Real-Time, High-Resolution, Space–Time Analysis of Sea Surface Temperatures from Multiple Platforms

Steven M. Lazarus Florida Institute of Technology, Melbourne, Florida

Search for other papers by Steven M. Lazarus in
Current site
Google Scholar
PubMed
Close
,
Corey G. Calvert Florida Institute of Technology, Melbourne, Florida

Search for other papers by Corey G. Calvert in
Current site
Google Scholar
PubMed
Close
,
Michael E. Splitt Florida Institute of Technology, Melbourne, Florida

Search for other papers by Michael E. Splitt in
Current site
Google Scholar
PubMed
Close
,
Pablo Santos National Weather Service, Miami, Florida

Search for other papers by Pablo Santos in
Current site
Google Scholar
PubMed
Close
,
David W. Sharp National Weather Service, Melbourne, Florida

Search for other papers by David W. Sharp in
Current site
Google Scholar
PubMed
Close
,
Peter F. Blottman National Weather Service, Melbourne, Florida

Search for other papers by Peter F. Blottman in
Current site
Google Scholar
PubMed
Close
, and
Scott M. Spratt National Weather Service, Melbourne, Florida

Search for other papers by Scott M. Spratt in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

A sea surface temperature (SST) analysis system designed to initialize short-term atmospheric model forecasts is evaluated for a month-long, relatively clear period in May 2004. System inputs include retrieved SSTs from the Geostationary Operational Environmental Satellite (GOES)-East and the Moderate Resolution Imaging Spectroradiometer (MODIS). The GOES SSTs are processed via a sequence of quality control and bias correction steps and are then composited. The MODIS SSTs are bias corrected and checked against the background field (GOES composites) prior to assimilation. Buoy data, withheld from the analyses, are used to bias correct the MODIS and GOES SSTs and to evaluate both the composites and analyses. The bias correction improves the identification of residual cloud-contaminated MODIS SSTs. The largest analysis system improvements are obtained from the adjustments associated with the creation of the GOES composites (i.e., a reduction in buoy/GOES composite rmse on the order of 0.3°–0.5°C). A total of 120 analyses (80 night and 40 day) are repeated for different experimental configurations designed to test the impact of the GOES composites, MODIS cloud mask, spatially varying background error covariance and decorrelation length scales, data reduction, and anisotropy. For the May 2004 period, the nighttime MODIS cloud mask is too conservative, at times removing good SST data and degrading the analyses. Nocturnal error variance estimates are approximately half that of the daytime and are relatively spatially homogeneous, indicating that the nighttime composites are, in general, superior. A 30-day climatological SST gradient is used to create anisotropic weights and a spatially varying length scale. The former improve the analyses in regions with significant SST gradients and sufficient data while the latter reduces the analysis rmse in regions where the innovations tend to be well correlated with distinct and persistent SST gradients (e.g., Loop Current). Data thinning reduces the rmse by expediting analysis convergence while simultaneously enhancing the computational efficiency of the analysis system. Based on these findings, an operational analysis configuration is proposed.

Corresponding author address: Dr. Steven M. Lazarus, Florida Institute of Technology, 150 W. University Blvd., Melbourne, FL 32901. Email: slazarus@fit.edu

Abstract

A sea surface temperature (SST) analysis system designed to initialize short-term atmospheric model forecasts is evaluated for a month-long, relatively clear period in May 2004. System inputs include retrieved SSTs from the Geostationary Operational Environmental Satellite (GOES)-East and the Moderate Resolution Imaging Spectroradiometer (MODIS). The GOES SSTs are processed via a sequence of quality control and bias correction steps and are then composited. The MODIS SSTs are bias corrected and checked against the background field (GOES composites) prior to assimilation. Buoy data, withheld from the analyses, are used to bias correct the MODIS and GOES SSTs and to evaluate both the composites and analyses. The bias correction improves the identification of residual cloud-contaminated MODIS SSTs. The largest analysis system improvements are obtained from the adjustments associated with the creation of the GOES composites (i.e., a reduction in buoy/GOES composite rmse on the order of 0.3°–0.5°C). A total of 120 analyses (80 night and 40 day) are repeated for different experimental configurations designed to test the impact of the GOES composites, MODIS cloud mask, spatially varying background error covariance and decorrelation length scales, data reduction, and anisotropy. For the May 2004 period, the nighttime MODIS cloud mask is too conservative, at times removing good SST data and degrading the analyses. Nocturnal error variance estimates are approximately half that of the daytime and are relatively spatially homogeneous, indicating that the nighttime composites are, in general, superior. A 30-day climatological SST gradient is used to create anisotropic weights and a spatially varying length scale. The former improve the analyses in regions with significant SST gradients and sufficient data while the latter reduces the analysis rmse in regions where the innovations tend to be well correlated with distinct and persistent SST gradients (e.g., Loop Current). Data thinning reduces the rmse by expediting analysis convergence while simultaneously enhancing the computational efficiency of the analysis system. Based on these findings, an operational analysis configuration is proposed.

