Air temperature is an important indicator of climate change, as well for understanding changes in hydrology, ecology, and other natural systems. However, meteorological stations that provide reliable observations are usually sparse areas complex terrain, thus limiting our ability to quantify high spatial resolution variability these regions. Here, we apply three statistical downscaling methods ...

The skill of state-of-the-art operational seasonal forecast models in extratropical latitudes is assessed using a multimodel ensemble from the Development of a European Multimodel Ensemble System for Seasonalto-Interannual Prediction (DEMETER) project. In particular, probabilistic forecasts of surface precipitation and maximum temperature in Spain are analyzed using a high-resolution observatio...

numerous studies yet have been carried out on downscaling of the large-scale climate data usingboth dynamical and statistical methods to investigate the hydrological and meteorological impacts of climatechange on different parts of the world. this study was also conducted to investigate the capability of feedforwardneural network with error back-propagation algorithm to downscale the provincial...

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...

introduction the city population, in particular at the industrialized cities and centers of provinces, has increased dramatically in iran during recent decades. arak city as center of markazi province is among those industrialized cities which has experienced a fast increase in population. these changes in population numbers tend to increase consuming water resources as well as increasing in en...

Based on the operational regional ensemble prediction system (REPS) in China Meteorological Administration (CMA), this paper carried out comparison of two initial condition perturbation methods: an ensemble transform Kalman filter (ETKF) and a dynamical downscaling of global ensemble perturbations. One month consecutive tests are implemented to evaluate the performance of both methods in the op...

I n this paper, we propose efficient techniques and architectures for realizing spatial-downscaling transcoders in the DCT domain. We also present methods for re-sampling motion vectors and determining coding modes. We propose a novel drift-free architecture which simplifies the cascaded DCT-domain downscaling transcoder (CDDT) by integrating the downscaling process into the DCT-domain motion c...

The objective of spatial downscaling strategies is to increase the information content of coarse datasets at smaller scales. In the case of quantitative precipitation estimation (QPE) for hydrological applications, the goal is to close the scale gap between the spatial resolution of coarse datasets (e.g., gridded satellite precipitation products at resolution L 3 L) and the high resolution (l 3...

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 arid regions (especially in deserts) because of inaccurate remote sensing LST products. In this study, LST was downscaled by a random forest model between LST and ...