Results 11 to 20 of about 4,321,601 (325)
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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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Deep learning techniques, especially convolutional neural networks (CNNs), have dramatically boosted the performance of statistical downscaling. In this study, we propose a CNN-based 2 m air temperature downscaling model named Terrain-Guided Attention ...
Guangyu Liu +5 more
semanticscholar +1 more source
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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Geophysical Research Letters, 2023 Under the perfect prognosis approach, statistical downscaling methods learn the relationships between large‐scale variables from reanalysis and local observational records.
M. N. Legasa +3 more
semanticscholar +1 more source
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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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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Hydrology and Earth System Sciences, 2021 . General circulation models (GCMs) are the primary tools for evaluating the possible impacts of climate change; however, their results are coarse in temporal and spatial dimensions. In addition, they often show systematic biases compared to observations.
H. Tabari +3 more
semanticscholar +1 more source
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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International Journal of Climatology, 2021 The Spanish Meteorological Agency (AEMET) is responsible for the elaboration of downscaled climate projections over Spain to feed the Second National Plan of Adaptation to Climate Change (PNACC‐2).
Alfonso Hernanz +5 more
semanticscholar +1 more source