Probing boundary layer turbulence with models and lidar measurements through data assimilation

This study represents an integrated research capability based on (1) data from a scanning water vapor lidar, (2) a hydrodynamic model (HIGRAD) with a observing routine (VIEWER) that simulates the lidar scanning, and (3) an extended Kalman filter (EKF) algorithm for data assimilation which merges data into a model for the best estimate of the system under study. The purpose is to understand the degree to which the lidar measurements represent faithfully the atmospheric boundary layer's spatial and temporal features and to extend this utility in studying other remote sensing capabilities employed in both field and laboratory experiments. Raman lidar water vapor data collected over the Pacific warm pool and the HIGRAD simulations were first compared with each other. Potential aliasing effects of the measurements are identified due to the relatively long duration of the lidar scanning. The problem is being handled by the EKF data assimilation technique which incorporates measurements, that are unevenly distributed in space and time, into a model that simulates the flow being observed. The results of this study in terms of assimilated data will help to resolve and describe the scales and mechanisms that govern the surface evaporation.