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Satellite-Based Ocean Surface Turbulent Fluxes

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Abstract

Ocean surface turbulent fluxes of momentum, heat, and water vapor respond to and determine the coupling between the atmosphere and the ocean and are excellent indicators of air–sea interactions at most temporal and spatial scales. These fluxes can be determined from bulk properties at the sea surface. By combining satellite observations of bulk properties such as sea surface temperature, wind, and humidity, estimates of these fluxes are available globally. The bulk aerodynamic formulations of these fluxes are first reviewed. Satellite retrieval techniques of these bulk properties and operational or semi-operational ocean surface flux products such as the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Observations (HOAPS), the Japanese Oceanic Fluxes with the Use of Remote Observations (J-OFURO), and the US NASA Goddard Space Flight Center Satellite-Based Sea Surface Turbulent Fluxes (GSSTF), as well as merged approach of the Objectively Analyzed Air–Sea Fluxes for the global ocean (OAFlux) are described, and their error and uncertainties are briefly discussed.

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Abbreviations

AATSR:

Advanced Along-Track Scanning Radiometer

ADEOS:

Advanced Earth Observing Satellite

ADEOS-2:

Advanced Earth Observing Satellite 2

AIRS:

Atmospheric Infrared Sounder

AMSR-E:

Advanced Microwave Scanning Radiometer-Earth Observing System

AMSU:

Advanced Microwave Sounding Unit

ASCAT:

Advanced Scatterometer

AVHRR:

Advanced Very High Resolution Radiometer

CFSR:

Climate Forecast System Reanalysis

COARE:

Coupled Ocean–Atmosphere Response Experiment

DMSP:

Defense Meteorological Satellite Program

DOE:

Department of Energy

ECMWF:

European Centre for Medium-Range Weather Forecasts

EIA:

Earth incidence angle

ERA-40:

European Centre for Medium-Range Weather Forecasts’ 40-year reanalysis

ERS-1:

Earth Resource Satellite 1

ERS-2:

Earth Resource Satellite 2

FGGE:

First Global Atmospheric Research Program Global Experiment

GARP:

Global Atmospheric Research Experiment

GES:

DISC Goddard Earth Sciences Data and Information Services Center

GOES:

Geostationary Operational Environmental Satellite

GSSTF:

Goddard Space Flight Center Satellite-based Sea surface Turbulent Fluxes

HOAPS:

Hamburg Ocean Atmosphere Parameters and fluxes from Satellite observations

JMA:

Japanese Meteorological Agency

J-OFURO:

Japanese Oceanic Fluxes with the Use of Remote Observations

JRA-25:

Japanese 25-year ReAnalysis

LHF:

Latent heat flux

MERRA:

Modern Era Retrospective Analysis for Research and Applications

MGDSST:

Merged satellite and in-situ data Global Daily SST

MODIS:

Moderate Resolution Imaging Spectroradiometer

NASA:

National Aeronautics and Space Administration

NCAR:

National Center for Atmospheric Research

NCEP:

National Centers for Environmental Prediction

NSCAT:

NASA Scatterometer

OAFlux:

Objectively Analyzed Air-sea Fluxes

QuikSCAT:

Quick Scatterometer

SAR:

Synthetic Aperture Radars

SASS:

Seasat-A Scatterometer System

SHF:

Sensible heat flux

SMMR:

Scanning Multichannel Microwave Radiometer

SSM/I:

Special Sensor Microwave Imager

SSMIS:

Special Sensor Microwave Imager/Sounder

SST:

Sea surface temperature

TMI:

Tropical Rainfall Measuring Mission Microwave Imager

TRMM:

Tropical Rainfall Measuring Mission

References

  • Andersson A, Fennig K, Klepp C, Bakan S, Graßl H, Schulz J (2010) The Hamburg ocean atmosphere parameters and fluxes from satellite data – HOAPS-3. Earth Syst Sci Data 2:215–234

    Article  Google Scholar 

  • Atlas R, Bloom SC, Hoffman RN, Ardizzone JV, Brin G (1991) Space-based surface wind vectors to aid understanding of air-sea interactions. EOS, Trans Am Geophys Union 72:201–208

    Article  Google Scholar 

  • Atlas RM, Hoffman RN, Ardizzone J et al (2011) A cross-calibrated, multi-platform ocean surface wind velocity product for meteorological and oceanographic applications. Bull Am Meteorol Soc 92:157–174

    Article  Google Scholar 

  • Beal RC, Young G, Monaldo F, Thompson DR, Winstead N.S, Scott CA (2005) High resolution wind monitoring with wide swath SAR: a user’s guide, Department of Commerce, NOAA, NESDIS and Office of Research and Applications, Washington D.C.

