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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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Regridding uncertainty for statistical downscaling of solar radiation [PDF]
Advances in Statistical Climatology, Meteorology and Oceanography, 2023 Initial steps in statistical downscaling involve being able to compare observed data from regional climate models (RCMs). This prediction requires (1) regridding RCM outputs from their native grids and at differing spatial resolutions to a common grid in
M. D. Bailey +6 more
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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
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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
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Projected Changes in Temperature and Precipitation over Kashafrood Basin Based on Statistical and Dynamical Downscaling Methods [PDF]
مخاطرات محیط طبیعی, 2021 It is well-known that climate is changing continuously under the intricate influences of natural and artificial factors at global and regional scales. The global Coupled Model Intercomparison Project (CMIP) already provides multi model data resources in ...
Hooshmand Ataei +3 more
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Climate change scenarios generated by using GCM outputs and statistical downscaling in an arid region [PDF]
Desert, 2015 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 ...
Zhaofei Liu, Zongxue Xu
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Repeatable high-resolution statistical downscaling through deep learning [PDF]
Geoscientific Model Development, 2022 One of the major obstacles for designing solutions against the imminent climate crisis is the scarcity of high spatio-temporal resolution model projections for variables such as precipitation.
D. Quesada-Chacón +2 more
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A statistical downscaling framework for environmental mapping [PDF]
The Journal of Supercomputing, 2018 In recent years, knowledge extraction from data has become increasingly popular, with many numerical forecasting models, mainly falling into two major categories—chemical transport models (CTMs) and conventional statistical methods. However, due to data and model variability, data-driven knowledge extraction from high-dimensional, multifaceted data in ...
Kassim S. Mwitondi +3 more
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Earth's Future, 2022 We present an intercomparison of a suite of high‐resolution downscaled climate projections based on a six‐member General Circulation Model (GCM) ensemble from Coupled Models Intercomparison Project (CMIP6).
Deeksha Rastogi +2 more
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Hydrology Research, 2022 One of the challenging issues in statistical downscaling of climate models is to select dominant large-scale climate variables (predictors). Correlation-based methods have been revealed to be efficacious to select the predictors; however, traditional ...
Aida Hosseini Baghanam +2 more
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