Results 11 to 20 of about 8,971 (314)
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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Interrogating empirical-statistical downscaling [PDF]
Climatic Change, 2013 The delivery of downscaled climate information is increasingly seen as a vehicle of climate services, a driver for impacts studies and adaptation decisions, and for informing policy development. Empirical-statistical downscaling (ESD) is widely used; however, the accompanying responsibility is significant, and predicated on effective understanding of ...
Hewitson, Bruce +4 more
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Earth and Space Science, 2022 Downscaling techniques are effective to bridge the scale gap between global circulation models and regional studies. Statistical downscaling methods are prevalent due to their advantages in high computational efficiency and accuracy. However, an implicit
Xintong Li +2 more
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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
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Differential Credibility Assessment for Statistical Downscaling [PDF]
Journal of Applied Meteorology and Climatology, 2020 AbstractClimate science is increasingly using (i) ensembles of climate projections from multiple models derived using different assumptions and/or scenarios and (ii) process-oriented diagnostics of model fidelity. Efforts to assign differential credibility to projections and/or models are also rapidly advancing.
S. C. Pryor, J. T. Schoof
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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
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Downscaling MODIS spectral bands using deep learning
GIScience & Remote Sensing, 2021 MODIS sensors are widely used in a broad range of environmental studies, many of which involve joint analysis of multiple MODIS spectral bands acquired at disparate spatial resolutions.
Rohit Mukherjee, Desheng Liu
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Statistically downscaled CMIP6 ocean variables for European waters [PDF]
Scientific Reports, 2023 Abstract Climate change impact studies need climate projections at scales relevant to planning and management and to be available across a range of climate scenarios. To address current gaps, we statistically downscaled (SD) 4–7 CMIP6 models for four key indicators of marine habitat conditions: temperature, salinity, oxygen, and chlorophyll ...
Trond Kristiansen +2 more
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Progress in Earth and Planetary Science, 2018 The hybrid dynamical-statistical downscaling approach is an effort to combine the ability of dynamical downscaling to resolve fine-scale climate changes with the low computational cost of statistical downscaling.
Quan Tran Anh, Kenji Taniguchi
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Statistically downscaled climate dataset for East Africa [PDF]
Scientific Data, 2019 AbstractFor many regions of the world, current climate change projections are only available at coarser spatial resolution from Global Climate Models (GCMs) that cannot directly be used in impact assessment and adaptation studies at regional and local scale.
Gebrechorkos, Solomon H. +2 more
openaire +4 more sources