Evaluation of Spatial Downscaling Methods for Enhancement of Spatial Precipitation Estimation [PDF]
Journal of korean society of hazard mitigation, 2013 In order to estimate a reliable spatial precipitation which has high resolution sub-measuring scale, a systematical research for spatial interpolation methods and evaluation of characteristics of them are required. This will be able to reduce disputes on selection of methodologies and estimate uncertainties of downscaled data.
Seok Hwan Hwang, Dae Heon Ham
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Geographically Weighted Area-to-Point Regression Kriging for Spatial Downscaling in Remote Sensing
Remote Sensing, 2018 Spatial downscaling of remotely sensed products is one of the main ways to obtain earth observations at fine resolution. Area-to-point (ATP) geostatistical techniques, in which regular fine grids of remote sensing products are regarded as points, have ...
Yan Jin +4 more
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Regional climate projections in two alpine river basins: Upper Danube and Upper Brahmaputra [PDF]
, 2011 Projections from coarse-grid global circulation models are not suitable for regional estimates of water balance or trends of extreme precipitation and temperature, especially not in complex terrain.
Ahrens, Bodo +4 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Land surface temperature (LST) is a key parameter in numerous environmental studies. However, currently, there is no satellite sensor that can completely provide LST data with both high spatial and high temporal resolutions simultaneously.
Xiaobo Luo +4 more
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Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies [PDF]
, 2016 Statistical downscaling is a commonly used technique for translating large-scale climate model output to a scale appropriate for assessing impacts. To ensure downscaled meteorology can be used in climate impact studies, downscaling must correct biases in
Ficklin, Darren L. +2 more
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Comparison of statistical and dynamical downscaling methods for seasonal-scale winter precipitation predictions over north India [PDF]
, 2019 The main aim of the present study is to analyse the capabilities of two downscaling approaches (statistical and dynamical) in predicting wintertime seasonal precipitation over north India.
Acharya +45 more
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Optimising predictor domains for spatially coherent precipitation downscaling [PDF]
Hydrology and Earth System Sciences, 2013 Abstract. Statistical downscaling is widely used to overcome the scale gap between predictors from numerical weather prediction models or global circulation models and predictands like local precipitation, required for example for medium-term operational forecasts or climate change impact studies.
S. Radanovics +4 more
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Towards an Accurate and Reliable Downscaling Scheme for High-Spatial-Resolution Precipitation Data
Remote Sensing, 2023 Accurate high-spatial-resolution precipitation is significantly important in hydrological and meteorological modelling, especially in rain-gauge-sparse areas.
Honglin Zhu +3 more
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Advances in Meteorology, 2017 Various downscaling approaches have been developed to overcome the limitation of the coarse spatial resolution of general circulation models (GCMs). Such techniques can be grouped into two approaches of dynamical and statistical downscaling.
Yoo-Bin Yhang, Soo-Jin Sohn, Il-Won Jung
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PreciPatch: A Dictionary-based Precipitation Downscaling Method
Remote Sensing, 2020 Climate and weather data such as precipitation derived from Global Climate Models (GCMs) and satellite observations are essential for the global and local hydrological assessment.
Mengchao Xu +8 more
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