Machine Learning as a Downscaling Approach for Prediction of Wind Characteristics under Future Climate Change Scenarios
Assessment of climate change impacts on wind characteristics is crucial for the design, operation, and maintenance of coastal and offshore infrastructures. In the present study, the Model Output Statistics (MOS) method was used to downscale a Coupled Model Intercomparison Project Phase 5 (CMIP5) wit...
Saved in:
Main Authors: | Abbas Yeganeh-Bakhtiary, Hossein EyvazOghli, Naser Shabakhty, Bahareh Kamranzad, Soroush Abolfathi |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2022-01-01
|
Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2022/8451812 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Regional Climate Change: Downscaling, Prediction, and Impact Assessment
by: Lian Xie, et al.
Published: (2015-01-01) -
Machine Learning Model Reveals Land Use and Climate’s Role in Caatinga Wildfires: Present and Future Scenarios
by: Rodrigo N. Vasconcelos, et al.
Published: (2024-12-01) -
Statistical Downscaling and Projection of Future Air Temperature Changes in Yunnan Province, China
by: Jiaxu Liu, et al.
Published: (2017-01-01) -
A Reliable Generative Adversarial Network Approach for Climate Downscaling and Weather Generation
by: Neelesh Rampal, et al.
Published: (2025-01-01) -
Dynamical Downscaling of Climate Change Impacts on Wind Energy Resources in the Contiguous United States by Using a Limited-Area Model with Scale-Selective Data Assimilation
by: Bin Liu, et al.
Published: (2014-01-01)