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...
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Wiley-Blackwell Publishing, Inc
2022
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ftuwarwick:oai:wrap.warwick.ac.uk:168542 2023-05-15T17:35:25+02:00 Machine learning as a downscaling approach for prediction of wind characteristics under future climate change scenarios Yeganeh-Bakhtiary, Abbas Eyvaz Oghli, Hossein Shabakhty, Naser Kamranzad, Bahareh Abolfathi, Soroush 2022-08-23 application/pdf http://wrap.warwick.ac.uk/168542/ http://wrap.warwick.ac.uk/168542/1/WRAP-Machine-learning-downscaling-approach-prediction-wind-characteristics-future-climate-change-scenarios-22.pdf https://doi.org/10.1155/2022/8451812 unknown Wiley-Blackwell Publishing, Inc http://wrap.warwick.ac.uk/168542/1/WRAP-Machine-learning-downscaling-approach-prediction-wind-characteristics-future-climate-change-scenarios-22.pdf Yeganeh-Bakhtiary, Abbas, Eyvaz Oghli, Hossein, Shabakhty, Naser, Kamranzad, Bahareh and Abolfathi, Soroush (2022) Machine learning as a downscaling approach for prediction of wind characteristics under future climate change scenarios. Complexity, 2022 . 8451812. doi:10.1155/2022/8451812 <http://dx.doi.org/10.1155/2022/8451812> ISSN 1076-2787. GC Oceanography TA Engineering (General). Civil engineering (General) TC Hydraulic engineering. Ocean engineering Journal Article NonPeerReviewed 2022 ftuwarwick https://doi.org/10.1155/2022/8451812 2023-02-02T23:50:38Z 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) with General Circulation Model (GCM) results for a case study in the North Atlantic Ocean, and a supervised machine learning method (M5’ Decision Tree model) was developed for the first time to establish a statistical relationship between predicator and predicant. To do so, the GCM simulation results and altimeter remote sensing data were employed to examine the capabilities of the M5’DT model in predicting future wind speed and identifying spatiotemporal trends in wind characteristics. For this purpose, three classes of M5′ models were developed to study the annual, seasonal, and monthly variations of wind characteristics. The developed decision tree (DT) models were employed to statistically downscale the Beijing Normal University Earth System Model (BNU-ESM) global climate model output. The M5′ models are calibrated and successfully validated against the GCM simulation results and altimeter remote sensing data. All the proposed models showed firm outputs in the training section. Predictions from the monthly model with a 70/30 training to test ratio demonstrated the best model performance. The monthly prediction model highlighted the decreasing trend in wind speed relative to the control period in 2030 to 2040 for the case study location and across all three future climate change scenarios tested within this study. This reduction in wind speed reduces wind energy by 13% to 19%. Article in Journal/Newspaper North Atlantic The University of Warwick: WRAP - Warwick Research Archive Portal |
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Open Polar |
collection |
The University of Warwick: WRAP - Warwick Research Archive Portal |
op_collection_id |
ftuwarwick |
language |
unknown |
topic |
GC Oceanography TA Engineering (General). Civil engineering (General) TC Hydraulic engineering. Ocean engineering |
spellingShingle |
GC Oceanography TA Engineering (General). Civil engineering (General) TC Hydraulic engineering. Ocean engineering Yeganeh-Bakhtiary, Abbas Eyvaz Oghli, Hossein Shabakhty, Naser Kamranzad, Bahareh Abolfathi, Soroush Machine learning as a downscaling approach for prediction of wind characteristics under future climate change scenarios |
topic_facet |
GC Oceanography TA Engineering (General). Civil engineering (General) TC Hydraulic engineering. Ocean engineering |
description |
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) with General Circulation Model (GCM) results for a case study in the North Atlantic Ocean, and a supervised machine learning method (M5’ Decision Tree model) was developed for the first time to establish a statistical relationship between predicator and predicant. To do so, the GCM simulation results and altimeter remote sensing data were employed to examine the capabilities of the M5’DT model in predicting future wind speed and identifying spatiotemporal trends in wind characteristics. For this purpose, three classes of M5′ models were developed to study the annual, seasonal, and monthly variations of wind characteristics. The developed decision tree (DT) models were employed to statistically downscale the Beijing Normal University Earth System Model (BNU-ESM) global climate model output. The M5′ models are calibrated and successfully validated against the GCM simulation results and altimeter remote sensing data. All the proposed models showed firm outputs in the training section. Predictions from the monthly model with a 70/30 training to test ratio demonstrated the best model performance. The monthly prediction model highlighted the decreasing trend in wind speed relative to the control period in 2030 to 2040 for the case study location and across all three future climate change scenarios tested within this study. This reduction in wind speed reduces wind energy by 13% to 19%. |
format |
Article in Journal/Newspaper |
author |
Yeganeh-Bakhtiary, Abbas Eyvaz Oghli, Hossein Shabakhty, Naser Kamranzad, Bahareh Abolfathi, Soroush |
author_facet |
Yeganeh-Bakhtiary, Abbas Eyvaz Oghli, Hossein Shabakhty, Naser Kamranzad, Bahareh Abolfathi, Soroush |
author_sort |
Yeganeh-Bakhtiary, Abbas |
title |
Machine learning as a downscaling approach for prediction of wind characteristics under future climate change scenarios |
title_short |
Machine learning as a downscaling approach for prediction of wind characteristics under future climate change scenarios |
title_full |
Machine learning as a downscaling approach for prediction of wind characteristics under future climate change scenarios |
title_fullStr |
Machine learning as a downscaling approach for prediction of wind characteristics under future climate change scenarios |
title_full_unstemmed |
Machine learning as a downscaling approach for prediction of wind characteristics under future climate change scenarios |
title_sort |
machine learning as a downscaling approach for prediction of wind characteristics under future climate change scenarios |
publisher |
Wiley-Blackwell Publishing, Inc |
publishDate |
2022 |
url |
http://wrap.warwick.ac.uk/168542/ http://wrap.warwick.ac.uk/168542/1/WRAP-Machine-learning-downscaling-approach-prediction-wind-characteristics-future-climate-change-scenarios-22.pdf https://doi.org/10.1155/2022/8451812 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_relation |
http://wrap.warwick.ac.uk/168542/1/WRAP-Machine-learning-downscaling-approach-prediction-wind-characteristics-future-climate-change-scenarios-22.pdf Yeganeh-Bakhtiary, Abbas, Eyvaz Oghli, Hossein, Shabakhty, Naser, Kamranzad, Bahareh and Abolfathi, Soroush (2022) Machine learning as a downscaling approach for prediction of wind characteristics under future climate change scenarios. Complexity, 2022 . 8451812. doi:10.1155/2022/8451812 <http://dx.doi.org/10.1155/2022/8451812> ISSN 1076-2787. |
op_doi |
https://doi.org/10.1155/2022/8451812 |
_version_ |
1766134584905302016 |