Impacts from assimilation of one data stream of AMSU‐A and MHS radiances on quantitative precipitation forecasts. (30th December 2016)

• Record Type: Journal Article
• Authors: Zou, X.; Qin, Z.; Weng, F.
• Is Part Of: Quarterly journal of the Royal Meteorological Society. Volume 143:Number 703(2017)
• Page Start: 731
• Page End: 743
• Publication Date: 2016-12-30
• Subjects: data assimilation -- satellite -- quantitative precipitation forecast; Meteorology -- Periodicals; 551.5
• DOI: 10.1002/qj.2960
• Languages: English
• ISSNs: 0035-9009

Abstract

Abstract : Since the launch of the NOAA‐15 satellite in 1998, the observations from microwave temperature and humidity sounders have been routinely disseminated to user communities through two separate data streams. In the Advanced Microwave Sounding Unit‐A (AMSU‐A) data stream, brightness temperatures in 15 channels are available primarily for profiling atmospheric temperature from the Earth's surface to the low stratosphere. In the Advanced Microwave Sounding Unit‐B (AMSU‐B) or Microwave Humidity Sounder (MHS) data stream, the brightness temperatures in five channels are included for sounding water vapour in the low troposphere. Assimilation of microwave radiance data in numerical weather prediction systems has also been carried out with AMSU‐A and AMSU‐B (MHS) data in two separate data streams. A new approach is to combine AMSU‐A and MHS radiances into one data stream for their assimilation. The National Centers for Environmental Prediction Gridpoint Statistical Interpolation analysis system and the Advanced Research Weather Research and Forecast model are employed for testing the impacts of the combined datasets. It is shown that the spatial collocation between MHS and AMSU‐A fields of view in the one data stream experiment allows for an improved quality control of MHS data, especially over the conditions where the liquid‐phase clouds are dominant. As a result, a closer fit of analyses to AMSU‐A and MHS observations is obtained, especially for AMSU‐A surface‐sensitive channels. The quantitative precipitation forecast skill is improved over a 10‐day period when Hurricane Isaac made landfall.