Bulk meteorological parameters for diagnosing cloudiness in the stochastic cloud forecast model
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Authors
Leach, Ryan N.
Subjects
Advisors
Pfeiffer, Karl D.
Date of Issue
2006-03
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
The three dimensional distribution of clouds is of great interest to the Air Force, and to the aviation community in general. The Stochastic Cloud Forecast Model (SCFM) is a novel, global cloud model currently operated at the Air Force Weather Agency (AFWA) which diagnoses cloud cover statistically using a minimal set of predictors from global numerical forecasts. Currently the four predictors are pressure, temperature, vertical velocity, and relative humidity. In this thesis, 330 sets of predictors are compared in the SCFM-R, a research version of the model programmed for this thesis. There are some differences in the SCFM and the SCFM-R that yield important information. It is found that the SCFM is very sensitive to how cloud cover in the boundary layer is diagnosed. An analysis of the diagnosis method used to initialize the model revealed a bias for over-diagnosing cloud at lower levels and under-diagnosing cloud at upper levels. Also, it is recommended that AFWA consider exchanging temperature for another predictor more related to moisture, such as cloud water, and that relative humidity is included as relative humidity to the fourth power. Other recommendations include improving the method for diagnosing cloud cover in the boundary layer and improving the model initial condition.
Type
Thesis
Description
Series/Report No
Department
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
xiv, 59 p. : (col. maps) ;
Citation
Distribution Statement
Approved for public release; distribution is unlimited.