Assimilating satellite SST/SSH and in-situ T/S profiles with the Localized Weighted Ensemble Kalman Filter

Meng Shen; Yan Chen; Pinqiang Wang; Weimin Zhang

doi:10.1007/s13131-021-1903-2

Volume 41 Issue 2

Feb. 2022

Turn off MathJax

Article Contents

Article Navigation > Acta Oceanologica Sinica > 2022 > 41(2): 26-40

Meng Shen, Yan Chen, Pinqiang Wang, Weimin Zhang. Assimilating satellite SST/SSH and in-situ T/S profiles with the Localized Weighted Ensemble Kalman Filter[J]. Acta Oceanologica Sinica, 2022, 41(2): 26-40. doi: 10.1007/s13131-021-1903-2

Citation:

Meng Shen, Yan Chen, Pinqiang Wang, Weimin Zhang. Assimilating satellite SST/SSH and in-situ T/S profiles with the Localized Weighted Ensemble Kalman Filter[J]. Acta Oceanologica Sinica, 2022, 41(2): 26-40. doi: 10.1007/s13131-021-1903-2

Citation:

PDF( 2177 KB)

Assimilating satellite SST/SSH and in-situ T/S profiles with the Localized Weighted Ensemble Kalman Filter

doi: 10.1007/s13131-021-1903-2

1.
College of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China
2.
Laboratory of Software Engineering for Complex Systems, Changsha 410073, China

Funds: The National Key Research and Development Program of China under contract No. 2018YFC1406202; the National Natural Science Foundation of China under contract No. 41830964.

More Information

Corresponding author: wmzhang104@139.com
Received Date: 2021-03-05
Accepted Date: 2021-07-27

Available Online: 2021-12-10

Publish Date: 2022-02-01

Abstract

Abstract

The Localized Weighted Ensemble Kalman Filter (LWEnKF) is a new nonlinear/non-Gaussian data assimilation (DA) method that can effectively alleviate the filter degradation problem faced by particle filtering, and it has great prospects for applications in geophysical models. In terms of operational applications, along-track sea surface height (AT-SSH), swath sea surface temperature (S-SST) and in-situ temperature and salinity (T/S) profiles are assimilated using the LWEnKF in the northern South China Sea (SCS). To adapt to the vertical S-coordinates of the Regional Ocean Modelling System (ROMS), a vertical localization radius function is designed for T/S profiles assimilation using the LWEnKF. The results show that the LWEnKF outperforms the local particle filter (LPF) due to the introduction of the Ensemble Kalman Filter (EnKF) as a proposal density; the RMSEs of SSH and SST from the LWEnKF are comparable to the EnKF, but the RMSEs of T/S profiles reduce significantly by approximately 55% for the T profile and 35% for the S profile (relative to the EnKF). As a result, the LWEnKF makes more reasonable predictions of the internal ocean temperature field. In addition, the three-dimensional structures of nonlinear mesoscale eddies are better characterized when using the LWEnKF.
- data assimilation,
- Localized Weighted Ensemble Kalman Filter,
- northern South China Sea,
- sea surface height,
- sea surface temperature,
- temperature and salinity profiles,
- mesoscale eddy

FullText(HTML)

References(34)

