Remotely sensed data are often used as proxies for indirect precipitation measures over data-scarce and complex-terrain areas such as the Peruvian Andes. Although this information might be appropriate for some research requirements, the extent at which local sites could be related to such information is very limited because of the resolution of the available satellite data. Downscaling techniqu...

sufficient measurements of effective parameters with suitable temporal and spatial distributions for all phenomena are needed. however, providing the data and informations are difficult and costly. so, temporal and/or spatial downscaling is of prime importance. by downscaling, one can generate data from available ones. there are diffent downscaling methods in literature but they are not compare...

Article history: Received 2 January 2008 Received in revised form 11 June 2008 Accepted 27 June 2008 Available online xxxx

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...

Content-based image indexing complexity often depends on image dimensions and data size. Reducing image dimensions before indexing affects the overall feature extraction and indexing complexity, as well as the retrieval performance. In this paper, the effects of Discrete Wavelet Transform (DWT) based downscaling on semantic retrieval performance are investigated via dedicated experiments. Sever...

To assess a species' vulnerability to climate change, we commonly use mapped environmental data that are coarsely resolved in time and space. Coarsely resolved temperature data are typically inaccurate at predicting temperatures in microhabitats used by an organism and may also exhibit spatial bias in topographically complex areas. One consequence of these inaccuracies is that coarsely resolved...

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...