{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T21:54:56Z","timestamp":1761429296425,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,4,9]],"date-time":"2019-04-09T00:00:00Z","timestamp":1554768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002830","name":"Centre National d\u2019Etudes Spatiales","doi-asserted-by":"publisher","award":["Postdoctoral grant"],"award-info":[{"award-number":["Postdoctoral grant"]}],"id":[{"id":"10.13039\/501100002830","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Labex Cominlabs","award":["SEACS"],"award-info":[{"award-number":["SEACS"]}]},{"name":"Teralab","award":["TIAMSEA"],"award-info":[{"award-number":["TIAMSEA"]}]},{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-13-MONU-0014"],"award-info":[{"award-number":["ANR-13-MONU-0014"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>From the recent developments of data-driven methods as a means to better exploit large-scale observation, simulation and reanalysis datasets for solving inverse problems, this study addresses the improvement of the reconstruction of higher-resolution Sea Level Anomaly (SLA) fields using analog strategies. This reconstruction is stated as an analog data assimilation issue, where the analog models rely on patch-based and Empirical Orthogonal Functions (EOF)-based representations to circumvent the curse of dimensionality. We implement an Observation System Simulation Experiment (OSSE) in the South China Sea. The reported results show the relevance of the proposed framework with a significant gain in terms of Root Mean Square Error (RMSE) for scales below 100 km. We further discuss the usefulness of the proposed analog model as a means to exploit high-resolution model simulations for the processing and analysis of current and future satellite-derived altimetric data with regard to conventional interpolation schemes, especially optimal interpolation.<\/jats:p>","DOI":"10.3390\/rs11070858","type":"journal-article","created":{"date-parts":[[2019,4,10]],"date-time":"2019-04-10T03:47:36Z","timestamp":1554868056000},"page":"858","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Data-Driven Interpolation of Sea Level Anomalies Using Analog Data Assimilation"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0226-9057","authenticated-orcid":false,"given":"Redouane","family":"Lguensat","sequence":"first","affiliation":[{"name":"IGE, Universit\u00e9 Grenoble Alpes, CNRS, IRD, Grenoble INP, 38000 Grenoble, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Phi Huynh","family":"Viet","sequence":"additional","affiliation":[{"name":"IMT Atlantique, Lab-STICC UMR CNRS 6285, UBL, 29200 Brest, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miao","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Ocean, National Marine Data and Information Service, Tianjin 300171, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ge","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Marine Information Technology, Ocean University of China, Qingdao 266100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3891-7327","authenticated-orcid":false,"given":"Tian","family":"Fenglin","sequence":"additional","affiliation":[{"name":"Department of Marine Information Technology, Ocean University of China, Qingdao 266100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bertrand","family":"Chapron","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019Oc\u00e9anographie Physique et Spatiale, IFREMER, 29200 Brest, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ronan","family":"Fablet","sequence":"additional","affiliation":[{"name":"IMT Atlantique, Lab-STICC UMR CNRS 6285, UBL, 29200 Brest, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep learning for remote sensing data: A technical tutorial on the state of the art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.gsf.2015.07.003","article-title":"Machine learning in geosciences and remote sensing","volume":"7","author":"Lary","year":"2016","journal-title":"Geosci. Front."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1175\/1520-0469(1969)26<636:APARBN>2.0.CO;2","article-title":"Atmospheric predictability as revealed by naturally occurring analogues","volume":"26","author":"Lorenz","year":"1969","journal-title":"J. Atmos. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"McDermott, P.L., and Wikle, C.K. (2015). A model-based approach for analog spatio-temporal dynamic forecasting. Environmetrics.","DOI":"10.1002\/env.2374"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Comeau, D., Giannakis, D., Zhao, Z., and Majda, A.J. (arXiv, 2017). Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting, arXiv.","DOI":"10.1007\/s00382-018-4459-x"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2890","DOI":"10.1175\/MWR-D-14-00342.1","article-title":"A Comparison of Two Techniques for Generating Nowcasting Ensembles. Part II: Analogs Selection and Comparison of Techniques","volume":"143","author":"Atencia","year":"2015","journal-title":"Mon. Weather Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3498","DOI":"10.1175\/MWR-D-12-00281.1","article-title":"Probabilistic weather prediction with an analog ensemble","volume":"141","author":"Eckel","year":"2013","journal-title":"Mon. Weather Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"531","DOI":"10.5194\/gmd-7-531-2014","article-title":"AnaWEGE: A weather generator based on analogues of atmospheric circulation","volume":"7","author":"Yiou","year":"2014","journal-title":"Geosci. Model Dev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3209","DOI":"10.1175\/MWR3237.1","article-title":"Probabilistic quantitative precipitation forecasts based on reforecast analogs: Theory and application","volume":"134","author":"Hamill","year":"2006","journal-title":"Mon. Weather Rev."