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 groundbased 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 fourdimensional data assimilation is employed to draw comparisons from parallel short-range 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 model !levels from 36 to 48. Over half of the additional !-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. In the last part, a quantitative precipitation forecast (QPF) verification study is conducted on multiple numerical weather models using the 22-23 April 2005 case. An objective precipitation cell isolation technique is employed to quantify the accuracy of each model with respect to magnitude and location of precipitation cells. 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.