Tools for stochastic weather series generation in R environment

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Authors Emanuele Cordano, Emanuele Eccel
Journal/Conference Name Italian Journal of Agrometeorology
Paper Category
Paper Abstract of a multi-site weather generator. Algorithms to represent historical spatial dependences of weather variables have been developed e.g. by Wilks, 1998, Khalili et al., 2009, Serinaldi, 2009, Bàrdossy and Pegram, 2009, Kleiber et al., 2013. Wilks (1998) simulated rainfall occurrences through a generation of combinations of Gaussian random variables and established a relationship for each pair of rain gauges between Gaussian variables correlation and binary (precipitation occurrence) values. In this way, weather generators can reproduce at least partially spatial correlations; this approach is widely cited in literature (Mehrotra et al., 2006, Brissette et al., 2007, Serinaldi, 2009, Thompson et al., 2007, Mhanna and Bauwens, 2011, Kleiber et al., 2012). Recently, statistical methods useful for weather generation, originally developed in environmetrics and econometrics, were made available in the R platform (R Core Team, 2014). In this context, a suite of two weather generator tools was developed within the R environment through the creation of two packages: RMAWGEN and RGENERATEPREC. In particular, RMAWGEN (R Multisite Auto-regressive Weather Generator Cordano and Eccel, 2011) was developed to cope with the demand for high-resolution climatic Tools for stochastic weather series generation in R environment
Date of publication 2016
Code Programming Language R

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