A Four-Season Impact Study of Rawinsonde, GOES, and POES Data in the Eta Data Assimilation System. Part II: Contribution of the Components

Abstract

Abstract The impact of in situ rawinsonde observations (raob), remotely sensed Geostationary Operational Environmental Satellite (GOES), and Polar-Orbiting Operational Environmental Satellite (POES) observations routinely used in NCEP’s Eta Data Assimilation/Forecast System (EDAS) is studied for extended-length time periods during four seasons. This work examines the contribution of nine individual components of the total observing system. The nine data types examined include rawinsonde mass and wind observations, GOES mass and wind observations, POES observations from the Microwave Sounding Unit (MSU), the Advanced Microwave Sounding Unit (AMSU-A and AMSU-B), the High Resolution Infrared Radiation Sounder (HIRS), and column total precipitable water and low-level wind observations from the Special Sensor Microwave Imager (SSM/I). The results are relevant for users of the Eta Model trying to compare/contrast the overall forecast impact of traditional, largely land-based rawinsonde observations against remotely sensed satellite observations, which are available domainwide. The case studies chosen consist of 15-day periods during fall 2001, winter 2001/02, spring 2002, and summer 2002. Throughout these periods, a November 2001 32-km version of the EDAS is run 10 times at both 0000 and 1200 UTC. The 10 runs include a control run, which utilizes all data types routinely used in the EDAS, and 9 experimental runs in which one of the component data types noted above is denied. Differences between the experimental and control runs are then accumulated over the 15-day periods and analyzed to demonstrate the 00-h sensitivity and 24-h forecast impact of these individual data types in the EDAS. The diagnostics are computed over the entire horizontal model domain and a subsection covering the continental United States (CONUS) and adjacent coastal waters on isobaric surfaces extending into the lower stratosphere. The 24-h forecast impact results show that a positive forecast impact is achieved from most of the nine component data sources during all four time periods. HIRS, MSU, and SSM/I wind observations yield only a slight positive forecast impact to all fields. Rawinsonde and GOES wind observations have the largest positive forecast impact for temperature over both the entire model domain and the extended CONUS. The same data types also provide the largest forecast impact to the u component of the wind, while GOES wind observations provide the largest forecast impact to moisture.