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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.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
310--317
Opis fizyczny
Bibliogr. 18 poz., rys., wykr.
Twórcy
autor
- Indian National Centre for Ocean Information Services, Hyderabad, India
autor
- Indian National Centre for Ocean Information Services, Hyderabad, India
autor
- Ministry of Earth Sciences, New Delhi, India
autor
- Jawaharlal Nehru Technological University, Hyderabad, India
autor
- Indian National Centre for Ocean Information Services, Hyderabad, India
autor
- Indian National Centre for Ocean Information Services, Hyderabad, India
Bibliografia
- 1. Chen, J., Gupta, A.K., 2012. Univariate Normal Model. In: Parametric statistical change point analysis: with applications to genetics, medicine, and finance. Springer Science & Business Media, 7-88.
- 2. Bendat, J.S., Piersol, A.G., 2011. Random data: analysis and measurement procedures. John Wiley & Sons, 640 pp.
- 3. Edson, J.B., Jampana, V., Weller, R.A., Bigorre, S.P., Plueddemann, A.J., Fairall, C.W., Miller, S.D., Mahrt, L., Vickers, D., Hersbach, H., 2013. On the exchange of momentum over the open ocean. J. Phys. Oceanogr. 43 (8), 1589-1610. https://doi.org/10.1175/JPO-D-12-0173.1
- 4. Fairall, C.W., Bradley, E.F., Hare, J.E., Grachev, A.A., Edson, J.B., 2003. Bulk parameterization of air—sea fluxes: Updates and verification for the COARE algorithm. J. Climate 16 (4), 571-591. https://doi.org/10.1175/1520-0442(2003)016〈0571:BPOASF〉2.0.CO;2
- 5. Fedorov, K.N., Ginzburg, A.I., 1992. The near-surface layer of the ocean. VSP, Utrecht, The Netherlands, 256 pp.
- 6. Haynes, K., Eckley, I.A., Fearnhead, P., 2017. Computationally efficient changepoint detection for a range of penalties. J. Comput. Graph. Stat. 26 (1), 134-143. https://doi.org/10.1080/10618600.2015.1116445
- 7. Hopkins, J., Challenor, P., Shaw, A.G.P., 2010. A new Statistical Modeling approach to ocean front detection from SST satellite images. J. Atmos. Ocean Tech. 27 (1), 173-191. https://doi.org/10.1175/2009JTECHO684.1
- 8. Horváth, L., 1993. The maximum likelihood method for testing changes in the parameters of normal observations. Ann. Stat. 21 (2), 671-680.
- 9. IMD, 2020. Super Cyclonic Strom "AMPHAN" ove the sourtheaast Bay of Bengal (16th-21st May 2020): Summary. [Online] Available at: https://internal.imd.gov.in/press_release/20200614_pr_840.pdf
- 10. Jackson, B., Scargle, J.D., Barnes, D., Arabhi, S., Alt, A., Gioumousis, P., Gwin, E., Sangtrakulcharoen, P., Tan, L., Tsai, T.T., 2005. An algorithm for optimal partitioning of data on an interval. IEEE Signal Proc. Let. 12 (2), 105-108. https://doi.org/10.1109/LSP.2001.838216
- 11. James, N.A., Matteson, D.S., 2015. ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data. J. Stat. Softw. 62 (7), 1-25. https://doi.org/10.18637/jss.v062.i07
- 12. Killick, R., Eckley, I., 2014. changepoint: An R package for change point analysis. J. Stat. Softw. 58 (3), 1-19. https://doi.org/10.18637/jss.v058.i03
- 13. Maidstone, R., Hocking, T., Rigaill, G., Fearnhead, P., 2017. On optimal multiple changepoint algorithms for large data. Stat. Comput. 27, 519-533. https://doi.org/10.1007/s11222-016-9636-3
- 14. Ross, G.J., 2015. Parametric and nonparametric sequential change detection in R: The cpm package. J. Stat. Softw. 66 (3), 1-20. https://doi.org/10.18637/jss.v066.i03
- 15. Truong, C., Oudre, L., Vayatis, N., 2020. Selective review of offline change point detection methods. Signal Process. 167, 107299. https://doi.org/10.1016/j.sigpro.2019.107299
- 16. Venkatesan, R., Shamji, V., Latha, G., Mathew, S., Rao, R., Muthiah, A., Atmanand, M., 2013. In situ ocean subsurface time-series measurements from OMNI buoy network in the Bay of Bengal. Curr. Sci. 104 (9), 1166-1177.
- 17. Yu, L., 2019. Global air-sea fluxes of heat, fresh water, and momentum: energy budget closure and unanswered questions. Annu. Rev. Mar. Sci. 11 (1), 227-248. https://doi.org/10.1146/annurev- marine- 010816- 060704
- 18. Zweers, N., Makin, V., De Vries, J., Burgers, G., 2010. A sea drag relation for hurricane wind speeds. Geophys. Res. Lett. (21) 37. https://doi.org/10.1029/2010GL045002
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023). (PL)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3f591ca5-55ab-4cda-b157-1a6005f49af0