• Advantech, 1997: ADAM 4000 series: Data acquisition modules. User’s Manual, 398 pp. [Available from Advantech Co. Ltd., 380 Fairview Way, Milpitas, CA 95035-3062.]

  • Baldridge, A., Hook S. , Grove C. , and Rivera G. , 2009: The ASTER spectral library version 2.0. Remote Sens. Environ., 113, 711715, doi:10.1016/j.rse.2008.11.007.

    • Search Google Scholar
    • Export Citation
  • Barker, A., Banks A. , Bell W. , Dowell M. , Fox N. P. , Green P. , and Whitney M. , 2015: Metrology for climate: Metrology priorities for the earth observation and climate community. A. Barker et al., Eds., National Physics Laboratory Tech. Rep., 36 pp. [Available online at http://www.npl.co.uk/upload/pdf/20150521-22_metrology_for_climate_report.pdf.]

  • Barton, I., Minnett P. , Maillet K. , Donlon C. , Hook S. , Jessup A. , and Nightingale T. , 2004: The Miami2001 infrared radiometer calibration and intercomparison. Part II: Shipboard results. J. Atmos. Oceanic Technol., 21, 268283, doi:10.1175/1520-0426(2004)021<0268:TMIRCA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bell, S., 2001: A beginner’s guide to uncertainty of measurement. Measurement Good Practice Guide 11, Issue 2 with Amendments, National Physical Laboratory Doc. PDB: 2284, 34 pp.

  • Berry, K. H., 1981: Emissivity of a cylindrical black-body cavity with a re-entrant cone end face. J. Phys., 14E, 629632, doi:10.1088/0022-3735/14/5/023.

    • Search Google Scholar
    • Export Citation
  • Best, F. A., and Coauthors, 2003: Traceability of absolute radiometric calibration for the atmospheric emitted radiance interferometer (AERI). [Available online at https://www.ssec.wisc.edu/gifts/blackbody/posters/calcon2003/calcon2003-best-aeri-traceability.pdf.]

  • Bojinski, S., Verstraete M. , Peterson T. C. , Richter C. , Simmons A. , and Zemp M. , 2014: The concept of essential climate variables in support of climate research, applications and policy. Bull. Amer. Meteor. Soc., 95, 14311443, doi:10.1175/BAMS-D-13-00047.1.

    • Search Google Scholar
    • Export Citation
  • Bourns, 2006: 4800P series—Thick film surface mounted medium body. 3 pp. [Available online at http://www.bourns.com/pdfs/4800P.pdf, 3 pages.]

  • CEOS, 2015: The earth observation handbook: Special 2015 COP21 edition. European Space Agency. [Available online at http://eohandbook.com/cop21/part1_4.html.]

  • Donlon, C. J., and Nightingale T. J. , 2000: Effect of atmospheric radiance errors in radiometric sea-surface skin temperature measurements. Appl. Opt., 39, 23872392, doi:10.1364/AO.39.002387.

    • Search Google Scholar
    • Export Citation
  • Donlon, C. J., Nightingale T. J. , Fiedler L. , Fisher G. , Baldwin D. , and Robinson I. , 1999: The calibration and intercalibration of sea-going infrared radiometer systems using a low cost blackbody cavity. J. Atmos. Oceanic Technol., 16, 11831197, doi:10.1175/1520-0426(1999)016<1183:TCAIOS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Donlon, C. J., and Coauthors, 2007: The Global Ocean Data Assimilation Experiment High-Resolution Sea Surface Temperature Pilot Project. Bull. Amer. Meteor. Soc., 88, 11971213, doi:10.1175/BAMS-88-8-1197.

    • Search Google Scholar
    • Export Citation
  • Donlon, C. J., Robinson I. , Reynolds M. , Wimmer W. , Fisher G. , Edwards R. , and Nightingale T. , 2008: An infrared sea surface temperature autonomous radiometer (ISAR) for deployment aboard volunteer observing ships (VOS). J. Atmos. Oceanic Technol., 25, 93113, doi:10.1175/2007JTECHO505.1.

    • Search Google Scholar
    • Export Citation
  • Donlon, C. J., Wimmer W. , Robinson I. , Fisher G. , Ferlet M. , Nightingale T. , and Bras B. , 2014: A second-generation blackbody system for the calibration and verification of seagoing infrared radiometers. J. Atmos. Oceanic Technol., 31, 11041127, doi:10.1175/JTECH-D-13-00151.1.

    • Search Google Scholar
    • Export Citation
  • Dybkjær, G., Tonboe R. , and Høyer J. , 2012: Arctic surface temperatures from Metop AVHRR compared to in situ ocean and land data. Ocean Sci., 8, 95970, doi:10.5194/os-8-959-2012.

