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

XUE Tong; GUAN Zhaoyong; XU Jianjun; SHAO Min

doi:10.7522/j.issn.1000-0534.2016.00087

Plateau Meteorology >

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

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

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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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

Cite this article

XUE Tong , GUAN Zhaoyong , XU Jianjun , SHAO Min . The Impact of ATMS and CrIS Data Assimilation on Weather Forecasts over the Qinghai-Tibetan Plateau[J]. Plateau Meteorology, 2017 , 36(4) : 912 -929 . DOI: 10.7522/j.issn.1000-0534.2016.00087

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