A method to assimilate all-sky radiances from the Advanced Microwave Scanning Radiometer 2 (AMSR2) was developed within the Weather Research and Forecasting (WRF) model's data assimilation (WRFDA) system. The four essential elements are: (1) extending the community radiative transform model's (CRTM) interface to include hydrometeor profiles; (2) using total water Qt as the moisture control variable; (3) using a warm-rain physics scheme for partitioning the Qt increment into individual increments of water vapour, cloud liquid water and rain; and (4) adopting a symmetric observation error model for all-sky radiance assimilation. Compared to a benchmark experiment with no AMSR2 data, the impact of assimilating clear-sky or allsky AMSR2 radiances on the analysis and forecast of Hurricane Sandy (2012) was assessed through analysis/ forecast cycling experiments using WRF and WRFDA's three-dimensional variational (3DVAR) data assimilation scheme. With more cloud/precipitation-affected data being assimilated around tropical cyclone (TC) core areas in the all-sky AMSR2 assimilation experiment, better analyses were obtained in terms of the TC's central sea level pressure (CSLP), warm-core structure and cloud distribution. Substantial (>20 %) error reduction in track and CSLP forecasts was achieved from both clear-sky and all-sky AMSR2 assimilation experiments, and this improvement was consistent from the analysis time to 72-h forecasts. Moreover, the allsky assimilation experiment consistently yielded better track and CSLP forecasts than the clear-sky did for all forecast lead times, due to a better analysis in the TC core areas. Positive forecast impact from assimilating AMSR2 radiances is also seen when verified against the European Center for Medium-Range Weather Forecasts (ECMWF) analysis and the Stage IV precipitation analysis, with an overall larger positive impact from the all-sky assimilation experiment.
CITATION STYLE
Yang, C., Liu, Z., Bresch, J., Rizvi, S. R. H., Huang, X. Y., & Min, J. (2016). AMSR2 all-sky radiance assimilation and its impact on the analysis and forecast of Hurricane Sandy with a limited-area data assimilation system. Tellus, Series A: Dynamic Meteorology and Oceanography, 68(1). https://doi.org/10.3402/tellusa.v68.30917
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