• Akella, S., R. Todling, and M. Suarez, 2017: Assimilation for skin SST in the NASA GEOS Atmospheric Data Assimilation System. Quart. J. Roy. Meteor. Soc., 143, 10321046, https://doi.org/10.1002/qj.2988.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J. E., and S. C. Riser, 2014: Near-surface variability of temperature and salinity in the near-tropical ocean: Observations from profiling floats. J. Geophys. Res. Oceans, 119, 74337448, https://doi.org/10.1002/2014JC010112.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Argo Data Management Team, 2019: Argo user’s manual V3.3. IFREMER Rep., 113 pp., https://doi.org/10.13155/29825.

    • Crossref
    • Export Citation
  • Atkinson, C. P., N. A. Rayner, J. Roberts-Jones, and R. O. Smith, 2013: Assessing the quality of sea surface temperature observations from drifting buoys and ships on a platform-by-platform basis. J. Geophys. Res. Oceans, 118, 35073529, https://doi.org/10.1002/jgrc.20257.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beggs, H. M., R. Verein, G. Paltoglou, H. Kippo, and M. Underwood, 2012: Enhancing ship of opportunity sea surface temperature observations in the Australian region. J. Oper. Oceanogr., 5, 5973, https://doi.org/10.1080/1755876X.2012.11020132.

    • Search Google Scholar
    • Export Citation
  • Brasnett, B., and D. Surcel Colan, 2016: Assimilating retrievals of sea surface temperature from VIIRS and AMSR2. J. Atmos. Oceanic Technol., 33, 361375, https://doi.org/10.1175/JTECH-D-15-0093.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brisson, A., P. Le Borgne, and A. Marsouin, 2002: Results of one year of preoperational production of sea surface temperatures from GOES-8. J. Atmos. Oceanic Technol., 19, 16381652, https://doi.org/10.1175/1520-0426(2002)019<1638:ROOYOP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Castro, S., G. Wick, D. Jackson, and W. Emery, 2008: Error characterization of infrared and microwave satellite sea surface temperature products for merging and analysis. J. Geophys. Res., 113, C03010, https://doi.org/10.1029/2006JC003829.

