A Study of Simultaneous Assimilation of Coastal Ground-Based and Airborne Radar Observations on the Prediction of Harvey (2017) With the Hourly 3DEnVar System for HWRF

This study investigates the relative impact of assimilating the ground-based WSR-88D radar (GBR) and the airborne Tail Doppler Radar (TDR) observations on the analysis and prediction of Hurricane Harvey (2017) during its landfalling stage. Results show that the assimilation of GBR (experiment "DAG") outperforms the assimilation of TDR (experiment "DAT") in multiple aspects. For example, "DAG" produces better predictions in V-max, radial velocity, brightness temperature, and precipitation than "DAT." The advantages of "DAG" over "DAT" are likely from the better analyzed thermodynamical structures in addition to the better data availability. The average Minimum Sea Level Pressure (MSLP) prediction error is the only aspect of "DAG" that is inferior to "DAT." Diagnostics show that such an inferior performance of MSLP for "DAG" is associated with the systematic bias from the Hurricane Weather Research and Forecasting model. The combined assimilation of both observations (experiment "DAB") shows complementary effects and performs the best overall among all experiments.