Advances in Atmospheric Sciences | 2021
Skill Assessment of Copernicus Climate Change Service Seasonal Ensemble Precipitation Forecasts over Iran
Abstract
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. 中长期降水预测对水资源管理和预警系统的发展至关重要。最近,哥白尼气候变化服务(C3S: Copernicus Climate Change Service)数据库对外发布了超前至三个月的逐月预测结果。本研究利用C3S三个模式1993年至2017年提前1-3个月的集合预测结果,采用概率和非概率的评判标准,对伊朗的8种降水类型进行了评估。此外,本文还分别对伊朗不同类型降水在干季和雨季下的模式预测技巧进行了诊断。结果表明,模式对西部型降水预测技巧最高,对北部湿润型呈现负的预测技巧。所有模式都能够较好地预测干季的强降水事件以及雨季的降水偏少事件。随着起报时间的增加,预测技巧也不断下降。虽然低估了几个干旱年且高估了几个多雨年的降水,模式对降水偏多和偏少年的预测结果与观测比较接近。此外,通过多元回归得到的多模式集合预测结果优于单个模式。总的来说,ECMWF和UKMO两个模式提前一个月的预测对大部分伊朗降水型的预测效果都较好。Météo France模式对伊朗降水型的长期预测能力较其他两个模式则偏低。