Results 11 to 20 of about 109,140 (374)
Diffusion model-based probabilistic downscaling for 180-year East Asian climate reconstruction [PDF]
npj Climate and Atmospheric ScienceAs our planet is entering into the “global boiling” era, understanding regional climate change becomes imperative. Effective downscaling methods that provide localized insights are crucial for this target.
Fenghua Ling +8 more
doaj +2 more sources
Scientific Reports, 2023 Ensemble simulations of climate models are used to assess the impact of climate change on precipitation, and require downscaling at the local scale. Statistical downscaling methods have been used to estimate daily and monthly precipitation from observed ...
Takao Yoshikane, Kei Yoshimura
doaj +2 more sources
Evaluating downscaling methods of GRACE (Gravity Recovery and Climate Experiment) data: a case study over a fractured crystalline aquifer in southern India [PDF]
Hydrology and Earth System Sciences, 2022 GRACE (Gravity Recovery and Climate Experiment) and its follow-on mission have provided since 2002 monthly anomalies of total water storage (TWS), which are very relevant to assess the evolution of groundwater storage (GWS) at global and regional scales.
C. Pascal +5 more
doaj +2 more sources
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
doaj +2 more sources
Residual corrective diffusion modeling for km-scale atmospheric downscaling [PDF]
Communications Earth & Environment, 2023 State of the art for weather and climate hazard prediction requires expensive km-scale numerical simulations. Here, a generative diffusion model is explored for downscaling global inputs to km-scale, as a cost-effective alternative.
Morteza Mardani +9 more
semanticscholar +1 more source
Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling [PDF]
arXiv.org, 2023 Climate simulations are essential in guiding our understanding of climate change and responding to its effects. However, it is computationally expensive to resolve complex climate processes at high spatial resolution.
Qidong Yang +7 more
semanticscholar +1 more source
A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts [PDF]
Journal of Advances in Modeling Earth Systems, 2022 Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables. A major contributing factor to this is that several key processes affecting precipitation distribution and ...
L. Harris +4 more
semanticscholar +1 more source
npj Climate and Atmospheric Science, 2023 Efforts to diagnose the risks of a changing climate often rely on downscaled and bias-corrected climate information, making it important to understand the uncertainties and potential biases of this approach.
David C. Lafferty, R. Sriver
semanticscholar +1 more source
Scientific Data, 2021 Dynamical downscaling is an important approach to obtaining fine-scale weather and climate information. However, dynamical downscaling simulations are often degraded by biases in the large-scale forcing itself.
Zhongfeng Xu +4 more
semanticscholar +1 more source
Using Machine Learning to Cut the Cost of Dynamical Downscaling
Earth's Future, 2023 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 GCMs can be dynamically downscaled, restricting uncertainty assessment.
S. Hobeichi +7 more
semanticscholar +1 more source