Retrieval of Surface Spectral Emissivity in Polar Regions Based on the Optimal Estimation Method
Surface spectral emissivity plays an important role in the polar radiation budget. The significance of surface emissivity in the far- infrared (far- IR) has been recognized by recent studies, yet there have been no observations to constrain far- IR surface spectral emissivity over the entire polar r...
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| Format: | Article in Journal/Newspaper |
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The TIMS Data User- s Workshop
2022
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| Online Access: | https://hdl.handle.net/2027.42/171894 https://doi.org/10.1029/2021JD035677 |
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ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/171894 |
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openpolar |
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Open Polar |
| collection |
University of Michigan: Deep Blue |
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ftumdeepblue |
| language |
unknown |
| topic |
optimal estimation surface spectral emissivity PREFIRE mission far- IR Atmospheric and Oceanic Sciences Science |
| spellingShingle |
optimal estimation surface spectral emissivity PREFIRE mission far- IR Atmospheric and Oceanic Sciences Science Xie, Yan Huang, Xianglei Chen, Xiuhong L’ecuyer, Tristan S. Drouin, Brian J. Wang, Jun Retrieval of Surface Spectral Emissivity in Polar Regions Based on the Optimal Estimation Method |
| topic_facet |
optimal estimation surface spectral emissivity PREFIRE mission far- IR Atmospheric and Oceanic Sciences Science |
| description |
Surface spectral emissivity plays an important role in the polar radiation budget. The significance of surface emissivity in the far- infrared (far- IR) has been recognized by recent studies, yet there have been no observations to constrain far- IR surface spectral emissivity over the entire polar regions. In preparation for the Polar Radiant Energy in the Far- InfraRed Experiment (PREFIRE) mission, this study develops and assesses an optimal estimation- based retrieval algorithm to estimate both mid- IR and far- IR polar surface emissivity from the future PREFIRE measurements. Synthetic PREFIRE spectra are simulated by feeding the ERA5 reanalysis and a global surface emissivity data set to a radiative transfer model. Information content analysis indicates that the far- IR surface emissivity retrievals can be more influenced by the atmospheric water vapor abundance than the mid- IR counterparts. When the total column water vapor is above 1 cm, the far- IR surface emissivity retrievals largely rely on the a priori constraints. Performance of the optimal- estimation algorithm is assessed using 960 synthetic PREFIRE clear- sky radiance spectra over the Arctic. The results based on current best estimate of instrument performance show that all retrievals converge within 15 iterations, the retrieved surface spectral emissivity has a mean bias within ±0.01 and a root- mean- square error less than 0.024. The far- IR surface emissivity retrievals are much more affected by the a priori choice than the mid- IR ones. A properly constructed a priori covariance can also help to improve the computational efficiency. Influences of other factors for future operational retrievals are also discussed.Key PointsAn optimal- estimation algorithm for surface spectral emissivity retrieval is developed and assessed for the forthcoming PREFIRE missionSurface spectral emissivity retrievals in the far- infrared can be significantly influenced by the atmospheric water vapor abundanceCompared to the mid- infrared, the far- infrared surface ... |
| format |
Article in Journal/Newspaper |
| author |
Xie, Yan Huang, Xianglei Chen, Xiuhong L’ecuyer, Tristan S. Drouin, Brian J. Wang, Jun |
| author_facet |
