TAMDAR Data Assimilation and Its Impact on Mesoscale Numerical Forecast

The AirDat Corporation has developed a unique aircraft-mounted multi-function atmospheric sensor called TAMDAR (Tropospheric Airborne Meteorological Data Reporting). The TAMDAR sensors provide high frequency measurements, in both space and time, of the atmospheric humidity, pressure, temperature, wind, icing, and turbulence. The TAMDAR sensors are intended to complement existing technologies, such as radiosondes and ACARS, by providing key observations in under-sampled regions, such as humidity over water.

Therefore, if the TAMDAR data can be used correctly to optimize the initial condition of numerical weather forecast, it will be very helpful to improve the forecast in the under-sampled regions. So this study will evaluate the performance of TAMDAR data on the short-range mesoscale numerical forecast using WRF (Weather Research & Forecasting Model) and WRFDA (The Weather Research and Forecasting Data Assimilation system). The operator to assimilate TAMDAR data had been developed in WRFDA. This study will include the following steps: 1) Estimate the TAMDAR data observational error by seasons for better understanding the data quality, and providing observation error information for WRFDA to assimilate TAMDAR data correctly; 2) Carry out 2-month cycling run in the Gulf of Mexico region to assess the performance and impact of TAMDAR data, especially to validate the impact of humidity in the mesoscale numerical forecast with 3D-Var (and 4D-Var if possible). The preliminary results will be presented.