Date of Award


Document Type


Degree Name

Master of Science


Department of Engineering Physics

First Advisor

Michael K. Walters, PhD


Air Force Global Weather Center's (AFGWC) Relocatable Window Model (RWM) total cloud forecasts were validated using data for selected days in May, June, and July, 1996. Forecasts were generated twice daily (00 UTC and 12 UTC) to determine the RWM's ability to accurately forecast total cloud cover during the late spring and early summer. The RWM forecasts were post-processed using the Slingo cloud forecast algorithm and compared against AFGWC's operational real time nephanalysis (RTNEPH) cloud analysis model. As a minimal-skill baseline comparison to the RWM's total cloud forecast, the RTNEPH initial analysis hour was persisted and evaluated against the same RTNEPH analysis as the RWM forecasts. The results indicate RWM total cloud forecasts did not show improved skill, sharpness, accuracy or bias when compared against RTNEPH persistence through the 36-hour forecast period. The results also suggest the Slingo algorithm, as tested, is not appropriate for use in the RWM as an accurate total cloud forecast method for the late spring and early summer months. The RWM's total cloud forecast performance during the late spring and early summer over the North American Window should be improved in the short term by incorporating convective parameterization within the Slingo algorithm or replacing the Slingo algorithm with an alternative algorithm designed for more accurate and skillful total cloud forecasts. While the suggested short-term improvements are incorporated into the RWM, the results of this and other related studies must be carefully communicated to the operational users of the RWM products to be useful. In the long term, the RWM should be replaced with a state of the art forecast model capable of forecasting clouds deterministically, rather than diagnostically.

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