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Chapter 4

Architecture of ocean monitoring and forecasting systems

CHAPTER
COORDINATORS
Avichal Mehra
CHAPTER
AUTHORS
Roland Aznar, Stefania Ciliberti, Laurence Crosnier, Marie Drevillon, Yann Drillet, Begoña Pérez Gómez, Antonio Reppucci, Joseph Sudheer, Marcos Garcia Sotillo, Marina Tonani, P. N. Vinaychandranand, and Aihong Zhong

4.3 Data Assimilation

Through data assimilation, OOFS combines observations and the numerical model solution with the scope of producing the best reconstruction of the ocean state to be used as initial condition of the forecasting cycle. According to Moore et al.(2019) and considering Figure 4.27, we can assume that a priori state estimate of the ocean computed from the numerical model (blue line in Figure 4.26) together with a priori direct but incomplete state estimate from ocean observations (black dots in Figure 4.26) produce a posteriori state estimate which “combines” all available information considering uncertainties in both model and observations (green line in Figure 4.26).

Figure 4.26. Data assimilation models (green) are helped by observations to produce more realistic forecasts, closerto real observations (source: MEDCLIC project, SOCIB-La Caixa Foundation).

Ocean data assimilation is then defined mathematically through a rigorous process that combines ocean observation statistics with statistics of ocean model behaviour to extract the most useful information, possibly from sparse observations of time-varying ocean circulation (Cummings et al., 2009). Broadening Step 1 in Figure 4.1, the main characteristics of the data assimilation modelling system can be presented as in Figure 4.27, which shows the major components of the data assimilation modelling system, which are defined by:

  • Access to observations;
  • Data quality control;
  • Data assimilation scheme.
Figure 4.27. Major components of a data assimilation modelling system.

Access to observations, quality, providers as well as examples have been presented in Section 4.2. Data quality control is performed by an automatic procedure, native in the assimilation scheme or performed in offline mode at the submission of the analysis cycle, which selects the best observational dataset from the one accessed. To do such selection, the procedure takes as input the quality flag value associated with each specific observation (see Figure 4.3): usually, observations with QC flag = 1 and/or 2 are selected and make eligible to be used by the data assimilation scheme.

Depending on the specific characteristics of the basin on which the system is working, the data quality control may include further checks to reject data which are not sufficiently good to be assimilated. Such criterion may be implemented in offline mode as pre-processing steps of the data access and management. This is the case, for example, of the Mediterranean Forecasting System (MedFS) delivered in the framework of Copernicus Marine Service: the system performs additional checks for Argo and SLA observations rejection based on specific criteria, which are listed in Table 4.8.

ARGO QC1 Check on the date and location quality flags: only the profiles with both flags equal to 1 are taken into account
ARGO QC2Out of the Mediterranean Sea region
ARGO QC3Retain only ascending profiles (descending are rejected)
ARGO QC4Check on the values of the quality flags of pressure, temperature and salinity for each depth: if one of the flags is not equal to 1, the layer is deleted
ARGO QC5 Check on the values of the temperature and salinity, data outside the following ranges are rejected: 0<T<35 ; 0<S<45
ARGO QC6Check on the thermocline: if distance between two subsequent measurements of temperature and salinity in the first 300 meters is larger than 40 m, the profile is rejected
ARGO QC7Measurement between 0 and 2 m are rejected
SLA QC1Check on the values of date, latitude, longitude, sea level anomaly and DAC: if one of these values is equal to missing value the measurement of sea level anomaly is rejected. Check on the quality flag of sea level anomaly: if the flag is not equal to 1 the measurement of sea level anomaly is rejected

Table 4.8. Quality control criteria adopted by the Mediterranean Analysis and Forecasting System (MedFS,🔗70) for in-situ (Argo) and SLA.

 

Data assimilation scheme is really the core of the system since it performs the mathematical work of combining model state and observations. Existing data assimilation methods are classified in 2 major groups (Bouttier and Courtier, 2002):

  • Sequential method, which considers past observations until the time of analysis: this is the case of NRT products (analysis);
  • Non-sequential method, which uses “future” observation: this is the case of the multi-year products (e.g.,reanalysis).

Another distinction can be made between continuous and intermittent assimilation in time:

  • Continuous assimilation: for a given period of time the observations are collected and the correction to the analysed state is smoothed over a specific assimilation window;
  • Intermittent assimilation: for a given period of time, the observations are collected within a specific assimilation window to compute a correction.

