A conditional approach for joint estimation of wind speed and direction under future climates
<p>This study develops a statistical conditional approach to evaluate climate model performance in wind speed and direction and to project their future changes under the Representative Concentration Pathway (RCP) 8.5 scenario over inland and offshore locations across the continental United Sta...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-12-01
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| Series: | Advances in Statistical Climatology, Meteorology and Oceanography |
| Online Access: | https://ascmo.copernicus.org/articles/8/205/2022/ascmo-8-205-2022.pdf |
| _version_ | 1811212966784139264 |
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| author | Q. Wu J. Bessac W. Huang J. Wang R. Kotamarthi |
| author_facet | Q. Wu J. Bessac W. Huang J. Wang R. Kotamarthi |
| author_sort | Q. Wu |
| collection | DOAJ |
| description | <p>This study develops a statistical conditional approach to evaluate climate model performance in wind speed and direction and to project their future changes under the Representative Concentration Pathway (RCP) 8.5 scenario over inland and offshore locations across the continental United States (CONUS). The proposed conditional approach extends the scope of existing studies by a combined characterization of the wind direction distribution and conditional distribution of wind on the direction, hence enabling an assessment of the joint wind speed and direction distribution and their changes. A von Mises mixture distribution is used to model wind directions across models and climate conditions. Wind speed distributions conditioned on wind direction are estimated using two statistical methods, i.e., a Weibull distributional regression model and a quantile regression model, both of which enforce the circular constraint to their resultant estimated distributions. Projected uncertainties associated with different climate models and model internal variability are investigated and compared with the climate change signal to quantify the robustness of the future projections. In particular, this work extends the concept of internal variability in the climate mean to the standard deviation and high quantiles to assess the relative magnitudes to their projected changes. The evaluation results show that the studied climate model captures both historical wind speed and wind direction and their dependencies reasonably well over both inland and offshore locations. Under the RCP8.5 scenario, most of the studied locations show no significant changes in the mean wind speeds in both winter and summer, while the changes in the standard deviation and 95th quantile show some robust changes over certain locations in winter. Specifically, high wind speeds (95th quantile) conditioned on direction in winter are projected to decrease in the northwestern, Colorado, and northern Great Plains locations in our study. In summer, high wind speeds conditioned on direction over the southern Great Plains increase slightly, while high wind speeds conditioned on direction over offshore locations do not change much. The proposed conditional approach enables a combined characterization of the wind speed distributions conditioned on direction and wind direction distributions, which offers a flexible alternative that can provide additional insights for the joint assessment of speed and direction.</p> |
| first_indexed | 2024-04-12T05:38:10Z |
| format | Article |
| id | doaj.art-d6c168173f3e489bb8d39d250994fdde |
| institution | Directory Open Access Journal |
| issn | 2364-3579 2364-3587 |
| language | English |
| last_indexed | 2024-04-12T05:38:10Z |
| publishDate | 2022-12-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Advances in Statistical Climatology, Meteorology and Oceanography |
| spelling | doaj.art-d6c168173f3e489bb8d39d250994fdde2022-12-22T03:45:45ZengCopernicus PublicationsAdvances in Statistical Climatology, Meteorology and Oceanography2364-35792364-35872022-12-01820522410.5194/ascmo-8-205-2022A conditional approach for joint estimation of wind speed and direction under future climatesQ. Wu0J. Bessac1W. Huang2J. Wang3R. Kotamarthi4Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USAMathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USASchool of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, USAEnvironmental Science Division, Argonne National Laboratory, Lemont, IL, USAEnvironmental Science Division, Argonne National Laboratory, Lemont, IL, USA<p>This study develops a statistical conditional approach to evaluate climate model performance in wind speed and direction and to project their future changes under the Representative Concentration Pathway (RCP) 8.5 scenario over inland and offshore locations across the continental United States (CONUS). The proposed conditional approach extends the scope of existing studies by a combined characterization of the wind direction distribution and conditional distribution of wind on the direction, hence enabling an assessment of the joint wind speed and direction distribution and their changes. A von Mises mixture distribution is used to model wind directions across models and climate conditions. Wind speed distributions conditioned on wind direction are estimated using two statistical methods, i.e., a Weibull distributional regression model and a quantile regression model, both of which enforce the circular constraint to their resultant estimated distributions. Projected uncertainties associated with different climate models and model internal variability are investigated and compared with the climate change signal to quantify the robustness of the future projections. In particular, this work extends the concept of internal variability in the climate mean to the standard deviation and high quantiles to assess the relative magnitudes to their projected changes. The evaluation results show that the studied climate model captures both historical wind speed and wind direction and their dependencies reasonably well over both inland and offshore locations. Under the RCP8.5 scenario, most of the studied locations show no significant changes in the mean wind speeds in both winter and summer, while the changes in the standard deviation and 95th quantile show some robust changes over certain locations in winter. Specifically, high wind speeds (95th quantile) conditioned on direction in winter are projected to decrease in the northwestern, Colorado, and northern Great Plains locations in our study. In summer, high wind speeds conditioned on direction over the southern Great Plains increase slightly, while high wind speeds conditioned on direction over offshore locations do not change much. The proposed conditional approach enables a combined characterization of the wind speed distributions conditioned on direction and wind direction distributions, which offers a flexible alternative that can provide additional insights for the joint assessment of speed and direction.</p>https://ascmo.copernicus.org/articles/8/205/2022/ascmo-8-205-2022.pdf |
| spellingShingle | Q. Wu J. Bessac W. Huang J. Wang R. Kotamarthi A conditional approach for joint estimation of wind speed and direction under future climates Advances in Statistical Climatology, Meteorology and Oceanography |
| title | A conditional approach for joint estimation of wind speed and direction under future climates |
| title_full | A conditional approach for joint estimation of wind speed and direction under future climates |
| title_fullStr | A conditional approach for joint estimation of wind speed and direction under future climates |
| title_full_unstemmed | A conditional approach for joint estimation of wind speed and direction under future climates |
| title_short | A conditional approach for joint estimation of wind speed and direction under future climates |
| title_sort | conditional approach for joint estimation of wind speed and direction under future climates |
| url | https://ascmo.copernicus.org/articles/8/205/2022/ascmo-8-205-2022.pdf |
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