    • Search Google Scholar
    • Export Citation
  • Chin, M., J. Vazquez-Cuervo, and E. Armstrong, 2017: A multi-scale high-resolution analysis of global sea surface temperature. Remote Sens. Environ., 200, 154169, https://doi.org/10.1016/j.rse.2017.07.029.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dash, P., and et al. , 2012: Group for High Resolution Sea Surface Temperature (GHRSST) analysis fields inter-comparisons—Part 2: Near real time web-based level 4 SST Quality Monitor (L4-SQUAM). Deep-Sea Res. II, 77–80, 3143, https://doi.org/10.1016/j.dsr2.2012.04.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Donlon, C. J., P. J. Minnett, C. Gentemann, T. J. Nightingale, I. J. Barton, B. Ward, and M. J. Murray, 2002: Toward improved validation of satellite sea surface skin temperature measurements for climate research. J. Climate, 15, 353369, https://doi.org/10.1175/1520-0442(2002)015<0353:TIVOSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fairall, C. W., E. F. Bradley, J. S. Godfrey, G. A. Wick, J. B. Edson, and G. S. Young, 1996: Cool-skin and warm-layer effects on sea surface temperature. J. Geophys. Res., 101, 12951308, https://doi.org/10.1029/95JC03190.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Freeman, E., and et al. , 2017: ICOADS release 3.0: A major update to the historical marine climate record. Int. J. Climatol., 37, 22112232, https://doi.org/10.1002/joc.4775.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Freeman, E., and et al. , 2019: The International Comprehensive Ocean-Atmosphere Data Set—Meeting users needs and future priorities. Front. Mar. Sci., 6, 435, https://doi.org/10.3389/fmars.2019.00435.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ingleby, B., 2010: Factors affecting ship and buoy data quality: A data assimilation perspective. J. Atmos. Oceanic Technol., 27, 14761489, https://doi.org/10.1175/2010JTECHA1421.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ingleby, B., and A. C. Lorenc, 1993: Bayesian quality control using multivariate normal distributions. Quart. J. Roy. Meteor. Soc., 119, 11951225, https://doi.org/10.1002/qj.49711951316.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ingleby, B., and M. Huddleston, 2007: Quality control of ocean temperature and salinity profiles—Historical and real-time data. J. Mar. Syst., 65, 158175, https://doi.org/10.1016/j.jmarsys.2005.11.019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kawai, Y., and A. Wada, 2007: Diurnal sea surface temperature variation and its impact on the atmosphere and ocean: A review. J. Oceanogr., 63, 721744, https://doi.org/10.1007/s10872-007-0063-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kent, E. C., and et al. , 2019: Observing requirements for long-term climate records at the ocean surface. Front. Mar. Sci., 6, 441, https://doi.org/10.3389/fmars.2019.00441.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kilpatrick, K. A., G. P. Podestá, and R. Evans, 2001: Overview of the NOAA/NASA Advanced Very High Resolution Radiometer Pathfinder algorithm for sea surface temperature and associated matchup database. J. Geophys. Res., 106, 91799197, https://doi.org/10.1029/1999JC000065.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Le Menn, M., P. Poli, A. David, J. Sagot, M. Lucas, A. O’Carroll, M. Belbeoch, and K. Herklotz, 2019: Development of surface drifting buoys for fiducial reference measurements of sea-surface temperature. Front. Mar. Sci., 6, 578, https://doi.org/10.3389/fmars.2019.00578.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Llewellyn-Jones, D. T., P. J. Minnett, R. W. Saunders, and A. M. Zavody, 1984: Satellite multichannel infrared measurements of sea surface temperature of the N.E. Atlantic Ocean using AVHRR/2. Quart. J. Roy. Meteor. Soc., 110, 613631, https://doi.org/10.1002/qj.49711046504.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenc, A. C., and O. Hammon, 1988: Objective quality control of observations using Bayesian methods. Theory, and a practical implementation. Quart. J. Roy. Meteor. Soc., 114, 515543, https://doi.org/10.1002/qj.49711448012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marsouin, A., P. Le Borgne, G. Legendre, S. Péré, and H. Roquet, 2015: Six years of OSI-SAF MetOp-A AVHRR sea surface temperature. Remote Sens. Environ., 159, 288306, https://doi.org/10.1016/j.rse.2014.12.018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martin, M., and et al. , 2012: Group for High Resolution Sea Surface Temperature (GHRSST) analysis fields inter-comparisons. Part 1: A GHRSST multi-product ensemble (GMPE). Deep-Sea Res. II, 77–80, 2130, https://doi.org/10.1016/j.dsr2.2012.04.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merchant, C. J., L. Horrocks, J. Eyre, and A. O’Carroll, 2006: Retrievals of sea surface temperature from infrared imagery: Origin and form of systematic errors. Quart. J. Roy. Meteor. Soc., 132, 12051223, https://doi.org/10.1256/qj.05.143.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Carroll, A. G., J. R. Eyre, and R. W. Saunders, 2008: Three-way error analysis between AATSR, AMSR-E, and in situ sea surface temperature observations. J. Atmos. Oceanic Technol., 25, 11971207, https://doi.org/10.1175/2007JTECHO542.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petrenko, B., A. Ignatov, Y. Kihai, J. Stroup, and P. Dash, 2014: Evaluation and selection of SST regression algorithms for JPSS VIIRS. J. Geophys. Res. Atmos., 119, 45804599, https://doi.org/10.1002/2013JD020637.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Poli, P., and et al. , 2019: The Copernicus Surface Velocity Platform drifter with Barometer and Reference Sensor for Temperature (SVP-BRST): Genesis, design, and initial results. Ocean Sci., 15, 199214, https://doi.org/10.5194/os-15-199-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., P. Brohan, D. E. Parker, C. K. Folland, J. J. Kennedy, M. Vanicek, T. J. Ansell, and S. F. B. Tett, 2006: Improved analyses of changes and uncertainties in sea surface temperature measured in situ since the mid-nineteenth century: The HadSST2 dataset. J. Climate, 19, 446469, https://doi.org/10.1175/JCLI3637.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution-blended analyses for sea surface temperature. J. Climate, 20, 54735496, https://doi.org/10.1175/2007JCLI1824.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roemmich, D., and et al. , 2009: Argo: The challenge of continuing 10 years of progress. Oceanography, 22 (3), 4655, https://doi.org/10.5670/oceanog.2009.65.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, K., A. Ignatov, X. M. Liang, and P. Dash, 2012: Selecting a first-guess sea surface temperature field as input to forward radiative transfer models. J. Geophys. Res., 117, C12001, https://doi.org/10.1029/2012JC008384.

