Weather generators (WG) became significant modules of crop models and decision support systems in the past decade. Using a large meteorological database from North America; two basic problems, related to the applicability of WGs in case of short or lacking data series, were investigated in the framework of the Multivariable weather generator (MVWG). First, the minimum data series length, required for adequate parameterization of the WG, was determined. Our results suggest that 15 years of observed data are enough for adequate parameterization of the MVWG. We then investigated a possibility of spatial interpolation of WG parameters using the outputs of the WG for sites with no meteorological observations. Coupled with the presented interpola...
The use and application of decision support systems (DDS) that consider variation in climate and soi...
International audienceNatural risk studies such as flood risk assessments require long series of wea...
In this paper, the weather generator (WG) used by the empirical statistical downscaling method, weat...
Copyright © 2013 Nándor Fodor et al. This is an open access article distributed under the Creative ...
Abstract. Climate change scenarios with a high spatial and temporal resolution are required in the e...
Abstract: This article reviews the historical development of statistical weather models, from simple...
International audienceTo simulate multivariate daily time series (minimum and maximum temperatures, ...
S3 and S4 functions are implemented for spatial multi-site stochastic generation of daily time s...
Simulation of agricultural risk assessment and environmental management requires long series of dail...
Although General Circulation Models (GCMs) are used to provide insight into scenario planning, ecolo...
Les générateurs stochastiques de temps sont des modèles numériques capables de générer des séquences...
Considering the importance of preserving spatial correlation between neighboring stations in many of...
Stochastic weather generators (SWGs) are designed to create simulations of synthetic weather data an...
Stochastic daily weather time-series models ("weather generators") are parameterized consi...
Wilks, 1992] four-variate stochastic weather generator [Dubrovsky, 1997]. It is designed to provide ...
The use and application of decision support systems (DDS) that consider variation in climate and soi...
International audienceNatural risk studies such as flood risk assessments require long series of wea...
In this paper, the weather generator (WG) used by the empirical statistical downscaling method, weat...
Copyright © 2013 Nándor Fodor et al. This is an open access article distributed under the Creative ...
Abstract. Climate change scenarios with a high spatial and temporal resolution are required in the e...
Abstract: This article reviews the historical development of statistical weather models, from simple...
International audienceTo simulate multivariate daily time series (minimum and maximum temperatures, ...
S3 and S4 functions are implemented for spatial multi-site stochastic generation of daily time s...
Simulation of agricultural risk assessment and environmental management requires long series of dail...
Although General Circulation Models (GCMs) are used to provide insight into scenario planning, ecolo...
Les générateurs stochastiques de temps sont des modèles numériques capables de générer des séquences...
Considering the importance of preserving spatial correlation between neighboring stations in many of...
Stochastic weather generators (SWGs) are designed to create simulations of synthetic weather data an...
Stochastic daily weather time-series models ("weather generators") are parameterized consi...
Wilks, 1992] four-variate stochastic weather generator [Dubrovsky, 1997]. It is designed to provide ...
The use and application of decision support systems (DDS) that consider variation in climate and soi...
International audienceNatural risk studies such as flood risk assessments require long series of wea...
In this paper, the weather generator (WG) used by the empirical statistical downscaling method, weat...