    Google Scholar 

  • Bentamy A, Katsaros KB, Mestas-Nuñez AM, Drennan WM, Forder EB, Roquet H (2003) Satellite estimates of wind speed and latent heat flux over the global oceans. J Climate 16:637–656

    Article  Google Scholar 

  • Black PG, D’Asaro EA, Drennan WM, French JR, Niiler PP, Sanford TB, Terrill EJ, Walsh EJ, Zhang JA (2007) Air-sea exchanges in hurricanes. Bull Am Meteorol Soc 88:357–374

    Article  Google Scholar 

  • Born GH, Lame DB, Rygh PJ (1981) A survey of the goals and accomplishments of the seasat mission. In: Gower JFR (ed) Oceanography from space. Plenum Press, New York

    Google Scholar 

  • Brunke MA, Zeng X, Anderson S (2002) Uncertainties in sea surface turbulent flux algorithms and data sets. J Geophys Res 107:3141. doi:10.1029/2001JC00092

    Article  Google Scholar 

  • Brunke MA, Fairall CW, Zeng X, Eymard L, Curry JA (2003) Which bulk aerodynamic algorithms are least problematic in computing ocean surface turbulent fluxes? J Climate 16:619–635

    Article  Google Scholar 

  • Brunke MA, Wang Z, Zeng X, Bosilovich M, Shie C-L (2011) An assessment of the uncertainties in ocean surface turbulent fluxes in 11 reanalysis, satellite-derived, and combined global data sets. J Climate 24:469–5493

    Article  Google Scholar 

  • Bunker AF (1976) Computation of surface energy flux and annual air-sea interaction cycle of the North Atlantic Ocean. Mon Weather Rev 104:1122–1140

    Article  Google Scholar 

  • Cayan DR (1992) Latent and sensible heat flux anomalies over the northern oceans: the connection to monthly atmospheric circulation. J Climate 5:354–369

    Article  Google Scholar 

  • Chiu LS, Chokngamwong R, Xing Y, Yang R, Shie C-L (2008) Trends and variations of global oceanic evaporation datasets from remote sensing. Acta Oceanol Sin 27:124–135

    Google Scholar 

  • Chiu LS, Gao S, Shie C-L (2012) Oceanic evaporation: variability and trends. In: Escalante-Ramirez B (ed) Remote sensing – applications. InTech, Rijeka

    Google Scholar 

  • Chou S-H, Atlas RM, Shie C-L, Ardizzone J (1995) Estimates of surface humidity and latent heat fluxes over oceans from SSM/I data. Mon Weather Rev 123:2405–2425

    Article  Google Scholar 

  • Chou S-H, Shie C-L, Atlas RM, Ardizzone J (1997) Air–sea fluxes retrieved from special sensor microwave imager data. J Geophys Res 102:12705–12726

    Article  Google Scholar 

  • Chou S-H, Nelkin E, Ardizzone J, Atlas RM, Shie C-L (2003) Surface turbulent heat and momentum fluxes over global oceans based on the Goddard satellite retrievals, version 2 (GSSTF2). J Climate 16:3256–3273

    Article  Google Scholar 

  • Curry JA et al (2004) SEAFLUX. Bull Am Meteorol Soc 85:409–424

    Article  Google Scholar 

  • da Silva AM, Young CC, Levitus S (1994) Atlas of surface marine data, vol 3, Anomalies of heat and momentum fluxes. NOAA/NESDIS, Washington, D.C