Carrassi et al. (2018) and De Mey (1997) detail more the nature of the assimilation schemes used in physical, biogeochemical, ice and wave forecasting systems, describing the formulation of the problem and numerical approximation. These concepts are detailed in the theoretical chapters from 5 to 9, which are dedicated to show how such methods are used for setting up an OOFS.

From the scheme in Figure 4.27, we can derive some key definitions at the basis of the assimilation cycle: the innovation, defined as the difference between the first guess (or forecast) and the observation. The data assimilation method tries to estimate with less uncertainty than either the model prediction or observation: it deals with the computation of the increment, defined as the analysis minus the model first guess. The data assimilation system itself has been used to monitor observations and data quality control (Hollingsworth et al., 1986) by computing statistics involving observations, such as observation increments used to setup the blacklisting; this is a list of observations that the data assimilation has rejected and represents valuable information to be shared also with data providers in order to fix potential issues or bugs in the observational datasets.

 
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References

Barnier, B., Siefridt, L., and Marchesiello, P. (1995). Thermal forcing for a global ocean circulation model using a three-year climatology of ECMWF analyses. Journal of Marine Systems, 6(4), 363-380, https://doi.org/10.1016/0924-7963(94)00034-9

Barnier, B. (1998). Forcing the ocean. In “Ocean Modeling and Parameterization”, Editors: E. P. Chassignet and J. Verron, Eds., Kluwer Academic, 45-80. 

Bell, M.J., Lefèbvre, M., Le Traon, P.-Y., Smith, N., Wilmer-Becker, K. (2009). GODAE the global ocean data assimilation experiment. Oceanography, 22, 14-21, https://doi.org/10.5670/oceanog.2009.62

Bellingham, J. (2009). Platforms: Autonomous Underwater Vehicles. In “Encyclopedia of Ocean Sciences”, Editors-in-Chief: J. K. Cochran, H. Bokuniewicz, P. Yager, ISBN: 9780128130827, doi:10.1016/B978-012374473- 9.00730-X 

Bourlès, B., Lumpkin, R., McPhaden, M.J., Hernandez, F., Nobre, P., Campos, E., Yu, L. Planton, S., Busalacchi, A., Moura, A.D., Servain, J., and Trotte, Y. (2008). The PIRATA program: History, accomplishments, and future directions. Bulletin of the American Meteorological Society, 89, 1111-1125, https://doi.org/10.1175/2008BAMS2462.1

Boyer, T.P., Baranova, O.K., Coleman, C., Garcia, H.E., Grodsky, A., Locarnini, R.A., Mishonov, A.V., Paver, C.R., Reagan, J.R., Seidov, D., Smolyar, I.V., Weathers, K.W., Zweng, M.M. (2019). World Ocean Database 2018. A. V. Mishonov, Technical Editor, NOAA Atlas NESDIS 87.

Bouttier, F., and Courtier, P. (2002). Data assimilation concepts and methods. Meteorological training course lecture series, ECMWF, 59. Available at https://www.ecmwf.int/en/elibrary/16928-data-assimilation-concepts-and-methods

Capet, A., Fernández, V., She, J., Dabrowski, T., Umgiesser, G., Staneva, J., Mészáros, L., Campuzano, F., Ursella, L., Nolan, G., El Serafy, G. (2020), Operational Modeling Capacity in European Seas - An EuroGOOS Perspective and Recommendations for Improvement. Frontiers in Marine Science, 7:129, https://doi.org/10.3389/fmars.2020.00129

Carrassi, A., Bocquet, M., Bertino, L., Evensen, G. (2018). Data assimilation in the geosciences: An overview of methods, issues, and perspectives. Wiley Interdisciplinary Reviews: Climate Change, 9(5), e535, https://doi.org/10.1002/wcc.535

Chang, Y.-S., Rosati, A. J., Zhang, S., and Harrison, M. J. (2009). Objective Analysis of Monthly Temperature and Salinity for the World Ocean in the 21st century: Comparison with World Ocean Atlas and Application to Assimilation Validation. Journal of Geophysical Research: Oceans, 114(C2), https://doi.org/10.1029/2008JC004970

Crocker, R., Maksymczuk, J., Mittermaier, M., Tonani, M., and Pequignet, C. (2020). An approach to the verification of high-resolution ocean models using spatial methods. Ocean Science, 16, 831-845, https://doi.org/10.5194/os-16-831-2020

