Editorial Type:
Article
Article Type:
Research Article

Feasibility of Estimating Sea Surface Height Anomalies from Surface Ocean Currents and Winds

Larry W. O’Neill aCollege of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, Oregon

Search for other papers by Larry W. O’Neill in
Current site
Google Scholar
PubMed Close
https://orcid.org/0000-0001-8884-6817
,
Dudley B. Chelton aCollege of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, Oregon

Search for other papers by Dudley B. Chelton in
Current site
Google Scholar
PubMed Close
,
Ernesto Rodríguez bJet Propulsion Laboratory, Pasadena, California

Search for other papers by Ernesto Rodríguez in
Current site
Google Scholar
PubMed Close
,
Roger Samelson aCollege of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, Oregon

Search for other papers by Roger Samelson in
Current site
Google Scholar
PubMed Close
, and
Alexander Wineteer bJet Propulsion Laboratory, Pasadena, California

Search for other papers by Alexander Wineteer in
Current site
Google Scholar
PubMed Close
Online Publication:
17 May 2024
Print Publication:
01 May 2024
DOI:
https://doi.org/10.1175/JTECH-D-23-0096.1
Page(s):
475–497
Restricted access

We are aware of a technical issue preventing figures and tables from showing in some newly published articles in the full-text HTML view.
While we are resolving the problem, please use the online PDF version of these articles to view figures and tables.

Abstract

We propose a method to reconstruct sea surface height anomalies (SSHA) from vector surface currents and winds. This analysis is motivated by the proposed satellite ODYSEA, which is a Doppler scatterometer that measures coincident surface vector winds and currents. If it is feasible to estimate SSHA from these measurements, then ODYSEA could provide collocated fields of SSHA, currents, and winds over a projected wide swath of ∼1700 km. The reconstruction also yields estimates of the low-frequency surface geostrophic, Ekman, irrotational, and nondivergent current components and a framework for separation of balanced and unbalanced motions. The reconstruction is based on a steady-state surface momentum budget including the Ekman drift, Coriolis acceleration, and horizontal advection. The horizontal SSHA gradient is obtained as a residual of these terms, and the unknown SSHA is solved for using a Helmholtz–Hodge decomposition given an imposed SSHA boundary condition. We develop the reconstruction using surface currents, winds, and SSHA off the U.S. West Coast from a 43-day coupled ROMS–WRF simulation. We also consider how simulated ODYSEA measurement and sampling errors and boundary condition uncertainties impact reconstruction accuracy. We find that temporal smoothing of the currents for periods of 150 h is necessary to mitigate large reconstruction errors associated with unbalanced near-inertial motions. For the most realistic case of projected ODYSEA measurement noise and temporal sampling, the reconstructed SSHA fields have an RMS error of 2.1 cm and a model skill (squared correlation) of 0.958 with 150-h resolution. We conclude that an accurate SSHA reconstruction is feasible using information measured by ODYSEA and external SSHA boundary conditions.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Larry W. O’Neill, larry.oneill@oregonstate.edu

Abstract

We propose a method to reconstruct sea surface height anomalies (SSHA) from vector surface currents and winds. This analysis is motivated by the proposed satellite ODYSEA, which is a Doppler scatterometer that measures coincident surface vector winds and currents. If it is feasible to estimate SSHA from these measurements, then ODYSEA could provide collocated fields of SSHA, currents, and winds over a projected wide swath of ∼1700 km. The reconstruction also yields estimates of the low-frequency surface geostrophic, Ekman, irrotational, and nondivergent current components and a framework for separation of balanced and unbalanced motions. The reconstruction is based on a steady-state surface momentum budget including the Ekman drift, Coriolis acceleration, and horizontal advection. The horizontal SSHA gradient is obtained as a residual of these terms, and the unknown SSHA is solved for using a Helmholtz–Hodge decomposition given an imposed SSHA boundary condition. We develop the reconstruction using surface currents, winds, and SSHA off the U.S. West Coast from a 43-day coupled ROMS–WRF simulation. We also consider how simulated ODYSEA measurement and sampling errors and boundary condition uncertainties impact reconstruction accuracy. We find that temporal smoothing of the currents for periods of 150 h is necessary to mitigate large reconstruction errors associated with unbalanced near-inertial motions. For the most realistic case of projected ODYSEA measurement noise and temporal sampling, the reconstructed SSHA fields have an RMS error of 2.1 cm and a model skill (squared correlation) of 0.958 with 150-h resolution. We conclude that an accurate SSHA reconstruction is feasible using information measured by ODYSEA and external SSHA boundary conditions.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Larry W. O’Neill, larry.oneill@oregonstate.edu
Save
Cover Journal of Atmospheric and Oceanic Technology
Journal of Atmospheric and Oceanic Technology

