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Journal of Hydrologic Engineering, 2015
AbstractDownscaling is a fundamental procedure in the assessment of the future climate change impact at regional and watershed scales. Hence, it is important to investigate the spatial variability of the climate conditions that are constructed by various downscaling methods to assess whether each method can properly model the climate conditions at ...
S. Jang, M. L. Kavvas
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AbstractDownscaling is a fundamental procedure in the assessment of the future climate change impact at regional and watershed scales. Hence, it is important to investigate the spatial variability of the climate conditions that are constructed by various downscaling methods to assess whether each method can properly model the climate conditions at ...
S. Jang, M. L. Kavvas
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Statistical downscaling of monthly forecasts
International Journal of Climatology, 2000Canonical correlation analysis (CCA) is used to downscale large-scale circulation forecasts by the Centre for Ocean‐Land‐Atmosphere studies (COLA) T30 general circulation model (GCM) statistically to regional rainfall in South Africa. Monthly GCM ensemble forecasts available from 1979 to 1995 have been generated using NCEP reanalysis data as initial ...
Willem A. Landman, Warren J. Tennant
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Statistical downscaling of river flows
Journal of Hydrology, 2010summary An extensive statistical ‘downscaling’ study is done to relate large-scale climate information from a general circulation model (GCM) to local-scale river flows in SW France for 51 gauging stations ranging from nival (snow-dominated) to pluvial (rainfall-dominated) river-systems.
Tisseuil, Clement +3 more
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International Journal of Climatology, 2020
Convolutional neural network (CNN) is an effective tool for extracting interpretable information from big data and has been recently used as a promising approach for statistical downscaling.
Lei Sun, Yufeng Lan
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Convolutional neural network (CNN) is an effective tool for extracting interpretable information from big data and has been recently used as a promising approach for statistical downscaling.
Lei Sun, Yufeng Lan
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Assessment of Various Statistical Downscaling Methods for Downscaling Precipitation in Florida
World Environmental and Water Resources Congress 2013, 2013Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by General Circulation Models (GCMs).
Aneesh Goly, Ramesh S. V. Teegavarapu
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Spatially Explicit Model for Statistical Downscaling of Satellite Passive Microwave Soil Moisture
IEEE Transactions on Geoscience and Remote Sensing, 2020We introduce a spatially explicit statistical downscaling (SESD) method that fuses multiscale geospatial data with the soil moisture (SM) product from NASA’s SM Active and Passive (SMAP) satellite. The multiscale data included the 9-km resolution SMAP SM
Yaping Xu +7 more
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Analog Models for Empirical-Statistical Downscaling
2021Global climate models (GCM) are fundamental tools for weather forecasting and climate predictions at different time scales, from intraseasonal prediction to climate change projections. Their design allows GCMs to simulate the global climate adequately, but they are not able to skillfully simulate local/regional climates.
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Automated regression-based statistical downscaling tool
Environmental Modelling & Software, 2008Many impact studies require climate change information at a finer resolution than that provided by Global Climate Models (GCMs). In the last 10 years, downscaling techniques, both dynamical (i.e. Regional Climate Model) and statistical methods, have been developed to obtain fine resolution climate change scenarios.
Hessami, Massoud +3 more
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Statistical-dynamical downscaling of wind climatologies
Journal of Wind Engineering and Industrial Aerodynamics, 1997Abstract A statistical-dynamical downscaling procedure is applied for an investigation into the availability of wind power over a region of 80 × 87 km which covers flat and hilly terrain. The approach is based on the statistical coupling of a regionally representative wind climate with a numerical atmospheric mesoscale model.
Heinz-Theo Mengelkamp +2 more
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Uncertainty analysis of statistical downscaling methods
Journal of Hydrology, 2006Three downscaling models namely Statistical Down-Scaling Model (SDSM), Long Ashton Research Station Weather Generator (LARS-WG) model and Artificial Neural Network (ANN) model have been compared in terms various uncertainty assessments exhibited in their downscaled results of daily precipitation, daily maximum and minimum temperatures. In case of daily
Mohammad Sajjad Khan +2 more
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