| dc.description.abstract | Improving the quantitative precipitation forecast is the key ongoing challenge in weather prediction. Despite the positive impact on the enhancement of numerical weather prediction, the assimilation of reflectivity and radial velocity cannot fully adjust the water vapor field to achieve an optimal short-term forecast. However, moisture information is proved to be critical for convection analysis and forecast. This thesis aims to investigate the additional assimilation of two different kinds of moisture data including the S-PolKa-retrieved water vapor and radar-retrieved refractivity.
In the first part of this dissertation, the S-PolKa-retrieved water vapor data which represents the environmental information outside the precipitation at the low level was assimilated with reflectivity and radial wind. The WRF local ensemble transform Kalman filter data assimilation system was employed to examine a series of experiments in three real cases of the Dynamics of the Madden-Julian Oscillation Experiment. The vertical profiles of humidity were thinned into one averaged and four-quadrant profiles and assimilated 1) with radar data for the entire 2 h and 2) alone in the first hour, followed by radial wind and reflectivity assimilation in the second hour. The results revealed that assimilating additional water vapor data more markedly improved the analysis at the convective scale, leading to more significant improvements in the rain forecast compared with assimilating radar data only. In addition, 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 in rain forecast compared with other experiments. Furthermore, assimilating moisture profiles into four quadrants achieved more accurate analyses and forecasts.
The second part of this dissertation focus on examining the assimilation of radar-retrieved refractivity which carries moisture information near the surface. Two real cases in the Southwest Monsoon Experiment were deployed with the high-resolution WRF local ensemble transform Kalman filter data assimilation system. Two different sets of experiments were investigated. In the first experimental group, the role of extra refractivity assimilation was investigated. The results indicated that additional refractivity assimilation improved the quantitative precipitation forecasting by generating the optimal moisture, temperature, and wind adjustment and enhancing the wind convergence. Moreover, the level impact of refractivity assimilation on the short-term forecast is markedly notable in dry-biased background moisture with broader refractivity distribution. The second experimental set was utilized for studying the refractivity assimilation before and after the weather system landed. The results revealed that assimilating radar refractivity continuously after the precipitation system landed on the island has advantages for the short-term forecast. Additionally, this study suggested starting assimilating refractivity before the weather system landed to obtain the optimal quantitative precipitation forecasting, particularly for heavy rainfall. | en_US |