Improving AHI Radiance Assimilation over Land with the Surface Skin Temperature Constrained by Station Observations and Its Impact for Quantitative Precipitation Forecasts

In this study, a new way to assimilate clear-sky Advanced Himawari Imager (AHI) surface-sensitive bright-ness temperature (TB) observations over land is investigated for improving quantitative precipitation forecasts (QPFs) in eastern China. To alleviate problems arising from inaccurate surface temperature in radiance simulations, surface station observations of land surface skin temperature (LSST) together with conventional and AMSU-A observations are assimi-lated to improve AHI surface-sensitive TB simulations of the Community Radiative Transfer Model (CRTM) before AHI data assimilation. First, the Gridpoint Statistical Interpolation (GSI) three-dimensional variational (3DVar) system is up-dated with the additional control variable of surface temperature and its background error covariances. Second, surface temperature and emissivity sensitivity checks are designed for the quality control of the surface-sensitive AHI channels. Finally, the impacts of a two-time data assimilation strategy are assessed for a local convection rainfall case and a synoptic-scale precipitation case. The experiment in which AHI data are assimilated after assimilating LSST data (ExpL2) outper-forms the traditional experiment in which the LSST is not updated (ExpL) in terms of its 24-h QPF skill score. A better description of atmospheric instability and moisture convergence forcing is obtained in ExpL2 than in ExpL. Both experi-ments show additional low-level temperature and humidity adjustments compared to the experiment that does not assimi-late AHI surface-sensitive channels (ExpNL). Lower AHI TB simulation biases are found in the ExpL2 experiment, which improve the analyzed field and subsequent QPFs. The results in this study suggest the importance of proper utilization of LSST observations for AHI surface-sensitive TB assimilations over land.