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...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.展开更多
使用TIGGE (the THORPEX interactive grand global ensemble)资料集下欧洲中期天气预报中心(the European Centre for Medium-Range Weather Forecasts, ECMWF)逐日起报的预报时效为24~168 h的日降水量集合预报资料,集合预报共包括51...使用TIGGE (the THORPEX interactive grand global ensemble)资料集下欧洲中期天气预报中心(the European Centre for Medium-Range Weather Forecasts, ECMWF)逐日起报的预报时效为24~168 h的日降水量集合预报资料,集合预报共包括51个成员,利用左删失的非齐次Logistic回归方法(left-Censored Non-homogeneous Logistic Regression, CNLR)和标准化的模式后处理方法(Standardized Anomaly Model Output Statistics, SAMOS)对具有复杂地形的中国东南部地区降水预报进行统计后处理。结果表明:采用CNLR方法能够有效改进原始集合预报的平均绝对误差(Mean Absolute Error, MAE)和连续分级概率评分(Continuous Ranked Probability Score, CRPS),提升了降水的定量预报和概率预报的预报技巧。而使用SAMOS方法对数据进行预处理,考虑地形等因素的影响,能在CNLR方法的基础上进一步订正由于地形影响造成的预报误差,并得到更加准确的全概率的降水概率预报。展开更多
文摘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.
文摘使用TIGGE (the THORPEX interactive grand global ensemble)资料集下欧洲中期天气预报中心(the European Centre for Medium-Range Weather Forecasts, ECMWF)逐日起报的预报时效为24~168 h的日降水量集合预报资料,集合预报共包括51个成员,利用左删失的非齐次Logistic回归方法(left-Censored Non-homogeneous Logistic Regression, CNLR)和标准化的模式后处理方法(Standardized Anomaly Model Output Statistics, SAMOS)对具有复杂地形的中国东南部地区降水预报进行统计后处理。结果表明:采用CNLR方法能够有效改进原始集合预报的平均绝对误差(Mean Absolute Error, MAE)和连续分级概率评分(Continuous Ranked Probability Score, CRPS),提升了降水的定量预报和概率预报的预报技巧。而使用SAMOS方法对数据进行预处理,考虑地形等因素的影响,能在CNLR方法的基础上进一步订正由于地形影响造成的预报误差,并得到更加准确的全概率的降水概率预报。
