{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/132711"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/132711","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Quality control and analysis of an updated wind gust data set in the United States","abstract":"This thesis presents the development of an updated 51-year peak wind gust data set for 679 Automated Surface Observing Systems (ASOS) stations across the Continental United States (CONUS) and explores limitations and implications for long return period wind speed estimation. In total, the data set contains more than 17.9 million peak wind gusts, including nearly double the station-years available compared to the data set used in the current ASCE/SEI 7-22 wind hazard maps. A rigorous quality control methodology was implemented using manual inspection of archived Meteorological Aerodrome Reports (METAR), 1-minute ASOS data, radar data, and surface analysis. In addition, 1-minute data is used to explore how the automated quality algorithm and power outages may impact long return period wind speed estimations. Peak wind speeds are classified into thunderstorm, non-thunderstorm, and tropical and standardized for instrumentation changes. Spatial analysis of annual maximum wind gusts shows the highest thunderstorm and non-thunderstorm wind speeds in the central Great Plains. Trends in median annual maximum wind gusts show a slight increasing trend, however, changes in ASOS averaging time and instrumentation, such as sonic anemometer installation, introduces three distinct eras in the data set. Overall, this work represents the most complete data set ever assembled for CONUS ASOS stations and demonstrates improvements over the previous data set, improving confidence in extreme wind speed estimation for future wind hazard maps.","abstract_html":"This thesis presents the development of an updated 51-year peak wind gust data set for 679 Automated Surface Observing Systems (ASOS) stations across the Continental United States (CONUS) and explores limitations and implications for long return period wind speed estimation. In total, the data set contains more than 17.9 million peak wind gusts, including nearly double the station-years available compared to the data set used in the current ASCE/SEI 7-22 wind hazard maps. A rigorous quality control methodology was implemented using manual inspection of archived Meteorological Aerodrome Reports (METAR), 1-minute ASOS data, radar data, and surface analysis. In addition, 1-minute data is used to explore how the automated quality algorithm and power outages may impact long return period wind speed estimations. Peak wind speeds are classified into thunderstorm, non-thunderstorm, and tropical and standardized for instrumentation changes. Spatial analysis of annual maximum wind gusts shows the highest thunderstorm and non-thunderstorm wind speeds in the central Great Plains. Trends in median annual maximum wind gusts show a slight increasing trend, however, changes in ASOS averaging time and instrumentation, such as sonic anemometer installation, introduces three distinct eras in the data set. Overall, this work represents the most complete data set ever assembled for CONUS ASOS stations and demonstrates improvements over the previous data set, improving confidence in extreme wind speed estimation for future wind hazard maps.","abstract_has_math":false,"creators":["Pagnanelli, Jr., Michael Joseph"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Lombardo, Franklin T"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12","date_published":"2025-12","updated_at":"2026-07-22T22:25:07Z","subjects":["wind engineering","ASOS","quality control","wind gusts"],"languages":["en"],"rights":["Copyright 2025 Michael Pagnanelli, Jr."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/132711","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Lombardo, Franklin T"]},{"key":"dc:creator","label":"Author","values":["Pagnanelli, Jr., Michael Joseph"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12","2025-12-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"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 Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["wind engineering","ASOS","quality control","wind gusts"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Michael Pagnanelli, Jr."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/132711"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This thesis presents the development of an updated 51-year peak wind gust data set for 679 Automated Surface Observing Systems (ASOS) stations across the Continental United States (CONUS) and explores limitations and implications for long return period wind speed estimation. In total, the data set contains more than 17.9 million peak wind gusts, including nearly double the station-years available compared to the data set used in the current ASCE/SEI 7-22 wind hazard maps. A rigorous quality control methodology was implemented using manual inspection of archived Meteorological Aerodrome Reports (METAR), 1-minute ASOS data, radar data, and surface analysis. In addition, 1-minute data is used to explore how the automated quality algorithm and power outages may impact long return period wind speed estimations. Peak wind speeds are classified into thunderstorm, non-thunderstorm, and tropical and standardized for instrumentation changes. Spatial analysis of annual maximum wind gusts shows the highest thunderstorm and non-thunderstorm wind speeds in the central Great Plains. Trends in median annual maximum wind gusts show a slight increasing trend, however, changes in ASOS averaging time and instrumentation, such as sonic anemometer installation, introduces three distinct eras in the data set. Overall, this work represents the most complete data set ever assembled for CONUS ASOS stations and demonstrates improvements over the previous data set, improving confidence in extreme wind speed estimation for future wind hazard maps.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-12-01","The student, Michael Pagnanelli, Jr., accepted the attached license on 2025-12-11 at 16:28.","The student, Michael Pagnanelli, Jr., submitted this Thesis for approval on 2025-12-11 at 16:56.","This Thesis was approved for publication on 2025-12-12 at 08:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #23140 on 2026-02-19 at 18:46:57"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Quality control and analysis of an updated wind gust data set in the United States"]}]}],"canonical_facts":{"dc:contributor":["Lombardo, Franklin T"],"dc:creator":["Pagnanelli, Jr., Michael Joseph"],"dc:date":["2025-12","2025-12-12"],"dc:description":["This thesis presents the development of an updated 51-year peak wind gust data set for 679 Automated Surface Observing Systems (ASOS) stations across the Continental United States (CONUS) and explores limitations and implications for long return period wind speed estimation. In total, the data set contains more than 17.9 million peak wind gusts, including nearly double the station-years available compared to the data set used in the current ASCE/SEI 7-22 wind hazard maps. A rigorous quality control methodology was implemented using manual inspection of archived Meteorological Aerodrome Reports (METAR), 1-minute ASOS data, radar data, and surface analysis. In addition, 1-minute data is used to explore how the automated quality algorithm and power outages may impact long return period wind speed estimations. Peak wind speeds are classified into thunderstorm, non-thunderstorm, and tropical and standardized for instrumentation changes. Spatial analysis of annual maximum wind gusts shows the highest thunderstorm and non-thunderstorm wind speeds in the central Great Plains. Trends in median annual maximum wind gusts show a slight increasing trend, however, changes in ASOS averaging time and instrumentation, such as sonic anemometer installation, introduces three distinct eras in the data set. Overall, this work represents the most complete data set ever assembled for CONUS ASOS stations and demonstrates improvements over the previous data set, improving confidence in extreme wind speed estimation for future wind hazard maps.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-12-01","The student, Michael Pagnanelli, Jr., accepted the attached license on 2025-12-11 at 16:28.","The student, Michael Pagnanelli, Jr., submitted this Thesis for approval on 2025-12-11 at 16:56.","This Thesis was approved for publication on 2025-12-12 at 08:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #23140 on 2026-02-19 at 18:46:57"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/132711"],"dc:language":["en"],"dc:rights":["Copyright 2025 Michael Pagnanelli, Jr."],"dc:subject":["wind engineering","ASOS","quality control","wind gusts"],"dc:title":["Quality control and analysis of an updated wind gust data set in the United States"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:07Z"}