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...

This study aims to evaluate effects of two downscaling methods; change-factor and statistical downscaling on therunoff of the Azam-Harat River located at Yazd province (with an arid climate) of Iran, under the A2 emission scenariofor the period of 2010-2039. For this purpose, CGCM3-AR4 model; a rainfall-runoff conceptual model, IHACRES; twodownscaling models, Change Factor and LARS-WG were appl...

Statistical downscaling is commonly used in climate modeling to obtain high-resolution spatial projections of future climate scenarios from the coarse-resolution outputs projected by global climate models. Unfortunately, most of the statistical downscaling approaches using standard regression methods tend to emphasize projecting the conditional mean of the data while paying scant attention to t...

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...

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 ...

Two statistical downscaling models, the non-homogeneous hidden Markov model (NHMM) and the Statistical Down–Scaling Model (SDSM) were used to generate future scenarios of both mean and extremes in the Tarim River basin,which were based on nine combined scenarios including three general circulation models (GCMs) (CSIRO30, ECHAM5,and GFDL21) predictor sets and three special report on emission sce...

xiii Chapter 1: Introduction 1 1.1 The growth of the Internet . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Predicting the performance of the Internet: existing techniques and limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Downscaling the Internet: a new direction for performance prediction . 5 1.4 The evolution of wireless networks . . . . . . ....

This paper presents the results of downscaling effects when modelling waves, tides and storm surge using a nested modelling system. In this study, the coupled POLCOMS/ProWAM models are used, with 3 nested computational domains, the largest of which covers part of north-eastern Atlantic Ocean with a coarse resolution grid and the smallest covers the surrounding waters of south-west Cornish coast...

In this paper, a multifractal downscaling technique is applied to adequately transformed and lag corrected normalized difference vegetation index (NDVI) in order to obtain daily estimates of rainfall in an area of the Peruvian Andean high plateau. This downscaling procedure is temporal in nature since the original NDVI information is provided at an irregular temporal sampling period between 8 a...

Downscaling is an important problem because consistent large-area assessments of forest habitat structure, while feasible, are only feasible when using relatively coarse data and indicators. Techniques are needed to enable more detailed and local interpretations of the national statistics. Using the results of national assessments from land-cover maps, this paper demonstrates downscaling in the...