Corresponding author address: Dr. Steven M. Lazarus, Florida Institute of Technology, 150 W. University Blvd., Melbourne, FL 32901. Email: slazarus@fit.edu

Save
  • Ackerman, S. A., K. I. Strabala, W. P. Menzel, R. A. Frey, C. C. Moeller, and L. E. Gumley, 1998: Discriminating clear sky from clouds with MODIS. J. Geophys. Res., 103 , D24. 141157.

    • Search Google Scholar
    • Export Citation
  • Barnes, S. L., 1964: A technique for maximizing details in numerical weather map analysis. J. Appl. Meteor., 3 , 396409.

  • Black, T. L., 1994: The new NMC mesoscale Eta model: Description and forecast examples. Wea. Forecasting, 9 , 265278.

  • Bormann, N., S. Saarinen, G. Kelly, and J-N. Thépaut, 2003: The spatial structure of observation errors in atmospheric motion vectors from geostationary satellite data. Mon. Wea. Rev., 131 , 706718.

    • Search Google Scholar
    • Export Citation
  • Bratseth, A. M., 1986: Statistical interpolation by means of successive corrections. Tellus, 38A , 439447.

  • Brewster, K., 1996: Implementation of a Bratseth analysis scheme including Doppler radar data. Preprints, 15th Conf. on Weather Analysis and Forecasting, Norfolk, VA, Amer. Meteor. Soc., 92–95.

  • Brown, O. B., and P. J. Minnett, 1999: MODIS infrared sea surface temperature algorithm theoretical basis document version 2.0. Rosenstiel School of Marine and Atmospheric Science, University of Miami, Internal Doc. under NASA Contract NAS5-31361, 91 pp.

  • Chelton, D. B., 2005: The impact of SST specification on ECMWF surface wind stress fields in the eastern tropical Pacific. J. Climate, 18 , 530549.

    • Search Google Scholar
    • Export Citation
  • Chelton, D. B., and F. J. Wentz, 2005: Global microwave satellite observations of sea surface temperature for numerical weather prediction and climate research. Bull. Amer. Meteor. Soc., 86 , 10971115.

    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and A. M. da Silva, 1999: Maximum-likelihood estimation of forecast and observation error covariance parameters. Part I: Methodology. Mon. Wea. Rev., 127 , 18221834.

    • Search Google Scholar
    • Export Citation
  • Donlon, C., 2002: Global Ocean Data Assimilation Experiment (GODAE) High Resolution Sea Surface Temperature Pilot Project (GHRSST-PP). Strategy and Initial Implementation Plan, GHRSST-PP Science Team, 47 pp.

  • Gandin, L. S., 1965: Objective Analysis of Meteorological Fields. Israel Program for Scientific Translations, 242 pp.

  • Gentemann, C. L., C. J. Donlon, A. Stuart-Menteth, and F. J. Wentz, 2003: Diurnal signals in satellite sea surface temperature measurements. Geophys. Res. Lett., 30 .1140, doi:10.1029/2002GL016291.

    • Search Google Scholar
    • Export Citation
  • Glenn, S. M., and M. F. Crowley, 1997: Gulf Stream and ring feature analyses for forecast model validation. J. Atmos. Oceanic Technol., 14 , 13661378.

    • Search Google Scholar
    • Export Citation
  • Haines, S. L., G. Jedlovec, S. Lazarus, and C. Calvert, 2006: A MODIS sea surface temperature composite product. Preprints, 14th Conf. on Satellite Meteorology and Oceanography, Atlanta, GA, Amer. Meteor. Soc., CD-ROM, P4.24.

  • He, R., R. W. Helber, R. H. Weisberg, H. Zhang, and F. Muller-Karger, 2004: Merging multiple satellite sea surface temperature products: A near-real-time cloud-free, sea surface temperature analysis for the southeast Atlantic coastal ocean. Preprints, 2004 Ocean Sciences Meeting, Portland, OR, Amer. Geophys. Union.

  • Hollingsworth, A., and P. Lönnberg, 1986: The statistical structure of short-range forecast errors as determined from radiosonde data. Part I: The wind field. Tellus, 38A , 111136.

    • Search Google Scholar
    • Export Citation
  • Johnson, R. X., and M. P. Weinreb, 1996: GOES-8 Imager midnight effects and slope correction, in GOES-8 and beyond. Proc. Soc. Photo-Opt. Instrum. Eng., 2812 , 596607.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation, and Predictability. Cambridge University Press, 341 pp.

  • Kawai, Y., and H. Kawamura, 1997: Seasonal and diurnal variability of differences between satellite-derived and in situ sea surface temperatures in the south of the Sea of Okhotsk. J. Oceanogr., 53 , 343354.

