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A Novel Methodology for Stochastic Formulation of Short Term Cloud Cover Forecasts

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dc.contributor.advisor Glimm, James en_US
dc.contributor.author Huang, Ya-Ting en_US
dc.contributor.other Department of Applied Mathematics and Statistics en_US
dc.date.accessioned 2017-09-20T16:53:24Z
dc.date.available 2017-09-20T16:53:24Z
dc.date.issued 2015-12-01 en_US
dc.identifier.uri http://hdl.handle.net/11401/77710 en_US
dc.description 71 pgs en_US
dc.description.abstract Following the chaos theory proposed by Lorenz, probabilistic approaches have been widely used in numerical weather prediction research. This paper introduces an innate methodology to measure the uncertainty of stochastic cloud boundary forecast. A stochastic partial differential equation is inserted into a numerical weather prediction model, and backtested to validate the probabilistic results of the model. This methodology can be applied to a variety of topics in numerical weather prediction research. The proposed method is applied to the short term forecast of cloud cover. A two parameter model based on physical principles of wind velocity dispersion and surface evaporation rate drives the stochastic model. They are used to couple a stochastic partial differential equation with a standard weather model (WRF) and satellite data to yield a probabilistic prediction of cloud cover. Results show good predictive capability of the model in forecasting cloud boundary for one half hour, with a gradual loss of predictive power over the following hour. en_US
dc.description.sponsorship This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. en_US
dc.format Monograph en_US
dc.format.medium Electronic Resource en_US
dc.language.iso en_US en_US
dc.publisher The Graduate School, Stony Brook University: Stony Brook, NY. en_US
dc.subject.lcsh Applied mathematics en_US
dc.subject.other backtesting, front tracking method, numerical weather prediction, partial differential equation en_US
dc.title A Novel Methodology for Stochastic Formulation of Short Term Cloud Cover Forecasts en_US
dc.type Dissertation en_US
dc.mimetype Application/PDF en_US
dc.contributor.committeemember Zhang, Minghua en_US
dc.contributor.committeemember Samulyak, Roman en_US
dc.contributor.committeemember Wu, Song en_US


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