Impacts from assimilation of one data stream of AMSU‐A and MHS radiances on quantitative precipitation forecasts

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.

[1]  Niels Bormann,et al.  Estimates of spatial and interchannel observation‐error characteristics for current sounder radiances for numerical weather prediction. I: Methods and application to ATOVS data , 2010 .

[2]  John Derber,et al.  The Use of TOVS Cloud-Cleared Radiances in the NCEP SSI Analysis System , 1998 .

[3]  Xiaolei Zou,et al.  Development and initial assessment of a new land index for microwave humidity sounder cloud detection , 2016, Journal of Meteorological Research.

[4]  X. Zou,et al.  Satellite data assimilation of upper-level sounding channels in HWRF with two different model tops , 2015, Journal of Meteorological Research.

[5]  Florence Rabier,et al.  Global 4DVAR Assimilation and Forecast Experiments Using AMSU Observations over Land. Part II: Impacts of Assimilating Surface-Sensitive Channels on the African Monsoon during AMMA , 2010 .

[6]  X. Zou,et al.  Evaluating Added Benefits of Assimilating GOES Imager Radiance Data in GSI for Coastal QPFs , 2013 .

[7]  X. Zou,et al.  Improved Coastal Precipitation Forecasts with Direct Assimilation ofGOES-11/12Imager Radiances , 2011 .

[8]  Vincent Guidard,et al.  Enhancements of Satellite Data Assimilation over Antarctica , 2010 .

[9]  Fuzhong Weng,et al.  Improved Quantitative Precipitation Forecasts by MHS Radiance Data Assimilation with a Newly Added Cloud Detection Algorithm , 2013 .

[10]  R. Purser,et al.  Three-Dimensional Variational Analysis with Spatially Inhomogeneous Covariances , 2002 .

[11]  Fuzhong Weng,et al.  Retrieval of Ice Cloud Parameters Using a Microwave Imaging Radiometer , 2000 .

[12]  A. Hollingsworth,et al.  Some aspects of the improvement in skill of numerical weather prediction , 2002 .

[13]  J. Thepaut,et al.  The assimilation of AIRS radiance data at ECMWF , 2006 .

[14]  Fuzhong Weng,et al.  Advanced microwave sounding unit cloud and precipitation algorithms , 2003 .

[15]  Philippe Courtier,et al.  Use of cloud‐cleared radiances in three/four‐dimensional variational data assimilation , 1994 .

[16]  J. R. Eyre,et al.  Assimilation of TOVS radiance information through one-dimensional variational analysis , 1993 .

[17]  N. Roberts,et al.  Numerical Aspects of the Application of Recursive Filters to Variational Statistical Analysis. Part II: Spatially Inhomogeneous and Anisotropic General Covariances , 2003 .