Xie, Yan Huang, Xianglei Chen, Xiuhong L’ecuyer, Tristan S. Drouin, Brian J. Wang, Jun |
| author_sort |
Xie, Yan |
| title |
Retrieval of Surface Spectral Emissivity in Polar Regions Based on the Optimal Estimation Method |
| title_short |
Retrieval of Surface Spectral Emissivity in Polar Regions Based on the Optimal Estimation Method |
| title_full |
Retrieval of Surface Spectral Emissivity in Polar Regions Based on the Optimal Estimation Method |
| title_fullStr |
Retrieval of Surface Spectral Emissivity in Polar Regions Based on the Optimal Estimation Method |
| title_full_unstemmed |
Retrieval of Surface Spectral Emissivity in Polar Regions Based on the Optimal Estimation Method |
| title_sort |
retrieval of surface spectral emissivity in polar regions based on the optimal estimation method |
| publisher |
The TIMS Data User- s Workshop |
| publishDate |
2022 |
| url |
https://hdl.handle.net/2027.42/171894 https://doi.org/10.1029/2021JD035677 |
| geographic |
Arctic |
| geographic_facet |
Arctic |
| genre |
Arctic Arctic |
| genre_facet |
Arctic Arctic |
| op_relation |
Xie, Yan; Huang, Xianglei; Chen, Xiuhong; L’ecuyer, Tristan S. Drouin, Brian J.; Wang, Jun (2022). "Retrieval of Surface Spectral Emissivity in Polar Regions Based on the Optimal Estimation Method." Journal of Geophysical Research: Atmospheres 127(5): n/a-n/a. 2169-897X 2169-8996 https://hdl.handle.net/2027.42/171894 doi:10.1029/2021JD035677 Journal of Geophysical Research: Atmospheres Palchetti, L., Barucci, M., Belotti, C., Bianchini, G., Cluzet, B., D’Amato, F., et al. ( 2021 ). Observations of the downwelling far- infrared atmospheric emission at the Zugspitze observatory. Earth System Science Data, 13 ( 9 ), 4303 - 4312. https://doi.org/10.5194/essd-13-4303-2021 Milstein, A. B., & Blackwell, W. J. ( 2016 ). Neural network temperature and moisture retrieval algorithm validation for AIRS/AMSU and CrIS/ATMS. Journal of Geophysical Research: Atmospheres, 121 ( 4 ), 1414 - 1430. https://doi.org/10.1002/2015JD024008 Murray, J. E., Brindley, H. E., Fox, S., Bellisario, C., Pickering, J. C., Fox, C., et al. ( 2020 ). Retrievals of high- latitude surface emissivity across the infrared from high- altitude aircraft flights. Journal of Geophysical Research: Atmospheres, 125 ( 22 ), e2020JD033672. https://doi.org/10.1029/2020JD033672 Nalli, N. R., Gambacorta, A., Liu, Q., Barnet, C. D., Tan, C., Iturbide- Sanchez, F., et al. ( 2018 ). Validation of atmospheric profile retrievals from the SNPP NOAA- unique combined atmospheric processing system. Part 1: Temperature and moisture. IEEE Transactions on Geoscience and Remote Sensing, 56 ( 1 ), 180 - 190. https://doi.org/10.1109/TGRS.2017.2744558 NygÃ¥rd, T., Vihma, T., Birnbaum, G., Hartmann, J., King, J., Lachlan- Cope, T., et al. ( 2016 ). Validation of eight atmospheric reanalyses in the Antarctic Peninsula region. Quarterly Journal of the Royal Meteorological Society, 142 ( 695 ), 684 - 692. https://doi.org/10.1002/qj.2691 Palchetti, L., Brindley, H., Bantges, R., Buehler, S. A., Camy- Peyret, C., Carli, B., et al. ( 2020 ). Forum: Unique far- infrared satellite observations to better understand how earth radiates energy to space. Bulletin of the American Meteorological Society, 101 ( 12 ), E2030 - E2046. https://doi.org/10.1175/bams-d-19-0322.1 Pan, F., Huang, X., Leroy, S. S., Lin, P., Strow, L. L., Ming, Y., & Ramaswamy, V. ( 2017 ). The stratospheric changes inferred from 10 years of AIRS and AMSU- A radiances. Journal of Climate, 30 ( 15 ), 6005 - 6016. https://doi.org/10.1175/jcli-d-17-0037.1 Peterson, D., & Wang, J. ( 2013 ). A sub- pixel- based calculation of fire radiative power from MODIS observations: 2. Sensitivity analysis and potential fire weather application. Remote Sensing of Environment, 129, 231 - 249. https://doi.org/10.1016/j.rse.2012.10.020 Pougatchev, N., August, T., Calbet, X., Hultberg, T., Oduleye, O., Schlüssel, P., et al. ( 2009 ). IASI temperature and