Assimilation Of Doppler Radar Data With An Ensemble Square Root Filter

Assimilation of Doppler radar data is important for the improvement of storm-scale numerical weather prediction. To retrieve dynamically consistent wind, thermodynamic and microphysical fields from radar radial velocity and reflectivity, advanced data assimilation methods are required. Compared with the four-dimensional variational (4DVAR) data assimilation method, the ensemble Kalman filter (EnKF) method with flow-dependent error covariance is very popular in recent years, because the algorithm does not require the adjoint code of either the forward observational operators or the prediction model.The goal of this research is to develop an EnKF data assimilation system and to investigate its ability in radar data assimilation for storm-scale numerical weather prediction. The following are the main conclusions and results:(1) An Ensemble Square Root Filter (EnSRF) data assimilation system concerning on storm-scale issues is built under the Weather Research Forecast (WRF) model framework and is tested with different types of severe weather events to examine the performance of this data assimilation system in convective scale application. One heavy rainfall event (5 July 2003) and one strong convective event (5 June 2009) are selected for conducting the tests. Results show that the analysis ensemble means at 60 min in both cases and the 30 min forecasts from these analyses are close to the observations. The impacts of different initial perturbation strategies are investigated in this study, namely, adding initial perturbations to the entire computational domain and to the limited region around the observed storm area, perturbations to the environmental wind field or not. Results show that perturbing the limited region does not improve the analysis results compared to perturbing the entire forecast domain while perturbing the environment wind field produces better results in the meiyu heavy rain case indicating the more significant impacts of environment uncertainty on relatively larger scale weather events. The impacts of data assimilation on precipitation forecast are also examined. In the heavy rain case, the forecast results show that within 6 hours, both the single forecast and the ensemble forecast initialized from analysis obtained through the assimilation of radar observations are better than the corresponding result in the experiment without data assimilation.(2) The effectiveness of the EnSRF data assimilation system for assimilating Doppler radar data at convective scales is investigated for cases whose behaviors span supercellular, linear, and multicellular organization. The EnSRF performs effectively and produces analyses of comparable quality for each of the cases. Additionally, the impacts of initial perturbations of different magnitude to potential temperature and water vapor on ensemble spread and analysis are also investigated. That the sensitivity of ensemble spread and analysis to the magnitude of perturbation in thermal field is found in results implies larger ensemble spread and better radial velocity analysis can be obtained by increasing the standard deviation of potential temperature and water vapor mix ratio appropriately used in initial perturbation.