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.

Ongoing data-denial studies, which now include parallel forecasts from the WRF-ARW, are being conducted over the continental US. The 72-h experimental (control) operational forecasts include (withhold) the TAMDAR data. Monthly and seasonal statistics, verified against other observing platforms (e.g., RAOBs), are compiled and analyzed for various domains and model 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, as well as (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 ongoing studies will present these findings, as well as the various degrees of forecast impacts provided by the additional airlines currently being equipped with TAMDAR sensors.