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Title:A case study of weather research and forecasting model over the Midwest USA
Author(s):Fu, Kan
Advisor(s):Koloutsou-Vakakis, Sotiria
Contributor(s):Rood, Mark J.
Department / Program:Civil & Environmental Eng
Discipline:Environ Engr in Civil Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Weather research and forecast (WRF)
Sensitivity analysis
Midwest USA
Numerical weather prediction
Abstract:Chemical Transport Models (CTMs) are important tools for air quality research, and it is of the same importance to provide accurate weather information as input data to CTMs. In this thesis, the Weather Research and Forecast (WRF) model was used as an input to a CTM and a sensitivity analysis of 17 WRF runs was conducted to explore the optimum physics configuration in 6 physics categories for the Midwest USA in May 2011, including cumulus, surface layer, microphysics, land surface model, planetary boundary layer, longwave radiation and shortwave radiation. Two domains were used: the coarse domain (12 km grid size) covering most parts of the North America and the nested domain (4 km grid size) covering the Illinois State and adjacent areas. The model output from the nested domain was evaluated statistically and results were compared with observation data using the Model Evaluation Tools (MET) software package and the National Center for Atmospheric Research Command Language (NCL). Benchmark values of several weather variables from the literature were adopted as a reference when discussing model statistical performance. After the sensitivity analysis was finished, the same optimum physics configuration for May was evaluated for October using measured meteorological data to test the applicability of the WRF model during different weather conditions. Finally, both the coarse domain and the fine domain were evaluated to investigate model sensitivity to the horizontal resolution. Compared with the starting run, the optimum run was found to produce better temperature (0.35 K decrease in hourly mean bias and 0.26 K decrease in hourly root mean square error), pressure (4.3 Pa decrease in hourly mean bias and 3.91 K decrease in hourly root mean square error) and relative humidity (1.44 % decrease in hourly mean bias and 1.76 % decrease in hourly root mean square error) results, while keeping the ability to simulate wind speed and wind direction accurately compared with other studies. In addition, all the statistical measures were within the benchmark value ranges that were available in the literature (Emery et al., 2001). When applying the same optimum physics configuration to October, WRF still produced acceptable results, with only gross error of wind direction out of the benchmark value range in hourly statistics (30.02° compared with 30° from the benchmark value). Comparison between the coarse domain and the fine domain suggested that decreasing horizontal resolution did not necessarily lead to increasing the model simulation skill. The unique contribution of this research is to provide a general method of sensitivity analysis in WRF and obtain the optimum WRF physics configurations for the Midwest USA. These contributions are important because CTMs need accurate weather inputs to produce reliable outputs, and it is not easy to find the optimum WRF outputs given that there are many choices to make when running WRF.
Issue Date:2016-11-09
Rights Information:Copyright 2016 Kan Fu
Date Available in IDEALS:2017-03-01
Date Deposited:2016-12

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