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Estimating pros and cons of statistical downscaling based on EQM bias adjustment as a complementary method to dynamical downscaling [PDF]
Scientific ReportsThe increasing availability of coarse-scale climate simulations and the need for ready-to-use high-resolution variables drive the climate community to the challenge of reducing computational resources and time for downscaling purposes.
Alfredo Reder +3 more
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High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method. [PDF]
Sci Data, 2023 High-resolution climate model projections for a range of emission scenarios are needed for designing regional and local adaptation strategies and planning in the context of climate change. To this end, the future climate simulations of global circulation
Rettie FM +4 more
europepmc +2 more sources
Journal of Water and Climate Change, 2023 Statistical downscaling (SD) is preferable to dynamic downscaling to derive local-scale climate change information from large-scale datasets. Many statistical downscaling models are available these days, but comparison of their performance is still ...
Rituraj Shukla +6 more
doaj +2 more sources
Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0) [PDF]
Geoscientific Model Development, 2019 In this paper I present new methods for bias adjustment and statistical downscaling that are tailored to the requirements of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP).
S. Lange
doaj +2 more sources
Weather and Climate Extremes, 2023 This study evaluates statistical downscaling techniques using different metrics and compares climate change signals and extreme precipitation and temperature changes under future climate change scenarios in the Bosque watershed, North-Central Texas.
Gebrekidan Worku Tefera +2 more
doaj +2 more sources
Deep learning for statistical downscaling of sea states [PDF]
Advances in Statistical Climatology, Meteorology and Oceanography, 2022 Numerous marine applications require the prediction of medium- and long-term sea states. Climate models are mainly focused on the description of the atmosphere and global ocean variables, most often on a synoptic scale.
M. Michel +5 more
doaj +2 more sources
Dynamical and statistical downscaling of precipitation and temperature in a Mediterranean area [PDF]
Italian Journal of Agronomy, 2012 In this paper we present and discuss a comparison between statistical and regional climate modeling techniques for downscaling GCM prediction . The comparison is carried out over the “Capitanata” region, an area of agricultural interest in south ...
Claudia Pizzigalli +5 more
doaj +4 more sources
Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models [PDF]
Neural Information Processing Systems, 2023 We introduce a two-stage probabilistic framework for statistical downscaling using unpaired data. Statistical downscaling seeks a probabilistic map to transform low-resolution data from a biased coarse-grained numerical scheme to high-resolution data ...
Z. Y. Wan +6 more
semanticscholar +1 more source
Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches [PDF]
Journal of Advances in Modeling Earth Systems, 2023 Deep learning (DL) has emerged as a promising tool to downscale climate projections at regional‐to‐local scales from large‐scale atmospheric fields following the perfect‐prognosis approach. Given their complexity, it is crucial to properly evaluate these
Jose González-Abad +2 more
semanticscholar +1 more source
Uncovering the shortcomings of a weather typing method [PDF]
Hydrology and Earth System Sciences, 2020 In recent years many methods for statistical downscaling of the precipitation climate model outputs have been developed. Statistical downscaling is performed under general and method-specific (structural) assumptions but those are rarely evaluated ...
E. Van Uytven +3 more
doaj +1 more source