Using machine learning to cut the cost of dynamical downscaling
Global climate models (GCMs) are commonly downscaled to understand future local climate
change. The high computational cost of regional climate models (RCMs) limits how many …
change. The high computational cost of regional climate models (RCMs) limits how many …
[HTML][HTML] Enhancing Regional Climate Downscaling through Advances in Machine Learning
Despite the sophistication of global climate models (GCMs), their coarse spatial resolution
limits their ability to resolve important aspects of climate variability and change at the local …
limits their ability to resolve important aspects of climate variability and change at the local …
[HTML][HTML] Regional climate model emulator based on deep learning: Concept and first evaluation of a novel hybrid downscaling approach
Providing reliable information on climate change at local scale remains a challenge of first
importance for impact studies and policymakers. Here, we propose a novel hybrid …
importance for impact studies and policymakers. Here, we propose a novel hybrid …
Deep learning regional climate model emulators: A comparison of two downscaling training frameworks
M van der Meer, S de Roda Husman… - Journal of Advances …, 2023 - Wiley Online Library
Regional climate models (RCMs) have a high computational cost due to their higher spatial
resolution compared to global climate models (GCMs). Therefore, various downscaling …
resolution compared to global climate models (GCMs). Therefore, various downscaling …
Regional climate downscaling: What's the point?
RA Pielke Sr, RL Wilby - Eos, Transactions American …, 2012 - Wiley Online Library
Dynamical and statistical downscaling of multidecadal global climate models provides finer
spatial resolution information for climate impact assessments [Wilby and Fowler, 2010] …
spatial resolution information for climate impact assessments [Wilby and Fowler, 2010] …
[HTML][HTML] An overview of the western united states dynamically downscaled dataset (wus-d3)
Predicting future climate change over a region of complex terrain, such as the western
United States (US), remains challenging due to the low resolution of global climate models …
United States (US), remains challenging due to the low resolution of global climate models …
How may the choice of downscaling techniques and meteorological reference observations affect future hydroclimate projections?
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 …
based on a six‐member General Circulation Model (GCM) ensemble from Coupled Models …
[HTML][HTML] Evaluating the stationarity assumption in statistically downscaled climate projections: is past performance an indicator of future results?
Empirical statistical downscaling (ESD) methods seek to refine global climate model (GCM)
outputs via processes that glean information from a combination of observations and GCM …
outputs via processes that glean information from a combination of observations and GCM …
Comparison of a novel machine learning approach with dynamical downscaling for Australian precipitation
Dynamical downscaling (DD), and machine learning (ML) based techniques have been
widely applied to downscale global climate models and reanalyses to a finer spatiotemporal …
widely applied to downscale global climate models and reanalyses to a finer spatiotemporal …
[HTML][HTML] Use-inspired, process-oriented GCM Selection: Prioritizing models for regional dynamical downscaling
N Goldenson, LR Leung, LO Mearns… - Bulletin of the …, 2023 - journals.ametsoc.org
Dynamical downscaling is a crucial process for providing regional climate information for
broad uses, using coarser-resolution global models to drive higher-resolution regional …
broad uses, using coarser-resolution global models to drive higher-resolution regional …