  • Ballarotta, M., and Coauthors, 2019: On the resolutions of ocean altimetry maps. Ocean Sci., 15, 1091–1109, https://doi.org/10.5194/os-15-1091-2019.

    • Search Google Scholar
    • Export Citation

  • Bhatia, H., G. Norgard, V. Pascucci, and P.-T. Bremer, 2013: The Helmholtz-Hodge decomposition—A survey. IEEE Trans. Visualization Comput. Graphics, 19, 1386–1404, https://doi.org/10.1109/TVCG.2012.316.

    • Search Google Scholar
    • Export Citation

  • Bijlsma, S. J., L. M. Hafkenscheid, and P. Lynch, 1986: Computation of the streamfunction and velocity potential and reconstruction of the wind field. Mon. Wea. Rev., 114, 1547–1551, https://doi.org/10.1175/1520-0493(1986)114<1547:COTSAV>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Bleck, R., 2002: An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates. Ocean Modell., 4, 55–88, https://doi.org/10.1016/S1463-5003(01)00012-9.

    • Search Google Scholar
    • Export Citation

  • Bourassa, M. A., E. Rodríguez, and D. Chelton, 2016: Winds and Currents Mission: Ability to observe mesoscale air/sea coupling. 2016 IEEE Int. Geoscience and Remote Sensing Symp., Beijing, China, IEEE, 7392–7395, https://doi.org/10.1109/IGARSS.2016.7730928.

  • Bretherton, F. P., R. E. Davis, and C. B. Fandry, 1976: A technique for objective analysis and design of oceanographic experiments applied to MODE-73. Deep-Sea Res. Oceanogr. Abstr., 23, 559–582, https://doi.org/10.1016/0011-7471(76)90001-2.

    • Search Google Scholar
    • Export Citation

  • Bühler, O., J. Callies, and R. Ferrari, 2014: Wave-vortex decomposition of one-dimensional ship-track data. J. Fluid Mech., 756, 1007–1026, https://doi.org/10.1017/jfm.2014.488.

    • Search Google Scholar
    • Export Citation

  • Chelton, D. B., 2023: Estimation of surface current divergence from satellite Doppler radar scatterometer measurements of surface ocean velocity. J. Atmos. Oceanic Technol., 40, 1171–1198, https://doi.org/10.1175/JTECH-D-23-0052.1.

    • Search Google Scholar
    • Export Citation

  • Chelton, D. B., J. C. Ries, B. J. Haines, L. L. Fu, and P. S. Callahan, 2001: Satellite altimetry. Satellite Altimetry and the Earth Sciences: A Handbook of Techniques and Applications, Academic Press, 1–131.

  • Chelton, D. B., M. G. Schlax, and R. M. Samelson, 2011: Global observations of nonlinear mesoscale eddies. Prog. Oceanogr., 91, 167–216, https://doi.org/10.1016/j.pocean.2011.01.002.

    • Search Google Scholar
    • Export Citation

  • Chelton, D. B., M. G. Schlax, R. M. Samelson, J. T. Farrar, M. J. Molemaker, J. C. McWilliams, and J. Gula, 2019: Prospects for future satellite estimation of small-scale variability of ocean surface velocity and vorticity. Prog. Oceanogr., 173, 256–350, https://doi.org/10.1016/j.pocean.2018.10.012.