    • Search Google Scholar
    • Export Citation
  • Saunders, P., 1967: Aerial measurements of sea surface temperature in the infrared. J. Geophys. Res., 72, 41094117, https://doi.org/10.1029/JZ072i016p04109.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Slutz, R. J., S. J. Lubker, J. D. Hiscox, S. D. Woodruff, R. L. Jenne, D. H. Joseph, P. M. Steurer, and J. D. Elms, 1985: Comprehensive ocean-atmosphere data set; release 1. NOAA Environmental Research Laboratories Climate Research Program Rep., 268 pp.

  • Storto, A., and P. Oddo, 2019: Optimal assimilation of daytime SST retrievals from SEVIRI in a regional ocean prediction system. Remote Sens., 11, 2776, https://doi.org/10.3390/rs11232776.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Strong, A., and P. McClain, 1984: Improved ocean surface temperatures from space—Comparison with drifting buoys. Bull. Amer. Meteor. Soc., 65, 138142, https://doi.org/10.1175/1520-0477(1984)065<0138:IOSTFS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • While, J., C. Mao, M. J. Martin, J. Roberts-Jones, P. A. Sykes, S. A. Good, and A. J. McLaren, 2017: An operational analysis system for the global diurnal cycle of sea surface temperature: Implementation and validation. Quart. J. Roy. Meteor. Soc., 143, 17871803, https://doi.org/10.1002/qj.3036.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wolter, K., 1997: Trimming problems and remedies in COADS. J. Climate, 10, 19801997, https://doi.org/10.1175/1520-0442(1997)010<1980:TPARIC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wong, A., R. Keeley, and T. Carval, 2020: Argo quality control manual for CTD and trajectory data. IFREMER Rep., 63 pp., https://doi.org/10.13155/33951.

    • Crossref
    • Export Citation
  • Woodruff, S. D., H. F. Diaz, E. C. Kent, R. W. Reynolds, and S. J. Worley, 2008: The evolving SST record from ICOADS. Advances in Global Change Research, S. Brönnimann et al., Eds., Springer International Publishing, 65–83, https://doi.org/10.1007/978-1-4020-6766-2_4.

    • Crossref
    • Export Citation
  • Woodruff, S. D., and et al. , 2011: ICOADS release 2.5: Extensions and enhancements to the surface marine meteorological archive. Int. J. Climatol., 31, 951967, https://doi.org/10.1002/joc.2103.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, F., and A. Ignatov, 2010: Evaluation of in situ sea surface temperatures for use in the calibration and validation of satellite retrievals. J. Geophys. Res., 115, C09022, https://doi.org/10.1029/2010JC006129.

