Articles | Volume 19, issue 6
https://doi.org/10.5194/os-19-1579-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/os-19-1579-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impact of surface and subsurface-intensified eddies on sea surface temperature and chlorophyll a in the northern Indian Ocean utilizing deep learning
Yingjie Liu
CAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
Xiaofeng Li
CORRESPONDING AUTHOR
CAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
Related authors
Qian Liu, Yingjie Liu, and Xiaofeng Li
Biogeosciences, 20, 4857–4874, https://doi.org/10.5194/bg-20-4857-2023, https://doi.org/10.5194/bg-20-4857-2023, 2023
Short summary
Short summary
In the Southern Ocean, abundant eddies behave opposite to our expectations. That is, anticyclonic (cyclonic) eddies are cold (warm). By investigating the variations of physical and biochemical parameters in eddies, we find that abnormal eddies have unique and significant effects on modulating the parameters. This study fills a gap in understanding the effects of abnormal eddies on physical and biochemical parameters in the Southern Ocean.
Haoyu Wang, Mengjiao Wang, and Xiaofeng Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-545, https://doi.org/10.5194/essd-2025-545, 2025
Preprint under review for ESSD
Short summary
Short summary
DeepFlux provides a global, gap-free, daily record of air temperature, humidity, and turbulent heat flux from 1992 to 2020. Using satellite data and deep learning, it fills missing observations and delivers continuous estimates. Tests against in situ measurements show it is closer to reality and more reliable than existing products. This open resource supports improved climate studies and model evaluation.
Yibin Ren, Xiaofeng Li, and Yunhe Wang
Geosci. Model Dev., 18, 2665–2678, https://doi.org/10.5194/gmd-18-2665-2025, https://doi.org/10.5194/gmd-18-2665-2025, 2025
Short summary
Short summary
This study developed a transformer-based deep learning model to predict the Arctic sea ice seasonally. By integrating the sea ice thickness data into the model, the spring prediction barrier of Arctic sea ice is optimized significantly. The model achieves better skills than the typical numerical model in predicting September’s sea ice extent seasonally. The sea ice thickness data play a key role in reducing the prediction errors of the Beaufort Sea, the East Siberian Sea and the Laptev Sea.
Xudong Zhang and Xiaofeng Li
Earth Syst. Sci. Data, 16, 5131–5144, https://doi.org/10.5194/essd-16-5131-2024, https://doi.org/10.5194/essd-16-5131-2024, 2024
Short summary
Short summary
Internal wave (IW) is an important ocean process and is frequently observed in the South China Sea (SCS). This study presents a detailed IW dataset for the northern SCS spanning from 2000 to 2022, with a spatial resolution of 250 m, comprising 3085 IW MODIS images. This dataset can enhance understanding of IW dynamics and serve as a valuable resource for studying ocean dynamics, validating numerical models, and advancing AI-driven model building, fostering further exploration into IW phenomena.
Le Gao, Yuan Guo, and Xiaofeng Li
Earth Syst. Sci. Data, 16, 4189–4207, https://doi.org/10.5194/essd-16-4189-2024, https://doi.org/10.5194/essd-16-4189-2024, 2024
Short summary
Short summary
Since 2008, the Yellow Sea has faced a significant ecological issue, the green tide, which has become one of the world's largest marine disasters. Satellite remote sensing plays a pivotal role in detecting this phenomenon. This study uses AI-based models to extract the daily green tide from MODIS and SAR images and integrates these daily data to introduce a continuous weekly dataset, which aids research in disaster simulation, forecasting, and prevention.
Qian Liu, Yingjie Liu, and Xiaofeng Li
Biogeosciences, 20, 4857–4874, https://doi.org/10.5194/bg-20-4857-2023, https://doi.org/10.5194/bg-20-4857-2023, 2023
Short summary
Short summary
In the Southern Ocean, abundant eddies behave opposite to our expectations. That is, anticyclonic (cyclonic) eddies are cold (warm). By investigating the variations of physical and biochemical parameters in eddies, we find that abnormal eddies have unique and significant effects on modulating the parameters. This study fills a gap in understanding the effects of abnormal eddies on physical and biochemical parameters in the Southern Ocean.
