Forecasting Front Range convection using radar data assimilation and RTFDDA

Presentation Type: 
Mei Xu (NCAR)
Juanzhen Sun (National Center for Atmospheric Research)
Sarah Tessendorf (NCAR, USA)
Yubao Liu (NCAR, USA)

The RTFDDA is a WRF-based numerical modeling system, typically run on multiple domains with a high-resolution inner domain at 1 3 km grid spacing and with 3-hourly or 6-hourly cycling schemes. The capability for assimilation of radar data has been designed to create dynamically and cloud/precipitation spun-up initial conditions such that very short-term convection forecasts can be improved. A hybrid method for radar data assimilation has been adopted, in which hourly radar data analysis is first obtained using the WRFDA 3DVAR, and hydrometeor and latent heat adjustment techniques. The radar analysis is then blended into the model using the grid-nudging method.

Extensive numerical experiments have been performed to evaluate the impact of radar data assimilation on 0 12 h RTFDDA forecasts of convection in the Rocky Mountains Front Range area. Through case studies, the capability of WRFDA 3DVAR and latent heat adjustment to retrieve convective features from radial velocity and reflectivity data in the initial conditions and to improve the forecast is examined through case studies. Several alternative configurations of 3DVAR and latent heating adjustment are tested. Impact of the radar data on the forecasts of precipitation, model microphysics and surface meteorological fields are evaluated. In addition, a real-time demonstration of the system is planned for July 7 August 15, 2014, along with several other high-resolution numerical systems. Results from the real-time test will be discussed at the conference.

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