{"id":{"repo_id":"creighton","oai_identifier":"oai:cdr.creighton.edu:10504/65327"},"canonical_url":"https://search.dev.ndltd.org/etd/creighton/oai:cdr.creighton.edu:10504/65327","repository":{"repo_id":"creighton","name":"Creighton University","base_url":"https://cdr.creighton.edu/server/oai/request"},"display":{"title":"Operational Application of the UW Cloud-Top Cooling Algorithm: Large Hail Forecasting","abstract":"Forecasting the initiation, intensity, and duration of severe convective events remains a significant challenge for operational weather forecasters. Even with continual increases in operational forecast model spatial resolution, numerical models remain unable to simulate mesoscale phenomena with the necessary degree of accuracy. Satellite remote sensing, however, has experienced major technological advancements over the last decade. Enhancements to sensing and software packages, the spatial and temporal resolution, and the development of various algorithms have bolstered a forecaster’s ability to nowcast thunderstorm growth and strength. The University of Wisconsin recently developed a cloud-top cooling algorithm, which has preliminarily demonstrated to provide forecast lead times of up to 1 hour on severe convection. Fifty three major events will be analyzed and evaluated during the Spring and Summer seasons from 2012-2014 over the Great Plains states. Correlations will also be performed between cooling rates, hail sizes, and various environmental parameters. Application and utility in operational forecasting of large hail should be considered, based on the findings of this study.","abstract_html":"Forecasting the initiation, intensity, and duration of severe convective events remains a significant challenge for operational weather forecasters. Even with continual increases in operational forecast model spatial resolution, numerical models remain unable to simulate mesoscale phenomena with the necessary degree of accuracy. Satellite remote sensing, however, has experienced major technological advancements over the last decade. Enhancements to sensing and software packages, the spatial and temporal resolution, and the development of various algorithms have bolstered a forecaster’s ability to nowcast thunderstorm growth and strength. The University of Wisconsin recently developed a cloud-top cooling algorithm, which has preliminarily demonstrated to provide forecast lead times of up to 1 hour on severe convection. Fifty three major events will be analyzed and evaluated during the Spring and Summer seasons from 2012-2014 over the Great Plains states. Correlations will also be performed between cooling rates, hail sizes, and various environmental parameters. Application and utility in operational forecasting of large hail should be considered, based on the findings of this study.","abstract_has_math":false,"creators":["DeCicco, Brian"],"institution":"Creighton University","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Wagner, Timothy J."],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-12-12","date_published":"2014-12-12","updated_at":"2026-07-24T01:51:19Z","subjects":[],"languages":["en_US"],"rights":["Copyright is retained by the Author. A non-exclusive distribution right is granted to Creighton University and to ProQuest following the publishing model selected above."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10504/65327","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Wagner, Timothy J."]},{"key":"dc:creator","label":"Author","values":["DeCicco, Brian"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-12-16T22:10:47Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-12-16T22:10:47Z"]},{"key":"dc:date.issued","label":"Date","values":["2014-12-12"]},{"key":"dc:publisher","label":"Institution","values":["Creighton University"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright is retained by the Author. A non-exclusive distribution right is granted to Creighton University and to ProQuest following the publishing model selected above."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10504/65327"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Forecasting the initiation, intensity, and duration of severe convective events remains a significant challenge for operational weather forecasters. Even with continual increases in operational forecast model spatial resolution, numerical models remain unable to simulate mesoscale phenomena with the necessary degree of accuracy. Satellite remote sensing, however, has experienced major technological advancements over the last decade. Enhancements to sensing and software packages, the spatial and temporal resolution, and the development of various algorithms have bolstered a forecaster’s ability to nowcast thunderstorm growth and strength. The University of Wisconsin recently developed a cloud-top cooling algorithm, which has preliminarily demonstrated to provide forecast lead times of up to 1 hour on severe convection. Fifty three major events will be analyzed and evaluated during the Spring and Summer seasons from 2012-2014 over the Great Plains states. Correlations will also be performed between cooling rates, hail sizes, and various environmental parameters. 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Enhancements to sensing and software packages, the spatial and temporal resolution, and the development of various algorithms have bolstered a forecaster’s ability to nowcast thunderstorm growth and strength. The University of Wisconsin recently developed a cloud-top cooling algorithm, which has preliminarily demonstrated to provide forecast lead times of up to 1 hour on severe convection. Fifty three major events will be analyzed and evaluated during the Spring and Summer seasons from 2012-2014 over the Great Plains states. Correlations will also be performed between cooling rates, hail sizes, and various environmental parameters. Application and utility in operational forecasting of large hail should be considered, based on the findings of this study."],"dc:identifier.uri":["http://hdl.handle.net/10504/65327"],"dc:language.iso":["en_US"],"dc:publisher":["Creighton University"],"dc:rights":["Copyright is retained by the Author. A non-exclusive distribution right is granted to Creighton University and to ProQuest following the publishing model selected above."],"dc:title":["Operational Application of the UW Cloud-Top Cooling Algorithm: Large Hail Forecasting"],"dc:type":["Thesis"]},"updated_at":"2026-07-24T01:51:19Z"}