    • Search Google Scholar
    • Export Citation
  • Embury, O., Merchant C. J. , and Filipiak M. J. , 2012: A reprocessing for climate of sea surface temperature from the along-track scanning radiometers: Basis in radiative transfer. Remote Sens. Environ., 116, 3246, doi:10.1016/j.rse.2010.10.016.

    • Search Google Scholar
    • Export Citation
  • GCOS, 2011: Systematic observation requirements for satellite-based data products for climate: 2011 update; Supplemental details to the satellite-based component of the “Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (2010 Update),” WMO Tech. Rep. GCOS–154, 127 pp.

  • Guan, L., Zhang K. , and Teng W. , 2011: Shipboard measurements of skin SST in the China Seas: Validation of satellite SST products. 2011 IEEE International Geoscience and Remote Sensing Symposium: Proceedings, IEEE, 2005–2008, doi:10.1109/IGARSS.2011.6049522.

  • Hanafin, J. A., and Minnett P. J. , 2005: Measurements of the infrared emissivity of a wind-roughened sea surface. Appl. Opt., 44, 398411, doi:10.1364/AO.44.000398.

    • Search Google Scholar
    • Export Citation
  • Heitronics, 2000: Infrared radiation pyrometer KT15D. 77 pp. [Available from Heitronics Infrarot Messtechnik GmbH, Kreuzberger Ring 40, 65205 Wiesbaden, Germany.]

  • JCGM, 2008: Evaluation of measurement data—Guide to the expression of uncertainty in measurement. Joint Committee for Guides in Metrology Tech. Rep. JCGM 100:2008. [Available online at http://www.iso.org/sites/JCGM/GUM/JCGM100/C045315e-html/C045315e.html?csnumber=50461.]

  • Lebigot, E. O., 2012: Uncertainties: A Python package for calculations with uncertainties. Version 1.8. [Available online at http://pythonhosted.org/uncertainties/.]

  • Masuda, K., 2006: Infrared sea surface emissivity including multiple reflection effect for isotropic Gaussian slope distribution model. Remote Sens. Environ., 103, 488496, doi:10.1016/j.rse.2006.04.011.

    • Search Google Scholar
    • Export Citation
  • Masuda, K., Takashima T. , and Takayama Y. , 1988: Emissivity of pure and sea waters for the model sea surface in the infrared window regions. Remote Sens. Environ., 24, 313329, doi:10.1016/0034-4257(88)90032-6.

    • Search Google Scholar
    • Export Citation
  • Maxim, 2008: MAX667: +5V/programmable low-dropout voltage regulator. Data Sheet 19-3894, Revision 4, 9 pp. [Available online at https://datasheets.maximintegrated.com/en/ds/MAX667.pdf.]

  • Measurement Specialties, 2008: 46041 super stable glass NTC thermistor. 3 pp. [Available online at http://www.datasheetlib.com/datasheet/160657/46031_msi-measurement-specialties-inc.html#datasheet.]

  • Minnett, P. J., 2011: Sea surface temperature algorithm refinement and validation though ship-based infrared spectroradiometry. [Available online at https://modis.gsfc.nasa.gov/sci_team/meetings/201105/presentations/ocean/minnett.pdf.]

  • Minnett, P. J., and Corlett G. K. , 2012: A pathway to generating climate data records of sea-surface temperature from satellite measurements. Deep-Sea Res. II, 77–80, 4451, doi:10.1016/j.dsr2.2012.04.003.

    • Search Google Scholar
    • Export Citation
  • Newman, S. M., Smith J. A. , Glew M. D. , Rogers S. M. , and Taylor J. P. , 2005: Temperature and salinity dependence of sea surface emissivity in the thermal infrared. Quart. J. Roy. Meteor. Soc., 131, 25392557, doi:10.1256/qj.04.150.

    • Search Google Scholar
    • Export Citation
  • Niclòs, R., Valor E. , Caselles V. , Coll C. , and Sánchez J. M. , 2005: In situ angular measurements of thermal infrared sea surface emissivity—Validation of models. Remote Sens. Environ., 94, 8393, doi:10.1016/j.rse.2004.09.002.

    • Search Google Scholar
    • Export Citation
  • Niclòs, R., Caselles V. , Valor E. , Coll C. , and Sánchez J. M. , 2009: A simple equation for determining sea surface emissivity in the 3–15 μm region. Int. J. Remote Sens., 30, 16031619, doi:10.1080/01431160802541523.

