IEEE Geoscience and Remote Sensing Magazine - September 2022 - 92

Sea, " Geophys. Res. Lett., vol. 35, no. 5, p. L05610, 2008, doi:
10.1029/2007GL032908.
[108] J. Sha, Y.-H. Jo, X.-H. Yan, and W. T. Liu, " The modulation of
the seasonal cross-shelf sea level variation by the cold pool in
the Middle Atlantic Bight, " J. Geophys. Res., Oceans, vol. 120, no.
11, pp. 7182-7194, 2015, doi: 10.1002/2015JC011255.
[109] F. Ienna, Y.-H. Jo, and X.-H. Yan, " A new method for tracking Meddies
by satellite altimetry, " J. Atmospheric Ocean. Technol., vol. 31,
no. 6, pp. 1434-1445, 2014, doi: 10.1175/JTECH-D-13-00080.1.
[110] A. K. Liu, Y. S. Chang, M.-K. Hsu, and N. K. Liang, " Evolution
of nonlinear internal waves in the East and South China Seas, "
J. Geophys. Res., Oceans, vol. 103, no. C4, pp. 7995-8008, 1998,
doi: 10.1029/97JC01918.
[111] R. Susanto, L. Mitnik, and Q. Zheng, " Ocean internal waves observed, "
Oceanography, vol. 18, no. 4, p. 80, 2005, doi: 10.5670/
oceanog.2005.08.
[112] I. S. Robinson, Measuring the Oceans from Space: The Principles
and Methods of Satellite Oceanography. Berlin, Germany: Springer
Science & Business Media, 2004.
[113] G. L. Mellor and T. Yamada, " A hierarchy of turbulence closure
models for planetary boundary layers, " J. Atmospheric Sci., vol.
31, no. 7, pp. 1791-1806, 1974, doi: 10.1175/1520-0469(1974)
031<1791:AHOTCM>2.0.CO;2.
[114] A. Carrassi, M. Bocquet, L. Bertino, and G. Evensen, " Data assimilation
in the geosciences: An overview of methods, issues,
and perspectives, " WIREs Climate Change, vol. 9, no. 5, p. e535,
2018, doi: 10.1002/wcc.535.
[115] I. Hoteit, X. Luo, M. Bocquet, A. Ko¨hl, and B. Ait-El-Fquih,
" Data assimilation in oceanography: Current status and new
directions, " in New Frontiers in Operational Oceanography, E.
P. Chassignet, A. Pascual, J. Tintore, and J. Verron, Eds. Santa
Cruz, CA, USA: GODAE OceanView, 2018, p. 815, doi:
10.17125/gov2018.ch17.
[116] A. M. Moore et al., " Synthesis of ocean observations using data
assimilation for operational, real-time and reanalysis systems:
A more complete picture of the state of the ocean, " Frontiers Mar.
Sci., vol. 6, p. 90, Mar. 2019, doi: 10.3389/fmars.2019.00090.
[117] K. Haines, " Assimilation of satellite altimetry in ocean models, "
in Ocean Forecasting: Conceptual Basis and Applications, N.
Pinardi and J. Woods, Eds. Berlin, Germany: Springer-Verlag,
2002, pp. 117-130.
[118] P. C. Chu, C. R. Fralick Jr., S. D. Haeger, and M. J. Carron,
" A parametric model for the Yellow Sea thermal variability, "
J. Geophys. Res., Oceans, vol. 102, no. C5, pp. 10,499-10,507,
1997, doi: 10.1029/97JC00444.
[119] P. C. Chu, Q. Wang, and R. H. Bourke, " A geometric model
for the Beaufort/Chukchi sea thermohaline structure, " J. Atmospheric
Ocean. Technol., vol. 16, no. 6, pp. 613-632, 1999, doi:
10.1175/1520-0426(1999)016<0613:AGMFTB>2.0.CO;2.
[120] P. C. Chu, C. Fan, and W. T. Liu, " Determination of vertical
thermal structure from sea surface temperature, " J. Atmospheric
Ocean. Technol., vol. 17, no. 7, pp. 971-979, 2000, doi:
10.1175/1520-0426(2000)017<0971:DOVTSF>2.0.CO;2.
