Date of Award


Document Type


Degree Name

Master of Science


Department of Engineering Physics

First Advisor

Gary R. Huffines, PhD


This research focuses on developing a linear regression formula that forecasters in the Midwest can use to accurately anticipate the formation of radiation fog. This was accomplished in three stages. First a study of the surface and upper air parameters and processes required to develop radiation fog were identified and explored. Next, a linear regression technique was applied to the 23 parameters identified. The top four indicators were then reprocessed and a new linear regression equation was developed. Finally, the new regression equation was compared to an existing fog forecasting technique. The existing forecast technique selected was the 2nd Weather Wings "Fog Stability Index." Hit rates, False Alarm Rates and Threat Scores for both methods were calculated and compared. In general the linear regression, while only accounting for 45 to 50 percent of the total error (S ST), outperformed the Fog Stability Index in ability to accurately forecast the development of radiation fog, and greatly reduced the number of incorrect forecasts. The new linear regression equation reduced the false alarm rate on fog forecasting by 23 to 43 percent and increased the threat score ability 30 to 60 percentage points.

AFIT Designator


DTIC Accession Number



The author's Vita page is omitted.

Included in

Meteorology Commons