Generalized linear modeling approach to stochastic weather generators

Furrer, Eva; Katz, Richard W.

doi:10.3354/cr034129

Cited by 105 publications

(115 citation statements)

References 23 publications

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“…However, as with other rainfall generators based on generalized linear models [e.g., Furrer and Katz, 2007], this approach can be easily generalized to incorporate multiple atmospheric forcing variables, seasonal cycles, and trends.…”

Section: Discussionmentioning

confidence: 99%

“…It is widely believed that the assumption of a power-transformed Gaussian distribution is largely responsible. Use of other distributions, such as the mixture of the exponential [Wilks, 1998;Brissette et al, 2007] and the gamma distribution [Furrer and Katz, 2007], has brought about some improvements, but extremal behavior is still underestimated. Second, spatial dependence is not well modeled.…”

Section: Introductionmentioning

confidence: 99%

“…They make up the most important step in construction of weather generators, which have wide applications in agriculture and ecosystem simulations [Richardson, 1981] and have application in climate change studies [Wilks, 1992;Furrer and Katz, 2007;Brissette et al, 2007]. Although much progress has been achieved in the development of precipitation simulation tools, current challenges include the accurate representation of extremal behavior, the generation of multisite sequences with realistic spatial dependence, the need to represent realistic levels of interannual variability in the generated sequences, and the representation of complex dynamical structures within a relatively cheap computational framework [e.g., Wheater et al, 2005].…”

Section: Introductionmentioning

confidence: 99%

“…During the 30 years since Katz introduced it, this stochastic precipitation model has been improved considerably. First, external forcing, internal cycles, and trends were incorporated by introducing threshold models [Katz and Parlange, 1993] and generalized linear models [e.g., Furrer and Katz, 2007]. Overdispersion was eliminated by introducing mixture models [e.g., Katz and Zheng, 1999;Zheng and Katz, 2008a] and other approaches [e.g., Katz and Parlange, 1998].…”

Section: Introductionmentioning

confidence: 99%

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Simulation of multisite precipitation using an extended chain‐dependent process

Zheng

Renwick

Clark

2010

Water Resources Research

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[1] The chain-dependent process is a popular stochastic model for precipitation sequence data. In this paper, the effect of daily regional precipitation occurrence is incorporated into the stochastic model. This model is applied to analyze the daily precipitation at a small number of sites in the upper Waitaki catchment, New Zealand. In this case study, the probability distributions of daily precipitation occurrence and intensity, spatial dependences, and the relation between precipitation and atmospheric forcings are simulated quite well. Specifically, some behaviors which are not well modeled by existing models, such as the extremal behavior of daily precipitation intensity, the lag 1 cross correlation of daily precipitation occurrence, spatial intermittency, and spatial correlation of seasonal precipitation totals, are significantly improved. Moreover, a new and simpler approach is proposed which successfully eliminates overdispersion, i.e., underestimation of the variance of seasonal precipitation totals.Citation: Zheng, X., J. Renwick, and A. Clark (2010), Simulation of multisite precipitation using an extended chain-dependent process, Water Resour. Res., 46, W01504,

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simulation of multisite precipitation using an extended chain‐dependent process

Zheng

Renwick

Clark

2010

Water Resources Research

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“…The recently introduced approach for stochastic weather generators, based generalized linear modeling (GLM), is convenient for this purpose, especially with covariates to account for seasonality and teleconnections with the El Niño-Southern Oscillation phenomenon (McCullagh and Nelder, 1989;Furrer and Katz, 2007). Yet one important limitation of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of seasonally aggregated variables (e.g., Buishand, 1978;Katz and Parlange, 1998).…”

Section: Introductionmentioning

confidence: 99%

Stochastic precipitation modeling based on Korean historical data

Kim

Kim²

2012

Journal of the Korean Data and Information Science Society

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Stochastic weather generators are commonly used to simulate time series of daily weather, especially precipitation amount. Recently, a generalized linear model (GLM) has been proposed as a convenient approach to fitting these weather generators. In this paper, a stochastic weather generator is considered to model the time series of daily precipitation at Seoul in South Korea. As a covariate, global temperature is introduced to relate long-term temporal scale predictor to short-term temporal predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate time series of seasonal total precipitation in the GLM weather generator as covariates. It is verified that the addition of these covariates does not distort the performance of the weather generator in other respects.

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