    Google Scholar 

  • Donelan MA, Haus BK, Reul N, Plant WJ, Stiassnie M, Graber HC, Brown OB, Saltzman ES (2004) On the limiting aerodynamic roughness of the ocean in very strong winds. Geophys Res Lett 31:L18306. doi:10.1029/2004GL019460

    Article  Google Scholar 

  • Donlon C et al (2007) The global ocean data assimilation project experiment high resolution sea surface temperature pilot project. Bull Am Meteorol Soc 88:1197–1213

    Article  Google Scholar 

  • Draper DW, Long DG (2004) Evaluating the effect of rain on SeaWinds scatterometer measurements. J Geophys Res 109:C02005. doi:10.1029/2002JC001741

    Article  Google Scholar 

  • Dzura MS, Etkin VS, Khrupin AS, Paspelov MN, Raev MD (1992) Radiometers-polarimeters: principles of design and applications for sea surface microwave emission polarimetry. In; Proceedings of the IGARSS 92 conference, Houston, 1992, IEEE Press, Piscataway

    Google Scholar 

  • Esbensen SK, Reynolds RW (1981) Estimating monthly averaged air-sea transfer of heat and momentum using the bulk aerodynamic method. J Phys Oceanogr 11:457–465

    Article  Google Scholar 

  • Esbensen SK, Chelton DB, Vockers D, Sun J (1993) An analysis of errors in spatial sensor microwave imager evaporation estimates over the global oceans. J Geophys Res 98:7081–7101

    Article  Google Scholar 

  • Fairall CW, Bradley EF, Rogers DP, Edson JB, Young GS (1996) Bulk parameterization of air-sea fluxes for tropical ocean-global atmosphere coupled-ocean atmosphere response experiment. J Geophys Res 101:3747–3764

    Article  Google Scholar 

  • Fairall CW, Bradley EF, Hare JE, Grachev AA, Edson JB (2003) Bulk parameterization of air-sea fluxes: updates and verification for the COARE algorithm. J Climate 16:571–591

    Article  Google Scholar 

  • Freilich MH, Dunbar RS (1999) The accuracy of the NSCAT-1 vector winds: comparisons with NDBC buoys. J Geophys Res 104:11231–11246

    Article  Google Scholar 

  • Gao S, Chiu LS (2010) Surface latent heat flux associated with rapidly intensifying tropical cyclone. Int J Remote Sens 31:4699–4710

    Article  Google Scholar 

  • Gao S, Chiu LS (2012) Development of statistical typhoon intensity prediction: application to satellite observed rain rate and surface evaporation (STIPER). Weather Forecast 27:240–250

    Article  Google Scholar 

  • Garratt JR (1992) The atmospheric boundary layer. Cambridge University Press, Cambridge

    Google Scholar 

  • Gasiewski AJ, Kunkee DB (1993) Calibration and application of polarization-correlating radiometers. IEEE Trans Microw Theory Tech 41(5):767–772

    Article  Google Scholar 

  • Gautam R, Cervone G, Singh RP, Kafatos M (2005) Characteristics of meteorological parameters associated with hurricane Isabel. Geophys Res Lett 32:L04801. doi:10.1029/2004GL021559

    Article  Google Scholar 

  • Goodberlet MA, Swift CT (1992) Improved retrievals from the DMSP wind speed algorithm under adverse weather conditions. IEEE Trans Geosci Remote Sens 30:1076–1077

    Article  Google Scholar 

  • Goodberlet MA, Swift CT, Wilkerson JC (1989) Remotes sensing of ocean surface wind with the special sensor microwave imager. J Geophys Res 94:14547–14555

    Article  Google Scholar 

  • Hastenrath S (1980) Heat budget of tropical ocean and atmosphere. J Phys Oceanogr 10:159–170

    Article  Google Scholar 

  • Hilburn KA, Shie C-L (2011) Decadal trends and variability in Special Sensor Microwave Imager (SSM/I) brightness temperatures and Earth incidence angle. Report no. 092811, Remote sensing systems, Santa Rosa

    Google Scholar 

  • Hsiung J (1985) Estimates of global oceanic meridional heat transport. J Phys Oceanogr 15:1405–1413