Cited articles
Assassi, C., Morel, Y., Vandermeirsch, F., Chaigneau, A., Pegliasco, C., Morrow, R., Colas, F., Fleury, S., Carton, X., and Klein, P.: An index to distinguish surface- and subsurface-intensified vortices from surface observations, J. Phys. Oceanogr., 46, 2529–2552, https://doi.org/10.1175/JPO-D-15-0122.1, 2016.
Babu, M. T., Kumar, S. P., and Rao, D. P.: A subsurface cyclonic eddy in the Bay of Bengal, J. Mar. Res., 49, 403–410, 1991.
Badin, G., Tandon, A., and Mahadevan, A.: Lateral mixing in the pycnocline by baroclinic mixed layer eddies, J. Phys. Oceanogr., 41, 2080–2101, https://doi.org/10.1175/JPO-D-11-05.1, 2011.
Banse, K. and English, D.: Geographical differences in seasonality of CZCS-derived phytoplankton pigment in the Arabian Sea for 1978–1986, Deep-Sea Res. Pt. II, 47, 1623–1677, https://doi.org/10.1016/S0967-0645(99)00157-5, 2000.
Bartier, P. M. and Keller, C. P.: Multivariate interpolation to incorporate thematic surface data using inverse distance weighting (IDW), Comput. Geosci., 22, 795–799, https://doi.org/10.1016/0098-3004(96)00021-0, 1996.
Bashmachnikov, I., Boutov, D., and Dias, J.: Manifestation of two meddies in altimetry and sea-surface temperature, Ocean Sci., 9, 249–259, https://doi.org/10.5194/os-9-249-2013, 2013.
Bisson, K., Boss, E., Westberry, T., and Behrenfeld, M.: Evaluating satellite estimates of particulate backscatter in the global open ocean using autonomous profiling floats, Opt. Express, 27, 30191–30203, https://doi.org/10.1364/OE.27.030191, 2019.
Bôas, A. B. V., Sato, O. T., Chaigneau, A., and Castelão, G. P.: The signature of mesoscale eddies on the air-sea turbulent heat fluxes in the South Atlantic Ocean, Geophys. Res. Lett., 42, 1856–1862, https://doi.org/10.1002/2015GL063105, 2015.
Brock, J. C., McClain, C. R., Luther, M. E., and Hay, W. W.: The phytoplankton bloom in the northwestern Arabian Sea during the southwest monsoon of 1979, J. Geophys. Res.-Oceans, 96, 20623–20642, https://doi.org/10.1029/91JC01711, 1991.
Caballero, A., Pascual, A., Dibarboure, G., and Espino, M.: Sea level and Eddy Kinetic Energy variability in the Bay of Biscay, inferred from satellite altimeter data, J. Marine Syst., 72, 116–134, https://doi.org/10.1016/j.jmarsys.2007.03.011, 2008.
Chelton, D. B., Gaube, P., Schlax, M. G., Early, J. J., and Samelson, R. M.: The influence of nonlinear mesoscale eddies on near-surface oceanic chlorophyll, Science, 334, 328–332, https://doi.org/10.1126/science.1208897, 2011a.
Chelton, D. B., Schlax, M. G., and Samelson, R. M.: Global observations of nonlinear mesoscale eddies, Prog. Oceanogr., 91, 167–216, https://doi.org/10.1016/j.pocean.2011.01.002, 2011b.
Chen, G. and Han, G.: Contrasting short–lived with long–lived mesoscale eddies in the global ocean, J. Geophys. Res.-Oceans, 124, 3149–3167, https://doi.org/10.1029/2019JC014983, 2019.
Chen, G., Wang, D., and Hou, Y.: The features and interannual variability mechanism of mesoscale eddies in the Bay of Bengal, Cont. Shelf Res., 47, 178–185, https://doi.org/10.1016/j.csr.2012.07.011, 2012.
Chen, G., Han, G., and Yang, X.: On the intrinsic shape of oceanic eddies derived from satellite altimetry, Remote Sens. Environ., 228, 75–89, https://doi.org/10.1016/j.rse.2019.04.011, 2019.
Chen, G., Yang, J., and Han, G.: Eddy morphology: Egg-like shape, overall spinning, and oceanographic implications, Remote Sens. Environ., 257, 112348, https://doi.org/10.1016/j.rse.2021.112348, 2021.