    • Search Google Scholar
    • Export Citation

  • Chelton, D. B., R. M. Samelson, and J. T. Farrar, 2022: The effects of uncorrelated measurement noise on SWOT estimates of sea-surface height, velocity and vorticity. J. Atmos. Oceanic Technol., 39, 1053–1083, https://doi.org/10.1175/JTECH-D-21-0167.1.

    • Search Google Scholar
    • Export Citation

  • Chereskin, T. K., 1995: Direct evidence for an Ekman balance in the California Current. J. Geophys. Res., 100, 18 261–18 269, https://doi.org/10.1029/95JC02182.

    • Search Google Scholar
    • Export Citation

  • Cleveland, W. S., 1979: Robust locally weighted regression and smoothing scatterplots. J. Amer. Stat. Assoc., 74, 829–836, https://doi.org/10.1080/01621459.1979.10481038.

    • Search Google Scholar
    • Export Citation

  • Cleveland, W. S., and S. J. Devlin, 1988: Locally weighted regression: An approach to regression analysis by local fitting. J. Amer. Stat. Assoc., 83, 596–610, https://doi.org/10.1080/01621459.1988.10478639.

    • Search Google Scholar
    • Export Citation

  • Cummings, J. A., 2005: Operational multivariate ocean data assimilation. Quart. J. Roy. Meteor. Soc., 131, 3583–3604, https://doi.org/10.1256/qj.05.105.

    • Search Google Scholar
    • Export Citation

  • Davies-Jones, R., 1988: On the formulation of surface geostrophic streamfunction. Mon. Wea. Rev., 116, 1824–1826, https://doi.org/10.1175/1520-0493(1988)116<1824:OTFOSG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828.

    • Search Google Scholar
    • Export Citation

  • Elipot, S., 2020: Measuring global mean sea level changes with surface drifting buoys. Geophys. Res. Lett., 47, e2020GL091078, https://doi.org/10.1029/2020GL091078.

    • Search Google Scholar
    • Export Citation

  • Elipot, S., and S. T. Gille, 2009: Ekman layers in the Southern Ocean: Spectral models and observations, vertical viscosity, and boundary layer depth. Ocean Sci., 5, 115–139, https://doi.org/10.5194/os-5-115-2009.

    • Search Google Scholar
    • Export Citation

  • Farrar, J. T., and Coauthors, 2020: S-MODE: The Sub-Mesoscale Ocean Dynamics Experiment. 2020 IEEE Int. Geoscience and Remote Sensing Symp., Waikoloa, HI, IEEE, 3533–3536, https://doi.org/10.1109/IGARSS39084.2020.9323112.

  • Fox, D. N., W. J. Teague, C. N. Barron, M. R. Carnes, and C. M. Lee, 2002: The Modular Ocean Data Assimilation System (MODAS). J. Atmos. Oceanic Technol., 19, 240–252, https://doi.org/10.1175/1520-0426(2002)019<0240:TMODAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Fox-Kemper, B., R. Ferrari, and J. Pedlosky, 2003: On the indeterminacy of rotational and divergent eddy fluxes. J. Phys. Oceanogr., 33, 478–483, https://doi.org/10.1175/1520-0485(2003)033<0478:OTIORA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Gaube, P., D. B. Chelton, R. M. Samelson, M. G. Schlax, and L. W. O’Neill, 2015: Satellite observations of mesoscale eddy-induced Ekman pumping. J. Phys. Oceanogr., 45, 104–132, https://doi.org/10.1175/JPO-D-14-0032.1.

    • Search Google Scholar
    • Export Citation

  • Hawkins, H. F., and S. L. Rosenthal, 1965: On the computation of stream functions from the wind field. Mon. Wea. Rev., 93, 245–252, https://doi.org/10.1175/1520-0493(1965)093<0245:OTCOSF>2.3.CO;2.