},{"key":"ref_10","unstructured":"Zhao, Z., and Giannakis, D. (arXiv, 2014). Analog Forecasting with Dynamics-Adapted Kernels, arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1175\/MWR-D-16-0093.1","article-title":"Global Optimization of an Analog Method by Means of Genetic Algorithms","volume":"145","author":"Horton","year":"2017","journal-title":"Mon. Weather Rev."},{"key":"ref_12","unstructured":"Tandeo, P., Ailliot, P., Chapron, B., Lguensat, R., and Fablet, R. (2015, January 24\u201325). The analog data assimilation: Application to 20 years of altimetric data. Proceedings of the CI 2015: 5th International Workshop on Climate Informatics, Boulder, CO, USA."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4093","DOI":"10.1175\/MWR-D-16-0441.1","article-title":"The Analog Data Assimilation","volume":"145","author":"Lguensat","year":"2017","journal-title":"Mon. Weather Rev."},{"key":"ref_14","first-page":"241","article-title":"Relating sardine recruitment in the Northern Benguela to satellite-derived sea surface height using a neural network pattern recognition approach","volume":"Volume 59","author":"Richardson","year":"2003","journal-title":"Progress in Oceanography: ENVIFISH: Investigating Environmental Causes of Pelagic Fisheries Variability in the SE Atlantic"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1175\/1520-0426(1998)015<0522:AIMMOM>2.0.CO;2","article-title":"An improved mapping method of multisatellite altimeter data","volume":"15","author":"Nadal","year":"1998","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1016\/0011-7471(76)90001-2","article-title":"A technique for objective analysis and design of oceanographic experiments applied to MODE-73","volume":"Volume 23","author":"Bretherton","year":"1976","journal-title":"Deep Sea Research and Oceanographic Abstracts"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2518","DOI":"10.1109\/TGRS.2017.2750491","article-title":"Improving mesoscale altimetric data from a multi-tracer convolutional processing of standard satellite-derived products","volume":"56","author":"Fablet","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1175\/JTECH-D-14-00152.1","article-title":"Dynamic Interpolation of Sea Surface Height and Potential Applications for Future High-Resolution Altimetry Mapping","volume":"32","author":"Ubelmann","year":"2014","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"L12603","DOI":"10.1029\/2009GL038359","article-title":"Diagnosis of vertical velocities in the upper ocean from high resolution sea surface height","volume":"36","author":"Klein","year":"2009","journal-title":"Geophys. Res. Lett."},{"key":"ref_20","first-page":"L24608","article-title":"Potential use of microwave sea surface temperatures for the estimation of ocean currents","volume":"33","author":"Chapron","year":"2006","journal-title":"Geophys. Res. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.1175\/JPO-D-13-0186.1","article-title":"On the Transfer Function between Surface Fields and the Geostrophic Stream Function in the Mediterranean Sea","volume":"44","author":"Shinde","year":"2014","journal-title":"J. Phys. Oceanogr."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Turiel, A., Sole, J., Nieves, V., Ballabrera-Poy, J., and Garcia-Ladona, E. (2009). Tracking oceanic currents by singularity analysis of Microwave Sea Surface Temperature images. Remote Sens. Environ., in press.","DOI":"10.1016\/j.rse.2007.10.007"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"447","DOI":"10.5194\/os-5-447-2009","article-title":"The multifractal structure of satellite sea surface temperature maps can be used to obtain global maps of streamlines","volume":"5","author":"Turiel","year":"2009","journal-title":"Ocean Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Fablet, R., Viet, P.H., and Lguensat, R. (2017). Data-driven Models for the Spatio-Temporal Interpolation of satellite-derived SST Fields. IEEE Trans. Comput. Imaging.","DOI":"10.1109\/TCI.2017.2749184"},{"key":"ref_25","unstructured":"Buades, A., Coll, B., and Morel, J.M. (2005, January 20\u201325). A non-local algorithm for image denoising. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR\u201905, San Diego, CA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fablet, R., Huynh Viet, P., Lguensat, R., Horrein, P.H., and Chapron, B. (2018). Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation. Remote Sens., 10.","DOI":"10.3390\/rs10020310"},{"key":"ref_27","first-page":"35","article-title":"A fifty-year eddy-resolving simulation of the world ocean: Preliminary outcomes of OFES (OGCM for the Earth Simulator)","volume":"1","author":"Masumoto","year":"2004","journal-title":"J. Earth Simul."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sasaki, H., Nonaka, M., Masumoto, Y., Sasai, Y., Uehara, H., and Sakuma, H. (2008). An eddy-resolving hindcast simulation of the quasi-global ocean from 1950 to 2003 on the Earth Simulator. High Resolution Numerical Modelling of the Atmosphere and Ocean, Springer.","DOI":"10.1007\/978-0-387-49791-4_10"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0399-1784(99)80028-0","article-title":"Sea surface height variations in the South China Sea from satellite altimetry","volume":"22","author":"Shaw","year":"1999","journal-title":"Oceanol. Acta"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2997","DOI":"10.1175\/2010MWR3164.1","article-title":"Beyond Gaussian statistical modeling in geophysical data assimilation","volume":"138","author":"Bocquet","year":"2010","journal-title":"Mon. Weather Rev."