Cheng, X., McCreary, J. P., Qiu, B., Qi, Y., Du, Y., and Chen, X.: Dynamics of Eddy Generation in the Central Bay of Bengal, J. Geophys. Res.-Oceans, 123, 6861–6875, https://doi.org/10.1029/2018jc014100, 2018.
Cui, W., Yang, J., and Ma, Y.: A statistical analysis of mesoscale eddies in the Bay of Bengal from 22 year altimetry data, Acta Oceanol. Sin., 35, 16–27, https://doi.org/10.1007/s13131-016-0945-3, 2016.
Dolz, J., Gopinath, K., Yuan, J., Lombaert, H., Desrosiers, C., and Ayed, I. B.: HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation, IEEE T. Med. Imaging, 38, 1116–1126, https://doi.org/10.1109/TMI.2018.2878669, 2018.
Dong, D., Brandt, P., Chang, P., Schütte, F., Yang, X., Yan, J., and Zeng, J.: Mesoscale Eddies in the Northwestern Pacific Ocean: Three-Dimensional Eddy Structures and Heat/Salt Transports, J. Geophys. Res.-Oceans, 122, 9795–9813, https://doi.org/10.1002/2017jc013303, 2017.
Faghmous, J. H., Frenger, I., Yao, Y., Warmka, R., Lindell, A., and Kumar, V.: A daily global mesoscale ocean eddy dataset from satellite altimetry, Scientific Data, 2, 150028, https://doi.org/10.1038/sdata.2015.28, 2015.
Findlater, J.: A major low-level air current near the Indian Ocean during the northern summer, Q. J. Roy. Meteor. Soc., 95, 362–380, https://doi.org/10.1002/qj.49709540409, 1969.
Garfield, N., Collins, C. A., Paquette, R. G., and Carter, E.: Lagrangian exploration of the California Undercurrent, 1992–95, J. Phys. Oceanogr., 29, 560–583, 1999.
Gaube, P., Chelton, D. B., Strutton, P. G., and Behrenfeld, M. J.: Satellite observations of chlorophyll, phytoplankton biomass, and Ekman pumping in nonlinear mesoscale eddies, J. Geophys. Res.-Oceans, 118, 6349–6370, https://doi.org/10.1002/2013jc009027, 2013.
Gaube, P., McGillicuddy, D. J., Chelton, D. B., Behrenfeld, M. J., and Strutton, P. G.: Regional variations in the influence of mesoscale eddies on near-surface chlorophyll, J. Geophys. Res.-Oceans, 119, 8195–8220, https://doi.org/10.1002/2014JC010111, 2014.
Gaube, P., Chelton, D. B., Samelson, R. M., Schlax, M. G., and O'Neill, L. W.: Satellite observations of mesoscale eddy-Induced Ekman pumping, J. Phys. Oceanogr., 45, 104–132, https://doi.org/10.1175/jpo-d-14-0032.1, 2015.
Greaser, S. R., Subrahmanyam, B., Trott, C. B., and Roman-Stork, H. L.: Interactions between mesoscale eddies and synoptic oscillations in the Bay of Bengal during the strong monsoon of 2019, J. Geophys. Res.-Oceans, 125, e2020JC016772, https://doi.org/10.1029/2020JC016772, 2020.
Gulakaram, V. S., Vissa, N. K., and Bhaskaran, P. K.: Characteristics and vertical structure of oceanic mesoscale eddies in the Bay of Bengal, Dynam. Atmos. Oceans, 89, 101131, https://doi.org/10.1016/j.dynatmoce.2020.101131, 2020.
Haëntjens, N., Della Penna, A., Briggs, N., Karp-Boss, L., Gaube, P., Claustre, H., and Boss, E.: Detecting mesopelagic organisms using biogeochemical-Argo floats, Geophys. Res. Lett., 47, e2019GL086088, https://doi.org/10.1029/2019GL086088, 2020.
Ham, Y. G., Kim, J. H., and Luo, J. J.: Deep learning for multi-year ENSO forecasts, Nature, 573, 568–572, https://doi.org/10.1038/s41586-019-1559-7, 2019.
Holte, J. and Talley, L.: A New Algorithm for Finding Mixed Layer Depths with Applications to Argo Data and Subantarctic Mode Water Formation, J. Atmos. Ocean. Tech., 26, 1920–1939, https://doi.org/10.1175/2009jtecho543.1, 2009.
