GNSS反演资料在GRAPES_Meso三维变分中的应用

为了进一步提高GRAPES_Meso的分析和预报效果，该文在GRAPES_Meso三维变分同化系统中建立了同化GNSS/RO反演的大气资料的观测算子，实现了对GNSS/RO反演的大气资料的同化应用，并通过2013年7月1个月的同化和预报试验分析了GNSS/RO反演大气资料对GRAPES_Meso模式系统分析和预报的影响。结果表明:增加了GNSS/RO反演大气资料的同化后，GRAPES_Meso位势高度场的分析误差明显减小，平均分析误差减小约8%，预报误差略有减小，平均预报误差减小约1%；湿度场的分析误差和预报误差变化不明显，常规观测资料稀少的青藏高原地区的降水预报技巧有所提高，小雨到大雨的ETS (equitable threat score) 评分提高约0.01，对全国及其他分区的降水预报技巧总体上有正效果。

Operational Assimilation of Data Retrieved by GNSS Observations into GRAPES Meso 3DVar System

Radio occultation (RO) observations using the Global Navigation Satellite System (GNSS) provides valuable data to support operational numerical weather prediction, and it is proved that the assimilation of RO data has the potential to significantly improve the accuracy of global and regional meteorological analysis and weather prediction. RO observations have many advantages such as high precision, global coverage and high vertical resolution compared with other observations. RO observations provide phase (Level 1a), bending angle (Level 1b), refractivity (Level 2a), retrieved pressure, temperature and humidity profiles (Level 2b), all of which can be assimilated into numerical model. Bending angle and refractivity are proved better choices for assimilation as observation operators are less complicated, and have no disadvantages associated with the modeling of ionospheric effects in the assimilation model. However, assimilation of water vapor, temperature or pressure derived from RO observations have advantages that data forms are same as model variables, and it is proved that assimilation of retrieved data also improve the accuracy of global analyses and forecasts significantly.In the operational GRAPES (Global/Regional Assimilation and Prediction System) regional (GRAPES_Meso) 3-dimensional variational (3DVar) system, the analysis is performed in model vertical level, but it can only assimilate the upper-level wind, pressure and humidity from radiosonde report (TEMP), upper-level wind and humidity by AIREP, cloud drift wind (SATOB), the pressure from surface station report over land (SYNOP) and over sea, integrated column precipitable water vapor (IPW) retrieved by ground-based GPS observations and radar wind profiles. In order to assimilate RO retrieved atmosphere data and address their impacts on analyses and forecasts, observation operators of retrieved humidity and pressur eassimilation are developed first, and then quality control and vertical thinning scheme are formed and discussed. Results of one month experiments show that the assimilation of GNSS/RO retrieved pressure and humidity data have considerable positive impacts on analyses of geopotential height, minor positive impacts on humidity analyses, geopotential height, humidity forecasts, rainfall forecasts, and major positive impacts on rainfall forecast for Qinghai-Tibet region.