Results 21 to 30 of about 4,321,601 (325)
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
doaj +1 more source
Symmetry, 2022 In recent years, climate change has demonstrated the volatility of unexpected events such as typhoons, flooding, and tsunamis that affect people, ecosystems and economies.
Nurul Ainina Filza Sulaiman +5 more
semanticscholar +1 more source
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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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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Journal of Hydrometeorology, 2022 Statistical processing of numerical model output has been a part of both weather forecasting and climate applications for decades. Statistical techniques are used to correct systematic biases in atmospheric model outputs and to represent local effects ...
E. Gutmann +5 more
semanticscholar +1 more source
Comparison of statistical and dynamical downscaling methods for seasonal-scale winter precipitation predictions over north India [PDF]
, 2019 The main aim of the present study is to analyse the capabilities of two downscaling approaches (statistical and dynamical) in predicting wintertime seasonal precipitation over north India.
Acharya +45 more
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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