• Ackermann, I. J., H. Hass, M. Memmesheimer, A. Ebel, F. S. Binkowski, and U. Shankar, 1998: Modal Aerosol Dynamics model for Europe: Development and first applications. Atmos. Environ., 32, 29812999, https://doi.org/10.1016/S1352-2310(98)00006-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aumann, H. H., and Coauthors, 2003: AIRS/AMSU/HSB on the Aqua mission: Design, science objectives, data products, and processing system. IEEE Trans. Geosci. Remote Sens., 41, 253264, https://doi.org/10.1109/TGRS.2002.808356.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aumann, H. H., and Coauthors, 2018: Evaluation of radiative transfer models with clouds. J. Geophys. Res. Atmos., 123, 61426157, https://doi.org/10.1029/2017JD028063.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chandrasekhar, S., 1960: Radiative Transfer. Dover Publications, 393 pp.

  • Clough, S. A., M. J. Iacono, and J.-L. Moncet, 1992: Line-by-line calculation of atmospheric fluxes and cooling rates: Application to water vapor. J. Geophys. Res., 97, 15 76115 785, https://doi.org/10.1029/92JD01419.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clough, S. A., M. W. Shephard, E. J. Mlawer, J. S. Delamere, M. J. Iacono, K. E. Cady-Pereira, S.-A. Boukabara, and P. D. Brown, 2005: Atmospheric radiative transfer modeling: A summary of the AER codes. J. Quant. Spectrosc. Radiat. Transfer, 91, 233244, https://doi.org/10.1016/j.jqsrt.2004.05.058.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crevoisier, C., S. Heilliette, A. Chédin, S. Serrar, R. Armante, and N. A. Scott, 2004: Midtropospheric CO2 concentration retrieval from AIRS observations in the tropics. Geophys. Res. Lett., 31, L17106, https://doi.org/10.1029/2004GL020141.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • d’Almeida, G. A., P. Koepke, and E. P. Shettle, 1991: Atmospheric Aerosol: Global Climatology and Radiative Characteristics. A. Deepak Publishing, 561 pp.

  • del Águila, A., D. S. Efremenko, V. M. Garcia, and J. Xu, 2019: Analysis of two dimensionality reduction technique for fast simulation of the spectral radiances in the Hartley-Huggins band. Atmosphere, 10, 142, https://doi.org/10.3390/atmos10030142.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duan, M., Q. Min, and J. Li, 2005: A fast radiative transfer model for simulating high-resolution absorption bands. J. Geophys. Res., 110, D15201, https://doi.org/10.1029/2004JD005590.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Efremenko, D. S., D. G. Loyola, R. Spurr, and A. Doicu, 2014a: Acceleration of radiative transfer model calculations for the retrieval of trace gases under cloudy conditions. J. Quant. Spectrosc. Radiat. Transfer, 135, 5865, https://doi.org/10.1016/j.jqsrt.2013.11.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Efremenko, D. S., A. Doicu, D. Loyola, and T. Trautmann, 2014b: Optical property dimensionality reduction techniques for accelerated radiative transfer performance: Application to remote sensing total ozone retrievals. J. Quant. Spectrosc. Radiat. Transfer, 133, 128135, https://doi.org/10.1016/j.jqsrt.2013.07.023.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eldering, A., and Coauthors, 2017: The Orbiting Carbon Observatory-2: First 18 months of science data products. Atmos. Meas. Tech., 10, 549563, https://doi.org/10.5194/amt-10-549-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eresmaa, R., and A. P. McNally, 2016: Diverse profile datasets based on the CAMS atmospheric composition forecasting system. ECMWF Rep., 12 pp.

  • Fu, D., T. J. Pongetti, J.-F. Blavier, T. J. Crawford, K. S. Manatt, G. G. Toon, K. W. Wong, and S. P. Sander, 2014: Near-infrared remote sensing of Los Angeles trace gas distributions from a mountaintop site. Atmos. Meas. Tech., 7, 713729, https://doi.org/10.5194/amt-7-713-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goody, R. M., and Y. L. Yung, 1989: Atmospheric Radiation. Oxford University Press, 519 pp.

