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A Solely Radiance-based Spectral Angular Distribution Model and Its Application in Deriving Clear-Sky Spectral Fluxes over Tropical Oceans


doi: 10.1007/s00376-015-5040-8

  • 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.
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  • 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 \upmum window. Mon. Wea. Rev., 118, 2377- 2388.10.1175/1520-0493(1990)1182.0.CO;279f5ba10-d09d-4aec-8536-0f343f9a6555b9379666eea3f06db1bdedb664825f78http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F240688897_The_2728_October_1986_FIRE_IFO_Cirrus_Case_Study_Spectral_Properties_of_Cirrus_Clouds_in_the_812_m_Windowrefpaperuri:(8571ac9b1ca4c4dddd9fa46758113c85)http://www.researchgate.net/publication/240688897_The_2728_October_1986_FIRE_IFO_Cirrus_Case_Study_Spectral_Properties_of_Cirrus_Clouds_in_the_812_m_WindowABSTRACT Lidar and radiometric observations are employed for detecting the presence of cirrus and describing their radiative properties. The study focuses on observations of the radiative properties of cirrus clouds within the 8-12 micron spectral region. The instrumentation that achieved the spectral radiance observations used in this study is described. Results are presented for October 28, 1986 FIRE case study day in terms of brightness temperature differences, cirrus effective emissivities, and the gross microphysical characteristics of the observed clouds. The HIS and lidar observations were combined to derive the spectral effective beam emissivity of the cirrus clouds. Fifty percent of clouds on this day displayed a spectral variation of effective beam emissivity from 2-10 percent. These differences, in conjunction with large differences in the HIS observed brightness temperatures, indicate that cirrus clouds cannot be considered gray in the 8-12 micron region. Also, the derived spectral transmittance of the cloud is used to infer the effective radii of the particle size distribution.
    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, D18107.10.1029/2004JD004816e4e16f95c063cd19825b9ea699d92642http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2004JD004816%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1029/2004JD004816/pdf[1] The radiation budget simulated by the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-year reanalysis (ERA40) is evaluated for the period 1979–2001 using independent satellite data and additional model data. This provides information on the quality of the radiation products and indirect evaluation of other aspects of the climate produced by ERA40. The climatology of clear-sky outgoing longwave radiation (OLR) is well captured by ERA40. Underestimations of about 10 W m 612 in clear-sky OLR over tropical convective regions by ERA40 compared to satellite data are substantially reduced when the satellite sampling is taken into account. The climatology of column-integrated water vapor is well simulated by ERA40 compared to satellite data over the ocean, indicating that the simulation of downward clear-sky longwave fluxes at the surface is likely to be good. Clear-sky absorbed solar radiation (ASR) and clear-sky OLR are overestimated by ERA40 over north Africa and high-latitude land regions. The observed interannual changes in low-latitude means are not well reproduced. Using ERA40 to analyze trends and climate feedbacks globally is therefore not recommended. The all-sky radiation budget is poorly simulated by ERA40. OLR is overestimated by around 10 W m 612 over much of the globe. ASR is underestimated by around 30 W m 612 over tropical ocean regions. Away from marine stratocumulus regions, where cloud fraction is underestimated by ERA40, the poor radiation simulation by ERA40 appears to be related to inaccurate radiative properties of cloud rather than inaccurate cloud distributions.
    Anderson G., 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.8e2c9a32f1e44c124848ac3fd8da0f9ehttp%3A%2F%2Fwww.researchgate.net%2Fpublication%2F234494463_Atmospheric_Sensitivity_to_Spectral_Top-of-Atmosphere_Solar_Irradiance_Perturbations_Using_MODTRAN-5_Radiative_Transfer_Algorithmhttp://www.researchgate.net/publication/234494463_Atmospheric_Sensitivity_to_Spectral_Top-of-Atmosphere_Solar_Irradiance_Perturbations_Using_MODTRAN-5_Radiative_Transfer_AlgorithmABSTRACT The opportunity to insert state-of-the-art solar irradiance measurements and calculations, with subtle perturbations, into a narrow spectral resolution radiative transfer model has recently been facilitated through release of MODTRAN-5 (MOD5). The new solar data are from: (1) SORCE satellite measurements of solar variability over solar rotation cycle, & (2) ultra-narrow calculation of a new solar source irradiance, extending over the full MOD5 spectral range, from 0.2 um to far-IR. MODTRAN-5, MODerate resolution radiance and TRANsmittance code, has been developed collaboratively by Air Force Research Laboratory and Spectral Sciences, Inc., with history dating back to LOWTRAN. It includes approximations for all local thermodynamic equilibrium terms associated with molecular, cloud, aerosol and surface components for emission, scattering, and reflectance, including multiple scattering, refraction and a statistical implementation of Correlated-k averaging. The band model is based on 0.1 cm-1 (also 1.0, 5.0 and 15.0 cm-1 statistical binning for line centers within the interval, captured through an exact formulation of the full Voigt line shape. Spectroscopic parameters are from HITRAN 2004 with user-defined options for additional gases. Recent validation studies show MOD5 replicates line-by-line brightness temperatures to within ~0.02&ordm;K average and <1.0&ordm;K RMS. MOD5 can then serve as a surrogate for a variety of perturbation studies, including the two modes for the solar source function, Io. (1) Data from the Solar Radiation and Climate Experiment (SORCE) satellite mission provide state-of-the-art measurements of UV, visible, near-IR, plus total solar radiation, on near real-time basis. These internally consistent estimates of Sun's output over solar rotation and longer time scales are valuable inputs for studying effects of Sun's radiation on Earth's atmosphere and climate. When solar rotation encounters bright plage and dark sunspots, relative variations are expected to be very small in visible wavelengths, although absolute power is substantial. SORCE's Spectral Irradiance Monitor measurements are readily included in comparative MOD5 calculations. (2) The embedded solar irradiance within MOD5 must be compatible with the chosen band model resolution binning. By matching resolutions some issues related to the correlated-k band model parameterizations can be tested. Two high resolution solar irradiances, the MOD5 default irradiance (Kurucz) and a new compilation associated with Solar Radiation Physical Modeling project (Fontenla), are compared to address the potential impact of discrepancies between any sets of irradiances. The magnitude of solar variability, as measured and calculated, can lead to subtle changes in heating/cooling rates throughout the atmosphere, as a function of altitude and wavelength. By holding chemical & dynamical responses constant, only controlled distributions of absorbing gases, aerosols and clouds will contribute to observed 1st order radiative effects.
    Aumann H.H., Coauthors, 2003a: AIRS/AMSU/HSB on the aqua mission: Design,science objectives, data products, and processing systems. IEEE Trans. Geosci. Remote Sens., 41(3), 253-264, doi: 10.1109/TGRS.2002.808356.10.1109/TGRS.2002.808356fb553f43-5a6f-44ae-a425-a8a68ffb31162d4b3af968d774e7668fe424230b2e5ehttp%3A%2F%2Fonlinelibrary.wiley.com%2Fresolve%2Freference%2FXREF%3Fid%3D10.1109%2FTGRS.2002.808356refpaperuri:(93035e9a93dcbd564e42b10d0e408668)http://onlinelibrary.wiley.com/resolve/reference/XREF?id=10.1109/TGRS.2002.808356The Atmospheric Infrared Sounder (AIRS), the Advanced Microwave Sounding Unit (AMSU), and the Humidity Sounder for Brazil (HSB) form an integrated cross-track scanning temperature and humidity sounding system on the Aqua satellite of the Earth Observing System (EOS). AIRS is an infrared spectrometer/radiometer that covers the 3.7-15.4-m spectral range with 2378 spectral channels. AMSU is a 15-channel microwave radiometer operating between 23 and 89 GHz. HSB is a four-channel microwave radiometer that makes measurements between 150 and 190 GHz. In addition to supporting the National Aeronautics and Space Administration's interest in process study and climate research, AIRS is the first hyperspectral infrared radiometer designed to support the operational requirements for medium-range weather forecasting of the National Ocean and Atmospheric Administration's National Centers for Environmental Prediction (NCEP) and other numerical weather forecasting centers. AIRS, together with the AMSU and HSB microwave radiometers, will achieve global retrieval accuracy of better than 1 K in the lower troposphere under clear and partly cloudy conditions. This paper presents an overview of the science objectives, AIRS/AMSU/HSB data products, retrieval algorithms, and the ground-data processing concepts. The EOS Aqua was launched on May 4, 2002 from Vandenberg AFB, CA, into a 705-km-high, sun-synchronous orbit. Based on the excellent radiometric and spectral performance demonstrated by AIRS during prelaunch testing, which has by now been verified during on-orbit testing, we expect the assimilation of AIRS data into the numerical weather forecast to result in significant forecast range and reliability improvements.