    • Search Google Scholar
    • Export Citation

  • Jin, X., C. Dong, J. Kurian, J. C. McWilliams, D. B. Chelton, and Z. Li, 2009: SST–wind interaction in coastal upwelling: Oceanic simulation with empirical coupling. J. Phys. Oceanogr., 39, 2957–2970, https://doi.org/10.1175/2009JPO4205.1.

    • Search Google Scholar
    • Export Citation

  • John, F., 1982: Partial Differential Equations. 4th ed. Springer-Verlag, 249 pp.

  • Lagerloef, G. S. E., G. T. Mitchum, R. B. Lukas, and P. P. Niiler, 1999: Tropical Pacific near-surface currents estimated from altimeter, wind, and drifter data. J. Geophys. Res., 104, 23 313–23 326, https://doi.org/10.1029/1999JC900197.

    • Search Google Scholar
    • Export Citation

  • Li, Z., Y. Chao, and J. C. McWilliams, 2006: Computation of the streamfunction and velocity potential for limited and irregular domains. Mon. Wea. Rev., 134, 3384–3394, https://doi.org/10.1175/MWR3249.1.

    • Search Google Scholar
    • Export Citation

  • Lilly, J. M., and S. Elipot, 2021: A unifying perspective on transfer function solutions to the unsteady Ekman problem. Fluids, 6, 85, https://doi.org/10.3390/fluids6020085.

    • Search Google Scholar
    • Export Citation

  • Lynch, P., 1989: Partitioning the wind in a limited domain. Mon. Wea. Rev., 117, 1492–1500, https://doi.org/10.1175/1520-0493(1989)117<1492:PTWIAL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Maximenko, N., P. Niiler, L. Centurioni, M.-H. Rio, O. Melnichenko, D. Chambers, V. Zlotnicki, and B. Galperin, 2009: Mean dynamic topography of the ocean derived from satellite and drifting buoy data using three different techniques. J. Atmos. Oceanic Technol., 26, 1910–1919, https://doi.org/10.1175/2009JTECHO672.1.

    • Search Google Scholar
    • Export Citation

  • National Academies of Sciences, Engineering, and Medicine, 2018: Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space. National Academies Press, 694 pp., https://doi.org/10.17226/24938.

  • Pascual, A., Y. Faugère, G. Larnicol, and P.-Y. L. Traon, 2006: Improved description of the ocean mesoscale variability by combining four satellite altimeters. Geophys. Res. Lett., 33, L02611, https://doi.org/10.1029/2005GL024633.

    • Search Google Scholar
    • Export Citation

  • Press, W. H., S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, 1992: Numerical Recipes in C: The Art of Scientific Computing. 2nd ed. Cambridge University Press, 994 pp.

  • Renault, L., J. C. McWilliams, and S. Masson, 2017: Satellite observations of imprint of oceanic current on wind stress by air-sea coupling. Sci. Rep., 7, 17747, https://doi.org/10.1038/s41598-017-17939-1.

    • Search Google Scholar
    • Export Citation

  • Ribeiro, P. C., H. F. de Campos Velho, and H. Lopes, 2016: Helmholtz–Hodge decomposition and the analysis of 2D vector field ensembles. Comput. Graphics, 55, 80–96, https://doi.org/10.1016/j.cag.2016.01.001.

    • Search Google Scholar
    • Export Citation

  • Rio, M.-H., and F. Hernandez, 2004: A mean dynamic topography computed over the World Ocean from altimetry, in situ measurements, and a geoid model. J. Geophys. Res., 109, C12032, https://doi.org/10.1029/2003JC002226.

    • Search Google Scholar
    • Export Citation

  • Rio, M.-H., S. Guinehut, and G. Larnicol, 2011: New CNES-CLS09 global mean dynamic topography computed from the combination of GRACE data, altimetry, and in situ measurements. J. Geophys. Res., 116, C07018, https://doi.org/10.1029/2010JC006505.

    • Search Google Scholar
    • Export Citation

  • Rodríguez, E., A. Wineteer, D. Perkovic-Martin, T. Gál, B. W. Stiles, N. Niamsuwan, and R. R. Monje, 2018: Estimating ocean vector winds and currents using a Ka-band pencil-beam Doppler scatterometer. Remote Sens., 10, 576, https://doi.org/10.3390/rs10040576.