References

[1]	Anderson J L. 2003. A local least squares framework for ensemble filtering. Monthly Weather Review, 131(4): 634–642. doi: 10.1175/1520-0493(2003)131<0634:ALLSFF>2.0.CO;2
[2]	Anderson J L. 2007. An adaptive covariance inflation error correction algorithm for ensemble filters. Tellus A, 59(2): 210–224. doi: 10.1111/j.1600-0870.2006.00216.x
[3]	Bonjean F, Lagerloef G S E. 2002. Diagnostic model and analysis of the surface currents in the tropical Pacific Ocean. Journal of Physical Oceanography, 32(10): 2938–2954. doi: 10.1175/1520-0485(2002)032<2938:DMAAOT>2.0.CO;2
[4]	Caruso M J, Gawarkiewicz G G, Beardsley R C. 2006. Interannual variability of the Kuroshio intrusion in the South China Sea. Journal of Oceanography, 62(4): 559–575. doi: 10.1007/s10872-006-0076-0
[5]	Chen Yan, Zhang Weimin, Wang Pinqiang. 2020a. An application of the localized weighted ensemble Kalman filter for ocean data assimilation. Quarterly Journal of the Royal Meteorological Society, 146(732): 3029–3047. doi: 10.1002/qj.3824
[6]	Chen Yan, Zhang Weimin, Zhu Mengbin. 2020b. A localized weighted ensemble Kalman filter for high-dimensional systems. Quarterly Journal of the Royal Meteorological Society, 146(726): 438–453. doi: 10.1002/qj.3685
[7]	Chustagulprom N, Reich S, Reinhardt M. 2016. A hybrid ensemble transform particle filter for nonlinear and spatially extended dynamical systems. SIAM/ASA Journal on Uncertainty Quantification, 4(1): 592–608. doi: 10.1137/15M1040967
[8]	Farchi A, Bocquet M. 2018. Review article: comparison of local particle filters and new implementations. Nonlinear Processes in Geophysics, 25(4): 765–807. doi: 10.5194/npg-25-765-2018
[9]	Gaspari G, Cohn S E. 1999. Construction of correlation functions in two and three dimensions. Quarterly Journal of the Royal Meteorological Society, 125(554): 723–757. doi: 10.1002/qj.49712555417
[10]	Good S A, Martin M J, Rayner N A. 2013. EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. Journal of Geophysical Research: Oceans, 118(12): 6704–6716. doi: 10.1002/2013JC009067
[11]	Hoteit I, Hoar T, Gopalakrishnan G, et al. 2013. A MITgcm/DART ensemble analysis and prediction system with application to the Gulf of Mexico. Dynamics of Atmospheres and Oceans, 63: 1–23. doi: 10.1016/j.dynatmoce.2013.03.002
[12]	Hoteit I, Pham D T, Triantafyllou G, et al. 2008. A new approximate solution of the optimal nonlinear filter for data assimilation in meteorology and oceanography. Monthly Weather Review, 136(1): 317–334. doi: 10.1175/2007MWR1927.1
[13]	Ingleby B, Huddleston M. 2007. Quality control of ocean temperature and salinity profiles -historical and real-time data. Journal of Marine Systems, 65(1–4): 158–175.
[14]	Jia Yinglai, Chassignet E P. 2011. Seasonal variation of eddy shedding from the Kuroshio intrusion in the Luzon Strait. Journal of Oceanography, 67(5): 601–611. doi: 10.1007/s10872-011-0060-1
[15]	Lee Y, Majda A J. 2016. State estimation and prediction using clustered particle filters. Proceedings of the National Academy of Sciences of the United States of America, 113(51): 14609–14614. doi: 10.1073/pnas.1617398113
[16]	Li Yi, Toumi R. 2017. A balanced Kalman filter ocean data assimilation system with application to the South Australian Sea. Ocean Modelling, 116: 159–172. doi: 10.1016/j.ocemod.2017.06.007
[17]	Metzger E J, Smedstad O M, Thoppil P G, et al. 2014. US Navy operational global ocean and Arctic ice prediction systems. Oceanography, 27(3): 32–43. doi: 10.5670/oceanog.2014.66
[18]	Nan Feng, Xue Huijie, Xiu Peng, et al. 2011. Oceanic eddy formation and propagation southwest of Taiwan. Journal of Geophysical Research: Oceans, 116(C12): C12045. doi: 10.1029/2011JC007386
[19]	Papadakis N, Mémin E, Cuzol A, et al. 2010. Data assimilation with the Weighted Ensemble Kalman Filter. Tellus A, 62(5): 673–697. doi: 10.1111/j.1600-0870.2010.00461.x
[20]	Penny S G, Miyoshi T. 2016. A local particle filter for high-dimensional geophysical systems. Nonlinear Processes in Geophysics, 23(6): 391–405. doi: 10.5194/npg-23-391-2016
[21]	Pham D T. 2001. Stochastic methods for sequential data assimilation in strongly nonlinear systems. Monthly Weather Review, 129(5): 1194–1207. doi: 10.1175/1520-0493(2001)129<1194:SMFSDA>2.0.CO;2
[22]	Poterjoy J. 2016. A localized particle filter for high-dimensional nonlinear systems. Monthly Weather Review, 144(1): 59–76. doi: 10.1175/MWR-D-15-0163.1
[23]	Poterjoy J, Wicker L, Buehner M. 2019. Progress toward the application of a Localized Particle Filter for numerical weather prediction. Monthly Weather Review, 147(4): 1107–1126. doi: 10.1175/MWR-D-17-0344.1
[24]	Rebeschini P, van Handel R. 2015. Can local particle filters beat the curse of dimensionality?. The Annals of Applied Probability, 25(5): 2809–2866
[25]	Sebastien B, Anne C, Sai S G, et al. 2013. Weighted ensemble transform Kalman filter for image assimilation. Tellus A, 65(1): 18803. doi: 10.3402/tellusa.v65i0.18803
[26]	Shen Zheqi, Tang Youmin, Gao Yanqiu. 2016. The theoretical framework of the ensemble-based data assimilation method and its prospect in oceanic data assimilation. Haiyang Xuebao (in Chinese), 38(3): 1–14
[27]	Shen Zheqi, Tang Youmin, Li Xiaojing. 2017. A new formulation of vector weights in localized particle filters. Quarterly Journal of the Royal Meteorological Society, 143(709): 3269–3278. doi: 10.1002/qj.3180
[28]	van Leeuwen P J, Cheng Yuan, Reich S. 2015. Nonlinear Data Assimilation. Cham: Springer, 31–41
[29]	van Leeuwen P J, Künsch H R, Nerger L, et al. 2019. Particle filters for high-dimensional geoscience applications: a review. Quarterly Journal of the Royal Meteorological Society, 145(723): 2335–2365. doi: 10.1002/qj.3551
[30]	Wang Pinqiang, Zhu Mengbin, Chen Yan, et al. 2020. Implicit equal-weights Variational particle smoother. Atmosphere, 11(4): 338. doi: 10.3390/atmos11040338
[31]	Wang Pinqiang, Zhu Mengbin, Chen Yan, et al. 2021. Ocean satellite data assimilation using the implicit equal-weights variational particle smoother. Ocean Modelling, 164: 101833. doi: 10.1016/j.ocemod.2021.101833
[32]	Zhang Zhiwei, Tian Jiwei, Qiu Bo, et al. 2016. Observed 3D structure, generation, and dissipation of oceanic Mesoscale eddies in the South China Sea. Scientific Reports, 6(1): 24349. doi: 10.1038/srep24349
[33]	Zhang Yongchui, Wang Ning, Zhou Lin, et al. 2020. The surface and three-dimensional characteristics of mesoscale eddies: a review. Advances in Earth Science, 35(6): 568–580
[34]	Zhang Zhiwei, Zhao Wei, Tian Jiwei, et al. 2013. A mesoscale eddy pair southwest of Taiwan and its influence on deep circulation. Journal of Geophysical Research: Oceans, 118(12): 6479–6494. doi: 10.1002/2013JC008994