    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:10.1117/12.506385.10.1117/12.506385b0b8dde912d489488c1e2a5bc356806chttp%3A%2F%2Fspie.org%2FPublications%2FProceedings%2FPaper%2F10.1117%2F12.506385http://spie.org/Publications/Proceedings/Paper/10.1117/12.506385ABSTRACT The comparison of global sea surface skin temperatures derived from cloud-free AIRS super window channel at 2616 cm-1 (sst2616) with the Real-Time Global Sea Surface Temperature (RTG.SST) for September 2002 shows a surprisingly small standard deviation of 0.44 K; however, sst2616 is colder than the RTG.SST by 0.67 K. About 0.35 K of the cold bias is due the expected bulk-skin gradient and the effect of using the day/night average RTG.SST for a nighttime comparison. The other 0.32 K is due to an absorbing layer in the atmosphere. There are large areas of the oceans where this absorbing layer is absent, and other areas where it is as large at 1.5 K. The layers persist regionally on a months timescale and might be related to some form of aerosol or marine haze. A correlation with major weather events, like the Monsoon season in the Indian ocean and, possibly, El Nino events is suspected, but has not been demonstrated. AIRS was lauched into polar orbit on the EOS Aqua spacecraft on May 4, 2002.
    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, D16S90.10.1029/2005JD0068226348c339b6aec4b8817ca4d85ffe277ahttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2005JD006822%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1029/2005JD006822/pdfAbstract Top of page Abstract 1.Introduction 2.Approach 3.Results and Discussion 4.Summary and Conclusions AppendixA::sst2616 and sst1231 Acknowledgments References Supporting Information [1] This paper evaluates the absolute accuracy and stability of the radiometric calibration of the Atmospheric Infrared Sounder (AIRS) by analyzing the difference between the brightness temperatures measured at 2616 cm 611 and those calculated at the top of the atmosphere (TOA), using the Real-Time Global Sea Surface Temperature (RTGSST) for cloud-free night tropical oceans between ±30° latitude. The TOA correction is based on radiative transfer. The analysis of the first 3 years of AIRS radiances verifies the absolute calibration at 2616 cm 611 to better than 200 mK, with better than 16 mK/yr stability. The AIRS radiometric calibration uses an internal full aperture wedge blackbody with the National Institute of Standards and Technology (NIST) traceable prelaunch calibration coefficients. The calibration coefficients have been unchanged since launch. The analysis uses very tight cloud filtering, which selects about 7000 cloud-free tropical ocean spectra per day, about 0.5% of the data. The absolute accuracy and stability of the radiometry demonstrated at 2616 cm 611 are direct consequences of the implementation of AIRS as a thermally controlled, cooled grating-array spectrometer and meticulous attention to details. Comparable radiometric performance is inferred from the AIRS design for all 2378 channels. AIRS performance sets the benchmark for what can be achieved with a state-of-the-art hyperspectral radiometer from polar orbit and what is expected from future hyperspectral sounders. AIRS was launched into a 705 km altitude polar orbit on NASA's Earth Observation System (EOS) Aqua spacecraft on 4 May 2002. AIRS covers the 3.7–15.4 micron region of the thermal infrared spectrum with a spectral resolution of ν/Δν = 1200 and has returned 3.7 million spectra of the upwelling radiance each day since the start of routine data gathering in September 2002.
    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- 2367.10.1364/AO.40.0023561835724430ebb6c07f4ebdc0dff2bd20893d0d05http%3A%2F%2Fmed.wanfangdata.com.cn%2FPaper%2FDetail%2FPeriodicalPaper_PM18357244http://med.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM18357244Abstract The Tropospheric Emission Spectrometer (TES) is an imaging infrared Fourier-transform spectrometer scheduled to be launched into polar Sun-synchronous orbit aboard the Earth Observing System's Aura satellite in June 2003. The primary objective of the TES is to make global three-dimensional measurements of tropospheric ozone and of the physical-chemical factors that control its formation, destruction, and distribution. Such an ambitious goal requires a highly sophisticated cryogenic instrument operating over a wide frequency range, which, in turn, demands state-of-the-art infrared detector arrays. In addition, the measurements require an instrument that can operate in both nadir and limb-sounding modes with a precision pointing system. The way in which these mission objectives flow down to the specific science and measurement requirements and in turn are implemented in the flight hardware are described. A brief overview of the data analysis approach is provided.
    Berk, A., 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:10.1117/12.578758.10.1117/12.546782a07feb62ec1f6558509c89d82f089562http%3A%2F%2Fspie.org%2FPublications%2FProceedings%2FPaper%2F10.1117%2F12.564634http://spie.org/Publications/Proceedings/Paper/10.1117/12.564634The MODTRAN5(1a, in press) radiation transport (RT) model is a major advancement over earlier versions of the MODTRAN(tm) atmospheric transmittance and radiance model. New model features include (1) finer spectral resolution via the Spectrally Enhanced Resolution MODTRAN(tm) (SERTRAN) molecular band model, (2) a fully coupled treatment of auxiliary molecular species, and (3) a rapid, high fidelity multiple scattering (MS) option. The finer spectral resolution improves model accuracy especially in the mid- and long-wave infrared atmospheric windows; the auxiliary species option permits the addition of any or all of the suite of HITRAN molecular line species, along with default and user-defined profile specification; and the MS option makes feasible the calculation of Vis-NIR databases that include high-fidelity scattered radiances. (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only. Conference Presentation Video Visit SPIE.TV
    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.74b2174998e0b07aa97cf4e6f793996chttp%3A%2F%2Fwww.researchgate.net%2Fpublication%2F234311482_The_NPOESS_Crosstrack_Infrared_Sounder_%28CrIS%29_and_Advanced_Technology_Microwave_Sounder_%28ATMS%29_as_a_Companion_to_the_New_Generation_AIRSAMSU_and_IASIAMSU_Sounder_Suiteshttp://www.researchgate.net/publication/234311482_The_NPOESS_Crosstrack_Infrared_Sounder_(CrIS)_and_Advanced_Technology_Microwave_Sounder_(ATMS)_as_a_Companion_to_the_New_Generation_AIRSAMSU_and_IASIAMSU_Sounder_SuitesABSTRACT The NPOESS Preparatory Project is serving the operations and research community as the bridge mission between the Earth Observing System and the National Polar-orbiting Operational Environmental Satellite System. The Cross-track Infrared Sounder (CrIS), combined with the Advanced Technology Microwave Sounder (ATMS) are the core instruments to provide the key performance temperature and humidity profiles (along with some other atmospheric constituent information). Both the high spectral resolution CrIS and the upgraded microwave sounder (ATMS) will be working in parallel with already orbiting Advanced Atmospheric Infrared Sounder (AIRS/AMSU) on EOS AQUA platform and Infrared Atmospheric Sounding Interferometer (IASI/AMSU) on METOP-A satellite. This presentation will review the CrIS/ATMS capabilities in the context of continuity with the excellent performance records established by AIRS and IASI. The CrIS sensor is in the process of its final calibration and characterization testing and the results and Sensor Data Record process are being validated against this excellent dataset. The comparison between CrIS, AIRS, and IASI will include spectral, spatial, radiometric performance and sounding capability comparisons.
    Chahine, M. T., 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-87-7-911.10.1175/BAMS-87-7-91147da7bc6-e736-4031-8dba-6e9e5f25d0982363d07b568f6b43e3373f27de59efd3http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F238426379_AIRS_Improving_Weather_Forecasting_and_Providing_New_Data_on_Greenhouse_Gasesrefpaperuri:(798eaf97a42da6e63f98d3b595dd6f75)http://www.researchgate.net/publication/238426379_AIRS_Improving_Weather_Forecasting_and_Providing_New_Data_on_Greenhouse_GasesThe Atmospheric Infrared Sounder (AIRS) and its two companion microwave sounders, AMSU and HSB were launched into polar orbit onboard the NASA Aqua Satellite in May 2002. NASA required the sounding system to provide high-quality research data for climate studies and to meet NOAA's requirements for improving operational weather forecasting. The NOAA requirement translated into global retrieval of temperature and humidity profiles with accuracies approaching those of radiosondes. AIRS also provides new measurements of several greenhouse gases, such as CO2, CO, CH4, O3, SO2, and aerosols. The assimilation of AIRS data into operational weather forecasting has already demonstrated significant improvements in global forecast skill. At NOAA/NCEP, the improvement in the forecast skill achieved at 6 days is equivalent to gaining an extension of forecast capability of six hours. This improvement is quite significant when compared to other forecast improvements over the last decade. In addition to NCEP, ECMWF and the Met Office have also reported positive forecast impacts due AIRS. AIRS is a hyperspectral sounder with 2,378 infrared channels between 3.7 and 15.4 0008m. NOAA/NESDIS routinely distributes AIRS data within 3 hours to NWP centers around the world. The AIRS design represents a breakthrough in infrared space instrumentation with measurement stability and accuracies far surpassing any current research or operational sounder. The results we describe in this paper are 0904work in progress,09-09 and although significant accomplishments have already been made much more work remains in order to realize the full potential of this suite of instruments.
