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中国沙漠 ›› 2016, Vol. 36 ›› Issue (2): 441-448.DOI: 10.7522/j.issn.1000-694X.2015.00024

• 天气与气候 • 上一篇    下一篇

GRAPES_SDM沙尘模式在新疆的客观检验

段海霞1, 李耀辉1, 霍文2, 秦贺3, 马玉芬2   

  1. 1. 中国气象局兰州干旱气象研究所, 甘肃省(中国气象局)干旱气候变化与减灾重点(开放)实验室, 甘肃 兰州 730020;<2r>2. 中国气象局乌鲁木齐沙漠气象研究所, 新疆 乌鲁木齐 830002;<2r>3. 新疆维吾尔自治区气象台, 新疆 乌鲁木齐 830002
  • 收稿日期:2014-11-02 修回日期:2015-01-12 出版日期:2016-03-20 发布日期:2016-03-20
  • 作者简介:段海霞(1980-),女,甘肃金昌人,副研究员,研究方向为天气动力学及数值模拟。E-mail:dhx8199@hotmail.com
  • 基金资助:
    中国沙漠气象科学研究基金项目(Sqj2012003)

Objective Verification of GRAPES_SDM Model in Xinjiang, China

Duan Haixia1, Li Yaohui1, Huo Wen2, Qin He3, Ma Yufen2   

  1. 1. Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province/Key Open Laboratory of Climatic Change and Disaster Reduction of CMA, Institute of Arid Meteorology, China Meteorological Administration, Lanzhou 730020, China;
    2. Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China;
    3. Xinjiang Meteorological Observatory, Urumqi 830002, China
  • Received:2014-11-02 Revised:2015-01-12 Online:2016-03-20 Published:2016-03-20

摘要: 使用天气学检验方法,对新疆2008-2013年春季沙尘天气GRAPES_SDM沙尘模式预报情况进行检验评估,通过TS、预报效率等检验统计量分析了在新疆的预报效果,通过平均误差、均方根误差、误差标准差等格点误差和站点误差量的分析,分析了主要预报要素的客观检验结果,指出近地面气温和风速的误差为初始条件的不确定以及观测和预报分辨率尺度不一致造成的随机性误差。在此基础上给出了新疆南疆盆地和新疆东部地区数值预报业务的误差特征,并根据检验结果定性地分析了模式预报系统性和非系统性误差的可能来源。

关键词: GRAPES_SDM, TS评分, 平均误差, 均方根误差

Abstract: By comparing the forecast product by the GRAPES_SDM model and the observed data of dust-storm weather in Xinjiang in the springs of 2008-2013, we tested the forecast product with synoptic verification method. By analyzing mean error, error standard deviation and root-mean-square error, the characteristics of error distribution of numerical forecast in Xinjiang are shown, and the possible sources of the systematic and non-systematic error are analyzed. The results showed that the 2-m height air temperature and 10-m height wind speed error mainly came from the uncertainty of the initial conditions and the inconsistencies of the resolution of observation and prediction. We also proposed some suggestions on improving the GRAPES_SDM model forecast accuracy.

Key words: GRAPES_SDM Model, TS score, mean error, root-mean-square error

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