    Article  Google Scholar 

  • Isemer H-J, Hasse L (1985) The bunker climate atlas of the north Atlantic ocean, vol 1, Observations. Springer, Heidelberg/New York/Tokyo

    Book  Google Scholar 

  • Isemer H-J, Hasse L (1987) The bunker climate atlas of the north Atlantic ocean, vol 2, Air-sea interactions. Springer, Heidelberg/New York/Tokyo

    Book  Google Scholar 

  • Jackson DL, Wick GA (2010) Near-surface air temperature retrieval derived from AMSU-A and sea surface temperature observations. J Atmos Oceanic Tech 27:1769–1776

    Article  Google Scholar 

  • Jackson DL, Wick GA, Bates JJ (2006) Near-surface retrieval of air temperature and specific humidity using multi-sensor microwave satellite observations. J Geophys Res 111:D10306. doi:10.1029/2005JD006431

    Article  Google Scholar 

  • Jackson DL, Wick GA, Robertson FR (2009) Improved multi-sensor approach to satellite retrieved near-surface specific humidity observations. J Geophys Res 114:D16303. doi:10.1029/2008JD011341

    Article  Google Scholar 

  • Jones C, Peterson P, Gautier C (1999) A new method for deriving ocean surface specific humidity and air temperature: an artificial neural network. J Appl Meteorol 38:1229–1245

    Article  Google Scholar 

  • Josey SA, Kent EC, Taylor PK (1998) The Southampton Oceanography Centre (SOC) ocean–atmosphere heat, momentum and freshwater flux atlas. Southampton Oceanography Centre, Report no. 6, Southampton

    Google Scholar 

  • Jourdan D, Gautier C (1995) Comparison between global latent heat flux computed from multisensor (SSM/I and AVHRR) and from in situ data. J Atmos Oceanic Tech 12:46–72

    Article  Google Scholar 

  • Konda M, Imasato N, Shibata A (1996) A new method to determine near-sea surface air temperature by using satellite data. J Geophys Res 101:14349–14360

    Article  Google Scholar 

  • Krasnopolsky V, Breaker LC, Gemmeill WH (1995) A neural network as a non-linear transfer function model for retrieving surface wind speeds from the special sensor microwave/imager. J Geophy Res 100:11033–11045

    Article  Google Scholar 

  • Kraus EB, Businger JA (1994) Atmosphere–ocean interaction. Oxford University Press, New York

    Google Scholar 

  • Kubota M, Shikauchi A (1995) Air temperature at ocean surface derived from surface-level humidity. J Oceanogr 51:619–634

    Article  Google Scholar 

  • Kummerow C (2009) Development of A fundamental climate data record for SSM/I, SSMIS and future microwave sensors, NOAA CDR selections. Available online at http://www1.ncdc.noaa.gov/pub/data/sds/cdr/abstracts/2009/kummerow.pdf. Accessed on 10 Dec 2011

  • Liu WT (1986) Statistical relation between monthly mean precipitable water and surface-level humidity over global oceans. Mon Weather Rev 114:1591–1602

    Article  Google Scholar 

  • Liu WT (1988) Moisture and latent heat flux variabilities in the Tropical Pacific derived from satellite data. J Geophys Res 93:6749–6760

    Article  Google Scholar 

  • Liu WT, Katsaros KB, Businger JA (1979) Bulk parameterization of the air-sea exchange of heat and water vapor including the molecular constraints at the interface. J Atmos Sci 36:1722–1735

    Article  Google Scholar 

  • Liu WT, Xie X, Tang W (2010) Scatterometer’s unique capability in measuring ocean surface stress. In: Barale V, Gower JFR, Alberotanza L (eds) Oceanography from space. Springer, Heidelberg

    Google Scholar 

  • Liu J, Curry JA, Clayson CA, Bourassa MA (2011) High-resolution satellite surface latent heat fluxes in North Atlantic hurricanes. Mon Weather Rev 139:2735–2747

    Article  Google Scholar 

  • Lo RC (1983) A comprehensive description of the Special Sensor Microwave Imager (SSM/I) environmental parameter extraction algorithm. Naval Research Laboratory Memo report 5199, Washington D.C.

