Impact of assimilating radar and lidar observations on improving the bore forecast during PECAN campaign

The assimilation of radar reflectivity and radial velocity from the WSR-88D radar and lidar water vapor profile observations could improve the forecast of location and timing of bore associated with nocturnal convections. This study describes the assimilation of such data observed during the 2015 Plains Elevated Convection at Night (PECAN) field campaign. The model and data assimilation system employed is the Pennsylvania State University Weather Research and Forecasting model Ensemble Kalman Filter (PSU-WRF-EnKF) cycling data assimilation system. The lidars include the Atmospheric Lidar for Validation, Interagency Collaboration and Education (ALVICE), University of Wyoming King Air compact Raman lidar, Atmospheric Radiation Measurement (ARM) Raman lidar, and National Center for Atmospheric Research (NCAR) micropulse Differential Absorption Lidar (DIAL). To better evaluate the strengths and limitations of radar and lidar observations, they are assimilated separately and jointly. The bore propagation was observed by both radar and ALVICE lidar in Kansas, on 14 July 2015, and a nocturnal convection initiated near the bore/density current. Without assimilating any observations, the WRF model didn’t well simulate the location of the bore, even though it captured the bore structure quite well. The assimilation of WSR-88D radar observations corrected the location of nocturnal convection, and assimilation of lidar observations improved the location and timing forecasts of the associated bore, through the comparison with independent observations. Deterministic forecasts initialized from the EnKF analysis are capable of predicting the timing, height, and shape of bore, even though different lead times vary. The improved predictions also accurately reveal the whole processes of mesoscale convection system and bore generation, i.e., convective storm, cold pool, gust cold front, and bore.