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Evaluation of High Density Surface Observations in Complex Terrain and Their Contribution to the MM5 Model

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Master's thesis

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This study evaluates the data assimilation capabilities of Three Dimensional Multiquadric Interpolation 3DMQ and the MM5 model when incorporating mesoscale observations from the United States Air Force Academy USAFA High Wind Alert system HWAS. These mesoscale observations are incorporated into a triple nested 12, 4, and 1.33 km high resolution model simulation and evaluated for their impact upon analyzed and forecasted wind values at USAFA during a severe down slope wind event that occurred on 6 March 2004. This evaluation is the first step in developing future forecasting and analysis tools for use by the military in various operations in complex terrain. The development of deployable automated tactical weather sensors in forward deployed locations requires an evaluation of the impact and usefulness these sensors would have on analysis forecast tools and mesoscale Numerical Weather Prediction NWP models. The juxtaposition of the HWAS network in complex terrain and the aviation training operations at USAFA provides an ideal set of data, mission and location for testing and evaluating a high resolution nested grid mesoscale NWP model. This study shows that incorporating HWAS observations into the 3DMQ data assimilation process has a significant impact upon verification of analyzed wind fields with the biggest impact occurring at the 1.33 km grid scale. Using these analyzed fields as initial conditions for MM5 model simulations, this study shows the ability of the 1.33 km model forecast wind fields to verify significantly better than either the 4 or 12 km through 18, 24, and 30 hour forecasts. Additionally, this study shows the limited, yet discernable impact HWAS observations have upon forecasted winds in the first several hours of MM5 model runs during a severe down slope wind event at USAFA.

Subject Categories:

  • Meteorology
  • Geology, Geochemistry and Mineralogy
  • Safety Engineering

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