Upper-air observations are disproportionately sparse, both temporally and geographically, when compared to surface observations. The lack of data is likely one of the largest limiting factors in numerical weather prediction.

Atmospheric measurements performed by the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) sensors of humidity, pressure, temperature, winds, icing, and turbulence provide significant additional information in the lower troposphere. The meteorological data, along with the corresponding location, time, and altitude from built-in GPS, are relayed to a ground-based network operations center via satellite in real-time. The TAMDAR sensors were deployed on a fleet of 63 Saab 340s operated by Mesaba Airlines in the Great Lakes region as a part of the NASA-sponsored Great Lakes Fleet Experiment (GLFE) by 15 July 2005. More than 800 soundings are generated from 400 flights to 75 regional airports daily.

A study of the impact of the TAMDAR data on mesoscale NWP is conducted using a mesoscale model which employs the NCAR-AirDat real-time four-dimensional data assimilation (RT-FDDA) and the available TAMDAR data. Four parallel 12-h simulations, where the two experimental (control) runs include (withhold) TAMDAR data, are performed, with one of the two pair of experimental and control simulations having 36 sigma-levels, while the other pair of experimental and control simulations has 47 sigma-levels. Over half of the additional sigma-levels are added to the lowest 1.5 km of the second set of parallel simulations.

The objectives of this study are to (i) optimize impacts that TAMDAR data may have on the RT-FDDA forecast system by increasing the horizontal distribution of vertical atmospheric profiles during initialization, and (ii) to isolate the ability of the model to utilize a higher vertical resolution during summer convective forecasts.

Preliminary results suggest that the addition of TAMDAR data improves the experimental simulation for certain output parameters. Additionally, when increasing the vertical resolution, the model data assimilation is able to take more details of the high-resolution TAMDAR observations and the results show significant improvements through the 12-h experimental simulation for parameters such as geopotential height, temperature, and winds from the surface level to above the 500-hPa level. The most notable improvements occur between the 900-hPa and 800-hPa level. Results also suggest that more accurate lower and mid-tropospheric model initializations feed back to the surface, thus improving surface parameters, as well as quantitative precipitation forecasts.