    • Search Google Scholar
    • Export Citation

  • Rodríguez, E., M. Bourassa, D. Chelton, J. T. Farrar, D. Long, D. Perkovic-Martin, and R. Samelson, 2019: The Winds and Currents Mission concept. Front. Mar. Sci., 6, 438, https://doi.org/10.3389/fmars.2019.00438.

    • Search Google Scholar
    • Export Citation

  • Rodríguez, E., A. Wineteer, D. Perkovic-Martin, T. Gál, S. Anderson, S. Zuckerman, J. Stear, and X. Yang, 2020: Ka-band Doppler scatterometry over a Loop Current eddy. Remote Sens., 12, 2388, https://doi.org/10.3390/rs12152388.

    • Search Google Scholar
    • Export Citation

  • Samelson, R. M., 2022: Wind drift in a homogeneous equilibrium sea. J. Phys. Oceanogr., 52, 1945–1967, https://doi.org/10.1175/JPO-D-22-0017.1.

    • Search Google Scholar
    • Export Citation

  • Samelson, R. M., L. W. O’Neill, D. B. Chelton, E. D. Skyllingstad, P. L. Barbour, and S. M. Durski, 2020: Surface stress and atmospheric boundary layer response to mesoscale SST structure in coupled simulations of the northern California Current System. Mon. Wea. Rev., 148, 259–287, https://doi.org/10.1175/MWR-D-19-0200.1.

    • Search Google Scholar
    • Export Citation

  • Sangster, W. E., 1960: A method of representing the horizontal pressure force without reduction of station pressures to sea level. J. Meteor., 17, 166–176, https://doi.org/10.1175/1520-0469(1960)017<0166:AMORTH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Schlax, M. G., and D. B. Chelton, 1992: Frequency domain diagnostics for linear smoothers. J. Amer. Stat. Assoc., 87, 1070–1081, https://doi.org/10.1080/01621459.1992.10476262.

    • Search Google Scholar
    • Export Citation

  • Seo, H., A. J. Miller, and J. R. Norris, 2016: Eddy–wind interaction in the California Current System: Dynamics and impacts. J. Phys. Oceanogr., 46, 439–459, https://doi.org/10.1175/JPO-D-15-0086.1.

    • Search Google Scholar
    • Export Citation

  • Seo, H., and Coauthors, 2023: Ocean mesoscale and frontal-scale ocean–atmosphere interactions and influence on large-scale climate: A review. J. Climate, 36, 1981–2013, https://doi.org/10.1175/JCLI-D-21-0982.1.

    • Search Google Scholar
    • Export Citation

  • Shchepetkin, A. F., and J. C. McWilliams, 2005: The Regional Oceanic Modeling System (ROMS): A split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Modell., 9, 347–404, https://doi.org/10.1016/j.ocemod.2004.08.002.

    • Search Google Scholar
    • Export Citation

  • Shukla, J., and K. R. Saha, 1974: Computation of non-divergent streamfunction and irrotational velocity potential from the observed winds. Mon. Wea. Rev., 102, 419–425, https://doi.org/10.1175/1520-0493(1974)102<0419:CONDSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN475+STR, 113 pp., https://doi.org/10.5065/D68S4MVH.

  • Stephens, J. J., and K. W. Johnson, 1978: Rotational and divergent wind potentials. Mon. Wea. Rev., 106, 1452–1457, https://doi.org/10.1175/1520-0493(1978)106<1452:RADWP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Sudre, J., C. Maes, and V. Garçon, 2013: On the global estimates of geostrophic and Ekman surface currents. Limnol. Oceanogr. Fluids Environ., 3, 1–20, https://doi.org/10.1215/21573689-2071927.

