A solely radiance-based spectral angular distribution model and its application in deriving clear-sky spectral fluxes over tropical oceans
Tóm tắt
The radiation budget at the top of the atmosphere plays a critical role in climate research. Compared to the broadband flux, the spectrally resolved outgoing longwave radiation or flux (OLR), with rich atmospheric information in different bands, has obvious advantages in the evaluation of GCMs. Unlike methods that need auxiliary measurements and information, here we take atmospheric infrared sounder (AIRS) observations as an example to build a self-consistent algorithm by an angular distribution model (ADM), based solely on radiance observations, to estimate clear-sky spectrally resolved fluxes over tropical oceans. As the key step for such an ADM, scene type estimations are obtained from radiance and brightness temperature in selected AIRS channels. Then, broadband OLR as well as synthetic spectral fluxes are derived by the spectral ADM and validated using both synthetic spectra and CERES (Clouds and the Earth’s Radiant Energy System) observations. In most situations, the mean OLR differences between the spectral ADM products and the CERES observations are within ±2 W m-2, which is less than 1% of the typical mean clear-sky OLR over tropical oceans. The whole algorithm described in this study can be easily extended to other similar hyperspectral radiance measurements.
Tài liệu tham khảo
Ackerman, S. A., W. L. Smith, H. E. Revercomb, and J. D. Spinhirne, 1990: The 27–28 October 1986 FIRE IFO cirrus case study: Spectral properties of cirrus clouds in the 8–12 µm window. Mon. Wea. Rev., 118, 2377–2388.
Allan, R. P., M. A. Ringer, J. A. Pamment, and A. Slingo, 2004: Simulation of the Earth’s radiation budget by the European Centre for Medium-Range Weather Forecasts 40-year reanalysis (ERA40). J. Geophys. Res., 109, D18107.
Anderson, G., and Coauthors, 2006: Atmospheric sensitivity to spectral top-of-atmosphere solar irradiance perturbations, using MODTRAN-5 radiative transfer algorithm. AGU Fall Meeting, Abstract A11C-05, San Francisco, CA, American Geophysical Union.
Aumann, H. H., and Coauthors, 2003a: AIRS/AMSU/HSB on the aqua mission: Design, science objectives, data products, and processing systems. IEEE Trans. Geosci. Remote Sens., 41(2), 253–264, doi: 10.1109/TGRS.2002.808356.
Aumann, H. H., M. T. Chahine, and D. Barron, 2003b: Sea surface temperature measurements with AIRS: RTG. SST comparison. Proc. SPIE 5151, Earth Observing Systems VIII, 252 (November 13, 2003), doi:10.1117/12.506385.
Aumann, H. H., S. Broberg, D. Elliott, S. Gaiser, and D. Gregorich, 2006: Three years of Atmospheric Infrared Sounder radiometric calibration validation using sea surface temperatures. J. Geophys. Res., 111, D16S90.
Beer, R., T. A. Glavich, and D. M. Rider, 2001: Tropospheric emission spectrometer for the Earth Observing System’s Aura satellite. Appl. Opt., 40(15), 2356–2367.
Berk, A., and Coauthors, 2005: MODTRAN5: A reformulated atmospheric band model with auxiliary species and practical multiple scattering options. Proc. SPIE 5655, Multispectral and Hyperspectral Remote Sensing Instruments and Applications II, 88 (January 20, 2005), doi:10.1117/12.578758.
Bingham, G. A., N. S. Pougatchev, M. P. Esplin, W. J. Blackwell, and C. D. Barnet, 2010: The NPOESS cross-track infrared sounder (CrIS) and advanced technology microwave sounder (ATMS) as a companion to the new generation AIRS/AMSU and IASI/AMSU sounder suites. Proc. 6th Annual Symposium on Future National Operational Environmental Satellite Systems, Atlanta, GA, American Meteorological Society.
