The use of conditional and potential instability axes for severe weather forceasting

In this thesis, we used an innovative approach to severe weather forecasting using traditional forecast parameters. The maximum theta-e (potential instability) in the lowest 300 mb and the deep-layer lapse rate (conditional instability) fields are two commonly used forecast parameters for severe weather forecasting, and the ridges of these fields, or more specifically, the ridge intersections, were hypothesized to indicate regions for enhanced severe convection. In addition, the sharpness of these ridge axes may correlate to an increase in severe potential. To test this theory, a mathematical formula was devised to quantitatively assess ridge strength. Then, using theta-e, lapse rate and their respective ridge strengths as predictors, a linear discriminate analysis was performed on dependent and independent datasets from the spring of 2008. Severe probabilistic forecasts were produced using the discriminate analysis and verified using two independent methods. Skill metrics calculated for the forecasts determined there is significant positive skill of the forecast technique. As a side note, this research attempted to determine the wavelength features (meso-a to meso-B ) that recorded the highest level of skill using this method. No significant difference was noted in the different wavelength feature forecasts, possibly due to the verification method.

Department dc:contributor.department

Meteorology (MR)

Grantor dc:publisher

Monterey, CA; Naval Postgraduate School

Year dc:date.issued

2009

Author dc:creator

• Oltmer, Douglas A.

Advisor dc:contributor.advisor

• Nuss, Wendell

Statement dc:rights

• This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.

Handle dc:identifier.uri

https://hdl.handle.net/10945/4800

OAI record oai:identifier

https://calhoun.nps.edu/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Acalhoun.nps.edu%3A10945%2F4800