A spatial analytical approach towards understanding racial residential segregation in Gauteng province (South Africa)
Samy Katumba
Gauteng City-Region Observatory, Johannesburg, South Africa
Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria, South Africa
Inger Fabris-Rotelli
Department of Statistics, University of Pretoria, Pretoria, South Africa
Alfred Stein
Department of Statistics, University of Pretoria, Pretoria, South Africa
University of Twente, The Netherlands
Serena Coetzee
Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria, South Africa
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S. Katumba
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Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-93, https://doi.org/10.5194/hess-2017-93, 2017
Manuscript not accepted for further review
Short summary
Short summary
Weather stations are often sparse and systematic under/overestimation of a global weather forecast system leads to bias. Most of the available bias correction methods do not consider higher order moments of a probability distribution and they use same distributions families to estimate both marginal and multivariate distributions. We propose three new copula-based bias correction methods, which describe the dependence structure between air temperature and covariates.
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Biogeosciences, 13, 1409–1422, https://doi.org/10.5194/bg-13-1409-2016, https://doi.org/10.5194/bg-13-1409-2016, 2016
Short summary
Short summary
We present a Bayesian estimation of uncertainty in half-hourly GPP partitioned from flux tower measurements of NEE. The results show that it is possible to do this at any desirable time step. This, in turn, can be used to quantify the propagated uncertainty when validating process-based simulators. We further show the importance of using non-informative priors compared to informative priors of the parameters of flux partitioning model as they speed up calculation without loss of precision.