Chahine, M. T., and Coauthors, 2006: AIRS: Improving weather forecasting and providing new data on greenhouse gases. Bull. Amer. Meteor. Soc., 87(7), 911–926, doi: 10.1175/BAMS-87-7-911.
Chen, X. H., and X. L. Huang, 2014: Usage of differential absorption method in the thermal IR: A case study of quick estimate of clear-sky column water vapor. Journal of Quantitative Spectroscopy and Radiative Transfer, 140, 99–106.
Clerbaux, C., and Coauthors, 2009: Monitoring of atmospheric composition using the thermal infrared IASI/MetOp sounder. Atmos. Chem. Phys., 9, 6041–6054.
Clough, S. A., and M. J. Iacono, 1995: Line-by-line calculation of atmospheric fluxes and cooling rates: 2. Application to carbon dioxide, ozone, methane, nitrous oxide and the halocarbons. J. Geophys. Res., 100(D8), 16519–16535
Clough, S. A., M. W. Shephard, E. J. Mlawer, J. S. Delamere, M. J. Iacono, K. Cady-Pereira, S. Boukabara, and P. D. Brown, 2005: Atmospheric radiative transfer modeling: A summary of the AER codes. Journal of Quantitative Spectroscopy and Radiative Transfer, 91, 233–244.
Frouin, R., and E. Middleton, 1990: A differential absorption technique to estimate atmospheric total water vapor amounts. American Meteorological Society Symposium on the First ISLSCP Field Experiment (FIFE), Anaheim, California, first ISLSCP Field Experiment, 135–139.
Goody, R., J. Anderson, and G. North, 1998: Testing climate models: An approach. Bull. Amer. Meteor. Soc., 79, 2541–2549.
Green, R. N., and P. O. R. Hinton, 1996: Estimation of angular distribution models from radiance pairs. J. Geophys. Res., 101(D12), 16951–16959.
Huang, X. L., and Y. L. Yung, 2005: Spatial and spectral variability of the outgoing thermal IR spectra from AIRS: A case study of July 2003. J. Geophys. Res., 110, D12102.
Huang, X. L., V. Ramaswamy, and M. D. Schwarzkopf, 2006: Quantification of the source of errors in AM2 simulated tropical clear-sky outgoing longwave radiation. J. Geophys. Res., 111, D14107.
Huang, X. L., W. Z. Yang, N. G. Loeb, and V. Ramaswamy, 2008: Spectrally resolved fluxes derived from collocated AIRS and CERES measurements and their application in model evaluation: Clear sky over the tropical oceans. J. Geophys. Res., 113, D09110.
Huang, X. L., N. G. Loeb, and W. Z. Yang, 2010: Spectrally resolved fluxes derived from collocated AIRS and CERES measurements and their application in model evaluation: 2. Cloudy sky and band-by-band cloud radiative forcing over the tropical oceans. J. Geophys. Res., 115, D21101.
Huang, Y., V. Ramaswamy, X. L. Huang, Q. Fu, and C. Bardeen, 2007: A strict test in climate modeling with spectrally resolved radiances: GCM simulation versus AIRS observations. Geophys. Res. Lett., 34, L24707.
Komhyr, W. D., R. D. Grass, R. K. Leonard, 1989: Dobson spectrophotometer 83: A standard for total ozone measurements, 1962–1987. J. Geophys. Res., 94, 9847–9861.
Le Marshall, J., and Coauthors, 2006: Improving global analysis and forecasting with AIRS. Bull. Amer. Meteor. Soc., 87(7), 891–894.
Loeb, N. G., P. O. R. Hinton, and R. N. Green, 1999: Top-ofatmosphere albedo estimation from angular distribution models: A comparison between two approaches. J. Geophys. Res., 104(D24), 31255–31260.
Loeb, N. G., F. Parol, J.-C. Buriez, and C. Vanbauce, 2000: Top-ofatmosphere albedo estimation from angular distribution models using scene identification from satellite cloud property retrievals. J. Climate, 13, 1269–1285.
