This paper reviews methods that have been used to evaluate global climate simulations and to downscale global climate scenarios for the assessment of climate impacts on hydrologic systems in the Pacific Northwest, USA. The approach described has been developed to facilitate integrated assessment research in support of regional resource management. Global climate model scenarios are evaluated an...

prediction of climatic variables on a local scale by general circulation models of the atmosphere is impossible because the models have large-scale network of resolution. therefore, downscaling methods are used to solve this problem. since the climate change phenomenon can affect different systems such as, water resources, agriculture, environment, industry and economy as well, selection of the...

General Circulation Models (GCMs) suggest that rising concentrations of greenhouse gases will have significant implications for climate at global and regional scales. Less certain is the extent to which meteorological processes at individual sites will be affected. So-called ‘downscaling’ techniques are used to bridge the spatial and temporal resolution gaps between what climate modellers are c...

This study aimed to forecast temperature variations in the western and southwestern part of Iran using a general circulation model artificial neural networks (ANN). The data included mean diurnal temperatures from synoptic stations, National Centers for Environmental Prediction/ Center Atmospheric Research (NCEP/NCAR) reanalysis data, outputs third-generation global climate model, Hadley Centre...

This study describes an EOF-based technique for statistical downscaling of high spatial resolution monthly mean precipitation from large scale reanalysis circulation fields. The method is demonstrated and evaluated for four widely-separated locations: the Southeast United States, the Upper Colorado River Basin, China’s Jiangxi Province, and Central Europe. For each location, the EOF-based downs...

Downscaling global weather prediction model outputs to individual locations or local scales is a common practice for operational weather forecast in order to correct the model outputs at subgrid scales. This paper presents an empirical-statistical downscaling method for precipitation prediction which uses a feed-forward multilayer perceptron MLP neural network. The MLP architecture was optimize...