Hsu, P.-C., Lin, C.-C., Huang, S.-J., and Ho, C.-R.: Effects of Cold Eddy on Kuroshio Meander and Its Surface Properties, East of Taiwan, IEEE J. Sel. Top. Appl., 9, 5055–5063, https://doi.org/10.1109/jstars.2016.2524698, 2016.
Jiang, H., Song, Y., Mironov, A., Yang, Z., Xu, Y., and Liu, J.: Accurate mean wave period from SWIM instrument on-board CFOSAT, Remote Sens. Environ., 280, 113149, https://doi.org/10.1016/j.rse.2022.113149, 2022.
Karstensen, J., Schütte, F., Pietri, A., Krahmann, G., Fiedler, B., Grundle, D., Hauss, H., Körtzinger, A., Löscher, C. R., Testor, P., Vieira, N., and Visbeck, M.: Upwelling and isolation in oxygen-depleted anticyclonic modewater eddies and implications for nitrate cycling, Biogeosciences, 14, 2167–2181, https://doi.org/10.5194/bg-14-2167-2017, 2017.
Klemas, V. and Yan, X.-H.: Subsurface and deeper ocean remote sensing from satellites: An overview and new results, Prog. Oceanogr., 122, 1–9, https://doi.org/10.1016/j.pocean.2013.11.010, 2014.
Kumar, S. P., Madhupratap, M., Kumar, M. D., Muraleedharan, P., De Souza, S., Gauns, M., and Sarma, V.: High biological productivity in the central Arabian Sea during the summer monsoon driven by Ekman pumping and lateral advection, Curr. Sci. India, 81, 1633–1638, 2001.
Kumar, S. P., Muraleedharan, P. M., Prasad, T. G., Gauns, M., Ramaiah, N., de Souza, S. N., Sardesai, S., and Madhupratap, M.: Why is the Bay of Bengal less productive during summer monsoon compared to the Arabian Sea?, Geophys. Res. Lett., 29, 88-1–88-4, https://doi.org/10.1029/2002gl016013, 2002.
Lecun, Y., Bengio, Y., and Hinton, G. E.: Deep learning, Nature, 521, 436–444, https://doi.org/10.1038/nature14539, 2015.
Ledwell, J. R., McGillicuddy Jr., D. J., and Anderson, L. A.: Nutrient flux into an intense deep chlorophyll layer in a mode-water eddy, Deep-Sea Res. Pt. II, 55, 1139–1160, 2008.
Lee, C. M., Jones, B. H., Brink, K. H., and Fischer, A. S.: The upper-ocean response to monsoonal forcing in the Arabian Sea: seasonal and spatial variability, Deep-Sea Res. Pt. II, 47, 1177–1226, 2000.
Lehahn, Y., d'Ovidio, F., Levy, M., Amitai, Y., and Heifetz, E.: Long range transport of a quasi isolated chlorophyll patch by an Agulhas ring, Geophys. Res. Lett., 38, L16610, https://doi.org/10.1029/2011gl048588, 2011.
Liu, Y., Chen, G., Sun, M., Liu, S., and Tian, F.: A parallel SLA-based algorithm for global mesoscale eddy identification, J. Atmos. Ocean. Tech., 33, 2743–2754, https://doi.org/10.1175/JTECH-D-16-0033.1, 2016.
Liu, Y. and Li, X.: A deep learning-based surface- and subsurface-intensified eddy detection model, figshare [data set], https://doi.org/10.6084/m9.figshare.23599473, 2023a.
Liu, Y. and Li, X.: Eddy-induced Chlorophyll-a Variations in the Northern Indian Ocean, figshare [code], https://doi.org/10.6084/m9.figshare.23599683, 2023b.
Lu, W., Su, H., Yang, X., and Yan, X.-H.: Subsurface temperature estimation from remote sensing data using a clustering-neural network method, Remote Sens. Environ., 229, 213–222, https://doi.org/10.1016/j.rse.2019.04.009, 2019.
Maritorena, S., d'Andon, O. H. F., Mangin, A., and Siegel, D. A.: Merged satellite ocean color data products using a bio-optical model: Characteristics, benefits and issues, Remote Sens. Environ., 114, 1791–1804, https://doi.org/10.1016/j.rse.2010.04.002, 2010.