Loeb, N. G., N. Manalo-Smith, S. Kato, W. F. Miller, S. K. Gupta, P. Minnis, and B. A. Wielicki, 2003: Angular distribution models for top-of-atmosphere radiative flux estimation from the clouds and the Earth’s Radiant Energy System instrument on the Tropical Rainfall Measuring Mission satellite. Part I: Methodology. J. Appl. Meteor., 42, 240–265.
Loeb, N. G., S. Kato, K. Loukachine, and N. Manalo-Smith, 2005: Angular distribution models for top-of-atmosphere radiative flux estimation from the Clouds and the Earth’s Radiant Energy System instrument on the Terra satellite. Part I: Methodology. J. Atmos. Oceanic Technol., 22, 338–351.
Loeb, N. G., S. Kato, K. Loukachine, N. Manalo-Smith, and D. R. Doelling, 2007: Angular distribution models for top-ofatmosphere radiative flux estimation from the Clouds and the Earth’s Radiant Energy System instrument on the Terra satellite. Part II: Validation. J. Atmos. Oceanic Technol., 24, 564–584.
Mann, M. E., R. S. Bradley, and M. K. Hughes, 1998: Globalscale temperature patterns and climate forcing over the past six centuries. Nature, 392, 779–787, doi: 10.1038/33859.
Ramanathan, V., R. D. Cess, E. F. Harrison, P. Minnis, B. R. Barkstrom, E. Ahmad, and D. Hartmann, 1989: Cloud-radiative forcing and climate: Results from the Earth Radiation Budget Experiment. Science, 243, 57–63.
Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and W. Q. Wang, 2002: An improved in situ and satellite SST analysis for climate. J. Climate, 15, 1609–1625.
Smith, G. L., R. N. Green, E. Raschke, L. M. Avis, J. T. Suttles, B. A. Wielicki, and R. Davies, 1986: Inversion methods for satellite studies of the Earth’s radiation budget: Development of algorithms for the ERBE mission. Rev. Geophys., 24(2), 407–421.
Strow, L. L., S. E. Hannon, M. Weiler, K. Overoye, S. L. Gaiser, and H. H. Aumann, 2003: Prelaunch spectral calibration of the Atmospheric Infrared Sounder (AIRS). IEEE Trans. Geosci. Remote Sens., 41(2), 274–286.
Strow, L. L., S. E. Hannon, S. De-Sousa Machado, H. E. Motteler, and D. C. Tobin, 2006: Validation of the atmospheric infrared sounder radiative transfer algorithm. J. Geophys. Res., 111, D09S06, doi: 10.1029/2005JD006146.
Susskind, J., C. D. Barnet, and J. M. Blaisdell, 2003: Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds. IEEE Trans. Geosci. Remote Sens., 41(2), 390–409.
Suttles, J. T., B. A. Wielicki, and S. Vemury, 1992: Top-ofatmosphere radiative fluxes: Validation of ERBE scanner inversion algorithm using Nimbus-7 ERB data. J. Appl. Meteor., 31, 784–796.
Wielicki, B. A., and R. N. Green, 1989: Cloud identification for ERBE radiation flux retrieval. J. Appl. Meteor., 28, 1133–1146.
Wielicki, B. A., and Coauthors, 2002: Evidence for large decadal variability in the tropical mean radiative energy budget. Science, 295, 841–844.
Wu, X. B., J. Li, W. J. Zhang, and F. Wang, 2005: Atmospheric profile retrieval with AIRS data and validation at the ARM CART site. Adv. Atmos. Sci., 22(5), 647–654, doi: 10.1007/BF02918708.
Zheng, J., J. Li, T. J. Schmit, J. L. Li, and Z. Q. Liu, 2015: The impact of AIRS atmospheric temperature and moisture profiles on hurricane forecasts: Ike and Irene. Adv. Atmos. Sci., 32(3), 319–335, doi: 10.1007/s00376-014-3162-z.