    Chen X. H., 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- 106.10.1016/j.jqsrt.2014.02.019f13e1b8120a5f4bb93734158e164f5e4http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0022407314000764http://www.sciencedirect.com/science/article/pii/S0022407314000764The concept of differential absorption has been widely used in UV and shortwave remote sensing. This study explores how to extend such concept to the thermal-IR for fast estimation of the total column water vapor (CWV) from clear-sky IR radiances. Using Atmospheric Infrared Sounder (AIRS) radiances as a case study, double difference of radiances at two pairs of pre-selected AIRS channels can be used to suppress the influence of continuum absorption and to highlight contrasts due to weak water vapor line absorptions. To take emission into account, another two AIRS channels are used as surrogates of surface temperature and lapse rate in the lower troposphere. As a result, a three-dimensional look-up table (LUT) can be constructed based on training data sets. Such LUT enables us a fast estimate of CWV directly from the spectral radiances without any a prior information or formal retrieval. The performance of the method is tested using synthetic AIRS radiances based on reanalysis as well as actual sounding profiles. It is also tested against AIRS L2 cloud-cleared radiances and CWV retrievals. The comparisons show that the mean bias of this method is within 0.07cm and the root-mean-square fractional error is about 33%.
    Clerbaux, C., Coauthors, 2009: Monitoring of atmospheric composition using the thermal infrared IASI/MetOp sounder. Atmos. Chem. Phys., 9, 6041- 6054.10.5194/acp-9-6041-20099c7a034739ba967f6d0b7eb2a4a63a03http%3A%2F%2Fonlinelibrary.wiley.com%2Fresolve%2Freference%2FXREF%3Fid%3D10.5194%2Facp-9-6041-2009http://onlinelibrary.wiley.com/resolve/reference/XREF?id=10.5194/acp-9-6041-2009The IASI instrument was launched onboard the METOP platform in October 2006. It is a nadir looking Fourier transform spectrometer that probes the Earth's atmosphere in the thermal infrared spectral range, with a spectral resolution of 0.5 cm-1 (apodized). IASI is monitoring the atmospheric composition at any location two times per day, and is measuring some of the chemical components playing a key role in the climate system and pollution issues. This talk will summarize the early results we have obtained from the analysis of the Eumetsat L1 products (nadir radiance spectra) since May 2007. We operationally retrieve CO, CH4, O3, as well as partial columns for O3. We also generate research products such as HNO3, H2O isotopes, and other atmospheric species. A special emphasis will be put on the study of fire and volcanic events.
    Clough S. A., 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), 16 519- 16 535.10.1029/95JD01386878ff384-a644-44c2-ae2a-c12ae251041e853cb942e261e6e8ebe05d5b433177a4http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F95JD01386%2Ffullrefpaperuri:(d3b4189c88230c99139a667dda0546d7)http://onlinelibrary.wiley.com/doi/10.1029/95JD01386/fullA line-by-line model (LBLRTM) has been applied to the calculation of clear-sky longwave fluxes and cooling rates for atmospheres including CO 2 , O 3 , CH 4 , N 2 O, CCl 4 , CFC-11, CFC-12, and CFC-22 in addition to water vapor. The present paper continues the approach developed in an earlier article in which the radiative properties of atmospheres with water vapor alone were reported. Tropospheric water vapor continues to be of principal importance for the longwave region due to the spectral extent of its absorbing properties, while the absorption bands of other trace species have influence over limited spectral domains. The principal effects of adding carbon dioxide are to reduce the role of the water vapor in the lower troposphere and to provide 72% of the 13.0 K d 611 cooling rate at the stratopause. In general, the introduction of uniformly mixed trace species into atmospheres with significant amounts of water vapor has the effect of reducing the cooling associated with water vapor, providing an apparent net atmospheric heating. The radiative consequences of doubling carbon dioxide from the present level are consistent with these results. For the midlatitude summer atmosphere the heating associated with ozone that occurs from 500 to 20 mbar reaches a maximum of 0.25 K d 611 at 50 mbar and partially offsets the cooling of 1.0 K d 611 contributed by H 2 O and CO 2 at this level. In the stratosphere the 704 cm 611 band of ozone, not included in many radiation models, contributes 25% of the ozone cooling rate. Radiative effects associated with anticipated 10-year constituent profile changes, 1990&ndash;2000, are presented from both a spectral and spectrally integrated perspective. The effect of the trace gases has been studied for three atmospheres: tropical, midlatitude summer, and midlatitude winter. Using these results and making a reasonable approximation for the polar regions, we obtain a value for the longwave flux at the top of the atmosphere of 265.5 W m 612 , in close agreement with the clear-sky Earth Radiation Budget Experiment (ERBE) observations. This agreement provides strong support for the present approach as a reference method for the study of radiative effects resulting from changes in the distributions of trace species on global radiative forcing. Many of the results from the spectral calculations reported here are archived at the Carbon Dioxide Information and Analysis Center for use by the community.
    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- 244.10.1016/j.jqsrt.2004.05.058fba207c2e1efb95c00ff46ceed83944bhttp%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0022407304002158http://www.sciencedirect.com/science/article/pii/S0022407304002158The radiative transfer models developed at AER are being used extensively for a wide range of applications in the atmospheric sciences. This communication is intended to provide a coherent summary of the various radiative transfer models and associated databases publicly available from AER ( http://www.rtweb.aer.com ). Among the communities using the models are the remote sensing community (e.g. TES, IASI), the numerical weather prediction community (e.g. ECMWF, NCEP GFS, WRF, MM5), and the climate community (e.g. ECHAM5). Included in this communication is a description of the central features and recent updates for the following models: the line-by-line radiative transfer model (LBLRTM); the line file creation program (LNFL); the longwave and shortwave rapid radiative transfer models, RRTM_LW and RRTM_SW; the Monochromatic Radiative Transfer Model (MonoRTM); the MT_CKD Continuum; and the Kurucz Solar Source Function. LBLRTM and the associated line parameter database (e.g. HITRAN 2000 with 2001 updates) play a central role in the suite of models. The physics adopted for LBLRTM has been extensively analyzed in the context of closure experiments involving the evaluation of the model inputs (e.g. atmospheric state), spectral radiative measurements and the spectral model output. The rapid radiative transfer models are then developed and evaluated using the validated LBLRTM model.
    Frouin R., 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- 139.10.1175/1520-0469(1988)0552.0.CO;2dd63a91a02d6510e28f8f1548d9d5a2fhttp%3A%2F%2Fwww.researchgate.net%2Fpublication%2F260884709_The_First_ISLSCP_Field_Experimenthttp://www.researchgate.net/publication/260884709_The_First_ISLSCP_Field_ExperimentABSTRACT This paper analyzes the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment site-average datasets for near-surface meteorology, soil moisture, and temperature; the surface fluxes of radiation, sensible, and latent heat; and the ground heat flux, for the period May 1987-November 1989. The diurnal and seasonal variation of surface albedo for this grassland site are discussed. The coupling of precipitation, soil moisture, evaporation, pressure height to the lifting condensation level, and equivalent potential temperature (E) on seasonal and diurnal timescales is also discussed. The 1988 data confirm the authors' result, shown earlier from the 1987 data that over moist soils increased evapotranspiration lowers afternoon lifting condensation level and increases afternoon E, suggesting a mechanism for a local positive feedback between soil moisture and precipitation on horizontal scales greater than 200 km. The seasonal cycle of ground heat flux and soil temperature is examined and the authors show that the coupling in the warm months between E and soil temperature on seasonal scales is similar over land to the coupling found over warm oceans despite very different controls on the surface fluxes. The boundary layer equilibrium over the ocean is contrasted with the diurnal cycle over land, which is soil moisture dependent.
    Goody R., J. Anderson, and G. North, 1998: Testing climate models: An approach. Bull. Amer. Meteor. Soc., 79, 2541- 2549.10.1175/1520-0477(1998)0792.0.CO;2a33b067d8235d503dca3a4c54ca460a6http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F247715665_Testing_Climate_Models_An_Approachhttp://www.researchgate.net/publication/247715665_Testing_Climate_Models_An_ApproachExamimes ways to test models used to predict climate change. Characteristics of observing systems, spectrally resolved radiances and GPS occultations; Detection of a projected climate change through benchmarks; Studies of high-order statistics.
    Green R. N., P. O. R. Hinton, 1996: Estimation of angular distribution models from radiance pairs. J. Geophys. Res., 101( D12), 16 951- 16 959.10.1029/96JD003681f858186a4367cad88a4cd7ce0905981http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F96JD00368%2Fabstracthttp://onlinelibrary.wiley.com/doi/10.1029/96JD00368/abstractA new approach to constructing angular distribution models (ADMs) from satellite data has been developed. The ADMs model the radiance anisotropy and are used to convert satellite measured radiance to flux at the top of the atmosphere. The radiance pairs method (RPM) processes radiance pairs that view approximately the same area at the same time. By ratioing the paired radiances, the flux or field strength is eliminated, producing ratios of anisotropies which are taken as the data source for the ADMs. The ADMs are modeled as random functions, and the RPM estimates the mean of the ADM. The RPM is compared to the standard method of sorting by angular bins (SAB) and is shown to remove questionable assumptions, converge faster, and give better accuracy than the SAB method. Both methods were applied to the same Nimbus 7 ERB data and resulted in statistically different longwave ADMs.
