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Operational Assimilation of Data Retrieved by GNSS Observations into GRAPES_Meso 3DVar System
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摘要: 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。
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关键词:
- GNSS掩星;
- 反演资料;
- 资料同化;
- GRAPES_Meso
Abstract: 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.-
Key words:
- GNSS radio occultation;
- retrieved data;
- data assimilation;
- GRAPES_Meso
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图 1 GNSS/RO反演的气压和相对湿度的观测误差
Fig. 1 The observation error of pressure and relative humidity retrieved by GNSS/RO data
图 2 2013年7月12日00:00同化GNSS/RO反演的大气气压和湿度的目标函数梯度范数和目标函数的收敛性
Fig. 2 Variations of the gradient norm and the cost-function for background and observation part with the iteration number for assimilating the GNSS/RO retrieved pressure and relative humidity at 0000 UTC 12 July 2013
图 3 2013年7月1日00:00—2013年7月31日18:00业务上接收的6 h同化时间窗的GRAPES_Meso模拟区域内GNSS/RO资料廓线数量
(a) 弯角，(b) 折射率，(c) 反演的大气廓线
Fig. 3 Number of profiles of bending angle (a), refractivity (b), retrieved atmosphere pressure, temperature and humidity (c) for [-3 h, +3 h) observation window located in GRAPES_Meso simulated domain from 0000 UTC 1 July 2013 to 1800 UTC 31 July 2013
图 4 2013年7月00:00, 12:00分析时刻GNSS/RO反演的气压及相对湿度标准差
(a) 反演的气压与背景场偏差的标准差，(b) 反演的气压与分析场偏差的标准差，(c) 反演的相对湿度与背景场偏差的标准差，(d) 反演的相对湿度与分析场偏差的标准差
Fig. 4 The standard deviation of retrieved pressure and relative humidity from GNSS/RO at analysis time of 0000 UTC and 1200 UTC in July 2013
(a) the standard deviation of difference between retrieved pressure and the background, (b) the standard deviation of difference between retrieved pressure and the analysis, (c) the standard deviation of difference between retrieved relative humidity and the background, (d) the standard deviation of difference between retrived relative humidity and the analysis
图 6 2013年7月00:00, 12:00分析时刻试验OPER及试验GNSS的分析误差
(a) 位势高度分析误差, (b) 比湿分析误差, (c) 位势高度月平均分析误差, (d) 比湿月平均分析误差 (图 6a, 6b中彩色阴影表示试验OPER分析误差，等值线表示试验GNSS与试验OPER分析误差的偏差，网格填充区域表示试验GNSS的分析误差大于试验OPER)
Fig. 6 Variations of the analysis error for OPER and GNSS for analysis time of 0000 UTC and 1200 UTC in July 2013
(a) analysis error of geopotential height, (b) analysis error of specific humidity, (c) monthly mean analysis error of geopotential height, (d) monthly mean analysis error of specific humidity (in Fig. 6a and Fig. 6b, the shaded denotes the analysis error of experiment OPER, the contour denotes the analysis error difference of GNSS to OPER, the net denotes the error of GNSS lager than that of OPER)
图 8 2013年7月00:00, 12:00分析时刻位势高度场24 h和48 h预报误差
(a) 位势高度场24 h预报误差, (b) 位势高度场48 h预报误差, (c) 位势高度场月平均24 h预报误差, (d) 位势高度场月平均48 h预报误差 (图 8a, 8b中，阴影表示试验OPER的位势高度的预报误差，等值线表示试验GNSS与OPER预报误差偏差，网格表示试验GNSS预报误差大于试验OPER)
Fig. 8 Variations of 24-h and 48-h forecast errors of geopotential height for analysis time of 0000 UTC and 1200 UTC in July 2013
(a)24-h forecast error, (b)48-h forecast error, (c) monthly mean 24-h forecast error, (d) monthly mean 48-h forecast error (in Fig. 8a and Fig. 8b, the shaded denotes the error of OPER, the contour denotes the forecast error difference of GNSS to OPER, the net denotes the forecast error of GNSS larger than that of OPER)
图 9 2013年7月00:00, 12：00分析时刻试验GNSS降水预报0~24 h预报 (a)，12~36 h预报 (b) 和24~48 h预报 (c) 的24 h累积降水ETS评分月平均值与试验OPER偏差
Fig. 9 Differences of ETS values of 0-24 h (a), 12-36 h (b) and 24-48 h (c) accumulated rainfall of GNSS to OPER for analysis time of 0000 UTC and 1200 UTC in July 2013
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