| dc.identifier.uri | http://hdl.handle.net/11401/77710 | |
| 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 | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Dissertation | |
| dcterms.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. | |
| dcterms.available | 2017-09-20T16:53:24Z | |
| dcterms.contributor | Zhang, Minghua | en_US |
| dcterms.contributor | Glimm, James | en_US |
| dcterms.contributor | Samulyak, Roman | en_US |
| dcterms.contributor | Wu, Song. | en_US |
| dcterms.creator | Huang, Ya-Ting | |
| dcterms.dateAccepted | 2017-09-20T16:53:24Z | |
| dcterms.dateSubmitted | 2017-09-20T16:53:24Z | |
| dcterms.description | Department of Applied Mathematics and Statistics. | en_US |
| dcterms.extent | 71 pg. | en_US |
| dcterms.format | Monograph | |
| dcterms.format | Application/PDF | en_US |
| dcterms.identifier | http://hdl.handle.net/11401/77710 | |
| dcterms.issued | 2015-12-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2017-09-20T16:53:24Z (GMT). No. of bitstreams: 1
Huang_grad.sunysb_0771E_12468.pdf: 3781104 bytes, checksum: da8e0990b5f1c1b012a07429510a458a (MD5)
Previous issue date: 1 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | backtesting, front tracking method, numerical weather prediction, partial differential equation | |
| dcterms.subject | Applied mathematics | |
| dcterms.title | A Novel Methodology for Stochastic Formulation of Short Term Cloud Cover Forecasts | |
| dcterms.type | Dissertation | |