    Google Scholar 

  • Lykossov VN (2001) Atmospheric and oceanic boundary layer physics. In: Jones I, Toba Y (eds) Wind stress over the oceans. Cambridge University Press, Cambridge

    Google Scholar 

  • Mears CA, Smith DK, Wentz FJ (2001) Comparison of special sensor microwave imager and buoy-measured wind speeds from 1987 to 1997. J Geophys Res 106:11719–11729

    Article  Google Scholar 

  • Meissner T, Smith DK, Wentz FJ (2001) A 10-year intercomparison between collocated special sensor microwave imager oceanic surface wind speed retrievals and global analyses. J Geophys Res 106:11731–11742

    Article  Google Scholar 

  • Murray FW (1967) On the computation of saturation vapor pressure. J Appl Meteorol 6:203–204

    Article  Google Scholar 

  • Oberhuber JM (1988) An atlas based on the ‘COADS’ data set: the budgets of heat, buoyancy and turbulent kinetic energy at the surface of the global ocean, Max Planck Institute for Meteorology, Report no. 15, Hamburg

    Google Scholar 

  • Powell MD, Vickery PJ, Reinhold TA (2003) Reduced drag coefficient for high wind speeds in tropical cyclones. Nature 422:279–283

    Article  Google Scholar 

  • Reynolds RW, Smith TM, Liu C, Chelton DB, Casey KS, Schlax MG (2007) Daily high-resolution-blended analyses for sea surface temperature. J Climate 20:5473–5496

    Article  Google Scholar 

  • Roberts B, Clayson CA, Robertson FR, Jackson DL (2010) Predicting near-surface characteristics from SSM/I using neural networks with a first guess approach. J Geophys Res 115:D19113. doi:10.1029/2009JD013099

    Article  Google Scholar 

  • Schlussel P, Schanz L, English G (1995) Retrieval of latent heat flux and longwave irradiance at sea surface differences at the sea surface from SSM/I and AVHRR measurements. Adv Space Res 16:107–116

    Article  Google Scholar 

  • Schulz J, Schlussel P, Grassl H (1993) Water vapour in atmospheric boundary layer over oceans from SSM/I measurements. Int J Remote Sens 14:2773–2789

    Article  Google Scholar 

  • Schulz J, Jeans M, Stefan E, Schlussel P (1997) Evaluation of satellite-derived latent heat fluxes. J Climate 10:2782–2795

    Article  Google Scholar 

  • Shie C-L (2010) Science background for the reprocessing and Goddard Satellite-based Surface Turbulent Fluxes (GSSTF2b) data set for global water and energy cycle research. Science document for the distributed GSSTF2b via Goddard Earth Sciences (GES) Data and Information Services Center (DISC), 18 pp. Available online at http://disc.sci.gsfc.nasa.gov/measures/documentation/Science-of-the-data.pdf. Accessed on 10 Dec 2011

  • Shie C-L (2011) Science background for the reprocessing and Goddard Satellite-based Surface Turbulent Fluxes (GSSTF2c) data set for global water and energy cycle research. Science document for the distributed GSSTF2c via Goddard Earth Sciences (GES) Data and Information Services Center (DISC), 19 pp. Available online at http://disc.sci.gsfc.nasa.gov/measures/documentation/Science-of-the-data.GSSTF2c.pdf. Accessed on 10 Dec 2011

  • Shie C-L, Hilburn K (2011) A satellite-based global ocean surface turbulent fluxes dataset and the impact of the associated SSM/I brightness temperature. In: Proceeding of the 2011 EUMESAT meteorological satellite conference, Oslo, 5–9 Sept 2011

    Google Scholar 

  • Shie C-L et al (2009) A note on reviving the Goddard Satellite-Based Surface Turbulent Fluxes (GSSTF) dataset. Adv Atmos Sci 26:1071–1080

    Article  Google Scholar 

  • Shie C-L, Chiu LS, Adler R, Lin I-I, Nelkin E, Ardizzone J (2010) The Goddard Satellite-Based Surface Turbulent Fluxes Dataset – Version 2b (GSSTF 2b) distributed via Goddard Earth Sciences (GES) Data and Information Services Center (DISC) in October 2010. Available online at http://disc.sci.gsfc.nasa.gov/daac-bin/DataHoldingsMEASURES.pl?PROGRAM_List=ChungLinShie_OldVer. Accessed on 10 Dec 2011