    Huang X. L., 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, D12102.10.1029/2004JD0055304f1ac9767fde8fd786c034085a244722http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2004JD005530%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/2004JD005530/fullAbstract Top of page Abstract 1.Introduction 2.Data Processing, Model and Methodology 3.Spectral EOF Analysis of AIRS Data Over the Tropical and Subtropical Oceans 4.Midlatitude Oceans 5.Results From CAM2 Simulation 6.Conclusion and Discussion AppendixA Acknowledgments References Supporting Information [1] Here we present a survey of the spatial variability in different climate zones seen from AIRS data using the spectral EOF analysis. Over the tropical and subtropical oceans, the first principal component (PC1) is mostly due to the thermal contrast between surface and thick cold cloud tops. The second principal component (PC2) is mainly due to the spatial variation of the lower tropospheric humidity (LTH) and the low clouds. The signature of dust aerosol over the Arabian Sea and the Atlantic off the coast of North Africa in the summertime can be clearly seen in the PC2. Both the PC1 and the PC2 capture the upper tropospheric water vapor variability due to the forced orthogonality of EOFs. The third principal component (PC3) is mainly due to the spatial variation of the lower stratospheric temperature. Over the midlatitude oceans, the PC1 is still due to the thermal contrast of emission temperature. During wintertime, the PC2 is mainly due to stratospheric temperature variations. In the summer, the PC2 over the southern hemisphere is still due to stratospheric temperature variations, but in the northern hemisphere it is mainly due to the variations of the LTH and the low clouds. An exploratory study using synthetic spectra based on a NCAR CAM2 simulation shows that the model could account for the essential features in the data as well as provide an explanation of the three leading PCs. Major disagreements exist in the location of the ITCZ, the dust aerosol, and the lower stratospheric temperature.
    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, D14107.10.1029/2005JD0065769bc7879d8e633626eb09798782d04937http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2005JD006576%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/2005JD006576/fullAbstract Top of page Abstract 1.Introduction 2.Model and Data Manipulation 3.Differences in the Clear-Sky OLR and Different Absorption Bands 4.Flux Differences in Different Groups of IR Channels 5.Conclusion Acknowledgments References Supporting Information [1] The global and tropical means of clear-sky outgoing longwave radiation (hereinafter OLRc) simulated by the new GFDL atmospheric general circulation model, AM2, tend to be systematically lower than ERBE observations by about 4 W m 612 , even though the AM2 total-sky radiation budget is tuned to be consistent with these observations. Here we quantify the source of errors in AM2-simulated OLRc over the tropical oceans by comparing the synthetic outgoing IR spectra at the top of the atmosphere on the basis of AM2 simulations to observed IRIS spectra. After the sampling disparity between IRIS and AM2 is reduced, AM2 still shows considerable negative bias in the simulated monthly mean OLRc over the tropical oceans. Together with other evidence, this suggests that the influence of spatial sampling disparity, although present, does not account for the majority of the bias. Decomposition of OLRc shows that the negative bias comes mainly from the H 2 O bands and can be explained by a too humid layer around 6–9 km in the model. Meanwhile, a positive bias exists in channels sensitive to near-surface humidity and temperature, which implies that the boundary layer in the model might be too dry. These facts suggest that the negative bias in the simulated OLRc can be attributed to model deficiencies, especially the large-scale water vapor transport. We also find that AM2-simulated OLRc has 651 W m 612 positive bias originating from the stratosphere; this positive bias should exist in simulated total-sky OLR as well.
    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, D09110.10.1029/2007JD00921905b3823f381e05ea2af9139b09471ee2http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2007JD009219%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/2007JD009219/full[1] Spectrally resolved outgoing thermal-IR flux, the integrand of the outgoing longwave radiation (OLR), has a unique value in evaluating model simulations. Here we describe an algorithm for deriving such clear-sky outgoing spectral flux through the entire thermal-IR spectrum from the collocated Atmospheric Infrared Sounder (AIRS) and the Clouds and the Earth's Radiant Energy System (CERES) measurements over the tropical oceans. On the basis of the predefined scene types in the CERES Single Satellite Footprint (SSF) data set, spectrally dependent ADMs are developed and used to estimate the spectral flux each AIRS channel. A multivariate linear prediction scheme is then used to estimate spectral fluxes at frequencies not covered by the AIRS instrument. The whole algorithm is validated using synthetic spectra as well as the CERES OLR measurements. Using the GFDL AM2 model simulation as a case study, applications of the derived clear-sky outgoing spectral fluxes in model evaluation are illustrated. By comparing the observed spectral fluxes and simulated ones for the year of 2004, compensating errors in the simulated OLR from different absorption bands are revealed, along with the errors from frequencies within a given absorption band. Discrepancies between the simulated and observed spatial distributions and seasonal evolutions of the spectral fluxes are further discussed. The methodology described in this study can be applied to other surface types as well as cloudy-sky observations and also to corresponding model evaluations.
    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, D21101.10.1029/2010JD0139327afa287d479b19cb32e3cbb0409be634http%3A%2F%2Fwww.agu.org%2Fpubs%2Fcrossref%2F2010%2F2010JD013932.shtmlhttp://www.agu.org/pubs/crossref/2010/2010JD013932.shtml[1] We first present an algorithm for deriving cloudy sky outgoing spectral flux through the entire longwave spectrum from the collocated Atmospheric Infrared Sounder (AIRS) and Cloud and the Earth's Radiant Energy System (CERES) measurements over the tropical oceans. The algorithm is similar to the one described in part 1 of this series of studies: spectral angular dependent models are developed to estimate the spectral flux of each AIRS channel, and then a multivariate linear prediction scheme is used to estimate spectral fluxes at frequencies not covered by the AIRS instrument. The entire algorithm is validated against synthetic spectra as well as the CERES outgoing longwave radiation (OLR) measurements. Mean difference between the OLR estimated in this way and the collocated CERES OLR is 2.15 W m 2 with a standard deviation of 5.51 W m 2 . The algorithm behaves consistently well for different combinations of cloud fractions and cloud-surface temperature difference, indicating the robustness of the algorithm for various cloudy scenes. Then, using the Geophysical Fluid Dynamics Laboratory AM2 model as a case study, we illustrate the merit of band-by-band cloud radiative forcings (CRFs) derived from this algorithm in model evaluation. The AM2 tropical annual mean band-by-band CRFs generally agree with the observed counterparts, but some systematic biases in the window bands and over the marine-stratus regions can be clearly identified. An idealized model is used to interpret the results and to explain why the fractional contribution of each band to the broadband CRF is worthy for studying: it is sensitive to cloud height but largely insensitive to the cloud fraction.
    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, L24707.10.1029/2007GL0314096da691162aac29850f0d9555bab3124ehttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2007GL031409%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/2007GL031409/fullAbstract Top of page Abstract 1.Introduction 2.Data and Model 3.Model-Satellite Comparison 4.Conclusion Acknowledgments References Supporting Information [1] The spectrally resolved infrared radiances observed by AIRS provide a strict and insightful test for general circulation models (GCMs). We compare the clear- and total- sky spectra simulated from the Geophysical Fluid Dynamics Laboratory GCM using a high resolution radiation code with the AIRS observations. After ensuring consistency in the sampling of the observed and simulated spectra and a proper representation of clouds in the radiance simulation, the observed and simulated global-mean radiances are shown to agree to within 2 K in the window region. Radiance discrepancies in the water vapor v 2 (1300–1650 cm 611 ) and carbon dioxide v 2 (650–720 cm 611 ) bands are consistent with the model biases in atmospheric temperature and water vapor. The existence of radiance biases of opposite signs in different spectral regions suggests that a seemingly good agreement of the model's broadband longwave flux with observations may be due to a fortuitous cancellation of spectral errors. Moreover, an examination of the diurnal difference spectrum indicates pronounced biases in the model-simulated diurnal hydrologic cycle over the tropical oceans, a feature seen to occur in other GCMs as well.
