Improved multisite stochastic weather generation with applications to historical data in South Korea

Lee, Donghwan; An, Hyungmi; Lee, Youngjo; Lee, Jae Yong; Lee, Hyo Shin; Oh, Hee Seok

doi:10.1007/s13143-010-0031-2

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…Usually, the sampling process of synthetic weather data is conditioned upon a sequence of whether states sampled from a temporal occurrence model, and its variability within a region is modeled by a spatial model (Richardson, 1981;D. Wilks, 1998;Lee et al, 2010;Chen et al, 2012;Kim et al, 2012;Carey-Smith et al, 2014;Allard & Bourotte, 2015) Nonparametric weather generators are in the other extreme -they are fully datadriven -they do not employ parametric probability distributions to specify the full joint probability distribution of weather variables. Popular methodologies for this include the use of empirical distributions (Semenov et al, 1998), neural networks (Trigo & Palutikof, 1999), and kernel density estimators (Rajagopalan et al, 1997).…”

Section: Stochastic Weather Generator Typesmentioning

confidence: 99%

Direct Sampling for Extreme Events Generation and Spatial Variability Enhancement of Weather Generators

Diaz¹,

Zadrozny²,

Watson³

et al. 2022

Preprint

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Weather generators based on resampling simulate new time series of weather variables by reordering the observed values such that the statistics of the simulated data are coherent with the observed ones. These weather generators are fully data-driven and simple to implement, do not rely on parametric distributions, and can reproduce the dynamics among the weather variables under analysis. However, although the simulated time series is new, the produced weather fields at arbitrary timesteps are copies of the weather fields found in the training dataset. Consequently, the spatial variability of simulations is restricted.Furthermore, these weather generators cannot create weather fields with out-of-sample extreme values because the scope of the resampling method is constrained to the observed values. In this work, we embedd the Direct Sampling algorithm -a data-driven method for producing simulations -into resampling-based weather generators to improve the spatial variability of the produced weather fields, and for generating extreme weather fields. We increase the spatial variability by applying Direct Sampling as a post-processing step on the weather generator outputs. Furthermore, we produce out-of-sample extreme weather fields using Direct Sampling in two ways: 1) applying quantile mappings on the Direct Sampling simulations for a given return period, and 2) using a set of control points jointly with Direct Sampling with values informed by return period analysis. We validate our approach using precipitation, temperature, and cloud cover weather-fields time series datasets, for a region in northwest India. The results are analyzed using a set of statistical and connectivity metrics.

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Section: Stochastic Weather Generator Typesmentioning

confidence: 99%

Direct Sampling for Extreme Events Generation and Spatial Variability Enhancement of Weather Generators

Diaz¹,

Zadrozny²,

Watson³

et al. 2022

Preprint

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show abstract

“…Notice our approach seems to imply a slight additive bias in the simulated probabilities, but removes the quite noticeable multiplicative bias exhibited by Wilks [1998] in an application to another precipitation data set. Other approaches have been able to further reduce this challenging criterion [ Lee et al , 2010], but often come at the cost of significant model complications. Finally, note that this additive bias is nearly negligible, only on the order of 0.01 to 0.02, which is unlikely to have a significant impact in practice.…”

Section: Applicationsmentioning

confidence: 99%

Daily spatiotemporal precipitation simulation using latent and transformed Gaussian processes

Kleiber

Katz

Rajagopalan

2012

Water Resources Research

131

133

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[1] A daily stochastic spatiotemporal precipitation generator that yields spatially consistent gridded quantitative precipitation realizations is described. The methodology relies on a latent Gaussian process to drive precipitation occurrence and a probability integral transformed Gaussian process for intensity. At individual locations, the model reduces to a Markov chain for precipitation occurrence and a gamma distribution for precipitation intensity, allowing statistical parameters to be included in a generalized linear model framework. Statistical parameters are modeled as spatial Gaussian processes, which allows for interpolation to locations where there are no direct observations via kriging. One advantage of such a model for the statistical parameters is that stochastic generator parameters are immediately available at any location, with the ability to adapt to spatially varying precipitation characteristics. A second advantage is that parameter uncertainty, generally unavailable with deterministic interpolators, can be immediately quantified at all locations. The methodology is illustrated on two data sets, the first in Iowa and the second over the Pampas region of Argentina. In both examples, the method is able to capture the local and domain aggregated precipitation behavior fairly well at a wide range of time scales, including daily, monthly, and annually.

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“…Parametric weather generators are those that rely on theoretical probability distributions to model the joint distribution of weather variables—for instance, precipitation is usually modeled by an exponential or gamma distribution (Todorovic & Woolhiser, 1975) and extreme rainfall by mixtures of gamma distributions (Kenabatho et al., 2012) or Generalized Pareto distribution (Lennartsson et al., 2008). Usually, the sampling process of synthetic weather data is conditioned upon a sequence of weather states sampled from a temporal occurrence model, and its variability within a region is modeled by a spatial model (Allard & Bourotte, 2015; Carey‐Smith et al., 2014; Chen et al., 2012; Kim et al., 2012; Lee et al., 2010; Richardson, 1981; D. Wilks, 1998).…”

Section: Introductionmentioning

confidence: 99%

Direct Sampling for Spatially Variable Extreme Event Generation in Resampling‐Based Stochastic Weather Generators

Guevara,

Garcia,

Avegliano

et al. 2023

J Adv Model Earth Syst

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Resampling‐based weather generators simulate new time series of weather variables by reordering the observed values such that the statistics of the simulated data are consistent with the observed ones. These generators are fully data‐driven, easy to implement, and capable of reproducing the dynamics among weather variables. However, although the simulated time series is new, the weather fields produced at arbitrary time steps are replicas of those found in observations, limiting the spatial variability of simulations and preventing the generation of extreme weather fields beyond the range of observed values. To address these limitations, we propose the integration of the Direct Sampling algorithm—a data‐driven method for producing simulations—into resampling‐based weather generators. By incorporating Direct Sampling as a post‐processing step on the outputs of the weather generator, we enhance the spatial variability of the generated weather fields and enable the generation of extreme weather fields. We introduce an approach for generating out‐of‐sample extreme weather fields using Direct Sampling. This method involves utilizing a set of control points in conjunction with Direct Sampling, where the values of these control points are informed by return period analysis. The proposed approach is validated using precipitation, temperature, and cloud cover weather fields in a region of northwest India. The experimental results confirm that Direct Sampling enhances the spatial variability of the weather fields and facilitates the generation of out‐of‐sample precipitation fields that accurately adhere to the spatial statistics provided by return precipitation level maps, as well as the observed precipitation weather field employed in the analysis.

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Improved multisite stochastic weather generation with applications to historical data in South Korea

Cited by 7 publications

References 14 publications

Direct Sampling for Extreme Events Generation and Spatial Variability Enhancement of Weather Generators

Direct Sampling for Extreme Events Generation and Spatial Variability Enhancement of Weather Generators

Daily spatiotemporal precipitation simulation using latent and transformed Gaussian processes

Direct Sampling for Spatially Variable Extreme Event Generation in Resampling‐Based Stochastic Weather Generators

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