water vapor retrievals - Error assessment and validation. Atmospheric Chemistry and Physics, 9 ( 17 ), 6453 - 6458. https://doi.org/10.5194/acp-9-6453-2009 Price, J. C. ( 1984 ). Land surface temperature measurements from the split window channels of the NOAA 7 Advanced Very High Resolution Radiometer. Journal of Geophysical Research: Atmospheres, 89 ( D5 ), 7231 - 7237. https://doi.org/10.1029/JD089iD05p07231 Rathke, C., Fischer, J., Neshyba, S., & Shupe, M. ( 2002 ). Improving IR cloud phase determination with 20 microns spectral observations. Geophysical Research Letters, 29 ( 8 ), 50 - 51. https://doi.org/10.1029/2001GL014594 Rockwell, B. W., & Hofstra, A. H. ( 2008 ). Identification of quartz and carbonate minerals across northern Nevada using ASTER thermal infrared emissivity data- Implications for geologic mapping and mineral resource investigations in well- studied and Frontier areas. Geosphere, 4 ( 1 ), 218 - 246. https://doi.org/10.1130/ges00126.1 Rodgers, C. D. ( 1976 ). Retrieval of atmospheric temperature and composition from remote measurements of thermal radiation. Reviews of Geophysics, 14 ( 4 ), 609 - 624. https://doi.org/10.1029/RG014i004p00609 Scarlat, R. C., Heygster, G., & Pedersen, L. T. ( 2017 ). Experiences with an optimal estimation algorithm for surface and atmospheric parameter retrieval from passive microwave data in the Arctic. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10 ( 9 ), 3934 - 3947. https://doi.org/10.1109/JSTARS.2017.2739858 Schlüssel, P., Hultberg, T. H., Phillips, P. L., August, T., & Calbet, X. ( 2005 ). The operational IASI Level 2 processor. Advances in Space Research, 36 ( 5 ), 982 - 988. https://doi.org/10.1016/j.asr.2005.03.008 Seemann, S. W., Borbas, E. E., Knuteson, R. O., Stephenson, G. R., & Huang, H.- L. ( 2008 ). Development of a global infrared land surface emissivity database for application to clear sky sounding retrievals from multispectral satellite radiance measurements. Journal of Applied Meteorology and Climatology, 47 ( 1 ), 108 - 123. https://doi.org/10.1175/2007jamc1590.1 Shannon, C. E., & Weaver, W. ( 1949 ). The mathematical theory of communication (p. 144). Champaign, IL: University of Illinois Press (ISBN: 0252725484). Smith, N., & Barnet, C. D. ( 2019 ). Uncertainty characterization and propagation in the community long- term infrared microwave combined atmospheric product system (CLIMCAPS). Remote Sensing, 11 ( 10 ), 1227. https://doi.org/10.3390/rs11101227 Susskind, J., Barnet, C. D., & Blaisdell, J. M. ( 2003 ). Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds. IEEE Transactions on Geoscience and Remote Sensing, 41 ( 2 ), 390 - 409. https://doi.org/10.1109/TGRS.2002.808236 Turner, D. D., Clough, S. A., Liljegren, J. C., Clothiaux, E. E., Cady- Pereira, K. E., & Gaustad, K. L. ( 2007 ). Retrieving liquid water path and precipitable water vapor from the atmospheric radiation measurement (ARM) microwave radiometers. IEEE Transactions on Geoscience and Remote Sensing, 45 ( 11 ), 3680 - 3690. https://doi.org/10.1109/TGRS.2007.903703 Turner, D. D., & Löhnert, U. ( 2014 ). Information content and uncertainties in thermodynamic profiles and liquid cloud properties retrieved from the ground- based atmospheric emitted radiance interferometer (AERI). Journal of Applied Meteorology and Climatology, 53 ( 3 ), 752 - 771. https://doi.org/10.1175/jamc-d-13-0126.1 Wan, Z., & Li, Z.