This study investigates dynamically different data-driven methods, specifically a statistical downscaling model (SDSM), a time lagged feedforward neural network (TLFN), and an evolutionary polynomial regression (EPR) technique for downscaling numerical weather ensemble forecasts generated by a medium range forecast (MRF) model. 5 Given the coarse resolution (about 200-km grid spacing) of the MR...

Papers published in Hydrology and Earth System Sciences Discussions are under open-access review for the journal Hydrology and Earth System Sciences Abstract Global Circulation Models (GCMs) are a major tool used for future projections of climate change using different emission scenarios. However, for assessing the hydrological impacts of climate change at the watershed and the regional scale, ...

We introduce an efficient downscaling methods with arbitrary downscaling factors for images that are transformed to DCT blocks as in the compression standard JPEG. Our hybrid approach combines the low complexity of DCT domain downscaling with the high image quality of B-spline based downscaling methods. A scalable parameter allows to adjust the trade-off between computational costs and image qu...

This study uses an empirical downscaling method based on self-organizing maps (SOMs) to produce highresolution, downscaled precipitation projections over the state of Pennsylvania in the mid-Atlantic region of the United States for the future period 2046–65. To examine the sensitivity of precipitation change to the water vapor increase brought by global warming, the authors test the following t...

Digital video databases are widely available in compressed format. In many applications such as video browsing, picture in picture, video conferencing etc. data transfer at lower bit rate is required. This requires downscaling of the video before transmission. The conventional spatial domain approach for downscaling video is computationally very expensive. The computation can greatly be reduced...

Aim Species atlases provide an economical way to collect data with national coverage, but are typically too coarse-grained to monitor fine-grain patterns in rarity, distribution and abundance. We test the performance of ten downscaling models in extrapolating occupancy across two orders of magnitude. To provide a greater challenge to downscaling models, we extend previous downscaling tests with...

This study investigates the skill of linear methods for downscaling provincial-scale precipitation over Indonesia from fields that describe the large-scale circulation and hydrological cycle. The study is motivated by the strong link between large-scale variations in the monsoon and the El Niño – Southern Oscillation (ENSO) phenomenon and regional precipitation, and the subsequent impact of reg...

We developed a global land-use allocation model that can be linked to integrated assessment models (IAMs) with a coarser spatial resolution. Using the model, we performed a downscaling of the IAMs' regional aggregated land-use projections to obtain a spatial land-use distribution, which could subsequently be used by Earth system models for global environmental assessments of ecosystem services,...

The assessment of local and regional impacts of climate change often requires downscaling of general circulation model (GCM) projections from coarser GCM-scale to finer localor catchmentscale spatial resolution. This paper provides an assessment of two downscaling approaches for simulation of daily rainfall over Sydney, Australia. The two downscaling alternatives compared include a multivariate...

Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in urban areas with several mixed surface types. In this study, LST was downscaled by a multiple linear regression model between LST and multiple scale factors in mix...