Crosnier, L., and Le Provost, C. (2007). Inter-comparing five forecast operational systems in the North Atlantic and Mediterranean basins: The MERSEA-strand1 Methodology. Journal of Marine Systems, 65(1-4), 354-375, https://doi.org/10.1016/j.jmarsys.2005.01.003

Cummings, J., Bertino, L., Brasseur, P., Fukumori, I., Kamachi, M., Martin, M.J., Mogensen, K., Oke, P., Testut, C.-E., Verron, J., Weaver, A. (2009). Ocean data assimilation systems for GODAE. Oceanography, 22, 96-109, https://doi.org/10.5670/oceanog.2009.69

Dai, A., and Trenberth, K. E. (2002). Estimates of freshwater discharge from continents: Latitudinal and seasonal variations. Journal of Hydrometeorology, 3(6), 660-687, https://doi.org/10.1175/1525-7541(2002)003<0660:EOFDFC>2.0.CO;2

Dai, A., Qian, T., Trenberth, K. E., Milliman, J. D. (2009). Changes in continental freshwater discharge from 1948-2004. Journal of Climate, 22(10), 2773-2791, https://doi.org/10.1175/2008JCLI2592.1

Dai, A. (2021). Hydroclimatic trends during 1950–2018 over global land. Climate Dynamics, 4027-4049, https://doi.org/10.1007/s00382-021-05684-1

De Mey, P. (1997). Data assimilation at the oceanic mesoscale: A review. Journal of Meteorological of Japan, 75(1b), 415-427, https://doi.org/10.2151/jmsj1965.75.1B_415

Carrassi, A., Bocquet, M., Bertino, L., Evensen, G. (2018). Data assimilation in the geosciences: An overview of methods, issues, and perspectives. Wiley Interdisciplinary Reviews: Climate Change, 9(5), e535, https://doi.org/10.1002/wcc.535

Donlon, C, Minnett, P., Gentemann, C., Nightingale, T.J., Barton, I., Ward, B., Murray, M. (2002). Toward improved validation of satellite sea surface skin temperature measurements for climate research. Journal of Climate, 15:353-369, https://doi.org/10.1175/1520-0442(2002)015<0353:TIVOSS>2.0.CO;2

Donlon, C., Casey, K., Robinson, I., Gentemann, C., Reynolds, R, Barton, I., Arino, O., Stark, J., Rayner, N., Le Borgne, P., Poulter, D., Vazquez-Cuervo, J., Armstrong, E., Beggs, H., Llewellyn-Jones, D., Minnett, P., Merchant, C., Evans, R. (2009). The GODAE High-Resolution Sea Surface Temperature Pilot Project. Oceanography, 22, 34-45, https://doi.org/10.5670/oceanog.2009.64

Drévillon, M., Greiner, E., Paradis, D., et al. (2013). A strategy for producing refined currents in the Equatorial Atlantic in the context of the search of the AF447 wreckage. Ocean Dynamics, 63, 63-82, https://doi.org/10.1007/s10236-012-0580-2

Eastwood, S., Le Borgne, P., Péré, S., Poulter, D. (2011). Diurnal variability in sea surface temperature in the Arctic. Remote Sensing of Environment, 115(10), 2594-2602, https://doi.org/10.1016/j.rse.2011.05.015

GHRSST Science Team (2012). The Recommended GHRSST Data Specification (GDS) 2.0, document revision 5, available from the GHRSST International Project Office, available at https://www.ghrsst.org/governance-documents/ghrsst-data-processing-specification-2-0-revision-5/

Griffies, S. M. (2006). Some ocean model fundamentals. In “Ocean Weather Forecasting”, Editoris: E. P. Chassignet and J. Verron, 19-73, Springer-Verlag, Dordrecht, The Netherlands, https://doi.org/10.1007/1-4020-4028-8_2 

Groom, S., Sathyendranath, S., Ban, Y,. Bernard, S., Brewin, R., Brotas, V., Brockmann, C., Chauhan, P., Choi, J-K., Chuprin, A., Ciavatta, S., Cipollini, P. Donlon, C., Franz, B., He, X., Hirata, T., Jackson, T., Kampel, M., Krasemann, H., Lavender, S., Pardo-Martinez, S., Mélin, F., Platt, T., Santoleri, R., Skakala, J., Schaeffer, B., Smith, M., Steinmetz, F., Valente, A., Wang, M. (2019). Satellite Ocean Colour: Current Status and Future Perspective. Frontiers in Marine Science, 6:485, https://doi.org/10.3389/fmars.2019.00485

