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A component-based probabilistic weather forecasting system for operational usage

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Title: A component-based probabilistic weather forecasting system for operational usage
Author: Nipen, Thomas N.
Degree: Doctor of Philosophy - PhD
Program: Atmospheric Science
Copyright Date: 2012
Issue Date: 2012-04-18
Publisher University of British Columbia
Abstract: 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 verifying observations from surface weather stations in North America. Each component's contribution to overall forecast quality is analysed.
Affiliation: Science, Faculty of
URI: http://hdl.handle.net/2429/42053
Scholarly Level: Graduate

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