    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., 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-of-atmosphere albedo estimation from angular distribution models: A comparison between two approaches. J. Geophys. Res., 104( D24), 31 255- 31 260.10.1029/1999JD900935a157d2d1ca936bd7a0aa79b665f14c2chttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F1999JD900935%2Fabstracthttp://onlinelibrary.wiley.com/doi/10.1029/1999JD900935/abstractEmpirical angular distribution models (ADMs) are commonly used to convert satellite-measured radiances to top-of-atmosphere (TOA) radiative fluxes. This study compares two methods of developing ADMs: (1) the radiance pairs method (RPM), which composits ratios of near-simultaneous radiance measurements over the same scene to construct the ADMs; (2) the sorting-into-angular-bins (SAB) method, which estimates ADM anisotropic factors from the ratio of the mean radiance in each angular bin to the mean flux determined by direct integration of the mean radiances. Theoretical simulations and analyses of measurements from the CERES (Clouds and Earth's Radiant Energy System) satellite instrument show that the RPM method provides a better estimate of the true mean ADM for a population of scenes, while the SAB method is better suited for top-of-atmosphere flux estimation. The CERES results also show that a variable field of view size with viewing zenith angle can cause an 10% (relative) change in estimated all-sky mean albedo with viewing zenith angle.
    Loeb N. G., F. Parol, J.-C. Buriez, and C. Vanbauce, 2000: Top-of-atmosphere albedo estimation from angular distribution models using scene identification from satellite cloud property retrievals. J.Climate, 13, 1269- 1285.10.1175/1520-0442(2000)013<1269:TOAAEF>2.0.CO;2559d2504ce0c72deb6ddac36da13f191http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F24162365_Top-of-Atmosphere_Albedo_Estimation_from_Angular_Distribution_Models_using_Scene_Identification_from_Satellite_Cloud_Property_Retrievalshttp://www.researchgate.net/publication/24162365_Top-of-Atmosphere_Albedo_Estimation_from_Angular_Distribution_Models_using_Scene_Identification_from_Satellite_Cloud_Property_RetrievalsThe next generation of earth radiation budget satellite instruments will routinely merge estimates of global top-of-atmosphere radiative fluxes with cloud properties. This information will offer many new opportunities for validating radiative transfer models and cloud parameterizations in climate models. In this study, five months of Polarization and Directionality of the Earth's Reflectances 670-nm radiance measurements are considered in order to examine how satellite cloud property retrievals can be used to define empirical angular distribution models (ADMs) for estimating top-of-atmosphere albedo. ADMs are defined for 19 scene types defined by satellite retrievals of cloud fraction and cloud optical depth. Two approaches are used to define the ADM scene types. The first assumes there are no biases in the retrieved cloud properties and defines ADMs for fixed discrete intervals of cloud fraction and cloud optical depth (fixed- t approach). The second approach involves the same cloud fraction intervals, but uses percentile intervals of cloud optical depth instead (percentile- t approach). Albedos generated using these methods are compared with albedos inferred directly from the mean observed reflectance field. Albedos based on ADMs that assume cloud properties are unbiased (fixed- t approach) show a strong systematic dependence on viewing geometry. This dependence becomes more pronounced with increasing solar zenith angle, reaching 12% (relative) between near-nadir and oblique viewing zenith angles for solar zenith angles between 608 and 708. The cause for this bias is shown to be due to biases in the cloud optical depth retrievals. In contrast, albedos based on ADMs built using percentile intervals of cloud optical depth (percentile- t approach) show very little viewing zenith angle dependence and are in good agreement with albedos obtained by direct integration of the mean observed reflectance field (,1% relative error). When the ADMs are applie.
    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-of-atmosphere 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: Global-scale temperature patterns and climate forcing over the past six centuries. Nature,392, 779-787, doi: 10.1038/33859.10.1038/33859fe7cda3f-99e4-41a3-b105-3198a17b9e099d76a9793a10e9c613da4259895fcb9fhttp%3A%2F%2Fwww.nature.com%2Fnature%2Fjournal%2Fv392%2Fn6678%2Fpdf%2F392779a0.pdfrefpaperuri:(1cdd061db9ed474c76d8492b054d5135)http://www.nature.com/nature/journal/v392/n6678/pdf/392779a0.pdfSpatially resolved global reconstructions of annual surface temperaturepatterns over the past six centuries are based on the multivariate calibrationof widely distributed high-resolution proxy climate indicators. Time-dependentcorrelations of the reconstructions with time-series records representingchanges in greenhouse-gas concentrations, solar irradiance, and volcanic aerosolssuggest that each of these factors has contributed to the climate variabilityof the past 400 years, with greenhouse gases emerging as the dominant forcingduring the twentieth century. Northern Hemisphere mean annual temperaturesfor three of the past eight years are warmer than any other year since (atleast) ad 1400.
    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- 63.10.1126/science.243.4887.57177804229aa1f2ecf2821401a087d632af5d67e0http%3A%2F%2Fmed.wanfangdata.com.cn%2FPaper%2FDetail%2FPeriodicalPaper_PM17780422http://med.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM17780422The study of climate and climate change is hindered by a lack of information on the effect of clouds on the radiation balance of the earth, referred to as the cloud-radiative forcing. Quantitative estimates of the global distributions of cloud-radiative forcing have been obtained from the spaceborne Earth Radiation Budget Experiment (ERBE) launched in 1984. For the April 1985 period, the global shortwave cloud forcing [-44.5 watts per square meter (W/m(2))] due to the enhancement of planetary albedo, exceeded in magnitude the longwave cloud forcing (31.3 W/m(2)) resulting from the greenhouse effect of clouds. Thus, clouds had a net cooling effect on the earth. This cooling effect is large over the mid-and high-latitude oceans, with values reaching -100 W/m(2). The monthly averaged longwave cloud forcing reached maximum values of 50 to 100 W/m(2) over the convectively disturbed regions of the tropics. However, this heating effect is nearly canceled by a correspondingly large negative shortwave cloud forcing, which indicates the delicately balanced state of the tropics. The size of the observed net cloud forcing is about four times as large as the expected value of radiative forcing from a doubling of CO(2). The shortwave and longwave components of cloud forcing are about ten times as large as those for a CO(2) doubling. Hence, small changes in the cloud-radiative forcing fields can play a significant role as a climate feedback mechanism. For example, during past glaciations a migration toward the equator of the field of strong, negative cloud-radiative forcing, in response to a similar migration of cooler waters, could have significantly amplified oceanic cooling and continental glaciation.
    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- 1625.10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;21ecd9ceedf9ede6f75f40eecf88c95f5http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F238183451_AN_IMPROVED_IN_SITU_AND_SATELLITE_SST_ANALYSIShttp://www.researchgate.net/publication/238183451_AN_IMPROVED_IN_SITU_AND_SATELLITE_SST_ANALYSISABSTRACT A weekly 18 spatial resolution optimum interpolation (OI) sea surface temperature (SST) analysis has been produced at the National Oceanic and Atmospheric Administration (NOAA) using both in situ and satellite data from November 1981 to the present. The weekly product has been available since 1993 and is widely used for weather and climate monitoring and forecasting. Errors in the satellite bias correction and the sea ice to SST conversion algorithm are discussed, and then an improved version of the OI analysis is developed. The changes result in a modest reduction in the satellite bias that leaves small global residual biases of roughly 20.038C. The major improvement in the analysis occurs at high latitudes due to the new sea ice algorithm where local differences between the old and new analysis can exceed 18C. Comparisons with other SST products are needed to determine the consistency of the OI. These comparisons show that the differences among products occur on large time- and space scales with monthly rms differences exceeding 0.58C in some regions. These regions are primarily the mid- and high-latitude Southern Oceans and the Arctic where data are sparse, as well as high- gradient areas such as the Gulf Stream and Kuroshio where the gradients cannot be properly resolved on a 18 grid. In addition, globally averaged differences of roughly 0.05 8C occur among the products on decadal scales. These differences primarily arise from the same regions where the rms differences are large. However, smaller unexplained differences also occur in other regions of the midlatitude Northern Hemisphere where in situ data should be adequate.
    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( 3), 407- 421.10.1029/RG024i002p00407b174a315553210e4bb2786f1514e0a34http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2FRG024i002p00407%2Fcitedbyhttp://onlinelibrary.wiley.com/doi/10.1029/RG024i002p00407/citedbyThe Earth Radiation Budget Experiment carries a three-channel scanning radiometer and a set of nadir-looking wide and medium field-of-view instruments for measuring the radiation emitted from earth and the solar radiation reflected from earth. This paper describes the algorithms which are used to compute the radiant exitances at a reference level (&ldquo;top of the atmosphere&rdquo;) from these measurements. Methods used to analyze data from previous radiation budget experiments are reviewed, and the rationale for the present algorithms is developed. The scanner data are converted to radiances by use of spectral factors, which account for imperfect spectral response of the optics. These radiances are converted to radiant exitances at the reference level by use of directional models, which account for anisotropy of the radiation as it leaves the earth. The spectral factors and directional models are selected on the basis of the scene, which is identified on the basis of the location and the long-wave and shortwave radiances. These individual results are averaged over 2.5 &times; 2.5 regions. Data from the wide and medium field-of-view instruments are analyzed by use of the traditional shape factor method and also by use of a numerical filter, which permits resolution enhancement along the orbit track.