- L. ( 1997 ). A physics- based algorithm for retrieving land- surface emissivity and temperature from EOS/MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 35 ( 4 ), 980 - 996. https://doi.org/10.1109/36.602541 Wood, N. B., & L’Ecuyer, T. S. ( 2021 ). What millimeter- wavelength radar reflectivity reveals about snowfall: An information- centric analysis. Atmospheric Measurement Techniques, 14 ( 2 ), 869 - 888. https://doi.org/10.5194/amt-14-869-2021 Xu, X., Wang, J., Zeng, J., Hou, W., Meyer, K. G., E Platnick, S., & Wilcox, E. ( 2018 ). A pilot study of shortwave spectral fingerprints of smoke aerosols above liquid clouds. Journal of Quantitative Spectroscopy and Radiative Transfer, 221, 38 - 50. https://doi.org/10.1016/j.jqsrt.2018.09.024 Zhou, D. K., Larar, A. M., Liu, X., Smith, W. L., Strow, L. L., Yang, P., et al. ( 2011 ). Global land surface emissivity retrieved from satellite ultraspectral IR measurements. IEEE Transactions on Geoscience and Remote Sensing, 49 ( 4 ), 1277 - 1290. https://doi.org/10.1109/TGRS.2010.2051036 Zhou, D. K., Smith, W. L., Liu, X., Larar, A. M., Mango, S. A., & Huang, H.- L. ( 2007 ). Physically retrieving cloud and thermodynamic parameters from ultraspectral IR measurements. Journal of the Atmospheric Sciences, 64 ( 3 ), 969 - 982. https://doi.org/10.1175/jas3877.1 Clough, S. A., Shephard, M. W., Mlawer, E. J., Delamere, J. S., Iacono, M. J., Cady- Pereira, K., et al. ( 2005 ). Atmospheric radiative transfer modeling: A summary of the AER codes. Journal of Quantitative Spectroscopy and Radiative Transfer, 91 ( 2 ), 233 - 244. https://doi.org/10.1016/j.jqsrt.2004.05.058 Coll, C., Caselles, V., Valor, E., & Rubio, E. ( 2003 ). Validation of temperature- emissivity separation and split- window methods from TIMS data and ground measurements. Remote Sensing of Environment, 85 ( 2 ), 232 - 242. https://doi.org/10.1016/S0034-4257(03)00003-8 Backus, G., Gilbert, F., & Bullard, E. C. ( 1970 ). Uniqueness in the inversion of inaccurate gross Earth data. Philosophical Transactions of the Royal Society of London - Series A: Mathematical and Physical Sciences, 266 ( 1173 ), 123 - 192. https://doi.org/10.1098/rsta.1970.0005 Bantges, R. J., Brindley, H. E., Chen, X. 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ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/171894 2023-08-20T04:03:12+02:00 Retrieval of Surface Spectral Emissivity in Polar Regions Based on the Optimal Estimation Method Xie, Yan Huang, Xianglei Chen, Xiuhong L’ecuyer, Tristan S. Drouin, Brian J. Wang, Jun 2022-03-16 application/pdf https://hdl.handle.net/2027.42/171894 https://doi.org/10.1029/2021JD035677 unknown The TIMS Data User- s Workshop Wiley Periodicals, Inc. Xie, Yan; Huang, Xianglei; Chen, Xiuhong; L’ecuyer, Tristan S. Drouin, Brian J.; Wang, Jun (2022). "Retrieval of Surface Spectral Emissivity in Polar Regions Based on the Optimal Estimation Method." Journal of Geophysical Research: Atmospheres 127(5): n/a-n/a. 2169-897X 2169-8996 https://hdl.handle.net/2027.42/171894 doi:10.1029/2021JD035677 Journal of Geophysical Research: Atmospheres Palchetti, L., Barucci, M., Belotti, C., Bianchini, G., Cluzet, B., D’Amato, F., et al. ( 2021 ). Observations of the downwelling far- infrared atmospheric emission at the Zugspitze observatory. Earth System Science Data, 13 ( 9 ), 4303 - 4312. https://doi.org/10.5194/essd-13-4303-2021 Milstein, A. B., & Blackwell, W. J. ( 2016 ). Neural network temperature and moisture retrieval algorithm validation for AIRS/AMSU and CrIS/ATMS. Journal of Geophysical Research: Atmospheres, 121 ( 4 ), 1414 - 1430. https://doi.org/10.1002/2015JD024008 Murray, J. E., Brindley, H. E., Fox, S., Bellisario, C., Pickering, J. C., Fox, C., et al. ( 2020 ). Retrievals of high- latitude surface emissivity across the infrared from high- altitude aircraft flights. Journal of Geophysical Research: Atmospheres, 125 ( 22 ), e2020JD033672. https://doi.org/10.1029/2020JD033672 Nalli, N. R., Gambacorta, A., Liu, Q., Barnet, C. D., Tan, C., Iturbide- Sanchez, F., et al. ( 2018 ). Validation of atmospheric profile retrievals from the SNPP NOAA- unique combined atmospheric processing system. Part 1: Temperature and moisture. IEEE Transactions on Geoscience and Remote Sensing, 56 ( 1 ), 180 - 190. https://doi.org/10.1109/TGRS.2017.2744558 NygÃ¥rd, T., Vihma, T., Birnbaum, G., Hartmann, J., King, J., Lachlan- Cope, T., et al. ( 2016 ). Validation of eight atmospheric reanalyses in the