Hamlington, B.D., Thompson, P.,. Hammond, W.C., Blewitt, G., Ray, R.D. (2016). Assessing the impact of vertical land motion on twentieth century global mean sea level estimates. Journal of Geophysical Research, 121(7), 4980-4993, https://doi.org/10.1002/2016JC011747

Hernandez, F., Bertino, L., Brassington, G.B., Chassignet, E., Cummings, J., Davidson, F., Drevillon, M., Garric, G., Kamachi, M., Lellouche, J.-M., et al. (2009). Validation and intercomparison studies within GODAE. Oceanography, 22(3), 128-143, https://doi.org/10.5670/oceanog.2009.71

Hernandez, F., Blockley, E., Brassington, G B., Davidson, F., Divakaran, P., Drévillon, M., Ishizaki, S., Garcia-Sotillo, M., Hogan, P. J., Lagemaa, P., Levier, B., Martin, M., Mehra, A., Mooers, C., Ferry, N., Ryan, A., Regnier, C., Sellar, A., Smith, G. C., Sofianos, S., Spindler, T., Volpe, G., Wilkin, J., Zaron, E D., Zhang, A. (2015). Recent progress in performance evaluations and near real-time assessment of operational ocean products. Journal of Operational Oceanography, 8(sup2), https://doi.org/10.1080/1755876X.2015.1050282

Hernandez, F., Smith, G., Baetens, K., Cossarini, G., Garcia-Hermosa, I., Drevillon, M., ... and von Schuckman, K. (2018). Measuring performances, skill and accuracy in operational oceanography: New challenges and approaches. In “New Frontiers in Operational Oceanography”, Editors: E. Chassignet, A. Pascual, J. Tintoré, and J. Verron, GODAE OceanView, 759-796, https://doi.org/10.17125/gov2018.ch29 

Hollingsworth, A., Shaw, D. B., Lönnberg, P., Illari, L., Arpe, K., and Simmons, A. J. (1986). Monitoring of Observation and Analysis Quality by a Data Assimilation System. Monthly Weather Review, 114(5), 861-879, https://doi.org/10.1175/1520-0493(1986)114<0861:MOOAAQ>2.0.CO;2

International Altimetry Team (2021). Altimetry for the future: building on 25 years of progress. Advances in Space Research, 68(2), 319-363, https://doi.org/10.1016/j.asr.2021.01.022

Kurihara, Y., Murakami, H., and Kachi, M. (2016). Sea surface temperature from the new Japanese geostationary meteorological Himawari-8 satellite. Geophysical Research Letters, 43(3), 1234-1240, https://doi.org/10.1002/2015GL067159

Josey, S.A., Kent, E.C., Taylor, P.K. (1999). New insights into the ocean heat budget closure problem from analysis of the SOC air-sea flux climatology. Journal of Climate, 12(9), 2856-2880, https://doi.org/10.1175/1520-0442(1999)012<2856:NIITOH>2.0.CO;2

Jolliffe, I.T., and Stephenson, D.B. (2003). Forecast Verification: A Practitioner’s Guide in Atmospheric Sciences, John Wiley & Sons Ltd., Hoboken, 296 pages, ISBN: 978-0-470-66071-3

Legates, D.R., McCabe, G.J. Jr. (1999). Evaluating the use of “Goodness of Fit” measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35, 233-241, https://doi.org/10.1029/1998WR900018

Legates, D., and Mccabe, G. (2013). A refined index of model performance: A rejoinder. International Journal of Climatology, 33(4), 1053-1056, https://doi.org/10.1002/joc.3487

Le Traon, P.Y., Larnicol, G., Guinehut, S., Pouliquen, S., Bentamy, A., Roemmich, D., Donlon, C., Roquet, H., Jacobs, G., Griffin, D., Bonjean, F., Hoepffner, N., Breivik, L.A. (2009). Data assembly and processing for operational oceanography 10 years of achievements. Oceanography, 22(3), 56-69, https://doi.org/10.5670/oceanog.2009.66

Levitus, S. (1982). Climatological Atlas of the World Ocean. NOAA/ERL GFDL Professional Paper 13, Princeton, N.J., 173 pp. (NTIS PB83-184093). Lindstrom, E., Gunn, J., Fischer, A., McCurdy, A. and Glover L.K. (2012). A Framework for Ocean Observing. IOC Information Document;1284, Rev. 2, https://doi.org/10.5270/OceanObs09-FOO 

