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Generating spatio-temporal maximum entropy ensembles using R

TitleGenerating spatio-temporal maximum entropy ensembles using R
Publication TypeConference Paper
Year of Publication2014
AuthorsSrivastav RK, Simonovic SP
Conference NameDailyMeteo.org/2014
Date Published06/2014
PublisherFaculty of Civil Engineering, University of Belgrade
Conference LocationBelgrade
Abstract

Multi-variate ensembles are very practical tools for assessment of uncertainty in the weather data due to changes in future climatic conditions. This paper presents a new multi-variate maximum entropy bootstrap for generating long samples of weather data using R. The model is able to mimic both the temporal and the spatial correlations present in the historical weather data in addition to the other statistical characteristics. The modeling process involves: (i) application of orthogonal transformation to de-correlate the multivariate data; (ii) generation of the samples of decorrelated data using maximum entropy bootstrap; and (iii) inverse orthogonal transformation. The multivariate weather data consists of daily precipitation, mean temperature, minimum temperature, and maximum temperature. An R package is developed to implement the multi-variate maximum entropy bootstrap. The main advantages of using R are: (i) open source; (ii) availability of a number of in-built statistical analysis packages, models and standard statistical tests; and (iii) access to one of the largest collections of user-developed statistical packages that can be easily downloaded, installed and most importantly modified.

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