    • Search Google Scholar
    • Export Citation
  • Noyes, E., Minnett P. , Remedios J. , Corlett G. , Good S. , and Llewellyn-Jones D. , 2006: The accuracy of the AATSR sea surface temperatures in the Caribbean. Remote Sens. Environ., 101, 3851, doi:10.1016/j.rse.2005.11.011.

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

    • Search Google Scholar
    • Export Citation
  • QA4EO Task Team, 2010: A quality assurance framework for earth observation: Principles. Version 4.0, Group on Earth Observations QA4EO Tech. Rep., 17 pp. [Available online at http://qa4eo.org/docs/QA4EO_Principles_v4.0.pdf.]

  • Theocharous, E., Usadi E. , and Fox N. P. , 2010: CEOS comparison of IR brightness temperature measurements in support of satellite validation. Part I: Laboratory and ocean surface temperature comparison of radiation thermometers. National Physics Laboratory Tech. Rep. OP 3, 130 pp.

  • Tokmakian, R., and Challenor P. , 1999: On the joint estimation of model and satellite sea surface height anomaly errors. Ocean Modell., 1, 3952, doi:10.1016/S1463-5003(99)00006-2.

    • Search Google Scholar
    • Export Citation
  • Watts, P. D., Allen M. R. , and Nightingale T. J. , 1996: Wind speed effects on sea surface emission and reflection for the Along Track Scanning Radiometer. J. Atmos. Oceanic Technol., 13, 126141, doi:10.1175/1520-0426(1996)013<0126:WSEOSS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wimmer, W., Robinson I. , and Donlon C. , 2012: Long-term validation of AATSR SST data products using shipborne radiometry in the Bay of Biscay and English Channel. Remote Sens. Environ., 116, 1731, doi:10.1016/j.rse.2011.03.022.

    • Search Google Scholar
    • Export Citation
  • Wu, X., and Smith W. L. , 1997: Emissivity of rough sea surface for 8–13 μm: Modeling and verification. Appl. Opt., 36, 26092619, doi:10.1364/AO.36.002609.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 215 109 7
PDF Downloads 140 73 8

The ISAR Instrument Uncertainty Model

View More View Less
  • 1 Earth and Ocean Science, University of Southampton, Southampton, United Kingdom
Restricted access

Abstract

Measurements of sea surface temperature at the skin interface () made by an Infrared Sea Surface Temperature Autonomous Radiometer (ISAR) have been used for a number of years to validate satellite sea surface temperature (SST), especially high-accuracy observations such as made by the Advanced Along-Track Scanning Radiometer (AATSR). The ISAR instrument accuracy for measuring is ±0.1 K (Donlon et al.), but to satisfy Quality Assurance Framework for Earth Observation (QA4EO) principles and metrological standards (Joint Committee for Guides in Metrology), an uncertainty model is required. To develop the ISAR uncertainty model, all sources of uncertainty in the instrument are analyzed and an uncertainty value is assigned to each component. Finally, the individual uncertainty components are propagated through the ISAR retrieval algorithm to estimate a total uncertainty for each measurement. The resulting ISAR uncertainty model applied to a 12-yr archive of measurements from the Bay of Biscay shows that 77.6% of the data are expected to be within ±0.1 K and a further 17.2% are within 0.2 K.

Corresponding author address: Werenfrid Wimmer, Earth and Ocean Science, University of Southampton, Waterfront Campus, European Way, Southampton SO14 3ZH, United Kingdom. E-mail: w.wimmer@soton.ac.uk

Abstract

Measurements of sea surface temperature at the skin interface () made by an Infrared Sea Surface Temperature Autonomous Radiometer (ISAR) have been used for a number of years to validate satellite sea surface temperature (SST), especially high-accuracy observations such as made by the Advanced Along-Track Scanning Radiometer (AATSR). The ISAR instrument accuracy for measuring is ±0.1 K (Donlon et al.), but to satisfy Quality Assurance Framework for Earth Observation (QA4EO) principles and metrological standards (Joint Committee for Guides in Metrology), an uncertainty model is required. To develop the ISAR uncertainty model, all sources of uncertainty in the instrument are analyzed and an uncertainty value is assigned to each component. Finally, the individual uncertainty components are propagated through the ISAR retrieval algorithm to estimate a total uncertainty for each measurement. The resulting ISAR uncertainty model applied to a 12-yr archive of measurements from the Bay of Biscay shows that 77.6% of the data are expected to be within ±0.1 K and a further 17.2% are within 0.2 K.

Corresponding author address: Werenfrid Wimmer, Earth and Ocean Science, University of Southampton, Waterfront Campus, European Way, Southampton SO14 3ZH, United Kingdom. E-mail: w.wimmer@soton.ac.uk
Save