A component-based probabilistic weather forecasting system for operational usage
department or school: Science, Faculty of
department or school: Earth, Ocean and Atmospheric Sciences, Department of
data provider: DSpace
degree campus: UBCV
creator: Nipen, Thomas N.
issued: 2012-04-18T16:57:41Z
issued: 2012
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: This dissertation presents a probabilistic weather prediction system for operational (real-time) usage. The proposed system provides complete probability distributions for both continuous weather variables, such as temperature, and mixed discrete-continuous variables like precipitation accumulations. The proposed system decomposes the process of generating probabilistic forecasts into a series of sequential steps, each of which is important in the overall goal of providing probabilistic forecasts of high quality. Starting with an ensemble of input predictors generated by numerical weather prediction models, the system uses the following four components: 1) correction; 2) uncertainty modeling; 3) calibration; and 4) updating. The correction component bias-corrects the input predictors. The uncertainty model converts these predictors into a suitable probability distribution. The calibration component improves this distribution by removing any distributional bias. The update component further improves the forecast by incorporating recently made observations of the true state. The system is designed to be modular. Namely, different implementations of each component can be used interchangeably with any combination of implementations for the other components. This allows future research into probabilistic forecasting to be focused on any one component and also allows new methods to be easily incorporated into the system. The system uses a number of existing correction and uncertainty models, but the dissertation also presents two new methods: Firstly, a new method for calibrating probabilistic forecasts is created. This method is shown to improve probabilistic forecasts that exhibit distributional bias. Secondly, a new method for incorporating recently made observations to existing probabilistic forecasts is developed. The system and its components are tested using meteorological data from daily operational runs of ensemble numerical weather prediction models and their ve…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/42053
note: A component-based probabilistic weather forecasting system for operational usage by Thomas N. Nipen B.Sc. Hons. Computer and Atmospheric Science, The University of British Columbia, 2006 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Doctor of Philosophy in THE FACULTY OF GRADUATE STUDIES (Atmospheric Science) The University of British Columbia (Vancouver) April 2012 c© Thomas N. Nipen, 2012 Abstract Abstract This dissertation presents a probabilistic weather prediction system for operational (real-time) us- age. The proposed system provides complete probability distributions for both continuous weather variables, such as temperature, and mixed discrete-continuous variables like precipitation accumu- lations. The proposed system decomposes the process of generating probabilistic forecasts into a series of sequential steps, each of which is important in the overall goal of providing probabilistic fore- casts of high quality. Starting with an ensemble of input predictors generated by numerical weather prediction models, the system uses the following four components: 1) correction; 2) uncertainty modeling; 3) calibration; and 4) updating. The correction component bias-corrects the input predic- tors. The uncertainty model converts these predictors into a suitable probability distribution. The calibration component improves this distribution by removing any distributional bias. The update component further improves the forecast by incorporating recently made observations of the true state. The system is designed to be modular. Namely, different implementations of each component can be used interchangeably with any combination of implementations for the other components. This allows future research into probabilistic forecasting to be focused on any one component and also allows new methods to be easily incorporated into the system. The system uses a number of existing correction and uncertainty models, but the dissertation also presents …
has type: Thesis/Dissertation
date issued: 2012-05
is shown at: 10.14288/1.0053570
language: eng
degree discipline: Atmospheric Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/42053