Joint 4D Data Assimilation of the Networks of Weather Radars and Ground-Based Profiling Platforms for Forecasting Convective Storms

Liu, Yubao; Liu, Yubao

Weather radars observe mainly observe precipitation particles and these particles are the “downstream” products of the atmospheric dynamical and thermodynamical processes. Thus, although it is straightforward to assimilate the radar observed hydrometeors to analyze the “clouds”, we face inherent difficulties in obtaining physically and dynamically consistent temperature, moisture, and wind circulations for the typical rapid ramp down of the forecast skills of convective storms right after a model begins forecasting after radar data assimilation. Thus, it is beneficial and necessary to constrain radar data assimilation with thermodynamical and wind observations. A summer convective precipitation case occurring in eastern China is selected to investigate the impact of joint assimilation of ground-based profiling platforms and weather radars on forecasting convective storms using observational system simulation experiments (OSSEs). The simulated profiling platforms include Doppler wind lidar (DWL), wind profiler (WP), and microwave radiometer (MWR).

Results show that joint assimilation of WP and radar data produces a better analysis of convective dynamical structure than joint assimilation of DWL and radar data, since WP detects deeper layer winds. Joint assimilation of MWR and radar data enables rapid adjustment of temperature and humidity and thus, avoids the potential errors introduced by the latent heat term of the radar diabatic initialization in the early stage. Profiling observations in a horizontal spacing of 80 km provide fewer benefits for convective forecasting, while reducing the spacing to 40 km can dramatically improve model analysis and forecasts. Joint assimilation of multiple profiling observations in a 20 km horizontal spacing with radar data exhibits a beneficial synergistic effect and mitigates "the ramp-down issue" during the forecast stage. Assimilating profiling observations with an update interval less than 30 mins does not have pronounced an effect on convective forecasts. Furthermore, assimilating profiling observations at a 20 km horizontal spacing can obtain accurate mesoscale background environment and forecast storms with an ability comparable to radar data assimilation.

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12A.5 Joint 4D Data Assimilation of the Networks of Weather Radars and Ground-Based Profiling Platforms for Forecasting Convective Storms