Modeling and Parameter Estimation of Hurricane Wind Fields with Asymmetry
© 2020 American Meteorological Society. This article presents an azimuthally asymmetric gradient hurricane wind field model that can be coupled with hurricane-track models for engineering wind risk assessments. The model incorporates low-wavenumber asymmetries into the maximum wind intensity paramet...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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American Meteorological Society
2021
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| Online Access: | https://hdl.handle.net/1721.1/133621 |
| _version_ | 1826189152117325824 |
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| author | Chang, Derek Amin, Saurabh Emanuel, Kerry |
| author_facet | Chang, Derek Amin, Saurabh Emanuel, Kerry |
| author_sort | Chang, Derek |
| collection | MIT |
| description | © 2020 American Meteorological Society. This article presents an azimuthally asymmetric gradient hurricane wind field model that can be coupled with hurricane-track models for engineering wind risk assessments. The model incorporates low-wavenumber asymmetries into the maximum wind intensity parameter of the Holland et al. wind field model. The am-plitudes and phases of the asymmetries are parametric functions of the storm-translation speed and wind shear. Model parameters are estimated by solving a constrained, nonlinear least squares (CNLS) problem that minimizes the sum of squared residuals between wind field intensities of historical storms and model-estimated winds. There are statistically significant wavenumber-1 asymmetries in the wind field resulting from both storm translation and wind shear. Adding the translation vector to the wind field model with wavenumber-1 asymmetries further improves the model’s estimation performance. In addition, inclusion of the wavenumber-1 asymmetry resulting from translation results in a greater decrease in modeling error than does inclusion of the wavenumber-1 shear-induced asymmetry. Overall, the CNLS estimation method can handle the inherently nonlinear wind field model in a flexible manner; thus, it is well suited to capture the radial variability in the hurricane wind field’s asymmetry. The article concludes with brief remarks on how the CNLS-estimated model can be applied for simulating wind fields in a statistically generated ensemble. |
| first_indexed | 2024-09-23T08:10:21Z |
| format | Article |
| id | mit-1721.1/133621 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:10:21Z |
| publishDate | 2021 |
| publisher | American Meteorological Society |
| record_format | dspace |
| spelling | mit-1721.1/1336212021-10-28T05:01:59Z Modeling and Parameter Estimation of Hurricane Wind Fields with Asymmetry Chang, Derek Amin, Saurabh Emanuel, Kerry © 2020 American Meteorological Society. This article presents an azimuthally asymmetric gradient hurricane wind field model that can be coupled with hurricane-track models for engineering wind risk assessments. The model incorporates low-wavenumber asymmetries into the maximum wind intensity parameter of the Holland et al. wind field model. The am-plitudes and phases of the asymmetries are parametric functions of the storm-translation speed and wind shear. Model parameters are estimated by solving a constrained, nonlinear least squares (CNLS) problem that minimizes the sum of squared residuals between wind field intensities of historical storms and model-estimated winds. There are statistically significant wavenumber-1 asymmetries in the wind field resulting from both storm translation and wind shear. Adding the translation vector to the wind field model with wavenumber-1 asymmetries further improves the model’s estimation performance. In addition, inclusion of the wavenumber-1 asymmetry resulting from translation results in a greater decrease in modeling error than does inclusion of the wavenumber-1 shear-induced asymmetry. Overall, the CNLS estimation method can handle the inherently nonlinear wind field model in a flexible manner; thus, it is well suited to capture the radial variability in the hurricane wind field’s asymmetry. The article concludes with brief remarks on how the CNLS-estimated model can be applied for simulating wind fields in a statistically generated ensemble. 2021-10-27T19:53:52Z 2021-10-27T19:53:52Z 2020 2021-09-15T17:48:18Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/133621 en 10.1175/JAMC-D-19-0126.1 Journal of Applied Meteorology and Climatology Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf American Meteorological Society American Meteorological Society (AMS) |
| spellingShingle | Chang, Derek Amin, Saurabh Emanuel, Kerry Modeling and Parameter Estimation of Hurricane Wind Fields with Asymmetry |
| title | Modeling and Parameter Estimation of Hurricane Wind Fields with Asymmetry |
| title_full | Modeling and Parameter Estimation of Hurricane Wind Fields with Asymmetry |
| title_fullStr | Modeling and Parameter Estimation of Hurricane Wind Fields with Asymmetry |
| title_full_unstemmed | Modeling and Parameter Estimation of Hurricane Wind Fields with Asymmetry |
| title_short | Modeling and Parameter Estimation of Hurricane Wind Fields with Asymmetry |
| title_sort | modeling and parameter estimation of hurricane wind fields with asymmetry |
| url | https://hdl.handle.net/1721.1/133621 |
| work_keys_str_mv | AT changderek modelingandparameterestimationofhurricanewindfieldswithasymmetry AT aminsaurabh modelingandparameterestimationofhurricanewindfieldswithasymmetry AT emanuelkerry modelingandparameterestimationofhurricanewindfieldswithasymmetry |