},{"key":"ref_31","first-page":"181","article-title":"Unified notation for data assimilation: operational, sequential and variational","volume":"75","author":"Ide","year":"1997","journal-title":"Practice"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Asch, M., Bocquet, M., and Nodet, M. (2016). Data Assimilation: Methods, Algorithms, and Applications, SIAM. Fundamentals of Algorithms.","DOI":"10.1137\/1.9781611974546"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2280","DOI":"10.1175\/1520-0485(1987)017<2280:AOAEFI>2.0.CO;2","article-title":"Assimilation of altimeter eddy fields in a limited-area quasi-geostrophic model","volume":"17","author":"Robinson","year":"1987","journal-title":"J. Phys. Oceanogr."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1002\/qj.49709239320","article-title":"Objective analysis of meteorological fields. By L. S. Gandin. Translated from the Russian. Jerusalem (Israel Program for Scientific Translations), 1965. Pp. vi, 242: 53 Figures; 28 Tables. \u00a34 1s. 0d","volume":"92","author":"Gandin","year":"1966","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1002\/qj.49711247414","article-title":"Analysis methods for numerical weather prediction","volume":"112","author":"Lorenc","year":"1986","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2148","DOI":"10.1002\/grl.50324","article-title":"Improvement of coastal and mesoscale observation from space: Application to the northwestern Mediterranean Sea","volume":"40","author":"Escudier","year":"2013","journal-title":"Geophys. Res. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ping, B., Su, F., and Meng, Y. (2016). An Improved DINEOF Algorithm for Filling Missing Values in Spatio-Temporal Sea Surface Temperature Data. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0155928"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2841","DOI":"10.1109\/TGRS.2017.2785240","article-title":"Predicting Missing Values in Spatio-Temporal Remote Sensing Data","volume":"56","author":"Gerber","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Fablet, R., and Rousseau, F. (2015, January 27\u201330). Missing data super-resolution using non-local and statistical priors. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7350884"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"024501","DOI":"10.1103\/PhysRevLett.98.024501","article-title":"Inverse Turbulent Cascades and Conformally Invariant Curves","volume":"98","author":"Bernard","year":"2007","journal-title":"Phys. Rev. Lett."},{"key":"ref_41","first-page":"584","article-title":"Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran","volume":"12","author":"Moazenzadeh","year":"2018","journal-title":"Eng. Appl. Comput. Fluid Mech."},{"key":"ref_42","first-page":"438","article-title":"Computational intelligence approach for modeling hydrogen production: A review","volume":"12","author":"Najafi","year":"2018","journal-title":"Eng. Appl. Comput. Fluid Mech."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yaseen, Z.M., Sulaiman, S.O., Deo, R.C., and Chau, K.W. (2018). An enhanced extreme learning machine model for river flow forecasting: state-of-the-art, practical applications in water resource engineering area and future research direction. J. Hydrol.","DOI":"10.1016\/j.jhydrol.2018.11.069"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1016\/j.jhydrol.2015.08.022","article-title":"Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and Extreme Learning Machines","volume":"529","author":"Taormina","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_45","first-page":"738","article-title":"Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network","volume":"12","author":"Mosavi","year":"2018","journal-title":"Eng. Appl. Comput. Fluid Mech."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.jhydrol.2011.01.017","article-title":"Rainfall runoff modeling using artificial neural network coupled with singular spectrum analysis","volume":"399","author":"Wu","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2227","DOI":"10.1109\/TPAMI.2014.2321376","article-title":"Scalable nearest neighbor algorithms for high dimensional data","volume":"36","author":"Muja","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1080\/01621459.1979.10481038","article-title":"Robust locally weighted regression and smoothing scatterplots","volume":"74","author":"Cleveland","year":"1979","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1175\/JTECH-D-15-0160.1","article-title":"The Challenge of Using Future SWOT Data for Oceanic Field Reconstruction","volume":"33","author":"Gaultier","year":"2015","journal-title":"J. Atmos. Ocean. Technol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/7\/858\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:44:02Z","timestamp":1760186642000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/7\/858"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,9]]},"references-count":49,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["rs11070858"],"URL":"https:\/\/doi.org\/10.3390\/rs11070858","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,4,9]]}}}