Maksymczuk J., Hernandez, F., Sellar, A., Baetens, K., Drevillon, M., Mahdon, R., Levier, B., Regnier, C., Ryan, A. (2016). Product Quality Achievements Within MyOcean. Mercator Ocean Journal #54. Available at https://www.mercator-ocean.eu/en/ocean-science/scientific-publications/mercator-ocean-journal/newsletter-54-focusing-on-the-main-outcomes-of-the-myocean2-and-follon-on-projects/

Mantovani C., Corgnati, L., Horstmann, J. Rubio, A., Reyes, E., Quentin, C., Cosoli, S., Asensio, J. L., Mader, J., Griffa, A. (2020). Best Practices on High Frequency Radar Deployment and Operation for Ocean Current Measurement. Frontiers in Marine Science, 7:210, https://doi.org/10.3389/fmars.2020.00210

Marks, K., and Smith, W. (2006). An Evaluation of Publicly Available Global Bathymetry Grids. Marine Geophysical Researches, 27, 19-34, https://doi.org/10.1007/s11001-005-2095-4

Martin, M.J. (2016). Suitability of satellite sea surface salinity data for use in assessing and correcting ocean forecasts. Remote Sensing of Environment, 180, 305-319. https://doi.org/10.1016/j.rse.2016.02.004

Martin, M.J, King, R.R., While, J., Aguiar, A.B. (2019). Assimilating satellite sea-surface salinity data for SMOS Aquarius and SMAP into a global ocean forecasting system. Quarterly Journal of the Royal Meteorological Society, 145(719), 705-726, https://doi.org/10.1002/qj.3461

McPhaden, M. J., Busalacchi, A.J., Cheney, R., Donguy, J.R., Gage, K.S., Halpern, D., Ji, M., Julian, P., Meyers, G., Mitchum, G.T., Niller, P.P., Picaut, J., Reynolds, R.W., Smith, N., Takeuchi, K. (1998). The Tropical Ocean-Global Atmosphere (TOGA) observing system: A decade of progress. Journal of Geophysical Research, 103, 14169-14240, https://doi.org/10.1029/97JC02906

McPhaden, M. J., Meyers, G., Ando, K., Masumoto, Y., Murty, V. S., Ravichandran, M., Syamsudin, F., Vialard, J., Yu, L., and Yu, W. (2009). RAMA: The Research Moored Array for African-Asia-Australian Monsoon Analysis and Prediction. Bulletin of the American Meteorological Society, 90, 459-480, https://doi.org/10.1175/2008BAMS2608.1

Moore, A.M., Martin, M.J., Akella, S., Arango, H.G., Balmaseda, M., Bertino, L., Ciavatta, S., Cornuelle, B., Cummings, J., Frolov, S., Lermusiaux, P., Oddo, P., Oke, P.R., Storto, A., Teruzzi, A., Vidard, A., Weaver, A.T. (2019). 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 in Marine Science, 6:90, https://doi.org/10.3389/fmars.2019.00090

Naeije, M., Schrama, E., and Scharroo, R. (2000). The Radar Altimeter Database System project RADS. Published in “IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment”, doi:10.1109/ IGARSS.2000.861605

Nurmi P. (2003). Recommendations on the verification of local weather forecasts (at ECMWF member states). Consultancy report to ECMWF Operations Department. Available at https://www.cawcr.gov.au/projects/verification/Rec_FIN_Oct.pdf

O’Carroll, A.G., Armstrong, E.M., Beggs, H., Bouali, M., Casey, K.S., Corlett, G.K., Dash, P., Donlon, C.J., Gentemann, C.L., Hoyer, J.L., Ignatov, A., Kabobah, K., Kachi, M. Kurihara, Y., Karagali, I., Maturi, E., Merchant, C.J., Minnett, P., Pennybacker, M., Ramakrishnan, B., Ramsankaran, R., Santoleri, R., Sunder, S., Saux Picart, S., Vazquez-Cuervo, J., Wimmer, W. (2019). Observational needs of sea surface temperature, Frontiers in Marine Science, 6:420, https://doi.org/10.3389/fmars.2019.00420

Peng, G., Downs, R.R., Lacagnina, C., Ramapriyan, H., Ivánová, I., Moroni, D., Wei, Y., Larnicol, G., Wyborn, L., Goldberg, M., Schulz, J., Bastrakova, I., Ganske, A., Bastin, L., Khalsa, S.J.S., Wu, M., Shie, C.-L., Ritchey, N., Jones, D., Habermann, T., Lief, C., Maggio, I., Albani, M., Stall, S., Zhou, L., Drévillon, M., Champion, S., Hou, C.S., Doblas-Reyes, F., Lehnert, K., Robinson, E. and Bugbee, K., (2021). Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets. Data Science Journal, 20(1), 19, http://doi.org/10.5334/dsj-2021-019