Mcdougall, T. J. and Barker, P. M.: Getting started with TEOS-10 and the Gibbs Seawater (GSW) Oceanographic Toolbox, SCOR/IAPSO WG, 127, 1–28, 2011.
McGillicuddy Jr., D. J.: Formation of Intrathermocline Lenses by Eddy-Wind Interaction, J. Phys. Oceanogr., 45, 606–612, https://doi.org/10.1175/jpo-d-14-0221.1, 2015.
McGillicuddy Jr., D. J., Anderson, L. A., Bates, N. R., Bibby, T., Buesseler, K. O., Carlson, C. A., Davis, C. S., Ewart, C., Falkowski, P. G., and Goldthwait, S. A.: Eddy/wind interactions stimulate extraordinary mid-ocean plankton blooms, Science, 316, 1021–1026, 2007.
Meunier, T., Tenreiro, M., Pallàs-Sanz, E., Ochoa, J., Ruiz-Angulo, A., Portela, E., Cusí, S., Damien, P., and Carton, X.: Intrathermocline eddies embedded within an anticyclonic vortex ring, Geophys. Res. Lett., 45, 7624–7633, https://doi.org/10.1029/2018GL077527, 2018.
Mignot, A., Claustre, H., Uitz, J., Poteau, A., d'Ortenzio, F., and Xing, X.: Understanding the seasonal dynamics of phytoplankton biomass and the deep chlorophyll maximum in oligotrophic environments: A Bio-Argo float investigation, Global Biogeochem. Cy., 28, 856–876, https://doi.org/10.1002/2013GB004781, 2014.
Paillet, J., Le Cann, B., Carton, X., Morel, Y., and Serpette, A.: Dynamics and evolution of a northern meddy, J. Phys. Oceanogr., 32, 55–79, 2002.
Piontkovski, S., Al-Azri, A., and Al-Hashmi, K.: Seasonal and interannual variability of chlorophyll a in the Gulf of Oman compared to the open Arabian Sea regions, Int. J. Remote Sens., 32, 7703–7715, https://doi.org/10.1080/01431161.2010.527393, 2011.
Prasad, T. G.: A comparison of mixed-layer dynamics between the Arabian Sea and Bay of Bengal: One-dimensional model results, J. Geophys. Res.-Oceans, 109, C03035, https://doi.org/10.1029/2003jc002000, 2004.
Pujol, M.-I., Faugère, Y., Taburet, G., Dupuy, S., Pelloquin, C., Ablain, M., and Picot, N.: DUACS DT2014: the new multi-mission altimeter data set reprocessed over 20 years, Ocean Sci., 12, 1067–1090, https://doi.org/10.5194/os-12-1067-2016, 2016.
Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., and Schlax, M. G.: Daily high-resolution-blended analyses for sea surface temperature, J. Climate, 20, 5473–5496, https://doi.org/10.1175/2007JCLI1824.1, 2007.
Roesler, C., Uitz, J., Claustre, H., Boss, E., Xing, X., Organelli, E., Briggs, N., Bricaud, A., Schmechtig, C., and Poteau, A.: Recommendations for obtaining unbiased chlorophyll estimates from in situ chlorophyll fluorometers: A global analysis of WET Labs ECO sensors, Limnol. Oceanogr., 15, 572–585, https://doi.org/10.1002/lom3.10185, 2017.
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation, Cham, Springer International Publishing, 234–241, https://doi.org/10.48550/arXiv.1505.04597, 2015.
Shafeeque, M., Balchand, A. N., Shah, P., George, G., B. R, S., Varghese, E., Joseph, A. K., Sathyendranath, S., and Platt, T.: Spatio-temporal variability of chlorophyll a in response to coastal upwelling and mesoscale eddies in the South Eastern Arabian Sea, Int. J. Remote Sens., 42, 4836–4863, https://doi.org/10.1080/01431161.2021.1899329, 2021.
Siegel, D. A., Peterson, P., McGillicuddy, D. J., Maritorena, S., and Nelson, N. B.: Bio-optical footprints created by mesoscale eddies in the Sargasso Sea, Geophys. Res. Lett., 38, L13608, https://doi.org/10.1029/2011gl047660, 2011.
Smitha, B., Sanjeevan, V., Padmakumar, K., Hussain, M. S., Salini, T., and Lix, J.: Role of mesoscale eddies in the sustenance of high biological productivity in North Eastern Arabian Sea during the winter-spring transition period, Sci. Total Environ., 809, 151173, https://doi.org/10.1016/j.scitotenv.2021.151173, 2022.
