Advances in Atmospheric Sciences

, Volume 35, Issue 4, pp 457–468 | Cite as

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

  • Saleh Aminyavari
  • Bahram SaghafianEmail author
  • Majid Delavar
Original Paper


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 NCEP, 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.

Key words

ensemble forecast NWP TIGGE evaluation post-processing 

摘 要

目前数值天气预报产品的应用正与日俱增. 已有研究表明: 集合预报的技巧比单一确定性预报的更高. 本文利用确定性评估, 二分型评估, 概率评估等判别指标评估了TIGGE资料集合预报2008年至2016年间伊朗降水的预报质量, 评估选取了分布于伊朗8个典型降水区域的13个雨量计观测资料为参考. 首先将提前1-3天的数值预报结果通过距离反比加权, Kriging插值等方法插值到选取的站点, 并利用贝叶斯模式平均方法对数值预报产品预处理以进一步提高预报质量. 结果表明: 欧洲中期天气预报中心(ECMWF)的预报比其他产品得分更高. 所有预报中心的产品都低估了降水较多地区的降水, 同时高估了其他地区的降水. 这表明TIGGE集合预报中存在着系统性偏差, 需进一步应用偏差校订技术. 基于二分型评估, 美国环境预报中心(NCEP)的预测在大部分站点中表现较好, 尽管所有的中心均高估了降水事件的发生频次. 相较于ECMWF和NCEP的预报, 英国气象局(UKMO)的预报在山区表现更好, 但在其他地区表现较差. 此外, 所有中心的预报在湿季都比在干季表现更好. 经过预处理的预报比未经过处理的预报质量更好. 总的来说, 本研究选择的各中心数值预报对伊朗降水预报的精度可以评为中等, 同时对预报产品进行预处理可提高预报质量.


集合预报 数值天气预报 TIGGE 评估 预处理 


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Copyright information

© Institute of Atmospheric Physics/Chinese Academy of Sciences, and Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Saleh Aminyavari
    • 1
  • Bahram Saghafian
    • 1
    Email author
  • Majid Delavar
    • 2
  1. 1.Department of Technical and Engineering, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Water Resources EngineeringTarbiat Modares UniversityTehranIran

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