  • Shie C-L, Hilburn K, Chiu LS, Adler R, Lin I-I, Nelkin E, Ardizzone J (2011) The Goddard Satellite-Based Surface Turbulent Fluxes Dataset – Version 2c (GSSTF 2c) distributed via Goddard Earth Sciences (GES) Data and Information Services Center (DISC) in October 2011. Available online at http://disc.sci.gsfc.nasa.gov/daac-bin/DataHoldingsMEASURES.pl?PROGRAM_List=ChungLinShie. Accessed on 10 Dec 2011

  • Singh R, Joshi PC, Kishtawal CM (2005) A new technique for estimation of surface latent heat fluxes using satellite-based observations. Mon Weather Rev 133:2692–2710

    Article  Google Scholar 

  • Singh R, Joshi PC, Kishtawal CM, Pal PK (2006) A new method for estimation of near surface specific humidity over global oceans. Meteorol Atmos Phys 94:1–10

    Article  Google Scholar 

  • Sun D (2011) Ocean remote sensing. In: Yang C, Wong D, Maio Q, Yang R (eds) Advanced geoinformation science. CRC Press, Boca Raton

    Google Scholar 

  • Tomita H, Kubota M, Cronin MF, Iwasaki S, Konda M, Ichikawa H (2010) An assessment of surface heat fluxes from J-OFURO2 at the KEO and JKEO sites. J Geophys Res 115:C03018. doi:10.1029/2009JC005545

    Article  Google Scholar 

  • Weare BC, Strub PT, Samuel MD (1981) Annual mean surface heat fluxes in the tropical Pacific Ocean. J Phys Oceanogr 11:705–717

    Article  Google Scholar 

  • Weissman DE, Bourassa MA, Tongue J (2002) Effects of rain rate and wind magnitude on SeaWinds scatterometer wind speed errors. J Atmos Oceanic Technol 19:738–746

    Article  Google Scholar 

  • Wentz FJ (1992) Measurement of oceanic wind vector using satellite microwave radiometers. IEEE Trans Geosci Remote Sens 30:960–972

    Article  Google Scholar 

  • Wentz FJ, Ricciardulli L, Hilburn KA, Mears CA (2007) How much more rain will global warming bring? Science 317:233–235

    Article  Google Scholar 

  • Wentz FJ, Mattox LA, Peteherych S (1986) New algorithms for microwave measurements of ocean winds: applications to SeaSat and the special sensor microwave imager. J Geophys Res 91:2289–2307

    Article  Google Scholar 

  • Williams BA, Long DG (2008) Estimation of hurricane winds from SeaWinds at ultra high resolution. IEEE Trans Geosci Remote Sens 46:2924–2935

    Article  Google Scholar 

  • Yu L, Weller RA (2007) Objectively analyzed Air-Sea heat fluxes for the global ice free oceans (1981–2005). Bull Am Meteorol Soc 88:527–539

    Article  Google Scholar 

  • Yu L, Jin X, Weller RA (2008) Multidecade global flux datasets from the Objectively Analyze Air-sea Fluxes (OAFlux) project: latent and sensible heat fluxes, ocean evaporation, and related surface meteorological variables. Woods Hole Oceanographic Institution OAFlux Project technical report OA-2008-01, Woods Hole

    Google Scholar 

  • Zeng X, Zhao M, Dickinson RE (1998) Intercomparison of bulk aerodynamic algorithms for the computation of sea surface fluxes using TOGA COARE and TAO data. J Climate 11:2628–2644

    Article  Google Scholar 

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Acknowledgments

This study is supported by the MEaSUREs Program of NASA Science Mission Directorate – Earth Science Division.

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Chiu, L.S., Gao, S., Shie, CL. (2013). Satellite-Based Ocean Surface Turbulent Fluxes. In: Qu, J., Powell, A., Sivakumar, M. (eds) Satellite-based Applications on Climate Change. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5872-8_11

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