    • Crossref
    • Export Citation
  • Gordon, I. E., and Coauthors, 2017: The HITRAN2016 molecular spectroscopic database. J. Quant. Spectrosc. Radiat. Transfer, 203, 369, https://doi.org/10.1016/j.jqsrt.2017.06.038.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, Y., and Coauthors, 2013: Suomi NPP CrIS measurements, sensor data record algorithm, calibration and validation activities, and record data quality. J. Geophys. Res. Atmos., 118, 12 73412 748, https://doi.org/10.1002/2013JD020344.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Havemann, S., J.-C. Thelen, J. P. Taylor, and R. C. Harlow, 2018: The Havemann-Taylor fast radiative transfer code (HT-FRTC): A multipurpose code based on principal components. J. Quant. Spectrosc. Radiat. Transfer, 220, 180192, https://doi.org/10.1016/j.jqsrt.2018.09.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hess, M., P. Koepke, and I. Schult, 1998: Optical properties of aerosols and clouds: The software package OPAC. Bull. Amer. Meteor. Soc., 79, 831844, https://doi.org/10.1175/1520-0477(1998)079<0831:OPOAAC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hilton, F., and Coauthors, 2012: Hyperspectral Earth observation from IASI: Five years of accomplishments. Bull. Amer. Meteor. Soc., 93, 347370, https://doi.org/10.1175/BAMS-D-11-00027.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kopparla, P., V. Natraj, R. Spurr, R.-L. Shia, D. Crisp, and Y. L. Yung, 2016: A fast and accurate PCA based radiative transfer model: Extension to the broadband shortwave region. J. Quant. Spectrosc. Radiat. Transfer, 173, 6571, https://doi.org/10.1016/j.jqsrt.2016.01.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kopparla, P., V. Natraj, D. Limpasuvan, R. Spurr, D. Crisp, R.-L. Shia, P. Somkuti, and Y. L. Yung, 2017: PCA-based radiative transfer: Improvements to aerosol scheme, vertical layering and spectral binning. J. Quant. Spectrosc. Radiat. Transfer, 198, 104111, https://doi.org/10.1016/j.jqsrt.2017.05.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Le, T., C. Liu, B. Yao, V. Natraj, and Y. L. Yung, 2020: Application of machine learning to hyperspectral radiative transfer simulations. J. Quant. Spectrosc. Radiat. Transfer, 246, 106928, https://doi.org/10.1016/j.jqsrt.2020.106928.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liou, K. N., 1973: A numerical experiment on Chandrasekhar’s discrete-ordinate method for radiative transfer: Application to cloudy and hazy atmospheres. J. Atmos. Sci., 30, 13031326, https://doi.org/10.1175/1520-0469(1973)030<1303:ANEOCD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liou, K. N., 2002: An Introduction to Atmospheric Radiation. Academic Press, 583 pp.

