A Statistical Approach to Diagnosing Storm Mode

Stang, David

A Statistical Approach to Diagnosing Storm Mode

dc.contributor.advisor	Brooks, Harold
dc.contributor.author	Stang, David
dc.contributor.committeeMember	Richman, Michael
dc.contributor.committeeMember	Wang, Xuguang
dc.date.accessioned	2021-12-13T15:04:03Z
dc.date.available	2021-12-13T15:04:03Z
dc.date.issued	2021-12-17
dc.date.manuscript	2021-12-10
dc.description.abstract	Determining storm mode (linear or isolated) is a crucial component of any severe weather forecast. Isolated storms are associated with a greater likelihood of significant (EF2+) tornadoes and very large (2”+) hail, while linear storms are more likely to produce straight-line wind damage. Current operational convection allowing models (CAMs), which are often used to diagnose storm mode, only run up to 48-60 hours into the future and can quickly lose accuracy with increasing lead time. To improve forecast accuracy and messaging on Day 3+ outlooks, a forecast tool was created to predict storm mode using only synoptic-scale variables. The approach uses a blend of theoretical modeling, stochastic modeling, and statistical modeling. The formulation generally performed well with reproducing past events and predicting future events 84+ hours in advance using 0.5° Global Forecasting System (GFS) and 0.5° Global Ensemble Forecasting System (GEFS) outputs.	en_US
dc.identifier.uri	https://hdl.handle.net/11244/332302
dc.language	en_US	en_US
dc.subject	storm mode	en_US
dc.subject	severe weather	en_US
dc.subject	forecast	en_US
dc.subject	prediction	en_US
dc.thesis.degree	Master of Science in Meteorology	en_US
dc.title	A Statistical Approach to Diagnosing Storm Mode	en_US
ou.group	College of Atmospheric and Geographic Sciences::School of Meteorology	en_US
shareok.nativefileaccess	restricted	en_US