高原气象

高原气象 >

2017 , Vol. 36 >Issue 4: 912 - 929

DOI: https://doi.org/10.7522/j.issn.1000-0534.2016.00087

论文

ATMS和CrIS卫星资料同化对青藏高原天气预报的影响

  • 薛童 ,
  • 管兆勇 ,
  • 徐建军 ,
  • 邵旻
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  • 南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心, 南京 210044;中国气象局气象干部培训学院, 北京 100081;广东海洋大学, 湛江 524088;美国乔治梅森大学 全球环境与自然资源研究所, 弗吉尼亚州 22030;NOAA卫星应用和研究中心, 马里兰州 20740

收稿日期: 2016-04-06

  网络出版日期: 2017-08-28

基金资助

国家自然科学基金项目（91437104）；公益性行业（气象）科研专项（GYHY201406024）

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The Impact of ATMS and CrIS Data Assimilation on Weather Forecasts over the Qinghai-Tibetan Plateau

  • XUE Tong ,
  • GUAN Zhaoyong ,
  • XU Jianjun ,
  • SHAO Min
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  • Key Laboratory of China Education Ministry for Meteorological Disasters/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China;China Meteorological Administration Training Centre, Beijing 100081, China;Guangdong Ocean University, Zhanjiang 524088, China;Global Environment and Natural Resources Institute, College of Science, George Mason University, Virginia 22030, USA;NOAA Center for Satellite Applications and Research(STAR), College Park, Maryland 20740, USA

Received date: 2016-04-06

  Online published: 2017-08-28

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摘要

利用WRF模式和GSI同化系统同化美国最新一代的ATMS和CrIS卫星资料，探讨卫星辐射资料同化对青藏高原天气要素预报准确性的影响。进行了四组试验模拟，即无资料同化的控制试验（CTRL）和三组同化试验，三组同化试验分别为：只用常规观测资料进行同化（CONV）、常规观测资料和ATMS卫星资料同化（ATMS）、常规观测资料和CrIS卫星资料同化（CRIS）。分析了2015年1月及7月的温度场、相对湿度场和风场的预报能力，除了分析近地表 2 m温度场、2 m相对湿度场及10 m风场外，也分析了高地形区域以及低地形区域不同高度层上气象要素的预报能力。结果表明：ATMS和CRIS同化对青藏高原天气要素预报效果改进并不具有普遍性，ATMS同化试验可以有效的增进7月低地形区域的2 m温度场、7月高地形区域2 m相对湿度场以及1月高地形区域10 m风场的24 h、48 h预报能力；CRIS同化对1月高地形区域2 m温度场24 h预报、1月与7月高地形区域10 m风场24 h与48 h预报有改善效果。就垂直分层来讨论，CRIS同化试验不管在哪个高度分层都无法有效地改进模式预报能力，ATMS同化试验则在不同分层、不同变量场有着不一样的预报效果。资料同化后温度场预报主要的误差来源是系统性误差，而相对湿度场和风场在同化后主要误差是由非系统性误差造成的。整体上ATMS同化试验效果优于CRIS同化试验。

关键词： 青藏高原; 卫星资料同化; GSI; 预报效果; 模式误差

本文引用格式

薛童 , 管兆勇 , 徐建军 , 邵旻 . ATMS和CrIS卫星资料同化对青藏高原天气预报的影响[J]. 高原气象, 2017 , 36(4) : 912 -929 . DOI: 10.7522/j.issn.1000-0534.2016.00087

Abstract

The impact of ATMS and CrIS data assimilation on weather forecasts over the Qinghai-Tibetan Plateau investigated by using NOAA's Gridpoint Statistical Interpolation (GSI) data assimilation system and NCAR's Advanced Research Weather Research and Forecasting (ARW-WRF) regional model. The experiment was designed with 4 parts:A control experiment (CTRL) and three data assimilation experiments with different data sets, including conventional data only (CONV), a combination of conventional and ATMS satellite data (ATMS), and a combination of conventional and CrIS satellite data (CRIS). The 2 m temperature (T), 2 m relative humidity (RH) and 10 m wind speed (WS) in January and July 2015 were evaluated to investigate the weather forecast ability. Furthermore, those variables in different vertical layers over the terrain were also analyzed to improve the forecast results. The simulation results showed that the improvement of three data assimilation experiments was not general. The forecast ability of 10 m WS in January and the 2 m RH in July could be modified by assimilating ATMS over high-elevation region, while 2 m T prognosis could be rectified over low-elevation region. CRIS showed a good performance over high-elevation region for 24 h 2 m T prediction in July. Meanwhile, CRIS could also improve the prediction accuracy of 10 m WS over high-elevation region in both January and July. Considering the vertical stratification, the CRIS data assimilation had a negative contribution in all vertical layers while ATMS data assimilation had different forecast accuracy in different vertical layers and variables. The forecast error in T was typically caused by the systematic error, which was controlled by the physical representation within the model. In contrast, the inaccuracies in the RH and WS forecasts were dominated by nonsystematic errors, derived from the random inadequacies of the initial conditions. In summary, the overall improvement of ATMS data assimilation over the Qinghai-Tibetan Plateau is better than the improvement of CRIS data assimilation.

Key words： Qinghai-Tibetan Plat; Radiance data assimi; GSI; Weather forecast eff; Model error

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