A daily stochastic spatiotemporal precipitation generator that yields precipitation realizations that are quantitatively consistent is described. The methodology relies on a latent Gaussian field that drives both the occurrence and intensity of the precipitation process. For the precipitation intensity, the marginal distributions, which are space and time dependent, are described by a composite model of a gamma distribution for observations below some threshold with a generalized Pareto distribution modeling the excesses above the threshold. Model parameters are estimated from data and extrapolated to locations and times with no direct observations using linear regression of position covariates. One advantage of such a model is that stochas...
Stochastic daily weather time-series models ("weather generators") are parameterized consi...
AbstractThis paper describes a versatile stochastic daily weather generator (WeaGETS) for producing ...
Weather generators are tools used to downscale monthly to seasonal climate forecasts, from numerical...
A daily stochastic spatiotemporal precipitation generator that yields precipitation realizations tha...
Capturing the spatial and temporal correlation of multiple variables in a weather generator is chall...
Traditional stochastic approaches for synthetic generation of weather variables often assume a prior...
International audienceWe propose a method for estimating the parameters in a latent Gaussian field u...
dissertationStochastic weather generators (SWGs) are statistically-based point-scale models of meteo...
Stochastic weather generators can generate very long time series of weather patterns, which are indi...
The objective of this study is to analyse the capability of a Weather Generator based on a multivari...
Although General Circulation Models (GCMs) are used to provide insight into scenario planning, ecolo...
The objective of the current work is to present a methodology for simulation of stochastic spatial d...
Les générateurs stochastiques de temps sont des modèles numériques capables de générer des séquences...
A model for generating daily spatial correlated rainfall fields suitable for evaluating the impacts ...
Stochastic precipitation generators (SPGs) are a class of statistical models which generate syntheti...
Stochastic daily weather time-series models ("weather generators") are parameterized consi...
AbstractThis paper describes a versatile stochastic daily weather generator (WeaGETS) for producing ...
Weather generators are tools used to downscale monthly to seasonal climate forecasts, from numerical...
A daily stochastic spatiotemporal precipitation generator that yields precipitation realizations tha...
Capturing the spatial and temporal correlation of multiple variables in a weather generator is chall...
Traditional stochastic approaches for synthetic generation of weather variables often assume a prior...
International audienceWe propose a method for estimating the parameters in a latent Gaussian field u...
dissertationStochastic weather generators (SWGs) are statistically-based point-scale models of meteo...
Stochastic weather generators can generate very long time series of weather patterns, which are indi...
The objective of this study is to analyse the capability of a Weather Generator based on a multivari...
Although General Circulation Models (GCMs) are used to provide insight into scenario planning, ecolo...
The objective of the current work is to present a methodology for simulation of stochastic spatial d...
Les générateurs stochastiques de temps sont des modèles numériques capables de générer des séquences...
A model for generating daily spatial correlated rainfall fields suitable for evaluating the impacts ...
Stochastic precipitation generators (SPGs) are a class of statistical models which generate syntheti...
Stochastic daily weather time-series models ("weather generators") are parameterized consi...
AbstractThis paper describes a versatile stochastic daily weather generator (WeaGETS) for producing ...
Weather generators are tools used to downscale monthly to seasonal climate forecasts, from numerical...