    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( 3), 274- 286.10.1109/TGRS.2002.808245fc5af9617dc4cf57a1798df14b4429eehttp%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1196045http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1196045The Atmospheric Infrared Sounder (AIRS) is a high-resolution infrared sounder launched aboard the National Aeronautics and Space Administration's Aqua satellite on May 4, 2002. AIRS is a grating spectrometer with 2378 channels located between 15 and 3.8 μm, with nominal resolving powers of ν/Δν=1200. As the first of a new generation of upcoming infrared instruments with similar spectral coverage and resolution, there will be much interest in the performance of AIRS. The ability to retrieve good atmospheric profiles from AIRS observations will depend in part upon our knowledge of the spectral response of AIRS to the upwelling radiance. This paper discusses the spectral calibration of AIRS based upon an extensive set of laboratory test data generated by the instruments prime contractor, BAE. In particular, we describe the calibration of the AIRS spectral response functions, showing that our requirement for accuracies of "1% of a width" have been achieved.
    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: 10.1029/2005JD006146.10.1029/2005JD006146f871a9ea4982d9f42461ff1606ea4247http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2005JD006146%2Fcitedbyhttp://onlinelibrary.wiley.com/doi/10.1029/2005JD006146/citedbyABSTRACT Comparisons between observed AIRS radiances and radiances computed from coincident in situ profile data are used to validate the accuracy of the AIRS radiative transfer algorithm (RTA) used in version 4 processing at Goddard Space Flight Center. In situ data sources include balloon-borne measurements with RS-90 sensors and frost point hygrometers and Raman lidar measurements of atmospheric water vapor. Estimates of the RTA accuracy vary with wave number region but approach 0.2 K in mid- to lower-tropospheric temperature and water vapor sounding channels. Temperature channel radiance biases using ECMWF forecast/analysis products are shown to be essentially identical to those observed with coincident sonde observations, with somewhat higher biases in water vapor channels. Some empirical adjustments to the RTA channel-averaged absorption coefficients were required to achieve these stated accuracies.
    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( 3), 390- 409.10.1109/TGRS.2002.808236f5373bf021af1e5300826f63ab71f1b9http%3A%2F%2Fonlinelibrary.wiley.com%2Fresolve%2Freference%2FXREF%3Fid%3D10.1109%2FTGRS.2002.808236http://onlinelibrary.wiley.com/resolve/reference/XREF?id=10.1109/TGRS.2002.808236New state-of-the-art methodology is described to analyze the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit/Humidity Sounder for Brazil (AIRS/AMSU/HSB) data in the presence of multiple cloud formations. The methodology forms the basis for the AIRS Science Team algorithm, which will be used to analyze AIRS/AMSU/HSB data on the Earth Observing System Aqua platform. The cloud-clearing methodology requires no knowledge of the spectral properties of the clouds. The basic retrieval methodology is general and extracts the maximum information from the radiances, consistent with the channel noise covariance matrix. The retrieval methodology minimizes the dependence of the solution on the first-guess field and the first-guess error characteristics. Results are shown for AIRS Science Team simulation studies with multiple cloud formations. These simulation studies imply that clear column radiances can be reconstructed under partial cloud cover with an accuracy comparable to single spot channel noise in the temperature and water vapor sounding regions; temperature soundings can be produced under partial cloud cover with RMS errors on the order of, or better than, 1 K in 1-km-thick layers from the surface to 700 mb, 1-km layers from 700-300 mb, 3-km layers from 300-30 mb, and 5-km layers from 30-1 mb; and moisture profiles can be obtained with an accuracy better than 20% absolute errors in 1-km layers from the surface to nearly 200 mb.
    Suttles J. T., B. A. Wielicki, and S. Vemury, 1992: Top-of-atmosphere radiative fluxes: Validation of ERBE scanner inversion algorithm using Nimbus-7 ERB data. J. Appl. Meteor., 31, 784- 796.10.1175/1520-0450(1992)0312.0.CO;27e8c6741-0cea-498a-ac6d-3c50172437c051e5ba3d785d39299a7977f073835dabhttp%3A%2F%2Fwww.researchgate.net%2Fpublication%2F23612097_Top-of-atmosphere_radiative_fluxes_-_Validation_of_ERBE_scanner_inversion_algorithm_using_Nimbus-7_ERB_data%3Fev%3Dauth_pubrefpaperuri:(4cbed86c9b5e6cfd0b95a00e93bf1cef)http://www.researchgate.net/publication/23612097_Top-of-atmosphere_radiative_fluxes_-_Validation_of_ERBE_scanner_inversion_algorithm_using_Nimbus-7_ERB_data?ev=auth_pubABSTRACT The ERBE algorithm is applied to the Nimbus-7 earth radiation budget (ERB) scanner data for June 1979 to analyze the performance of an inversion method in deriving top-of-atmosphere albedos and longwave radiative fluxes. The performance is assessed by comparing ERBE algorithm results with appropriate results derived using the sorting-by-angular-bins (SAB) method, the ERB MATRIX algorithm, and the 'new-cloud ERB' (NCLE) algorithm. Comparisons are made for top-of-atmosphere albedos, longwave fluxes, viewing zenith-angle dependence of derived albedos and longwave fluxes, and cloud fractional coverage. Using the SAB method as a reference, the rms accuracy of monthly average ERBE-derived results are estimated to be 0.0165 (5.6 W/sq m) for albedos (shortwave fluxes) and 3.0 W/sq m for longwave fluxes. The ERBE-derived results were found to depend systematically on the viewing zenith angle, varying from near nadir to near the limb by about 10 percent for albedos and by 6-7 percent for longwave fluxes. Analyses indicated that the ERBE angular models are the most likely source of the systematic angular dependences. Comparison of the ERBE-derived cloud fractions, based on a maximum-likelihood estimation method, with results from the NCLE showed agreement within about 10 percent.
    Wielicki B. A., R. N. Green, 1989: Cloud identification for ERBE radiation flux retrieval. J. Appl. Meteor., 28, 1133- 1146.
    Wielicki, B. A., Coauthors, 2002: Evidence for large decadal variability in the tropical mean radiative energy budget. Science, 295, 841- 844.10.1126/science.10658371182363841858d810489c490f13c92431448e379http%3A%2F%2Fonlinelibrary.wiley.com%2Fresolve%2Freference%2FPMED%3Fid%3D11823638http://med.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM11823638It is widely assumed that variations in Earth's radiative energy budget at large time and space scales are small. We present new evidence from a compilation of over two decades of accurate satellite data that the top-of-atmosphere (TOA) tropical radiative energy budget is much more dynamic and variable than previously thought. Results indicate that the radiation budget changes are caused by changes in tropical mean cloudiness. The results of several current climate model simulations fail to predict this large observed variation in tropical energy budget. The missing variability in the models highlights the critical need to improve cloud modeling in the tropics so that prediction of tropical climate on interannual and decadal time scales can be improved.
    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/ BF02918708.10.1007/BF02918708c38e498c46eb097bd1e632fa0d5df78chttp%3A%2F%2Fwww.cqvip.com%2Fqk%2F71135x%2F201107%2F20503224.htmlhttp://d.wanfangdata.com.cn/Periodical_dqkxjz-e200505004.aspxThe physical retrieval algorithm of atmospheric temperature and moisture distribution from the Atmospheric InfraRed Sounder (AIRS) radiances is presented. The retrieval algorithm is applied to AIRS clear-sky radiance measurements. The algorithm employs a statistical retrieval followed by a subsequent nonlinear physical retrieval. The regression coefficients for the statistical retrieval are derived from a dataset of global radiosonde observations (RAOBs) comprising atmospheric temperature, moisture, and ozone profiles. Evaluation of the retrieved profiles is performed by a comparison with RAOBs from the Atmospheric Radiation Measurement (ARM) Program Cloud And Radiation Testbed (CART) in Oklahoma,U. S. A.. Comparisons show that the physically-based AIRS retrievals agree with the RAOBs from the ARM CART site with a Root Mean Square Error (RMSE) of 1 K on average for temperature profiles above 850 hPa, and approximately 10% on average for relative humidity profiles. With its improved spectral resolution, AIRS depicts more detailed structure than the current Geostationary Operational Environmental Satellite (GOES) sounder when comparing AIRS sounding retrievals with the operational GOES sounding products.
    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(4), 319-335, doi: 10.1007/s00376-014-3162-z.10.1007/s00376-014-3162-z670f5c63-727f-4429-abde-ef34167dcc62dd634f2e17370380b7ac1195f55380d6http%3A%2F%2Flink.springer.com%2F10.1007%2Fs00376-014-3162-zrefpaperuri:(8ade020f6c3bc68975955280459d0560)http://d.wanfangdata.com.cn/Periodical_dqkxjz-e201503004.aspx
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Manuscript received: 18 March 2015
Manuscript revised: 05 August 2015
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A Solely Radiance-based Spectral Angular Distribution Model and Its Application in Deriving Clear-Sky Spectral Fluxes over Tropical Oceans

