{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/95564"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/95564","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A case study of weather research and forecasting model over the Midwest USA","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Fu, Kan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Environ Engr in Civil Engr","degree_department":null,"school":null,"contributors":["Koloutsou-Vakakis, Sotiria","Rood, Mark J."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-03-01T17:01:09Z","date_published":"2017-03-01T17:01:09Z","updated_at":"2026-07-22T22:26:37Z","subjects":["Weather research and forecast (WRF)","Sensitivity analysis","Midwest USA","Numerical weather prediction"],"languages":["en"],"rights":["Copyright 2016 Kan Fu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/95564","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Koloutsou-Vakakis, Sotiria","Rood, Mark J."]},{"key":"dc:creator","label":"Author","values":["Fu, Kan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-03-01T17:01:09Z","2019-03-02T10:15:14Z","2016-11-09","2016-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Environ Engr in Civil Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Weather research and forecast (WRF)","Sensitivity analysis","Midwest USA","Numerical weather prediction"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Kan Fu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/95564"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-12-01","The student, Kan Fu, accepted the attached license on 2016-11-04 at 13:18.","The student, Kan Fu, submitted this Thesis for approval on 2016-11-04 at 13:28.","This Thesis was approved for publication on 2016-11-09 at 16:37.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10220 on 2017-02-28 at 14:41:21","Made available in DSpace on 2017-03-01T17:01:09Z (GMT). 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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.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-12-01","The student, Kan Fu, accepted the attached license on 2016-11-04 at 13:18.","The student, Kan Fu, submitted this Thesis for approval on 2016-11-04 at 13:28.","This Thesis was approved for publication on 2016-11-09 at 16:37.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10220 on 2017-02-28 at 14:41:21","Made available in DSpace on 2017-03-01T17:01:09Z (GMT). No. of bitstreams: 3 FU-THESIS-2016.pdf: 2837617 bytes, checksum: 431b584df2bca3645da929e36d244d05 (MD5) Kan_Thesis_final.docx: 12473077 bytes, checksum: 0550a54130c0c33d06a06ff658cb09a0 (MD5) LICENSE.txt: 4203 bytes, checksum: 9b1a49eba8f4091005935f1a6e165be1 (MD5) Previous issue date: 2016-11-09","Embargo set by: Seth Robbins for item 98680 Lift date: 2019-03-01T17:02:22Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 98680 Lift date: 2019-03-01T17:03:32Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 98680 Lift date: 2019-03-01T17:05:02Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 98680 Lift date: 2019-03-01T17:06:55Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 98680 on 2019-03-02T10:15:14Z."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/95564"],"dc:language":["en"],"dc:rights":["Copyright 2016 Kan Fu"],"dc:subject":["Weather research and forecast (WRF)","Sensitivity analysis","Midwest USA","Numerical weather prediction"],"dc:title":["A case study of weather research and forecasting model over the Midwest USA"],"dc:type":["text"],"thesis:degree_discipline":["Environ Engr in Civil Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:37Z"}