Add to ORKGMitchell describes a maximum likelihood method using historical weather data to estimate a parametric model of daily precipitation and maximum and minimum air temperatures. Historical weather data from Brookings, SD, and Boone, IA, are used to create the model. Mitchell describes the process of estimation for the precipitation parametric model, and then reports the actual parametric estimates. Next, he presents an algorithm designed to generate a simulated time series of weather variables using the parametric model. Finally, he describes a model that determines soil temperatures as functions of air temperatures and precipitation
Considering the importance of preserving spatial correlation between neighboring stations in many of...
Abstract: This article reviews the historical development of statistical weather models, from simple...
One major acknowledged challenge in daily precipitation is the inability to model extreme events in ...
Mitchell describes a maximum likelihood method using historical weather data to estimate a parametri...
Traditional stochastic approaches for synthetic generation of weather variables often assume a prior...
Although General Circulation Models (GCMs) are used to provide insight into scenario planning, ecolo...
A nonparametric wet/dry spell model is developed for resampling daily precipitation at a site. The m...
Abstract. Generated weather that represents alternative realizations of a particular historical reco...
A nonparametric resampling technique for generating daily weather variables at a site is presented. ...
Weather generators are tools used to downscale monthly to seasonal climate forecasts, from numerical...
We present the logical and algorithmic framework of a numerical model which generates daily interpol...
Stochastic weather generators (SWGs) are designed to create simulations of synthetic weather data an...
Wet/dry spell characteristics of daily precipitation are of interest for a number of...
The Richardson model is a popular technique for stochastic simulation of daily weather variables, in...
WeaGETS is a MATLAB-based versatile random day-to-day weather generator. It can produce everyday r...
Considering the importance of preserving spatial correlation between neighboring stations in many of...
Abstract: This article reviews the historical development of statistical weather models, from simple...
One major acknowledged challenge in daily precipitation is the inability to model extreme events in ...
Mitchell describes a maximum likelihood method using historical weather data to estimate a parametri...
Traditional stochastic approaches for synthetic generation of weather variables often assume a prior...
Although General Circulation Models (GCMs) are used to provide insight into scenario planning, ecolo...
A nonparametric wet/dry spell model is developed for resampling daily precipitation at a site. The m...
Abstract. Generated weather that represents alternative realizations of a particular historical reco...
A nonparametric resampling technique for generating daily weather variables at a site is presented. ...
Weather generators are tools used to downscale monthly to seasonal climate forecasts, from numerical...
We present the logical and algorithmic framework of a numerical model which generates daily interpol...
Stochastic weather generators (SWGs) are designed to create simulations of synthetic weather data an...
Wet/dry spell characteristics of daily precipitation are of interest for a number of...
The Richardson model is a popular technique for stochastic simulation of daily weather variables, in...
WeaGETS is a MATLAB-based versatile random day-to-day weather generator. It can produce everyday r...
Considering the importance of preserving spatial correlation between neighboring stations in many of...
Abstract: This article reviews the historical development of statistical weather models, from simple...
One major acknowledged challenge in daily precipitation is the inability to model extreme events in ...