  • 1. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

Abstract: 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.

1. Introduction
  • The global outgoing longwave radiation or flux (OLR) at the top of the atmosphere (TOA) is uniquely important because of its reflection of the net radiation budget of the climate system. For a long time, OLR has been a critical quantity in climate research, both observationally and in simulations (Ramanathan et al., 1989; Wielicki et al., 2002; Allan et al., 2004). The spectrally resolved radiance (or flux) has significant advantages in evaluating climate models due to its rich information about various atmospheric and surface physical parameters (Goody et al., 1998). One of the advantages of using spectrally resolved flux to evaluate models is the ability to avoid the compensating errors from different bands in a GCM (Huang et al., 2006; Huang et al., 2007). However, the application of spectrally resolved radiance and flux is not as prevalent as that of broadband OLR, mainly because of the difficulties involved in taking measurements.

    Satellite instruments can only obtain radiances measured at a certain viewing zenith angle, while fluxes require information from all angles. One working method to convert measured radiance to radiative fluxes is using an angular distribution model (ADM) (Smith et al., 1986; Suttles et al., 1992; Green and Hinton, 1996; Loeb et al., 1999). The ADM is usually constructed for different scene types and even for different sub-scene types (hereafter, discrete intervals), which are defined by surface and atmospheric physical quantities (Wielicki and Green, 1989; Loeb et al., 2000, 2003, 2005). These physical quantities largely determine the anisotropic distribution at the TOA and always have to be estimated by utilizing multiple measurements. For example, ADMs applied by the Clouds and the Earth's Radiant Energy System (CERES) for broadband OLR have to combine the cloud mask product and the cloud and aerosol product of MODIS (Moderate Resolution Imaging Spectroradiometer) (Loeb et al., 2003, 2005). Modern satellite sensors such as the atmospheric infrared sounder (AIRS) (Aumann et al., 2003a; Chahine et al., 2006), the cross-track infrared Sounder (Bingham et al., 2010), the tropospheric emission spectrometer (Beer et al., 2001), and the infrared atmospheric sounding interferometer (Clerbaux et al., 2009), can provide high spectral resolution infrared spectra with rich information about the atmosphere and surface. Numerous studies on the reliability of products of atmospheric and surface parameters retrieved by AIRS radiances have demonstrated an abundance of information content in thermal-IR spectra (e.g., Susskind et al., 2003; Wu et al., 2005; Le Marshall et al., 2006; Zheng et al., 2015). Hence, we are motivated to explore whether it is possible to develop a solely radiance-based algorithm to derive the spectrally resolved flux according to hyperspectral measurements, such as those from AIRS.

    Huang et al. (2008, 2010) developed a spectral ADM and successfully derived the spectral flux from collocated AIRS and CERES measurements. Due to the requirement of near-simultaneous observations as well as the collocation strategy to overcome the differences in resolution or observational area, the combinations between AIRS and CERES increase the difficulties in deriving fluxes and largely reduce the available samples. Here, still taking AIRS measurements as an example, we investigate the possibility of developing a scene-type classification algorithm that is only based on spectral radiance and, consequently, construct solely radiance-based spectral ADMs. The algorithm would not need to combine any other observations and can largely improve the amount of available samples and make better use of hyperspectral information.

    As the first step, in this study, we focus on developing the solely radiance-based algorithm for clear skies over tropical oceans, to show the feasibility of, and the procedure for, obtaining the spectrally resolved flux by AIRS only. Section 2 describes the datasets and forward model used in this study. The key new feature of this algorithm is to develop a radiance-based method for estimating the correct scene types and constructing spectral ADMs. The details are described in section 3. Then, validations of the entire algorithm are shown in section 4. Section 5 concludes with a summary and further discussion.

2. Data sets and model
  • AIRS, onboard NASA's (National Aeronautics and Space Administration) Earth Observing System Aqua satellite, is an infrared grating array spectrometer with 2378 channels (Aumann et al., 2003a). It measures radiances across three bands (3.74-4.61, 6.20-8.22 and 8.8-15.4 μm) with a resolving power (Λ/∆Λ) of 1200(λ is the wavelength). AIRS scans from -49° to 49° with a horizontal resolution of 13.5 km at the nadir on the surface. AIRS records about 2.9 million spectra per day with good calibration performance and global coverage (Chahine et al., 2006). In this study, the AIRS calibrated radiances (level 1B) in the channels recommended by the AIRS team for level-2 retrieval purposes are applied. It is well-known that AIRS radiances from the 2169-3673 cm-1 band contribute little to the longwave flux. So, the spectral fluxes are derived only for 10-2000 cm-1. As in (Huang and Yung, 2005), we also screen the data with a strict quality control procedure to exclude possible bad spectra.

    To validate the predicted broadband OLR, collocated CERES measurements are needed. Two identical CERES instruments (FM3 and FM4) were also aboard Aqua. The instrument field of view of CERES is an approximate 20 km nadir-view footprint on the surface. We only apply the cross-track CERES observations, since AIRS always operates in such a mode. The CERES datasets used here are the Aqua-CERES level 2 footprint data product, and the Single Satellite Footprint TOA/Surface Fluxes and Clouds Edition 2A (Loeb et al., 2005). For Aqua-CERES-derived regional mean OLR, the estimated bias is 0.2-0.4 W m-2 and the estimated RMSE is less than 0.7 W m-2 (Loeb et al., 2007).

  • We use MODTRAN TM-5 version 2 revision 11 (hereafter, MODTRAN5) as the forward radiative transfer model to construct the whole algorithm. MODTRAN5 is collaboratively developed by Air Force Research Laboratory and Spectral Sciences Inc. (Berk et al., 2005). Comparisons between MODTRAN5 and a line-by-line radiative transfer model (Clough and Iacono, 1995; Clough et al., 2005) show good agreement in the thermal IR transmittances and radiances (Anderson et al., 2006). In this study, a synthetic AIRS spectrum is derived by convolving the MODTRAN5 output at a 0.1 cm-1 resolution with the spectral response functions of individual AIRS channels (Strow et al., 2003, 2006). The spectral fluxes at frequencies not covered by AIRS instruments are also estimated by MODTRAN5 simulations.

3. Algorithm
  • The anisotropic factor R is the key parameter in an ADM to obtain flux from radiance measured at any zenith angle. It is defined as \begin{equation} \label{eq1} R_\nu(\theta)=\dfrac{\pi I_\nu(\theta)}{F_\nu} , (1)\end{equation} where IΝ(θ) and FΝ are the upwelling radiance and spectra flux at the TOA, respectively. Different from the broadband anisotropic factor used in conventional ADMs, the R here is not only a function of zenith angle θ but also a function of frequency, Ν.

    Similar to those used in the CERES LW ADM (Loeb et al., 2005), clear-sky scenes are further categorized to different discrete intervals according to the precipitable water (PW), lapse rate (∆ T) and surface skin temperature (T s). For clear sky over the tropical oceans, 14 discrete intervals are enough for all possible clear-sky scenes observed over the ocean (Huang et al., 2008). Therefore, only 14 discrete intervals are included to construct the spectral ADMs in this study. Table 1 lists the details of the 14 discrete intervals.

