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

Advances in Atmospheric Sciences 2021年第3期38卷 504-521页

作者： Masoud NOBAKHT Bahram SAGHAFIAN saleh aminyavari Department of Civil Engineering Science and Research BranchIslamic Azad UniversityTehran ***Iran Department of Civil Engineering Chalous BranchIslamic Azad UniversityChalous 46615/397Iran

Medium to long-term precipitation forecasting plays a pivotal role in water resource management and development of warning ***,the Copernicus Climate Change Service(C3S)database has been releasing monthly forecasts for lead times of up to three months for public *** 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 *** and non-probabilistic criteria were used for ***,the skill of selected models was analyzed in dry and wet periods in different precipitation *** results indicated that the models performed best in western precipitation clusters,while in the northern humid cluster the models had negative skill *** models were better at forecasting upper-tercile events in dry seasons and lower-tercile events in wet ***,with increasing lead time,the forecast skill of the models *** 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 ***,the multi-model forecasts generated via multivariate regression of the forecasts of the three models yielded better results compared with those of individual *** general,the ECMWF and UKMO models were found to be appropriate for one-month-ahead precipitation forecasting in most clusters of *** 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

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Evaluation of TIGGE Ensemble Forecasts of Precipitation in Distinct Climate Regions in Iran

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Advances in Atmospheric Sciences 2018年第4期35卷 457-468页

作者： saleh aminyavari Bahram SAGHAFIAN Majid DELAVAR Department of Technical and Engineering Science and Research Branch Islamic Azad University Department of Water Resources Engineering Tarbiat Modares University

The application of numerical weather prediction （NWP） products is increasing dramatically. Existing reports indicate that ensemble predictions have better skill than deterministic forecasts. In this study, numerical ensemble precipitation forecasts in the TIGGE database were evaluated using deterministic, dichotomous （yes/no）, and probabilistic techniques over Iran for the period 2008-16. Thirteen rain gauges spread over eight homogeneous precipitation regimes were selected for evaluation. The Inverse Distance Weighting and Kriging methods were adopted for interpolation of the prediction values, downscaled to the stations at lead times of one to three days. To enhance the forecast quality, NWP values were post-processed via Bayesian Model Averaging. The results showed that ECMWF had better scores than other products. However, products of all centers underestimated precipitation in high precipitation regions while overestimating precipitation in other regions. This points to a systematic bias in forecasts and demands application of bias correction techniques. Based on dichotomous evaluation, NCEP did better at most stations, although all centers overpredicted the number of precipitation events. Compared to those of ECMWF and NCER UKMO yielded higher scores in mountainous regions, but performed poorly at other selected stations. Furthermore, the evaluations showed that all centers had better skill in wet than in dry seasons. The quality of post-processed predictions was better than those of the raw predictions. In conclusion, the accuracy of the NWP predictions made by the selected centers could be classified as medium over Iran, while post-processing of predictions is recommended to improve the quality.

关键词： ensemble forecast NWP TIGGE evaluation post-processing

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