Evaluation of temporal and spatial distribution of TAMDAR data in short-range mesoscale forecasts

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) 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. The TAMDAR sensors are 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). More than 800 soundings are generated from 400 flights to 75 regional airports during a 24-h period.

A two-part case study is conducted using the 22-23 April 2005 cyclogenetic event over the Great Lakes region. A mesoscale model using real-time four-dimensional data assimilation (RT-FDDA) is employed to draw comparisons from parallel 12-h simulations where the experimental (control) run includes (withholds) TAMDAR data. The second part of this study varies the vertical resolution by increasing the number of sigma levels from 36 to 48. Over half of the additional sigma levels are added to the lowest 1.5 km. This is done for both the control and experimental simulations for the same 22-23 April 2005 case.

The objectives of this study are to (i) identify impacts that TAMDAR data may have on mesoscale model forecasts by increasing the horizontal distribution of vertical atmospheric profiles during initialization, and (ii) to isolate the model's ability to utilize higher vertical data resolution; thus quantifying any impacts it may have for this particular case.

Results suggest that the addition of TAMDAR data improves the experimental simulation for certain output parameters. Additionally, when increasing the vertical resolution, 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 sea-level pressure and 10-m wind fields, as well as quantitative precipitation forecasts.