    • Search Google Scholar
    • Export Citation
  • Xu, F., and A. Ignatov, 2014: In situ SST quality monitor (iQuam). J. Atmos. Oceanic Technol., 31, 164180, https://doi.org/10.1175/JTECH-D-13-00121.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, F., and A. Ignatov, 2016: Error characterization in iQuam SSTs using triple collocations with satellite measurements. Geophys. Res. Lett., 43, 10 82610 834, https://doi.org/10.1002/2016GL070287.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, H., H. Beggs, L. Majewski, X. H. Wang, and A. Kiss, 2016: Investigating sea surface temperature diurnal variation over the tropical warm pool using MTSAT-1R data. Remote Sens. Environ., 183, 112, https://doi.org/10.1016/j.rse.2016.05.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, H., A. V. Babanin, Q. Liu, and A. Ignatov, 2019: Cool skin signals observed from Advanced Along-Track Scanning Radiometer (AATSR) and in situ SST measurements. Remote Sens. Environ., 226, 3850, https://doi.org/10.1016/j.rse.2019.03.035.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Evaluation of the In Situ Sea Surface Temperature Quality Control in the NOAA In Situ SST Quality Monitor (iQuam) System

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  • 1 a NOAA/Center for Satellite Applications and Research, College Park, Maryland
  • | 2 b Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado
  • | 3 c Global Science and Technology, Inc., College Park, Maryland
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Abstract

In situ sea surface temperature (SST) measurements play a critical role in the calibration/validation (Cal/Val) of satellite SST retrievals and ocean data assimilation. However, their quality is not always optimal, and proper quality control (QC) is required before they can be used with confidence. The in situ SST Quality Monitor (iQuam) system was established at the National Oceanic and Atmospheric Administration (NOAA) in 2009, initially to support the Cal/Val of NOAA satellite SST products. It collects in situ SST data from multiple sources, performs uniform QC, monitors the QCed data online, and distributes them to users. In this study, the iQuam QC is compared with other QC methods available in some of the in situ data ingested in iQuam. Overall, the iQuam QC performs well on daily to monthly time scales over most global oceans and under a wide variety of environmental conditions. However, it may be less accurate in the daytime, when a pronounced diurnal cycle is present, and in dynamic regions, because of the strong reliance on the “reference SST check,” which employs daily low-resolution level-4 analyses with no diurnal cycle resolved. The iQuam “performance history check,” applied to all in situ platforms, is an effective alternative to the customary “black/gray” lists, available only for some platforms (e.g., drifters and Argo floats). In the future, iQuam QC will be upgraded [e.g., using improved reference field(s), with enhanced temporal and spatial resolutions]. More comparisons with external QC methods will be performed to learn and employ the best QC practices.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Haifeng Zhang, Haifeng.zhang@noaa.gov

Abstract

In situ sea surface temperature (SST) measurements play a critical role in the calibration/validation (Cal/Val) of satellite SST retrievals and ocean data assimilation. However, their quality is not always optimal, and proper quality control (QC) is required before they can be used with confidence. The in situ SST Quality Monitor (iQuam) system was established at the National Oceanic and Atmospheric Administration (NOAA) in 2009, initially to support the Cal/Val of NOAA satellite SST products. It collects in situ SST data from multiple sources, performs uniform QC, monitors the QCed data online, and distributes them to users. In this study, the iQuam QC is compared with other QC methods available in some of the in situ data ingested in iQuam. Overall, the iQuam QC performs well on daily to monthly time scales over most global oceans and under a wide variety of environmental conditions. However, it may be less accurate in the daytime, when a pronounced diurnal cycle is present, and in dynamic regions, because of the strong reliance on the “reference SST check,” which employs daily low-resolution level-4 analyses with no diurnal cycle resolved. The iQuam “performance history check,” applied to all in situ platforms, is an effective alternative to the customary “black/gray” lists, available only for some platforms (e.g., drifters and Argo floats). In the future, iQuam QC will be upgraded [e.g., using improved reference field(s), with enhanced temporal and spatial resolutions]. More comparisons with external QC methods will be performed to learn and employ the best QC practices.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Haifeng Zhang, Haifeng.zhang@noaa.gov
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