Hide

To give you the best possible experience, this site uses cookies. Continuing to use this site means you agree
to our use of cookies. If you'd like to learn more about the cookies we use please find out more

Reference documents

 project header 2

 

Reference documents

Project relevant papers and articles

 

RD 1    Reul N., J. Tenerelli , B. Chapron, D. Vandemark, Y. Quilfen and Y. Kerr (2012): SMOS satellite L-band radiometer: A new capability for ocean surface remote sensing in hurricanes. Journal Of Geophysical Research-oceans, 117, 117, C02006, doi:10.1029/2011JC007474. Publisher's official version: http://dx.doi.org/10.1029/2011JC007474

RD 2    Reul N., S. Fournier, J. Boutin, O. Hernandez, C. Maes, B. Chapron, G. Alory, Y. Quilfen, J. Tenerelli, S. Morisset, Y. Kerr, S. Mecklenburg and S. Delwart (2013): Sea Surface Salinity Observations from Space with the SMOS Satellite: A New Means to Monitor the Marine Branch of the Water Cycle,Surveys in Geophysics, p 1-42, DOI: 10.1007/s10712-013-9244-0, http://link.springer.com/article/10.1007%2Fs10712-013-9244-0

RD 3    Lee,T., G. Lagerloef, M. M. Gierach, H.‐Y. Kao, S. Yueh, K. Dohan (2012): Aquarius reveals salinity structure of tropical instability waves, Vol 39, issue 12, DOI:10.1029/2012GL052232, http://onlinelibrary.wiley.com/doi/10.1029/2012GL052232/full

RD 4     Lagerloef, G.S.E, C. Swift, and D. Le Vine, (1995): Sea surface salinity: The next remote sensing challenge, Oceanography 8(2):44–50. Technical offer CLS-CAL-PR-16-0143 V1.0 May.25, 2016 Observing System Experiment with satellite SSS, https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19970001845.pdf

RD5     Zhu, J., B. Huang,R.-H. Zhang, Z.-Z. Hu, A. Kumar, M. A. Balmaseda, L. Marx and J. L. Kinter III. (2014): Salinity anomaly as a trigger for ENSO events. Scientific Reports 4, 6821, doi:10.1038/srep06821, http://www.nature.com/articles/srep06821

RD 6    Meissner, T., Wentz, F., and Scott, J. (2015): Remote sensing systems SMAP Level 3 Ocean Surface Salinities [running 8-day, monthly] on 0.25 deg grid, version 1.0 (BETA). Technical report, Remote Sensing Systems, Santa Rosa, CA. Available at http://www-remss.com/missions/smap.

RD 7    Tranchant, B., Testut, C.E., Renault, L., Ferry, N., Birol, F. and Brasseur P., (2008): Expected impact of the future SMOS and Aquarius Ocean surface salinity missions in the Mercator Ocean operational systems : New perspectives to monitor ocean circulation, Remote Sensing of Environment, 112, pp 1476-1487, http://fulltext.study/preview/pdf/4460378.pdf

RD 8    Tranchant B., Testut C.E., Renault L. and Ferry N., Obligis E., Boone C. and G. Larnicol(2008): Data assimilation of simulated SSS SMOS products in an ocean forecasting system, Journal of operational Oceanography, Vol. 2008, No 2, August 2008., pp 19-27(9), http://www.tandfonline.com/doi/abs/10.1080/1755876X.2008.11020099

RD 9    Obligis E., Boone, C., Philipps S., Larnicol G., Tranchant B. and P-T. Le Traon (2008): Benefits of the future Sea Surface Salinity measurements from SMOS. generation and characteristics of SMOS geophysical products, IEEE Trans. Geoscience and Remote Sensing, vol. 46, No 3, p8.

RD 10    Tranchant, B., Greiner, E., Garric, Drevillon and Regnier, C. (2014): Sea Surface Salinity from Space: a Future for Operational Oceanography? Ocean salinity science and salinity remote sensing workshop, 26-28 November 2014, Met. Office, Exeter, UK.

RD 11    Tranchant, B., Greiner, E., Legalloudec, O. and P-Y. Le Traon (2015): Sea Surface Salinity Data Assimilation Improvement in a Global Ocean Forecasting System at 1/4° from SMOS and Aquarius Data, 2nd science SMOS conference, 25-29 May 2015, ESA-ESAC, (near Madrid) Spain.

RD 12    Martin, J. M. (2016): Suitability of satellite sea surface salinity data for use in assessing and correcting ocean forecasts, Remote Sens. Environ., in press, http://dx.doi.org/10.1016/j.rse.2016.02.004.

RD 13    Reul,N, B. Chapron, T. Lee, C. Donlon, J. Boutin and G. Alory (2014): Sea surface salinity structure of the meandering gulf stream revealed by SMOS sensor, Geophys. Res. Lett., 41 (2014), pp. 31413141p, http://dx.doi.org/10.1002/2014GL059215

RD 14    Hackert, E., A. J. Busalacchi, and J. Ballabrera-Poy (2014): Impact of Aquarius sea surface salinity observations on coupled forecasts for the tropical Indo-Pacific Ocean, J. Geophys. Res. Oceans, 119, doi:10.1002/2013JC009697.

RD 15    Terray, L., L. Corre, S. Cravatte, T. Delcroix, G. Reverdin, and A. Ribes, 2012: Near-surface salinity as Nature’s rain gauge to detect human inflence on the tropical water cycle. J. Climate, 25-3, 958–977, doi: 10.1175/JCLI-D-10-05025.

