Lower and middle-tropospheric observations are disproportionately sparse, both temporally and geographically, when compared to surface observations. The unsubstantial density of observations is likely one of the largest limiting factors in numerical weather prediction.

Atmospheric measurements performed by the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) sensor of humidity, pressure, temperature, winds aloft, icing, and turbulence, along with the corresponding location, time, and altitude from built-in GPS are relayed via satellite in real-time to a ground-based network operations center.

Since December 2004, the TAMDAR sensors have been operating 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). Equipage of sensors on additional aircraft across the continental US and Alaska is currently underway. More than 800 soundings are generated from 400 flights to 75 regional airports during a 24-h period. Upon completion of the 2007-08 installation, more than 5000 daily sounding will be produced.

A 3-way study is conducted using a limited-area mesoscale model to draw comparisons from eight parallel 72-h simulations where the four experimental (control) runs include (withhold) TAMDAR data. Two of the four pair of experimental and control simulations have 36 sigma-levels, while the other two pair of experimental and control simulations have more than 60 sigma-levels. Over half of the additional sigma-levels are added to the lowest 2 km. Within these two pair, each set of parallel simulations has an outer grid spacing of either 36 km or 10 km.

The objectives of this study are to (i) optimize impacts that TAMDAR data may have on the forecast system by increasing the horizontal distribution of vertical atmospheric profiles during initialization, and (ii) to isolate the effects of data assimilation at a higher horizontal and vertical resolution with respect to temperature and relative humidity forecast variables.

Preliminary results suggest that the addition of TAMDAR data improves the experimental simulations for certain output parameters. Additionally, when increasing the horizontal and vertical resolution, results show varying degrees of improvement through the 72-h experimental simulations. The most notable increase in forecast skill occurs between the 900-hPa and 800-hPa level. Results also suggest that more accurate lower and mid-tropospheric data assimilation and model initializations feed back to the surface, thus improving surface parameters, as well as quantitative precipitation forecasts.