  • Liu, C., P. Yang, S. L. Nasiri, S. Platnick, K. G. Meyer, C. Wang, and S. Ding, 2015: A fast visible infrared imaging radiometer suite simulator for cloudy atmospheres. J. Geophys. Res. Atmos., 120, 240255, https://doi.org/10.1002/2014JD022443.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, X., W. L. Smith, D. K. Zhou, and A. Larar, 2006: Principal component-based radiative transfer model for hyperspectral sensors: Theoretical concept. Appl. Opt., 45, 201209, https://doi.org/10.1364/AO.45.000201.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, X., D. K. Zhou, A. M. Larar, W. L. Smith, P. Schluessel, S. M. Newman, J. P. Taylor, and W. Wu, 2009: Retrieval of atmospheric profiles and cloud properties from IASI spectra using super-channels. Atmos. Chem. Phys., 9, 91219142, https://doi.org/10.5194/acp-9-9121-2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, X., Q. Yang, H. Li, Z. Jin, W. Wu, S. Kizer, D. K. Zhou, and P. Yang, 2016: Development of a fast and accurate PCRTM radiative transfer model in the solar spectral region. Appl. Opt., 55, 82368247, https://doi.org/10.1364/AO.55.008236.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Y., Z. Cai, D. Yang, M. Duan, and D. , 2013: Optimization of the instrument configuration for TANSAT CO2 spectrometer. Chin. Sci. Bull., 58, 27872789, https://doi.org/10.1360/972013-518.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matricardi, M., 2010: A principal component based version of the RTTOV fast radiative transfer model. Quart. J. Roy. Meteor. Soc., 136, 18231835, https://doi.org/10.1002/qj.680.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matricardi, M., and A. McNally, 2014: The direct assimilation of principal components of IASI spectra in the ECMWF 4D-Var. Quart. J. Roy. Meteor. Soc., 140, 573582, https://doi.org/10.1002/qj.2156.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moncet, J.-L., G. Uymin, A. E. Lipton, and H. E. Snell, 2008: Infrared radiance modeling by optimal spectral sampling. J. Atmos. Sci., 65, 39173934, https://doi.org/10.1175/2008JAS2711.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moncet, J.-L., G. Uymin, P. Liang, and A. E. Lipton, 2015: Fast and accurate radiative transfer in the thermal regime by simultaneous optimal spectral sampling over all channels. J. Atmos. Sci., 72, 26222641, https://doi.org/10.1175/JAS-D-14-0190.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Natraj, V., X. Jiang, R.-L. Shia, X. Huang, J. S. Margolis, and Y. L. Yung, 2005: Application of principal component analysis to high spectral resolution radiative transfer: A case study of the O2 A band. J. Quant. Spectrosc. Radiat. Transfer, 95, 539556, https://doi.org/10.1016/j.jqsrt.2004.12.024.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Natraj, V., R.-L. Shia, and Y. L. Yung, 2010: On the use of principal component analysis to speed up radiative transfer calculations. J. Quant. Spectrosc. Radiat. Transfer, 111, 810816, https://doi.org/10.1016/j.jqsrt.2009.11.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oreopoulos, L., and Coauthors, 2012: The continual intercomparison of radiation codes: Results from phase I. J. Geophys. Res., 117, D06118, https://doi.org/10.1029/2011JD016821.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Dell, C. W., 2010: Acceleration of multiple-scattering hyperspectral radiative transfer calculations via low-stream interpolation. J. Geophys. Res., 115, D10206, https://doi.org/10.1029/2009JD012803.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Dell, C. W., and Coauthors, 2012: The ACOS CO2 retrieval algorithm—Part I: Description and validation against synthetic observations. Atmos. Meas. Tech., 5, 99121, https://doi.org/10.5194/amt-5-99-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Plass, G. N., and G. W. Kattawar, 1968: Monte Carlo calculations of light scattering from clouds. Appl. Opt., 7, 415419, https://doi.org/10.1364/AO.7.000415.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Randles, C., and Coauthors, 2013: Intercomparison of shortwave radiative transfer schemes in global aerosol modeling: Results from the AeroCom Radiative Transfer Experiment. Atmos. Chem. Phys., 13, 23472379, https://doi.org/10.5194/acp-13-2347-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saunders, R., and Coauthors, 2018: An update on the RTTOV fast radiative transfer model (currently at version 12). Geosci. Model Dev., 11, 27172737, https://doi.org/10.5194/gmd-11-2717-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seeman, S. W., E. Borbas, R. O. Knuteson, G. R. Stephenson, and H.-L. Huang, 2008: Development of a global infrared land surface emissivity database for application to clear sky sounding retrievals from multi-spectral satellite radiances measurements. J. Appl. Meteor. Climatol., 47, 108123, https://doi.org/10.1175/2007JAMC1590.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Somkuti, P., H. Boesch, V. Natraj, and P. Kopparla, 2017: Application of a PCA-based fast radiative transfer model to XCO2 retrievals in the shortwave infrared. J. Geophys. Res. Atmos., 122, 10 47710 496, https://doi.org/10.1002/2017JD027013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Spurr, R. J. D., T. P. Kurosu, and K. V. Chance, 2001: A linearized discrete ordinate radiative transfer model for atmospheric remote sensing retrieval. J. Quant. Spectrosc. Radiat. Transfer, 68, 689735, https://doi.org/10.1016/S0022-4073(00)00055-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Spurr, R. J. D., V. Natraj, C. Lerot, M. Van Roozendael, and D. Loyola, 2013: Linearization of the principal component analysis method for radiative transfer acceleration: Application to retrieval algorithms and sensitivity studies. J. Quant. Spectrosc. Radiat. Transfer, 125, 117, https://doi.org/10.1016/j.jqsrt.2013.04.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Twomey, S., H. Jacobowitz, and H. B. Howell, 1966: Matrix methods for multiple-scattering problems. J. Atmos. Sci., 23, 289298, https://doi.org/10.1175/1520-0469(1966)023<0289:MMFMSP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vidot, J., P. Brunel, M. Dumont, C. Carmagnola, and J. Hocking, 2018: The VIS/NIR land and snow BRDF atlas for RTTOV: Comparison between MODIS MCD43C1 C5 and C6. Remote Sens., 10, 21, https://doi.org/10.3390/rs10010021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, C., P. Yang, S. Platnick, A. K. Heidinger, B. A. Baum, T. Greenwald, Z. Zhang, 2013: Retrieval of ice cloud properties from AIRS and MODIS observations based on a fast high-spectral-resolution radiative transfer model. J. Appl. Meteor. Climatol., 52, 710726, https://doi.org/10.1175/JAMC-D-12-020.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, C., P. Yang, and X. Liu, 2015: A high-spectral-resolution radiative transfer model for simulating multilayered clouds and aerosols in the infrared spectral region. J. Atmos. Sci., 72, 926942, https://doi.org/10.1175/JAS-D-14-0046.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, W., and Coauthors, 2017: The application of PCRTM physical retrieval methodology for IASI cloudy scene analysis. IEEE Trans. Geosci. Remote Sens., 55, 50425056, https://doi.org/10.1109/TGRS.2017.2702006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wunch, D., and Coauthors, 2011: The Total Carbon Column Observing Network. Philos. Trans. Roy. Soc., 369A, 20872112, https://doi.org/10.1098/rsta.2010.0240.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, J., Z. Zhang, C. Wei, F. Lu, and Q. Guo, 2017: Introducing the new generation of Chinese geostationary weather satellites, Fengyun-4. Bull. Amer. Meteor. Soc., 98, 16371658, https://doi.org/10.1175/BAMS-D-16-0065.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, Q., X. Liu, W. Wu, S. Kizer, and R. R. Baize, 2016: Fast and accurate hybrid stream PCRTM-SOLAR radiative transfer model for reflected solar spectrum simulation in the cloudy atmosphere. Opt. Express, 24, A1514A1527, https://doi.org/10.1364/OE.24.0A1514.