Somayajulu, Y. K., Murty, V. S. N., and Sarma, Y. V. B.: Seasonal and inter-annual variability of surface circulation in the Bay of Bengal from TOPEX/Poseidon altimetry, Deep-Sea Res. Pt. II, 50, 867–880, https://doi.org/10.1016/S0967-0645(02)00610-0, 2003.
Su, H., Wu, X., Yan, X.-H., and Kidwell, A.: Estimation of subsurface temperature anomaly in the Indian Ocean during recent global surface warming hiatus from satellite measurements: A support vector machine approach, Remote Sens. Environ., 160, 63–71, https://doi.org/10.1016/j.rse.2015.01.001, 2015.
Su, H., Zhang, T., Lin, M., Lu, W., and Yan, X.-H.: Predicting subsurface thermohaline structure from remote sensing data based on long short-term memory neural networks, Remote Sens. Environ., 260, 112465, https://doi.org/10.1016/j.rse.2021.112465, 2021.
Su, J., Strutton, P. G., and Schallenberg, C.: The subsurface biological structure of Southern Ocean eddies revealed by BGC-Argo floats, J. Marine Syst., 220, 103569, https://doi.org/10.1016/j.jmarsys.2021.103569, 2021.
Sun, B., Liu, C., and Wang, F.: Global meridional eddy heat transport inferred from Argo and altimetry observations, Sci. Rep.-UK, 9, 1345, https://doi.org/10.1038/s41598-018-38069-2, 2019.
Thomas, L. N.: Formation of intrathermocline eddies at ocean fronts by wind-driven destruction of potential vorticity, Dynam. Atmos. Oceans, 45, 252–273, https://doi.org/10.1016/j.dynatmoce.2008.02.002, 2008.
Trott, C. B., Subrahmanyam, B., Chaigneau, A., and Delcroix, T.: Eddy tracking in the northwestern Indian Ocean during southwest monsoon regimes, Geophys. Res. Lett., 45, 6594–6603, https://doi.org/10.1029/2018gl078381, 2018.
Trott, C. B., Subrahmanyam, B., Chaigneau, A., and Roman-Stork, H. L.: Eddy-induced temperature and salinity variability in the Arabian Sea, Geophys. Res. Lett., 46, 2734–2742, https://doi.org/10.1029/2018GL081605, 2019.
Wang, Z.-F., Sun, L., Li, Q.-Y., and Cheng, H.: Two typical merging events of oceanic mesoscale anticyclonic eddies, Ocean Sci., 15, 1545–1559, https://doi.org/10.5194/os-15-1545-2019, 2019.
Yang, G., Wang, F., Li, Y., and Lin, P.: Mesoscale eddies in the northwestern subtropical Pacific Ocean: Statistical characteristics and three-dimensional structures, J. Geophys. Res.-Oceans, 118, 1906–1925, https://doi.org/10.1002/jgrc.20164, 2013.
Yang, X., Xu, G., Liu, Y., Sun, W., Xia, C., and Dong, C.: Multi-Source Data Analysis of Mesoscale Eddies and Their Effects on Surface Chlorophyll in the Bay of Bengal, Remote Sens.-Basel, 12, 3485, https://doi.org/10.3390/rs12213485, 2020.
Zhan, P., Guo, D., and Hoteit, I.: Eddy-induced transport and kinetic energy budget in the Arabian Sea, Geophys. Res. Lett., 47, e2020GL090490, https://doi.org/10.1029/2020GL090490, 2020.
Zhang, Z., Qiu, B., Klein, P., and Travis, S.: The influence of geostrophic strain on oceanic ageostrophic motion and surface chlorophyll, Nat. Commun., 10, 2838, https://doi.org/10.1038/s41467-019-10883-w, 2019.
Short summary
The study developed a deep learning model that effectively distinguishes between surface- and subsurface-intensified eddies in the northern Indian Ocean by integrating sea surface height and temperature data. The accurate distinction between these types of eddies provides valuable insights into their dynamics and their impact on marine ecosystems in the northern Indian Ocean and contributes to understanding the complex interactions between eddy dynamics and biogeochemical processes in the ocean.
The study developed a deep learning model that effectively distinguishes between surface- and...