Description: Three data types are compared in the low-current-velocity regime in the southeastern North Atlantic, between 12-degrees-N and 30-degrees-N, 29-degrees-W and 18-degrees-W: Geosat altimetric sea level and derived surface geostrophic velocities, shallow current meter velocities, and dynamic heights derived from hydrographic data from cruises 4, 6, and 9 of the research vessel Meteor. The four current meter daily time series, at depths around 200 m, were smoothed over 1 month; the altimetric geostrophic velocities were computed from sea surface slopes over 142 km every 17 days. The correlation coefficients between the current meter and altimetric geostrophic velocities range between 0.64 and 0.90 for the moorings near 29-degrees-N but between 0.32 and 0.71 for the two around 21-degrees-N; the associated rms discrepancies between the two measurement types range between 1.5 and 4.4 cm/s, which is 49% to 127% of the rms of the respective current meter time series. Dynamic heights relative to 1950 dbar for the months of November 1986 (d(M4)), November 1987 (d(M6)), and February 1989 (d(M9)) were computed from Meteor cruises 4, 6, and 9. Both dynamic heights and altimetric heights (h(M4), h(M6), h(M9)) were averaged over 1-degrees boxes for the duration of each cruise. Differences d(M4) - d(M6) and d(M9) - d(M6) were computed only at bins where at least one station from both cruises existed, Assuming that dynamic heights d in dynamic centimeters are equivalent to sea level h in centimeters, the standard deviation sigma of the differences ((h(M4) - h(M6)) - (d(M4) - d(M6))) and corresponding M9 - M6 values was 2.1 cm. This value (squared) is only 13% of the (5.8 cm)2 variance of the dynamic height differences and is indistinguishable from the 2.7- to 5.6-cm natural variability of sea level in the area expected between the times when the ship and the satellite sampled the ocean. The areally averaged discrepancy for M9 - M6 was only 0.7 cm, but the corresponding value for M4 - M6 was 5.2 cm. A systematic difference between the water vapor corrections used before and after July 1987 is responsible for the M4 - M6 difference. The average M4 - M6 discrepancy is only 0.1 cm using the Fleet Numerical Oceanography Center correction, with a standard deviation of 3.1 cm. In spite of the underlying differences in sampling and physics, including unknown barotropic components not included in our hydrographic dynamic heights, and in data errors, including water vapor, ionospheric, and orbital effects on the altimetry, consistent interannual changes of the mean sea level from the independently obtained altimetric and hydrographic data sets are obtained, and correlated seasonal changes in surface currents are observed with both altimetry and current meters.

Description: Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February, 1983

Description: Altimetric, gravimetric and oceanographic data over the North Atlantic are combined -using techniques of optimum estimation- to infer the surface expression of the time averaged circulation (ζ) and to estimate the marine geoid (γ), both in the wavelength band 100 km-2000 km. Optimum inverse methods in geophysics are reviewed. They are then used to analyze the estimation of the geoid from gravity data, emphasizing the wavenumber spectrum of resolution functions. It is found that accurate bandpassed versions of the geoid can be recovered from restricted data sets. The accuracy and distribution of publicly available gravity data are shown to define an estimate γ whose expected errors, σγ, range between 30 and 260 cm, assuming the Wagner and Colombo (1978) spectrum describes the average geoid behaviour. The σγ underestimate the actual differences between 'y and an altimetric surface (s) derived from Seasat, but the spatial variation of σγ follows closely the differences s-γ. The discrepancy is attributable to a partial failure of the spectral model at short wavelengths. The differences s-γ are dominated by geoid error that masks much of the signal ζ. The main North Atlantic gyre emerges clearly only after the σγ and the simplest model for ζ -as a spatially uncorrelated process with (30 cm)2 variance- are taken into account. To obtain a corrected geoid, a hydrographic estimate of ζ is combined with sand γ, and their expected errors.

Description: NASA's research Grant NAG6-9 funded this work

Description: © The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Gentemann, C. L., Clayson, C. A., Brown, S., Lee, T., Parfitt, R., Farrar, J. T., Bourassa, M., Minnett, P. J., Seo, H., Gille, S. T., & Zlotnicki, V. FluxSat: measuring the ocean-atmosphere turbulent exchange of heat and moisture from space. Remote Sensing, 12(11), (2020): 1796, doi:10.3390/rs12111796.

Description: Recent results using wind and sea surface temperature data from satellites and high-resolution coupled models suggest that mesoscale ocean–atmosphere interactions affect the locations and evolution of storms and seasonal precipitation over continental regions such as the western US and Europe. The processes responsible for this coupling are difficult to verify due to the paucity of accurate air–sea turbulent heat and moisture flux data. These fluxes are currently derived by combining satellite measurements that are not coincident and have differing and relatively low spatial resolutions, introducing sampling errors that are largest in regions with high spatial and temporal variability. Observational errors related to sensor design also contribute to increased uncertainty. Leveraging recent advances in sensor technology, we here describe a satellite mission concept, FluxSat, that aims to simultaneously measure all variables necessary for accurate estimation of ocean–atmosphere turbulent heat and moisture fluxes and capture the effect of oceanic mesoscale forcing. Sensor design is expected to reduce observational errors of the latent and sensible heat fluxes by almost 50%. FluxSat will improve the accuracy of the fluxes at spatial scales critical to understanding the coupled ocean–atmosphere boundary layer system, providing measurements needed to improve weather forecasts and climate model simulations.

Description: C.L.G. was funded by NASA grant 80NSSC18K0837. C.A.C. was funded by NASA grants 80NSSC18K0778 and 80NSSC20K0662. J.T.F. was funded by NASA grants NNX17AH54G, NNX16AH76G, and 80NSSC19K1256. S.T.G. was funded by the National Science Foundation grant PLR-1425989 and by the NASA Ocean Vector Winds Science Team grant 80NSSC19K0059. M.B. was funded in part by the Ocean Observing and Monitoring Division, Climate Program Office (FundRef number 100007298), National Oceanic and Atmospheric Administration, U.S. Department of Commerce, and by the NASA Ocean Vector Winds Science Team grant through NASA/JPL. H.S. was funded by National Oceanic and Atmospheric Administration (NOAA) grant NA19OAR4310376 and the Andrew W. Mellon Foundation Endowed Fund for Innovative Research at Woods Hole Oceanographic Institution.