    • Search Google Scholar
    • Export Citation

  • Thompson, R. O. R. Y., 1979: Coherence significance levels. J. Atmos. Sci., 36, 2020–2021, https://doi.org/10.1175/1520-0469(1979)036<2020:CSL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Torres, H., P. Klein, D. Menemenlis, B. Qiu, Z. Su, J. Wang, S. Chen, and L.-L. Fu, 2018: Partitioning ocean motions into balanced motions and internal gravity waves: A modeling study in anticipation of future space missions. J. Geophys. Res. Oceans, 123, 8084–8105, https://doi.org/10.1029/2018JC014438.

    • Search Google Scholar
    • Export Citation

  • Torres, H., and Coauthors, 2022: Separating energetic internal gravity waves and small-scale frontal dynamics. Geophys. Res. Lett., 49, e2021GL096249, https://doi.org/10.1029/2021GL096249.

    • Search Google Scholar
    • Export Citation

  • Torres, H., A. Wineteer, P. Klein, T. Lee, J. Wang, E. Rodriguez, D. Menemenlis, and H. Zhang, 2023: Anticipated capabilities of the ODYSEA wind and current mission concept to estimate wind work at the air–sea interface. Remote Sens., 15, 3337, https://doi.org/10.3390/rs15133337.

    • Search Google Scholar
    • Export Citation

  • Ubelmann, C., G. Dibarboure, L. Gaultier, A. Ponte, F. Ardhuin, M. Ballarotta, and Y. Faugère, 2021: Reconstructing ocean surface current combining altimetry and future spaceborne Doppler data. J. Geophys. Res. Oceans, 126, e2020JC016560, https://doi.org/10.1029/2020JC016560.

    • Search Google Scholar
    • Export Citation

  • van Meurs, P., and P. P. Niiler, 1997: Temporal variability of the large-scale geostrophic surface velocity in the northeast Pacific. J. Phys. Oceanogr., 27, 2288–2297, https://doi.org/10.1175/1520-0485(1997)027<2288:TVOTLS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Villas Bôas, A. B., and W. R. Young, 2020: Directional diffusion of surface gravity wave action by ocean macroturbulence. J. Fluid Mech., 890, R3, https://doi.org/10.1017/jfm.2020.116.

    • Search Google Scholar
    • Export Citation

  • Wang, J., H. Torres, P. Klein, A. Wineteer, H. Zhang, D. Menemenlis, C. Ubelmann, and E. Rodriguez, 2023: Increasing the observability of near inertial oscillations by a future ODYSEA satellite mission. Remote Sens., 15, 4526, https://doi.org/10.3390/rs15184526.

    • Search Google Scholar
    • Export Citation

  • Warner, J. C., B. Armstrong, R. He, and J. B. Zambon, 2010: Development of a Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) modeling system. Ocean Modell., 35, 230–244, https://doi.org/10.1016/j.ocemod.2010.07.010.

    • Search Google Scholar
    • Export Citation

  • Watterson, I. G., 2001: Decomposition of global ocean currents using a simple iterative method. J. Atmos. Oceanic Technol., 18, 691–703, https://doi.org/10.1175/1520-0426(2001)018<0691:DOGOCU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Wentz, F. J., L. A. Mattox, and S. Peteherych, 1986: New algorithms for microwave measurements of ocean winds: Applications to Seasat and the special sensor microwave imager. J. Geophys. Res., 91, 2289–2307, https://doi.org/10.1029/JC091iC02p02289.

    • Search Google Scholar
    • Export Citation

  • Wineteer, A., H. S. Torres, and E. Rodríguez, 2020a: On the surface current measurement capabilities of spaceborne Doppler scatterometry. Geophys. Res. Lett., 47, e2020GL090116, https://doi.org/10.1029/2020GL090116.

    • Search Google Scholar
    • Export Citation

  • Wineteer, A., and Coauthors, 2020b: Measuring winds and currents with Ka-band Doppler scatterometry: An airborne implementation and progress towards a spaceborne mission. Remote Sens., 12, 1021, https://doi.org/10.3390/rs12061021.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 1544 1477 284
Full Text Views 255 230 4
PDF Downloads 287 258 0