RD 16    Oke, P.R., G. Larnicol, Y. Fujii, G.C. Smith, D.J. Lea, S. Guinehut, E. Remy, M. Alonso Balmaseda, T. Rykova, D. Surcel-Colan, M.J. Martin, A.A. Sellar, S. Mulet & V. Turpin (2015): Assessing the impact of observations on ocean forecasts and reanalyses: Part 1, Global studies, Journal of Operational Oceanography, 8:sup1, s49-s62 Technical offer CLS-CAL-PR-16-0143 V1.0 May.25, 2016 Observing System Experiment with satellite sss

RD 17    Oke, P.R., G. Larnicol, E.M. Jones, V. Kourafalou, A.K. Sperrevik, F. Carse, C.A.S. Tanajura, B. Mourre, M. Tonani, G.B. Brassington, M. Le Henaff, G.R. Halliwell Jr., R. Atlas, A.M. Moore, C.A. Edwards, M.J. Martin, A.A. Sellar, A. Alvarez, P. De Mey & M. Iskandarani (2015) Assessing the impact of observations on ocean forecasts and reanalyses: Part 2, Regional applications, Journal of Operational Oceanography, 8:sup1, s63-s79

RD 18    Lea, D. J., Martin, M. J. and Oke, P. R. (2014), Demonstrating the complementarity of observations in an operational ocean forecasting system. Q.J.R. Meteorol. Soc., 140: 2037-2049. doi: 10.1002/qj.2281.

RD 19    Hernandez, F. et al., 2015: Recent progress in performance evaluations and near real-time assessment of operational ocean products, Journal of Operational Oceanography, 8:sup2, s221-s238, DOI: 10.1080/1755876X.2015.1050282

RD 20    Ryan, A.G., C. Regnier, P. Divakaran, T. Spindler, A. Mehra, G.C. Smith, F. Davidson, F. Hernandez, J. Maksymczuk and Y. Liu, 2015: GODAE OceanView Class 4 forecast verification framework: global ocean inter-comparison. 8:sup1, s98-s111, doi: 10.1080/1755876X.2015.1022330.

RD 21    Lea, D.J. Observation Impact Statements for operational ocean forecasting . Met Office Forecasting R&D Technical Report no. 568, 9 October 2012.

RD 22     Lellouche, J.-M., Le Galloudec, O., Drévillon, M., Régnier, C., Greiner, E., Garric, G., Ferry, N., Desportes, C., Testut, C.-E., Bricaud, C., Bourdallé-Badie, R., Tranchant, B., Benkiran, M., Drillet, Y., Daudin, A., and De Nicola, C.: Evaluation of global monitoring and forecasting systems at Mercator Océan, Ocean Sci., 9, 57–81, doi:10.5194/os-9-57-2013, 2013.

RD 23    Blockley, E. W., Martin, M. J., McLaren, A. J., Ryan, A. G., Waters, J., Lea, D. J., Storkey, D. (2014). Recent development of the Met Office operational ocean forecasting system: An overview and assessment of the new Global FOAM forecasts. Geosci. Model Dev., 7, 2613–2638. http://dx.doi.org/10.5194/gmd-7-2613-2014.

RD 24    Martin, M. 2015: Suitability of satellite sea surface salinity data for assessing and correcting ocean forecasts. Forecasting Research Technical Report No. 599, Met Office, Exeter, UK.

RD 25    Maes, C., J. Picaut, and S. Belamari (2005), Importance of salinity barrier layer for the buildup of El Nino, J. Clim., 18, 104 – 118.

RD 26    Turpin, V., Remy, E., and Le Traon, P. Y.: How essential are Argo observations to constrain a global ocean data assimilation system?, Ocean Sci., 12, 257-274, doi:10.5194/os-12-257-2016, 2016.

RD 27     Larnicol G. and P. Oke, (2015), Key requirements and GOV OSEval-TT priorities for the OSE impact studies on SSS (SMOS) data in operational systems, available from ESA, June 9, 2015.

RD 28     Good, S. A., M. J. Martin, and N. A. Rayner (2013), EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates, J. Geophys. Res. Oceans, 118, doi:10.1002/2013JC009067. Technical offer CLS-CAL-PR-16-0143 V1.0 May.25, 2016 Observing System Experiment with satellite sss

RD29    Waters, J., Lea, D. J., Martin, M. J., Mirouze, I., Weaver, A. and While, J. (2015), Implementing a variational data assimilation system in an operational 1/4 degree global ocean model. Q.J.R. Meteorol. Soc., 141: 333-349. doi: 10.1002/qj.2388

RD 30    Weaver, A. T., C. Deltel, E. Machu, S. Ricci and N. Daget (2005). A multivariate balance operator for variational ocean data assimilation. Q. J. R. Meteorol. Soc., 131, 3605–3625.

RD 31    Donlon CJ, Martin M, Stark J, Roberts-Jones J, Fiedler E, Wimmer W. 2012. The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system. Remote Sensing of the Environment 116: 140–158. doi:10.1016/j.rse.2010.10.017.

RD 32     Dai, A. and Trenberth, K. E.: Estimates of freshwater discharge from continents: Latitudinal and seasonal variations, J. Hydrometeorol., 3, 660–687, 2002.

RD 33    Köhl,A., M. Sena, and D. Stammer (2014): Impact of assimilating surface salinity from SMOS on ocean circulation estimates, J. of Geophys. Res., Oceans, 119 (2014), pp. 5449–5464, http://dx.doi.org/10.1002/2014JC01004

 

 footer

Home Contact Login CLS Met Office Mercator Ocean