2004
DOI: 10.3354/cr026175
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Comparison of LARS-WG and AAFC-WG stochastic weather generators for diverse Canadian climates

Abstract: Two weather generators -LARS-WG, developed at Long Ashton Research Station (UK), and AAFC-WG, developed at Agriculture and Agri-Food Canada -were compared in order to gauge their capabilities of reproducing probability distributions, means and variances of observed daily precipitation, maximum temperature and minimum temperature for diverse Canadian climates. Climatic conditions, such as wet and dry spells, interannual variability and agroclimatic indices, were also used to assess the performance of the 2 weat… Show more

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Cited by 87 publications
(79 citation statements)
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“…It has been shown (Qian et al 2004, Semenov 2008b) that for locations where temperature residuals are not normally distributed, simulation of extreme high or low tempera- There is a global concern regarding environmental and social sustainability and more effort in introducing clean technologies. The global population reaches 7 billion by 2100. )…”
Section: Revision Of Lars-wgmentioning
confidence: 99%
“…It has been shown (Qian et al 2004, Semenov 2008b) that for locations where temperature residuals are not normally distributed, simulation of extreme high or low tempera- There is a global concern regarding environmental and social sustainability and more effort in introducing clean technologies. The global population reaches 7 billion by 2100. )…”
Section: Revision Of Lars-wgmentioning
confidence: 99%
“…This subsection summarizes how our model performs in reproducing inter-annual variances of annual as well as summer and winter total precipitation and also of average Tmax and Tmin; see e.g. Mavromatis & Hansen (2001), Qian et al (2004) concerning overdispersion in temperature variables.…”
Section: Aggregative Statisticsmentioning
confidence: 99%
“…This model was implemented as WGEN (Weather GENerator) by Richardson and Wright (1984) [1], which used a simple Markov Chain for precipitation occurrence, a gamma distribution for simulation of rainfall amounts, and an autoregressive model for the remaining variables. A number of subsequent WGs, such as WXGEN [8], CLIGEN [9,10], LARS-WG [11][12][13], ClimGen [14], WeaGETS [15,16], Met and Roll [17], MOFRBC [18,19], WeatherMan [20], MarkSim [21], AAFC-WG [22,23], WM2 [24], KnnCAD [25][26][27], and the WG used by the UK Met Office (UKCP09) [28,29], all share the basic principles of stochastic simulation presented in WGEN. These WGs are station-scale generators, with time scales that range from daily (or even hourly in the case of rainfall) to annual, daily resolution being the most common.…”
Section: Introductionmentioning
confidence: 99%