Assimilating Retrieved Water Vapor and Radar Data from NCAR SPol-Ka: Performance and Validation Using Real Cases

The effect of assimilating S- and Ka-band dual‐wavelength-retrieved water vapor which provides environmental information before precipitation occurs was examined in this study. The S-PolKa vertical profile of humidity was obtained at low levels and thinned into averaged and four-quadrant profiles. Two different strategies were utilized to assimilate the retrieved moisture information: (1) assimilation of water vapor data with radar data for the entire 2 h and (2) assimilation of water vapor data in the first hour, and radial velocity and reflectivity data in the second hour. By using the WRF local ensemble transform Kalman filter data assimilation system, three real cases of the Dynamics of the Madden-Julian Oscillation Experiment were examined through a series of experiments. The results revealed that assimilating additional water vapor data more markedly improved the analysis at the convective scale than assimilating radial wind and reflectivity data alone, leading to more significant improvements in the rain forecast compared with assimilating radar data only. When moisture data were assimilated, improved nowcasting could be extended up to 4 h. Additionally, the strategy of assimilating only retrieved water vapor data in the first hour and radial wind and reflectivity data in the second hour achieved the optimal analysis, resulting in the most improvement of rain forecast compared with other experiments. Furthermore, assimilating moisture profiles into four quadrants achieved more accurate analysis and forecast. Overall, our study demonstrated that the humidify information in nonprecipitation areas is critical for further improving the analysis and forecast of convective weather systems.