During the summer of 2007, AirDat added a version of the NCAR Advanced Research WRF (ARW) to the operational fleet of grid-scale mesoscale models that currently assimilate atmospheric measurements performed by the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) sensor. The TAMDAR sensor measures humidity, pressure, temperature, winds aloft, icing, and turbulence, along with the corresponding location, time, and altitude from built-in GPS. These observations are transmitted in real time to a ground-based network operations center via a global satellite network.

Findings from the ongoing data-denial WRF-ARW study will be presented along with updates to the new focus of testing various data assimilation schemes. The primary schemes that will be tested are the WRF 4DVAR and the WRF-based Real-Time Four-Dimensional Data Assimilation (RTFDDA) and forecasting system. The experimental (control) forecasts were conducted by including (withholding) the TAMDAR data, while a parallel version of the 3DVAR WRF will also be employed as a baseline for statistical improvement. Monthly and seasonal verification statistics against other observing platforms (e.g., RAOBs) are compiled and analyzed for different model domains and configurations.

The objectives of this study are to (i) optimize impacts that TAMDAR data may have on the WRF-ARW forecast system by testing various assimilation methods, weighting schemes, error statistics, and parameterization configurations, and (ii) monitor the accuracy, contribution, and health of the TAMDAR observing system.

Preliminary results suggest that the proper assimilation of the TAMDAR data improves the short-range forecast skill of the WRF-ARW when parameters such as wind, temperature, and relative humidity are verified against observations. The findings will be presented., In addition, we are evaluating the forecast impacts provided by the additional airlines currently being equipped with TAMDAR sensors and the result will also be reported.