    • Search Google Scholar
    • Export Citation
  • Lazarus, S. M., J. D. Horel, and C. M. Ciliberti, 2002: Application of a near-real-time analysis system in complex terrain. Wea. Forecasting, 17 , 9711000.

    • Search Google Scholar
    • Export Citation
  • Lorenc, A. C., R. S. Bell, and B. Macpherson, 1991: The Meteorological Office analysis correction data assimilation scheme. Quart. J. Roy. Meteor. Soc., 117 , 5989.

    • Search Google Scholar
    • Export Citation
  • Maturi, E., A. Harris, C. Merchant, X. Li, and B. Potash, 2006: Geostationary sea surface temperature products (current and future). Preprints, 14th Conf. on Satellite Meteorology and Oceanography, Atlanta, GA, Amer. Meteor. Soc., CD-ROM, P4.22.

  • Minnett, P. J., R. O. Knuteson, F. A. Best, B. J. Osborne, J. A. Hanafin, and O. B. Brown, 2001: The Marine-Atmospheric Emitted Radiance Interferometer (M-AERI), a high-accuracy, sea-going infrared spectroradiometer. J. Atmos. Oceanic Technol., 18 , 9941013.

    • Search Google Scholar
    • Export Citation
  • O’Neill, L. W., D. B. Chelton, S. K. Esbensen, and F. J. Wentz, 2005: High-resolution satellite measurements of the atmospheric boundary layer response to SST variations along the Agulhas Return Current. J. Climate, 18 , 27062722.

    • Search Google Scholar
    • Export Citation
  • Otkin, J. A., M. C. Anderson, J. R. Mecikalski, and G. R. Diak, 2005: Validation of GOES-based insolation estimates using data from the U.S. Climate Reference Network. J. Hydrometeor., 6 , 460475.

    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., 1988: A real-time global sea surface temperature analysis. J. Climate, 1 , 7587.

  • Reynolds, R. W., and T. M. Smith, 1994: Improved global sea surface temperature analyses using optimum interpolation. J. Climate, 7 , 929948.

    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., H-M. Zhang, T. M. Smith, C. L. Gentemann, and F. Wentz, 2005: Impacts of in situ and additional satellite data on the accuracy of a sea-surface temperature analysis for climate. Int. J. Climatol., 25 , 857864.

    • Search Google Scholar
    • Export Citation
  • Smith, N., 2001: Report of the GODAE high resolution SST workshop, 30th October–1st November 2000. International GODAE Project Office Rep., 64 pp. [Available from International GODAE Project Office, Bureau of Meteorology, Melbourne 3001, Australia.].

  • Thiébaux, H. J., 1976: Anisotropic correlation functions for objective analysis. Mon. Wea. Rev., 104 , 9941002.

  • Thiébaux, H. J., H. L. Mitchell, and D. W. Shantz, 1986: Horizontal structure of hemispheric forecast error correlations for geopotential and temperature. Mon. Wea. Rev., 114 , 10481066.

    • Search Google Scholar
    • Export Citation
  • Thiébaux, H. J., E. Rogers, W. Wang, and B. Katz, 2003: A new high-resolution blended real-time global sea surface temperature analysis. Bull. Amer. Meteor. Soc., 84 , 645656.

    • Search Google Scholar
    • Export Citation
  • Walker, N., S. Myint, A. Babin, and A. Haag, 2003: Advances in satellite radiometry for the surveillance of surface temperatures, ocean eddies and upwelling processes in the Gulf of Mexico using GOES-8 measurements during summer. Geophys. Res. Lett., 30 .1854, doi:10.1029/2003GL017555.

    • Search Google Scholar
    • Export Citation
  • Warner, T. T., M. N. Lakhtakia, J. D. Doyle, and R. A. Pearson, 1990: Marine atmospheric boundary layer circulations forced by Gulf Stream sea surface temperature gradients. Mon. Wea. Rev., 118 , 309323.

    • Search Google Scholar
    • Export Citation
  • Wick, G. A., J. J. Bates, and D. J. Scott, 2002: Satellite and skin-layer effects on the accuracy of sea surface temperature measurements from the GOES satellites. J. Atmos. Oceanic Technol., 19 , 18341848.

    • Search Google Scholar
    • Export Citation
  • Xue, M., K. K. Droegemeier, and V. Wong, 2001: The Advanced Regional Prediction System (ARPS)—A multiscale nonhydrostatic atmospheric simulation and prediction tool. Part I: Model dynamics and verification. Meteor. Atmos. Phys., 75 , 161193.

    • Search Google Scholar
    • Export Citation
  • Young, G. S., and T. D. Sikora, 2003: Mesoscale stratocumulus bands caused by Gulf Stream meanders. Mon. Wea. Rev., 131 , 21772191.

  • Zeng, X., and A. Beljaars, 2005: A prognostic scheme of sea surface skin temperature for modeling and data assimilation. Geophys. Res. Lett., 32 .L14605, doi:10.1029/2005GL023030.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 658 329 12
PDF Downloads 103 23 2