Study of hybrid radar data assimilation on cloud microphysics scheme

Abstract:

Radar provides three-dimensional distribution, intensity, and movement of precipitation hydrometeors in the atmosphere at high resolution, which have been utilized in numerical weather prediction (NWP) models to improve quantitative precipitation forecasts (QPF). The 3D-Variational (VAR) method has been widely used for radar data assimilation (DA) because its application is simple and requires less computation resources, and has few equation constraints. However, 3D-VAR only considers climatological background error (BE) and the effect of DA is homogeneous and isotropic often leading to poor performance. The hybrid DA method, on the other hand, considers model error of the time and the effect of DA is flow dependent making it more adaptable to changing weather conditions. This study assimilated reflectivity and radial velocity into NWP model for three heavy rainfall cases. The effect of radar DA and the accuracy of precipitation forecast by 3D-VAR and Hybrid DA methods are studied.

In numerical experiments of 3D-VAR the precipitation areas was narrower than the observations and cumulative total precipitation error increased. The most improvement of rainfall accuracy came from hybrid DA. The root mean square error was reduced by 9.83 mm with 3D-VAR and 11.27 mm from Hybrid when compared to CTRL. The mixing ratio of water vapor and hydrometeors increased through radar DA, and the amount of Hybrid increased more than 3D-VAR. Increased mixing ratio of water vapor changed into hydrometeors. Hydrometeors grew to larger hydrometeors through various microphysical processes, eventually producing precipitation. Radar DA changed the mixing ratio of water vapor, snow and graupel but the increased water vapor mixing ratio had the greatest impact on precipitation formation and the effect of snow, rain and graupel mixing ratios were relatively small.

Acknowledgments: This research was funded by the Korea Environmental Industry & Technology Institute (KEITI) of the Korea Ministry of Environment (MOE) as “Advanced Water Management Research Program”. (79615).