOBAKHT Bahram SAGHAFIAN Saleh AMINYAVARI 

机构地区：[1]Department of Civil Engineering,Science and Research Branch,Islamic Azad University,Tehran 1477893855,Iran [2]Department of Civil Engineering,Chalous Branch,Islamic Azad University,Chalous 46615/397,Iran

出　　处：《Advances in Atmospheric Sciences》2021年第3期504-521,共18页大气科学进展（英文版）

摘　　要：Medium to long-term precipitation forecasting plays a pivotal role in water resource management and development of warning systems.Recently,the Copernicus Climate Change Service(C3S)database has been releasing monthly forecasts for lead times of up to three months for public use.This study evaluated the ensemble forecasts of three C3S models over the period 1993-2017 in Iran’s eight classified precipitation clusters for one-to three-month lead times.Probabilistic and non-probabilistic criteria were used for evaluation.Furthermore,the skill of selected models was analyzed in dry and wet periods in different precipitation clusters.The results indicated that the models performed best in western precipitation clusters,while in the northern humid cluster the models had negative skill scores.All models were better at forecasting upper-tercile events in dry seasons and lower-tercile events in wet seasons.Moreover,with increasing lead time,the forecast skill of the models worsened.In terms of forecasting in dry and wet years,the forecasts of the models were generally close to observations,albeit they underestimated several severe dry periods and overestimated a few wet periods.Moreover,the multi-model forecasts generated via multivariate regression of the forecasts of the three models yielded better results compared with those of individual models.In general,the ECMWF and UKMO models were found to be appropriate for one-month-ahead precipitation forecasting in most clusters of Iran.For the clusters considered in Iran and for the long-range system versions considered,the Météo France model had lower skill than the other models.

关 键 词：ensemble forecasts Copernicus Climate Change Service long-term forecasting model evaluation Iran 

分 类 号：P467[天文地球—大气科学及气象学] P457.6