92nd American Meteorological Society Annual Meeting (January 22-26, 2012)

Tuesday, 24 January 2012: 2:00 PM
Radar Data Assimilation for Short-Term Forecasting of Convection Using a Hybrid Approach of Latent Heat Nudging, 3DVAR, and Grid Nudging
Room 340 and 341 (New Orleans Convention Center )
Mei Xu, NCAR, Boulder, CO; and Y. Liu, W. Yu, J. Sun, M. Ge, J. C. Knievel, and J. C. Pace

Techniques are tested for assimilating Doppler radar radial winds and reflectivity into the Real-Time Four-Dimensional Data Assimilation and forecasting system (RTFDDA) developed jointly by NCAR and ATEC (Army Test and Evaluation Command). The current RTFDDA is WRF-based, 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. A capability for assimilation of radar data has been designed to improve RTFDDA in creating 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.

In this study, extensive numerical experiments are performed to evaluate the impact of radar data assimilation on 0–12 h RTFDDA forecasts. The tests are conducted for two regional areas, the Front Range of Colorado and the eastern U.S.. 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. Two extended retrospective simulations, for the Front Range domain during June 4-18, 2009 and the eastern domain during February 20–March 07, 2011, have also been conducted. Verification shows that the hybrid radar data assimilation technique apparently improves 0–6 h forecasts of precipitation and surface temperature statistically in both retrospective case studies.

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