Evaluating Added Benefits of Assimilating GOES Imager Radiance Data in GSI for Coastal QPFs

被引:68
作者
Qin, Zhengkun [1 ,3 ]
Zou, Xiaolei [1 ]
Weng, Fuzhong [2 ]
机构
[1] Florida State Univ, Dept Earth Ocean & Atmospher Sci, Tallahassee, FL 32306 USA
[2] NOAA, Natl Environm Satellite Data & Informat Serv, Washington, DC 20233 USA
[3] Nanjing Univ Informat & Sci & Technol, Ctr Data Assimilat Res & Applicat, Nanjing, Jiangsu, Peoples R China
关键词
NUMERICAL WEATHER PREDICTION; VARIATIONAL STATISTICAL-ANALYSIS; CYCLONE TRACK FORECASTS; CLOUD-CLEARED RADIANCES; RECURSIVE FILTERS; WIND INFORMATION; PART II; IMPACT; COVARIANCES; MODEL;
D O I
10.1175/MWR-D-12-00079.1
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
The Geostationary Operational Environmental Satellites (GOES) provide high-resolution, temporally continuous imager radiance data over the West Coast (GOES-West currently known as GOES-11) and East Coast (GOES-East currently GOES-12) of the United States. Through a real case study, benefits of adding GOES-11/12 imager radiances to the satellite data streams in NWP systems for improved coastal precipitation forecasts are examined. The Community Radiative Transfer Model (CRTM) is employed for GOES imager radiance simulations in the National Centers for Environmental Prediction (NCEP) gridpoint statistical interpolation (GSI) analysis system. The GOES imager radiances are added to conventional data for coastal quantitative precipitation forecast (QPF) experiments near the northern Gulf of Mexico and the derived precipitation threat score was compared with those from six other satellite instruments. It is found that the GOES imager radiance produced better precipitation forecasts than those from any other satellite instrument. However, when GOES imager radiance and six different types of satellite instruments are all assimilated, the score becomes much lower than the individual combination of GOES and any other instrument. Our analysis shows that an elimination of Advance Microwave Sounding Unit-B (AMSU-B)/Microwave Humidity Sounder (MHS) data over areas where GOES detects clouds significantly improved the forecast scores from AMSU-B/MHS data assimilation.
引用
收藏
页码:75 / 92
页数:18
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