Antarctic Peninsula region. Quarterly Journal of the Royal Meteorological Society, 142 ( 695 ), 684 - 692. https://doi.org/10.1002/qj.2691 Palchetti, L., Brindley, H., Bantges, R., Buehler, S. A., Camy- Peyret, C., Carli, B., et al. ( 2020 ). Forum: Unique far- infrared satellite observations to better understand how earth radiates energy to space. Bulletin of the American Meteorological Society, 101 ( 12 ), E2030 - E2046. https://doi.org/10.1175/bams-d-19-0322.1 Pan, F., Huang, X., Leroy, S. S., Lin, P., Strow, L. L., Ming, Y., & Ramaswamy, V. ( 2017 ). The stratospheric changes inferred from 10 years of AIRS and AMSU- A radiances. Journal of Climate, 30 ( 15 ), 6005 - 6016. https://doi.org/10.1175/jcli-d-17-0037.1 Peterson, D., & Wang, J. ( 2013 ). A sub- pixel- based calculation of fire radiative power from MODIS observations: 2. Sensitivity analysis and potential fire weather application. Remote Sensing of Environment, 129, 231 - 249. https://doi.org/10.1016/j.rse.2012.10.020 Pougatchev, N., August, T., Calbet, X., Hultberg, T., Oduleye, O., Schlüssel, P., et al. ( 2009 ). IASI temperature and water vapor retrievals - Error assessment and validation. Atmospheric Chemistry and Physics, 9 ( 17 ), 6453 - 6458. https://doi.org/10.5194/acp-9-6453-2009 Price, J. C. ( 1984 ). Land surface temperature measurements from the split window channels of the NOAA 7 Advanced Very High Resolution Radiometer. Journal of Geophysical Research: Atmospheres, 89 ( D5 ), 7231 - 7237. https://doi.org/10.1029/JD089iD05p07231 Rathke, C., Fischer, J., Neshyba, S., & Shupe, M. ( 2002 ). Improving IR cloud phase determination with 20 microns spectral observations. Geophysical Research Letters, 29 ( 8 ), 50 - 51. https://doi.org/10.1029/2001GL014594 Rockwell, B. W., & Hofstra, A. H. ( 2008 ). Identification of quartz and carbonate minerals across northern Nevada using ASTER thermal infrared emissivity data- Implications for geologic mapping and mineral resource investigations in well- studied and Frontier areas. Geosphere, 4 ( 1 ), 218 - 246. https://doi.org/10.1130/ges00126.1 Rodgers, C. D. ( 1976 ). Retrieval of atmospheric temperature and composition from remote measurements of thermal radiation. Reviews of Geophysics, 14 ( 4 ), 609 - 624. https://doi.org/10.1029/RG014i004p00609 Scarlat, R. C., Heygster, G., & Pedersen, L. T. ( 2017 ). Experiences with an optimal estimation algorithm for surface and atmospheric parameter retrieval from passive microwave data in the Arctic. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10 ( 9 ), 3934 - 3947. https://doi.org/10.1109/JSTARS.2017.2739858 Schlüssel, P., Hultberg, T. H., Phillips, P. L., August, T., & Calbet, X. ( 2005 ). The operational IASI Level 2 processor. Advances in Space Research, 36 ( 5 ), 982 - 988. https://doi.org/10.1016/j.asr.2005.03.008 Seemann, S. W., Borbas, E. E., Knuteson, R. O., Stephenson, G. R., & Huang, H.- L. ( 2008 ). Development of a global infrared land surface emissivity database for application to clear sky sounding retrievals from multispectral satellite radiance measurements. Journal of Applied Meteorology and Climatology, 47 ( 1 ), 108 - 123. https://doi.org/10.1175/2007jamc1590.1 Shannon, C. E., & Weaver, W. ( 1949 ). The mathematical theory of communication (p. 144). Champaign, IL: University of Illinois Press (ISBN: 0252725484). Smith, N., & Barnet, C. D. ( 2019 ). Uncertainty characterization and propagation in the community long- term infrared microwave combined atmospheric product system (CLIMCAPS). Remote Sensing, 11 ( 10 ), 1227. https://doi.org/10.3390/rs11101227 Susskind, J., Barnet, C. D., & Blaisdell, J. M. ( 2003 ). Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds. IEEE Transactions on Geoscience and Remote Sensing, 41 ( 2 ), 390 - 409. https://doi.org/10.1109/TGRS.2002.808236 Turner, D. D., Clough, S. A., Liljegren, J. C., Clothiaux, E. E., Cady- Pereira, K. E., & Gaustad, K. L. ( 2007 ). Retrieving liquid water path and precipitable water vapor from the atmospheric radiation measurement (ARM) microwave radiometers. IEEE Transactions on Geoscience and Remote Sensing, 45 ( 11 ), 3680 - 3690. https://doi.org/10.1109/TGRS.2007.903703 Turner, D. D., & Löhnert, U. ( 2014 ). Information content and uncertainties in thermodynamic profiles and liquid cloud properties retrieved from the ground- based atmospheric emitted radiance interferometer (AERI). Journal of Applied Meteorology and Climatology, 53 ( 3 ), 752 - 771. https://doi.org/10.1175/jamc-d-13-0126.1 Wan, Z., & Li, Z.- L. ( 1997 ). A physics- based algorithm for retrieving land- surface emissivity and temperature from EOS/MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 35 ( 4 ), 980 - 996. https://doi.org/10.1109/36.602541 Wood, N. B., & L’Ecuyer, T. S. ( 2021 ). What millimeter- wavelength radar reflectivity reveals about snowfall: An information- centric analysis. Atmospheric Measurement Techniques, 14 ( 2 ), 869 - 888. https://doi.org/10.5194/amt-14-869-2021 Xu, X., Wang, J., Zeng, J., Hou, W., Meyer, K. G., E Platnick, S., & Wilcox, E. ( 2018 ). A pilot study of shortwave spectral fingerprints of smoke aerosols above liquid clouds. 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Quarterly Journal of the Royal Meteorological Society, 137 ( 656 ), 553 - 597. https://doi.org/10.1002/qj.828 IndexNoFollow optimal estimation surface spectral emissivity PREFIRE mission far- IR Atmospheric and Oceanic Sciences Science Article 2022 ftumdeepblue https://doi.org/10.1029/2021JD03567710.5194/essd-13-4303-202110.1029/2020JD03367210.1109/TGRS.2017.274455810.1002/qj.269110.1175/bams-d-19-0322.110.5194/acp-9-6453-200910.1029/JD089iD05p0723110.1029/RG014i004p0060910.1109/TGRS.2010.205103610.1016/j.jqsrt. 2023-07-31T20:45:59Z Surface spectral emissivity plays an important role in the polar radiation budget. The significance of surface emissivity in the far- infrared (far- IR) has been recognized by recent studies, yet there have been no observations to constrain far- IR surface spectral emissivity over the entire polar regions. In preparation for the Polar Radiant Energy in the Far- InfraRed Experiment (PREFIRE) mission, this study develops and assesses an optimal estimation- based retrieval algorithm to estimate both mid- IR and far- IR polar surface emissivity from the future PREFIRE measurements. Synthetic PREFIRE spectra are simulated by feeding the ERA5 reanalysis and a global surface emissivity data set to a radiative transfer model. Information content analysis indicates that the far- IR surface emissivity retrievals can be more influenced by the atmospheric water vapor abundance than the mid- IR counterparts. When the total column water vapor is above 1 cm, the far- IR surface emissivity retrievals largely rely on the a priori constraints. Performance of the optimal- estimation algorithm is assessed using 960 synthetic PREFIRE clear- sky radiance spectra over the Arctic. The results based on current best estimate of instrument performance show that all retrievals converge within 15 iterations, the retrieved surface spectral emissivity has a mean bias within ±0.01 and a root- mean- square error less than 0.024. The far- IR surface emissivity retrievals are much more affected by the a priori choice than the mid- IR ones. A properly constructed a priori covariance can also help to improve the computational efficiency. Influences of other factors for future operational retrievals are also discussed.Key PointsAn optimal- estimation algorithm for surface spectral emissivity retrieval is developed and assessed for the forthcoming PREFIRE missionSurface spectral emissivity retrievals in the far- infrared can be significantly influenced by the atmospheric water vapor abundanceCompared to the mid- infrared, the far- infrared surface ... Article in Journal/Newspaper Arctic Arctic University of Michigan: Deep Blue Arctic |