Evaluating conditional density estimation networks as a probabilistic downscaling tool: application to precipitation in Newfoundland

Biswas, Rasel (2015) Evaluating conditional density estimation networks as a probabilistic downscaling tool: application to precipitation in Newfoundland. Masters thesis, Memorial University of Newfoundland.

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Abstract

The objective of this research is to better quantify the distribution of extreme precipitation within Newfoundland, for the current climate conditions. The province of Newfoundland is interested in this information as guidance for climate adaptation and longterm infrastructure planning purposes. Extreme analyses are commonly limited by short periods of observation (often only 30 years or less), resulting in large uncertainty regarding rare events. These limitations can be addressed by increasing the period of observation, or partially addressed using synthetic time series generated by stochastic weather generators. These weather generators attempt to replicate relevant statistics of the observed climate, using various statistical modelling tools. Here, a probabilistic neural network-based downscaling method is used to predict the probability distribution of precipitation at a study site conditional upon the synoptic state of the atmosphere; that is, features of a given day’s large-scale atmospheric state are used to estimate the likelihood of all possible precipitation amounts for that day. My results show that CDEN-based probabilistic downscaling generally agrees better with observations than uncorrected reanalysis-based precipitation estimates. However, it gives significantly higher estimates for extreme events at low frequency (e.g., 100 year return periods). This approach is demonstrated for St. John’s airport. Comparison with nearby stations, Windsor Lake, suggests that these higher estimates may be reasonable; however, further work and a longer observational record is needed to determine whether these estimates represent added value to the raw observational record or a shortcoming of our CDEN-based methodology.

Item Type: Thesis (Masters)
URI: http://research.library.mun.ca/id/eprint/8516
Item ID: 8516
Additional Information: Includes bibliographical references (pages 35-43).
Keywords: Neural Network, Multilayer perceptron, Downscaling
Department(s): Science, Faculty of > Computational Science
Date: June 2015
Date Type: Submission
Library of Congress Subject Heading: Probable maximum precipitation (Hydrometeorology)--Statistical methods; Probability forecasts (Meteorology); Climatic extremes--Forecasting

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