    • Crossref
    • Search Google Scholar
    • Export Citation
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A Spectral Data Compression (SDCOMP) Radiative Transfer Model for High-Spectral-Resolution Radiation Simulations

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  • 1 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, and School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
  • 2 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
  • 3 Chinese Academy of Meteorological Sciences, Beijing, China
  • 4 Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California
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Abstract

With the increasing use of satellite and ground-based high-spectral-resolution (HSR) measurements for weather and climate applications, accurate and efficient radiative transfer (RT) models have become essential for accurate atmospheric retrievals, for instrument calibration, and to provide benchmark RT solutions. This study develops a spectral data compression (SDCOMP) RT model to simulate HSR radiances in both solar and infrared spectral regions. The SDCOMP approach “compresses” the spectral data in the optical property and radiance domains, utilizing principal component analysis (PCA) twice to alleviate the computational burden. First, an optical-property-based PCA is performed for a given atmospheric scenario (atmospheric, trace gas, and aerosol profiles) to simulate relatively low-spectral-resolution radiances at a small number of representative wavelengths. Second, by using precalculated principal components from an accurate radiance dataset computed for a large number of atmospheric scenarios, a radiance-based PCA is carried out to extend the low-spectral-resolution results to desired HSR results at all wavelengths. This procedure ensures both that individual monochromatic RT calculations are efficiently performed and that the number of such computations is optimized. SDCOMP is approximately three orders of magnitude faster than numerically exact RT calculations. The resulting monochromatic radiance has relative errors less than 0.2% in the solar region and brightness temperature differences less than 0.1 K for over 95% of the cases in the infrared region. The efficiency and accuracy of SDCOMP not only make it useful for analysis of HSR measurements, but also hint at the potential for utilizing this model to perform RT simulations in mesoscale numerical weather and general circulation models.

© 2020 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: Vijay Natraj, vijay.natraj@jpl.nasa.gov

Abstract

With the increasing use of satellite and ground-based high-spectral-resolution (HSR) measurements for weather and climate applications, accurate and efficient radiative transfer (RT) models have become essential for accurate atmospheric retrievals, for instrument calibration, and to provide benchmark RT solutions. This study develops a spectral data compression (SDCOMP) RT model to simulate HSR radiances in both solar and infrared spectral regions. The SDCOMP approach “compresses” the spectral data in the optical property and radiance domains, utilizing principal component analysis (PCA) twice to alleviate the computational burden. First, an optical-property-based PCA is performed for a given atmospheric scenario (atmospheric, trace gas, and aerosol profiles) to simulate relatively low-spectral-resolution radiances at a small number of representative wavelengths. Second, by using precalculated principal components from an accurate radiance dataset computed for a large number of atmospheric scenarios, a radiance-based PCA is carried out to extend the low-spectral-resolution results to desired HSR results at all wavelengths. This procedure ensures both that individual monochromatic RT calculations are efficiently performed and that the number of such computations is optimized. SDCOMP is approximately three orders of magnitude faster than numerically exact RT calculations. The resulting monochromatic radiance has relative errors less than 0.2% in the solar region and brightness temperature differences less than 0.1 K for over 95% of the cases in the infrared region. The efficiency and accuracy of SDCOMP not only make it useful for analysis of HSR measurements, but also hint at the potential for utilizing this model to perform RT simulations in mesoscale numerical weather and general circulation models.

© 2020 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: Vijay Natraj, vijay.natraj@jpl.nasa.gov
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