Petrenko, B., Ignatov, A., Kihai, Y., Dash, P. (2016). Sensor-Specific Error Statistics for SST in the Advanced Clear-Sky Processor for Oceans. Journal of Atmospheric and Oceanic Technology, 33(2), 345-359, https://doi.org/10.1175/JTECH-D-15-0166.1

Roarty, H., Cook, T., Hazard, L., George, D., Harlan, J., Cosoli, S., Wyatt, L., Alvarez Fanjul, E., Terrill, E., Otero, M., Largier, J., Glenn, S., Ebuchi, N., Whitehouse, Br., Bartlett, K., Mader, J., Rubio, A., Corgnati, L., Mantovani, C., Griffa, A., Reyes, E., Lorente, P., Flores-Vidal, X., Saavedra-Matta, K. J., Rogowski, P., Prukpitikul, S., Lee, S-H., Lai, J-W., Guerin, C-A., Sanchez, J., Hansen, B., Grilli, S. (2019). The Global High Frequency Radar Network. Frontiers in Marine Science, 6:164, https://doi.org/10.3389/fmars.2019.00164

Ryan, A. G., Regnier, C., Divakaran, P., Spindler, T., Mehra, A., Smith, G. C., et al. (2015). GODAE OceanView Class 4 forecast verification framework: global ocean inter-comparison. Journal of Operational Oceanography, 8(sup1), S112-S126, https://doi.org/10.1080/1755876X.2015.1022330

Scharroo, R. (2012). RADS version 3.1: User Manual and Format Specification. Available at http://rads.tudelft.nl/rads/radsmanual.pdf

Sotillo, M. G., Garcia-Hermosa, I., Drévillon, M., Régnier, C., Szczypta, C., Hernandez, F., Melet, A., Le Traon, P.Y. (2021). Communicating CMEMS Product Quality: evolution & achievements along Copernicus-1 (2015- 2021). Mercator Ocean Journal #57. Available at https://marine.copernicus.eu/news/copernicus-1-marine-service-achievements-2015-2021 

Tonani, M., Balmaseda, M., Bertino, L., Blockley, E., Brassington, G., Davidson, F., Drillet, Y., Hogan, P., Kuragano, T., Lee, T., Mehra, A., Paranathara, F., Tanajura, C., Wang, H. (2015). Status and future of global and regional ocean prediction systems. Journal of Operational Oceanography, 8, s201-s220, https://doi.org/10.1080/1755876X.2015.1049892

Tournadre, J., Bouhier, N., Girard-Ardhuin, F., Remy, F. (2015). Large icebergs characteristics from altimeter waveforms analysis. Journal of Geophysical Research: Oceans, 120(3), 1954-1974, https://doi.org/10.1002/2014JC010502

Tozer, B., Sandwell, D.T., Smith, W.H.F., Olson, C., Beale, J. R., and Wessel, P. (2019). Global bathymetry and topography at 15 arc sec: SRTM15+. Earth and Space Science, 6, 1847-1864, https://doi.org/10.1029/2019EA000658

Vinogradova, N.T., Ponte, R.M., Fukumori, I., and Wang, O. (2014). Estimating satellite salinity errors for assimilation of Aquarius and SMOS data into climate models. Journal of Geophysical Research: Oceans, 119(8), 4732-4744, https://doi.org/10.1002/2014JC009906

Vinogradova N., Lee, T., Boutin, J., Drushka, K., Fournier, S., Sabia, R., Stammer, D., Bayler, E., Reul, N., Gordon, A., Melnichenko, O., Li, L., Hackert, E., Martin, M., Kolodziejczyk, N., Hasson, A., Brown, S., Misra, S., and Linkstrom, E. (2019). Satellite Salinity Observing System: Recent Discoveries and the Way Forward. Frontiers in Marine Science, 6:243, https://doi.org/10.3389/fmars.2019.00243

Zhang H., Beggs, H., Wang, X.H., Kiss, A. E., Griffin, C. (2016). Seasonal patterns of SST diurnal variation over the Tropical Warm Pool region. Journal of Geophysical Research: Oceans, 121(11), 8077-8094, https://doi.org/10.1002/2016JC012210

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Architecture of ocean monitoring and forecasting systems

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