Evaluation of TAMDAR data impact on predicting warm-season convection using the NCAR-AirDat WRF-based RTFDDA system

During the summer of 2009, AirDat and NCAR jointly implemented an operational CONUS-scale cloud-resolvable (4-km grid) Real-Time Four Dimensional Data Assimilation (RTFDDA) and forecasting system, known as NCAR-AirDat RTFDDA-WRF. RTFDDA was originally developed jointly by NCAR and the US Army Test and Evaluation Command (ATEC). RTFDDA is built upon the Weather Research and Forecasting (WRF) ARW-core. It contains a Newtonian-relaxation observational-nudging based data assimilation algorithm which allows the model to continuously assimilate measurements from fixed-location platforms, as well as continuous, moving platforms such as the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) sensor. The real-time TAMDAR temperature, winds and humidity reports are continuously assimilated into the NCAR-AirDat RTFDDA-WRF modeling system along with standard WMO upper-air and surface observations and diverse platforms included in the NOAA MADIS data stream. Continuous four-dimensional data assimilation approach provides a unique ability for producing dynamically consistent, convection “spun-up” initial conditions to initialize model forecasts, which is crucial for nowcasting and forecasting warn-seasonal precipitation with NWP models. In this study, we evaluate the capability of NCAR-AirDat RTFDDA system for short-term (0-24h) forecasts of convection over CONUS. Different behavior and impact of the TAMDAR data on model analyses and forecasts of convention systems on the implicit (12km grid) and the convection-resolvable (4km grid) will be reported. Sensitivity studies using the same model configurations are conducted to study the benefit of the TAMDAR data in comparison to the radiosondes and ACARS data and the results will also be presented.