On the non-parametric changepoint detection of flow regimes in cyclone Amphan
The Bay of Bengal was witness to a severe cyclone named Amphan during the summer of the year 2020. The National Institute of Ocean Technology (NIOT), INDIA moorings BD08 and BD09 happened to be in the vicinity of the cyclone. The highly instrumented mooring recorded near-surface meteorological param...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | Oceanologia |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0078323422000823 |
| _version_ | 1797844304448716800 |
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| author | Venkat Shesu Reddem Venkata Jampana Ravichandran Muthalagu Venkateswara Rao Bekkam Pattabhi Rama Rao Eluri Srinivasa Kumar Tummala |
| author_facet | Venkat Shesu Reddem Venkata Jampana Ravichandran Muthalagu Venkateswara Rao Bekkam Pattabhi Rama Rao Eluri Srinivasa Kumar Tummala |
| author_sort | Venkat Shesu Reddem |
| collection | DOAJ |
| description | The Bay of Bengal was witness to a severe cyclone named Amphan during the summer of the year 2020. The National Institute of Ocean Technology (NIOT), INDIA moorings BD08 and BD09 happened to be in the vicinity of the cyclone. The highly instrumented mooring recorded near-surface meteorological parameters like wind speed, sea surface temperature, and near-surface pressure. This article explores the possibility of using a non-parametric algorithm to identify different flow regimes using a one-month long time-series data of the near-surface parameters. The changes in the structure of the time series signal were statistically segmented using an unconstrained non-parametric algorithm. The non-parametric changepoint method was applied to time series of near-surface winds, sea surface temperature, sea level pressure, air temperature and salinity and the segmentations are consistent with visual observations. Identifying different data segments and their simple parameterization is a crucial component and relating them to different flow regimes is useful for the development of parametrization schemes in weather and climate models. The segmentations can considerably simplify the parametrization schemes when expressed as linear functions. Moreover, the usefulness of non-parametric automatic detection of data segments of similar statistical properties shall be more apparent when dealing with relatively long time series data. |
| first_indexed | 2024-04-09T17:20:08Z |
| format | Article |
| id | doaj.art-092ef5775ee449098a62111663a60558 |
| institution | Directory Open Access Journal |
| issn | 0078-3234 |
| language | English |
| last_indexed | 2024-04-09T17:20:08Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Oceanologia |
| spelling | doaj.art-092ef5775ee449098a62111663a605582023-04-19T04:21:38ZengElsevierOceanologia0078-32342023-04-01652310317On the non-parametric changepoint detection of flow regimes in cyclone AmphanVenkat Shesu Reddem0Venkata Jampana1Ravichandran Muthalagu2Venkateswara Rao Bekkam3Pattabhi Rama Rao Eluri4Srinivasa Kumar Tummala5Indian National Centre for Ocean Information Services, Hyderabad, India; Corresponding author at: Indian National Centre for Ocean Information Services, Hyderabad, India.Indian National Centre for Ocean Information Services, Hyderabad, IndiaMinistry of Earth Sciences, New Delhi, IndiaJawaharlal Nehru Technological University, Hyderabad, IndiaIndian National Centre for Ocean Information Services, Hyderabad, IndiaIndian National Centre for Ocean Information Services, Hyderabad, IndiaThe Bay of Bengal was witness to a severe cyclone named Amphan during the summer of the year 2020. The National Institute of Ocean Technology (NIOT), INDIA moorings BD08 and BD09 happened to be in the vicinity of the cyclone. The highly instrumented mooring recorded near-surface meteorological parameters like wind speed, sea surface temperature, and near-surface pressure. This article explores the possibility of using a non-parametric algorithm to identify different flow regimes using a one-month long time-series data of the near-surface parameters. The changes in the structure of the time series signal were statistically segmented using an unconstrained non-parametric algorithm. The non-parametric changepoint method was applied to time series of near-surface winds, sea surface temperature, sea level pressure, air temperature and salinity and the segmentations are consistent with visual observations. Identifying different data segments and their simple parameterization is a crucial component and relating them to different flow regimes is useful for the development of parametrization schemes in weather and climate models. The segmentations can considerably simplify the parametrization schemes when expressed as linear functions. Moreover, the usefulness of non-parametric automatic detection of data segments of similar statistical properties shall be more apparent when dealing with relatively long time series data.http://www.sciencedirect.com/science/article/pii/S0078323422000823AmphanChangepoint detectionCyclone |
| spellingShingle | Venkat Shesu Reddem Venkata Jampana Ravichandran Muthalagu Venkateswara Rao Bekkam Pattabhi Rama Rao Eluri Srinivasa Kumar Tummala On the non-parametric changepoint detection of flow regimes in cyclone Amphan Oceanologia Amphan Changepoint detection Cyclone |
| title | On the non-parametric changepoint detection of flow regimes in cyclone Amphan |
| title_full | On the non-parametric changepoint detection of flow regimes in cyclone Amphan |
| title_fullStr | On the non-parametric changepoint detection of flow regimes in cyclone Amphan |
| title_full_unstemmed | On the non-parametric changepoint detection of flow regimes in cyclone Amphan |
| title_short | On the non-parametric changepoint detection of flow regimes in cyclone Amphan |
| title_sort | on the non parametric changepoint detection of flow regimes in cyclone amphan |
| topic | Amphan Changepoint detection Cyclone |
| url | http://www.sciencedirect.com/science/article/pii/S0078323422000823 |
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