[121] H.-Y. Kao and J.-Y. Yu, " Contrasting Eastern-Pacific and Central-Pacific
types of ENSO, " J. Climate, vol. 22, no. 3, pp. 615-
632, 2009, doi: 10.1175/2008JCLI2309.1.
92
[122] A. Capotondi et al., " Understanding ENSO diversity, " Bull.
Amer. Meterological Soc., vol. 96, no. 6, pp. 921-938, 2015, doi:
10.1175/BAMS-D-13-00117.1.
[123] C. Wang, C. Deser, J.-Y. Yu, P. DiNezio, and A. Clement, " El
Niño and Southern oscillation (ENSO): A review, " in Coral
Reefs of the Eastern Tropical Pacific: Persistence and Loss in a
Dynamic Environment, P. W. Glynn, D. P. Manzello, and I. C.
Enochs, Eds. Dordrecht, Netherlands: Springer Science & Business
Media, 2017, pp. 85-106.
[124] R. Morrow et al., " Global observations of fine-scale ocean surface
topography with the Surface Water and Ocean Topography
(SWOT) mission, " Frontiers Mar. Sci., vol. 6, p. 232, May 2019,
doi: 10.3389/fmars.2019.00232.
[125] B. N. Holben, " Characteristics of maximum-value composite
images from temporal AVHRR data, " Int. J. Remote Sens., vol. 7,
no. 11, pp. 1417-1434, 1986, doi: 10.1080/01431168608948945.
[126] L. Ricciardulli and F. J. Wentz, " A Scatterometer geophysical
model function for climate-quality winds: QuikSCAT Ku2011, "
J. Atmospheric Ocean. Technol., vol. 32, no. 10, pp. 1829-
1846, 2015, doi: 10.1175/JTECH-D-15-0008.1.
[127] A. Bentamy and D. C. Fillon, " Gridded surface wind fields from
Metop/ASCAT measurements, " Int. J. Remote Sens., vol. 33, no.
6, pp. 1729-1754, 2012, doi: 10.1080/01431161.2011.600348.
[128] S. B. Hooker, " SeaWiFS technical report series: An overview
of SeaWiFS and ocean color, " NASA Technical Memorandum,
Washington, DC, USA, 1992. [Online]. Available: https://
ntrs.nasa.gov/citations/19920020443
[129] W. E. Esaias et al., " An overview of MODIS capabilities for
ocean science observations, " IEEE Trans. Geosci. Remote Sens.,
vol. 36, no. 4, pp. 1250-1265, 1998, doi: 10.1109/36.701076.
[130] L. Meng, C. Yan, W. Zhuang, W. Zhang, and X.-H. Yan, " Reconstruction
of three-dimensional temperature and salinity fields
from satellite observations, " J. Geophys. Res., Oceans, vol. 126,
no. 11, p. e2021JC017605, 2021, doi: 10.1029/2021JC017605.
[131] I. J. Goodfellow et al., " Generative adversarial networks, " 2014,
arXiv:1406.2661.
[132] A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta,
and A. A. Bharath, " Generative adversarial networks: An
overview, " IEEE Signal Process. Mag., vol. 35, no. 1, pp. 53-65,
2018, doi: 10.1109/MSP.2017.2765202.
[133] M. Hussain, J. J. Bird, and D. R. Faria, " A study on CNN transfer
learning for image classification, " in U.K. Workshop on computational
Intelligence, A. Lotfi, H. Bouchachia, A. Gegov, C. Langensiepen,
and M. McGinnity, Eds. Cham: Springer Nature
Switzerland AG, 2018, pp. 191-202.
[134] E. Aljalbout, V. Golkov, Y. Siddiqui, M. Strobel, and D. Cremers,
" Clustering with deep learning: Taxonomy and new
methods, " 2018, arXiv:1801.07648.
[135] H. Su, T. Zhang, M. Lin, W. Lu, and X.-H. Yan, " Predicting subsurface
thermohaline structure from remote sensing data based on
long short-term memory neural networks, " Remote Sens. Environ.,
vol. 260, p. 112,465, Jul. 2021, doi: 10.1016/j.rse.2021.112465.
[136] I. Kozlov, D. Romanenkov, A. Zimin, and B. Chapron, " SAR
observing large-scale nonlinear internal waves in the White
Sea, " Remote Sens. Environ., vol. 147, pp. 99-107, May 2014,
doi: 10.1016/j.rse.2014.02.017.
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