An application study of merging GNSS/PWV and FY-4A/GIIRS water vapor profiles
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摘要: 中国新一代地球静止气象卫星风云四号A星（FY-4A）搭载的干涉式大气垂直探测仪（Geostationary Interferometric Infrared Sounder, GIIRS）以红外高光谱干涉分光方式探测三维大气温湿结构，取得了在静止轨道上探测大气的突破性进展。地基全球导航卫星系统（Global Navigation Satellite System，GNSS）是一种连续监测大气可降水量（Precipitable Water Vapor，PWV）的有效手段，基于2018年6—8月中国地基GNSS站监测的PWV和FY-4A/GIIRS水汽廓线的业务产品以及常规无线电探空资料，开展GNSS/PWV与FY-4A/GIIRS水汽廓线快速融合应用，以提高卫星资料反演大气水汽廓线的精度。结果表明：与常规无线电探空相比，FY-4A/GIIRS水汽廓线产品在大气低层均方根误差（Root Mean Square Error，RMSE）为4.5 g/kg，700 hPa为2.4 g/kg，500 hPa以上因水汽含量较低RSME小于1.5 g/kg。GNSS/PWV与FY-4A/GIIRS水汽廓线融合后，FY-4A/GIIRS水汽廓线误差整层RMSE减小20%，从近地层到600 hPa RMSE平均减小20%—25%，尤其是850—700 hPa改善最明显，极大改善了卫星水汽反演资料的可用性。对一次多系统影响的暴雨天气过程应用分析表明，GNSS/PWV和FY-4A/GIIRS融合产品可获得高时、空密度的大气水汽廓线，对强降水的临近预报有非常重要的支撑作用。Abstract: The hyperspectral data from the Geostationary Interferometric Infrared Sounder (GIIRS) of the new-generation geostationary meteorological satellite FY-4A can be used to detect three-dimensional structure of atmosphere temperature and humidity by means of infrared hyperspectral interference spectroscopy, and a breakthrough has been made in detecting the atmosphere in geostationary orbit. Precipitable water vapor (PWV) from ground-based Global Navigation Satellite System (GNSS) is an effective means for continuous monitoring of atmospheric water vapor. In order to improve the reliability of water vapor from FY-4A hyperspectral sounder data, the GNSS/PWV, the atmospheric water vapor profiles retrieved by FY-4A/GIIRS and conventional radiosonde data from June to August in 2018 are analyzed. The high-precision atmospheric water vapor data monitored by GNSS and the GNSS/PWV and FY-4A/GIIRS water vapor profiles are rapidly merged to improve the accuracy of satellite retrievals of atmospheric water vapor profile. The results show that compared with conventional radiosonde data, the RMSE of the real-time product for atmospheric profile retrievals of FY-4A/GIIRS is 4.5 g/kg in the lower atmosphere, 2.4 g/kg at 700 hPa, and less than 1.5 g/kg above 500 hPa due to less water vapor content. By merging GNSS/PWV and water vapor profiles of FY-4A/GIIRS, the root mean square error of the whole layer is reduced by about 20%, and the root mean square error from near the surface to 600 hPa is reduced by 20%—25%, especially between 850 hPa and 700 hPa. The merging method can greatly improve the availability of satellite retrieval data. Through the application analysis of a rainstorm process with multiple system effects, it is found that the merging of GNSS/PWV and FY-4A/GIIRS can obtain atmospheric water vapor profiles with high spatial and temporal resolutions, which play a very important role in the nowcast of heavy rainfall.
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Key words:
- Atmospheric profile /
- Merging /
- GNSS /
- PWV /
- FY-4A /
- Hyperspectral
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图 1 GNSS气象站与无线电探空站匹配后分布示意
Figure 1. Distribution of matched GNSS sites and radiosondes
图 3 风云四号A星GIIRS比湿 （q） 廓线反演精度检验 （08和20为观测时间；a. 偏差、均方根误差和比湿廓线均值，b. 相对误差与样本数目）
Figure 3. Accuracy evaluation of specific humidity profiles for FY-4A GIIRS retrievals （08 and 20 represent observation time；a. Bias，RMSEs and mean profiles of specific humidity，b. mean relative errors and sample numbers）
图 4 水汽廓线融合效果检验 （08和20为观测时间；a. 融合前后均方根误差 （RMSE为融合前，RMSE merged为融合后），b. 融合后相对误差 （MRE为融合前，MRE merged为融合后））
Figure 4. Accuracy evaluation of updated specific humidity profiles with merging method （08 and 20 represent observation time；a. RMSEs before and after merging （RMSE merged is the RSME after merging），b. mean relative errors before and after merging （MRE merged is the MRE after merging））
图 5 水汽廓线融合后均方根误差 （a） 和相对误差 （b） 减少比例 （08和20为观测时间）
Figure 5. Reduction ratios of RMSE （a） and （b） after updating specific humidity profile with merging method （08 and 20 represent observation time）
图 6 融合前后误差概率分布 （横坐标表示RMSE比例，stdev1和stdev2分别代表融合前后标准偏差统计；a. 925 hPa， b. 850 hPa，c. 700 hPa，d. 500 hPa）
Figure 6. Bias PDFs before and after merging （x-axis represents the RMSE ratio at corresponding height，stdev1 and stdev2 represent the standard deviations before and after merging，respectively；a. 925 hPa，b. 850 hPa， c. 700 hPa，b. 500 hPa）
图 7 2018年8月15日08时FY-4A 10.8 μm通道卫星云图
Figure 7. FY-4A satellite image of channel 10.8 μm at 08：00 BT 15 August 2018
图 8 2018年8月15日水汽融合比湿 （色阶） 与融合后水汽增量场 （等值线） （红色方框为暴雨发生的区域；单位：g/kg；a. 08时700 hPa，b. 14时700 hPa，c. 08时850 hPa，d. 14时850 hPa）
Figure 8. Specific humidity after merging （shaded） and increment field （contours） on 15 August 2018 （the red box shows the rainstorm area，unit：g/kg；a. 700 hPa at 08：00 BT，b. 700 hPa at 14：00 BT，c. 850 hPa at 08：00 BT，d. 850 hPa at 14：00 BT）
图 9 2018年8月15日08时至16日20时江淮区域平均比湿变化 （a） 与3 h降水时间序列 （b）
Figure 9. Average specific humidity （a） and 3 h accumulative rainfall （b） from 08：00 BT 15 August to 20：00 BT 16 August 2018
图 10 江淮地区水汽廓线融合效果 （a. 融合前后均方根误差 （RMSE为融合前，RMSE merged为融合后），b. 融合后相对误差 （MRE为融合前，MRE merged为融合后））
Figure 10. Accuracy evaluation of updated specific humidity profiles with merging method in Jianghuai area （a. RMSE before and after merging （RMSE merged is the RSME after merging），b. mean relative errors before and after merging （MRE merged is the MRE after merging））
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