Spatial‐temporal rainfall simulation using generalized linear models

By C. Yang, Richard E. Chandler, V. Isham, Howard S. Wheater. 2005

204 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/f4d0fc201d14b94ade1ae006097d9cc8c76bb30f

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In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Stochastic Model Output Statistics for Bias Correcting and Downscaling Precipitation Including Extremes (2014, background): “Furthermore one might also transfer probabilistic multistation models (e.g., Yang et al. 2005) to our stochastic MOS that explicitly model spatial dependence.”
Modeling of daily precipitation at multiple locations using a mixture of distributions to characterize the extremes (2009, methodology): “Extension to a multisite model is also possible using this framework [Yang et al., 2005].”
Comparison of transfer functions in statistical downscaling models for daily temperature and precipitation over Canada (2012, background): “If the precipitation amounts vector, P, follows a gamma distribution, the distribution of transformed vector by the cube-root, called Anscombe residuals, is approximately normal, as has been described by Terrell (2003) and Yang et al. (2005).”
Multivariate—Intervariable, Spatial, and Temporal—Bias Correction* (2015, methodology): “…postprocessing to statistical downscaling models, taking explicitly into account the multidimensional structure of the data to be downscaled [e.g., Yang et al. (2005), Flecher et al. (2010), and Vrac et al. (2007), for multisite, multivariable, and temporal dependences modeling, respectively].”
Multisite multivariate modeling of daily precipitation and temperature in the Canadian Prairie Provinces using generalized linear models (2016, methodology): “…vector ξ i, the precipitation intensity process for the ith wet day is modelled as gamma-distributed with mean μi and shape parameter ν, where with the shape parameter ν assumed to be constant (e.g., Yang et al. 2005) for all observations at all sites, and ϕ is a column vector of coefficients.”
Coupled stochastic weather generation using spatial and generalized linear models (2015, methodology): “The daily rainfall amounts are modeled by fitting a gamma density function (Katz 1977; Buishand 1978; Yang et al. 2005; Furrer and Katz 2007).”
Daily spatiotemporal precipitation simulation using latent and transformed Gaussian processes (2012, background): “Seasonal variation and other covariates can be included in a generalized linear model (GLM) framework, which imparts significant flexibility in the statistical model [Yang et al., 2005; Furrer and Katz, 2007].”