%0 Journal Article %A XUE Tong %A GUAN Zhaoyong %A XU Jianjun %A SHAO Min %T The Impact of ATMS and CrIS Data Assimilation on Weather Forecasts over the Qinghai-Tibetan Plateau %D %R 10.7522/j.issn.1000-0534.2016.00087 %J Plateau Meteorology %P 912-929 %V 36 %N 4 %X 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. %U http://www.gyqx.ac.cn/EN/10.7522/j.issn.1000-0534.2016.00087