    Figure 1.  Flowchart illustration of the solely radiance-based algorithm for deriving spectral fluxes from 10 to 2000 cm$^-1$ by AIRS observations only.

    As mentioned in the introduction, the centerpiece of this algorithm is to develop a radiance-based method to estimate the correct ranges of PW, ∆ T and T s. Then, the appropriate discrete interval can be identified. To do so, we feed profiles from the ERA-Interim [European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis] six-hourly output to MODTRAN5 to generate sufficient synthetic AIRS spectra for exploring feasible ways to classify the correct ranges of PW, ∆ T and T s from synthetic radiances alone. Combined with the corresponding spectrally dependent ADMs and the algorithm developed by (Huang et al., 2008) for frequencies not covered by the AIRS instrument, spectral flux over the entire longwave spectrum can then be estimated. A flowchart summarizing the whole procedure of the algorithm in this section is shown in Fig. 1.

  • The PW is also called the total column water vapor. It is defined as the vertical integration of atmospheric water vapor mass. The relationship between the radiance due to the water vapor line absorption and the PW is not simply linear. The continuum absorption of water vapor is the main reason while the atmosphere and surface emissions make the problem more complicated. However, the water vapor continuum in the window region varies slowly with frequency compared to the fast variation of absorption lines. Meanwhile, water vapor in the lower troposphere is the dominant part in water vapor absorption. Since only an estimation of PW is needed in this case, instead of exact retrieval, a double-differential technique is used here to categorize the PW. The double-differential technique is based on the different absorption dependencies on frequency to discriminate different absorbers and has been widely used in UV and visible remote sensing (Komhyr et al., 1989; Frouin and Middleton, 1990). Here, this technique is applied to remove the continuum contribution so as to improve the accuracy of the estimation of PW from radiance.

    Two pairs of AIRS channels with similar frequency intervals in each pair are chosen as channel 1 to 4, as listed in Table 2. The wavenumbers in the selected channels are 779.5 and 780.8 cm-1 in one pair, and 827.7 and 829.3 cm-1 in the other pair, respectively. Each pair includes a relatively strong absorption line and a relatively weak one. The double differential radiance, Rad, can then be derived as \begin{equation} \label{eq2} \Delta { Rad}=({ Rad}_4-{ Rad}_3)-({ Rad}_2-{ Rad}_1) , (2)\end{equation} where Radn is the radiance of the nth channel. In each pair, the spectral interval is 1.3 and 1.6 cm-1, respectively. Given the linear shape of the water vapor continuum in the spectral range, which is very narrow and close, the contribution of slow-varying continuum absorption to Rad is largely removed. The radiance differences caused by water vapor absorption should be closely related to absorption differences between the relatively strong and weak water vapor absorption lines. Then, the PW can be classified exactly by radiance, without any a priori information. Furthermore, a transparent channel at 963.8 cm-1, listed as channel 5 in Table 2, is also selected to show the impact of T s. This method is very similar to the classical application for the retrieval of ozone by removing the scattering effect.

    To set up a look-up table, more than 560 000 ECMWF six-hourly profiles over the ocean between 50°S and 50°N in 2005 January are fed into MODTRAN5 to generate synthetic AIRS spectra. Then, the look-up table can be constructed according to the radiances of the four water vapor absorption channels 1-4, the brightness temperature of channel 5, and the corresponding PW value. Figure 2 shows the look-up table at nadir-view. The smaller PW values of less than 2 cm in "cooler" colors is the most complicated part, mainly due to the weaker sensitivity of radiance to a small amount of water vapor and the existence of an inversion boundary layer. In order to validate the look-up table, about 45 000 ECMWF profiles over the ocean between 30°S and 30°N in January, April, July and October 2002 are randomly selected to generate synthetic AIRS spectra by MODTRAN5. Then, the brightness temperature and radiances in selected channels are applied to the look-up table to estimate the PW and consequently its sub-intervals. Compared with true PW sub-intervals determined by the input ECMWF profiles, the accuracy of this method is about 82% at nadir-view, as shown in Table 3.

    Figure 1.  The look-up table derived from ECMWF data in January 2005 over the ocean between 50°S and 50°N, simulated by MODTRAN5 at nadir-view. The color scale shows the total PW.

  • The ∆ T used here is defined as the vertical temperature change of the first 300 hPa above the surface. Listed as channel 6 in Table 2, a CO2 absorption channel at 748.6 cm-1 with the peak of weighting function around 753.6 hPa is selected to represent the temperature of 300 hPa above the surface. Together with channel 5, which represents the surface temperature, the ∆ T is related with the difference between the brightness temperatures of two selected channels (hereafter, ∆ T B).

    In the 14 discrete intervals used in this study, there are only two ∆ T sub-intervals that are <15 and 15-30 K. Given the near-linear relationship between ∆ T and ∆ T B, a simple threshold method is sufficient for classification. Similarly, about 91 000 ECMWF six-hourly profiles over the ocean between 30°S and 30°N in January 2006 are fed into MODTRAN5 to generate synthetic AIRS spectra. So, the brightness temperatures of channel 6 at 748.6 cm-1 and channel 5 at 963.8 cm-1 (as listed in Table 2) can be obtained and the ∆ TB values are classified to different sub-intervals according to the corresponding ∆ T value from the input ECMWF profiles. Then, for each pair of adjacent sub-intervals, the proper threshold of ∆ TB that can categorize the corresponding ∆ T into the correct sub-interval is derived as ∆ T B-thres. The same validation dataset as PW is applied to show the accuracy of this method. Each ∆ T B between channel 6 at 748.6 cm-1 and channel 5 at 963.8 cm-1 in the validation dataset are compared with the ∆ T B-thres. If ∆ T B is less than ∆ T B-thres, the spectra are classified to sub-intervals with ∆ T<15 K. Otherwise, the spectra should be classified to sub-intervals with 15 K <∆ T<30 K. The accuracy of this simple threshold method for the lapse rate is about 91% at nadir-view, as shown in Table 3.

    For T s, the brightness temperature of the window region listed as channel 5 in Table 2 (hereafter T B5) is directly used to represent T s. As listed in Table 1, there are three T s sub-intervals: 270-290, 290-310 and 310-330 K. Similar to the lapse rate, the simple threshold method is also applied for the estimation of T s sub-intervals due to the linear relationship between T s and T B5. The only difference for T s is that there are two thresholds for three sub-intervals. Using the same training and validation dataset as the lapse rate, the two thresholds of T B5 are selected to categorize proper sub-intervals of T s and the accuracy of the estimation of T s is over 99% at nadir-view, as shown in Table 3.

  • The methods described above are all based on the dataset at nadir-view and have to be extended to multiple viewing zenith angles. Technically, similar training processes are needed at every observational angle because AIRS scans from -49° to 49° with an interval of 1.1° (Aumann et al., 2003a). However, according to the definition of transmittance, the radiance at the TOA has correlations with 1/cos θ. To simplify the problem, the linear fitting along 1/cos θ is sufficient for our case. Similar with the training process at nadir-view, ECMWF six-hourly profiles over the ocean between 30°S and 30°N in January 2006 are fed into MODTRAN5 to generate synthetic AIRS spectra at 15°, 30° and 45°. Together with the dataset at 0°, four groups of the radiances and brightness temperatures in the selected channels in Table 2 are derived.

    For T s, there is no obvious zenith angle dependence due to the transparent feature of the window channel. So, the same thresholds of T B5 as in section 3.2 are chosen at different θ. For the lapse rate, if we assume that the thresholds are changing monotonically along 1/cos θ, the thresholds at all angles can be derived by linear fitting. We obtain the thresholds by training the dataset of ∆ T and corresponding ∆ T B at 15°, 30° and 45°, respectively. Together with the thresholds at 0°, the coefficients for each threshold at different angles are ready by linear fitting of four points along 1/cos θ. Then, the thresholds at all zenith angles from 0° to 45° are derived.

    For the look-up table of PW, the relationship is not exactly the same in each PW interval. In fact, only Rad is changing with θ, while the brightness temperature of the window channel has no obvious zenith angle dependence due to its transparent feature. So, if the Rad at different zenith angle can be converted to an equivalent range at 0°, the look-up table at 0° can then be easily applied to estimate the PW and to determine the corresponding sub-intervals. According to the training dataset of PW and Rad at 0°, 15°, 30° and 45°, PW data are first grouped as <1 cm, 1-5 cm per 0.2 cm, and >5 cm. Then, the corresponding Rad values are classified into different groups. For each pair of adjacent groups, a threshold of Rad that can categorize the corresponding PW into a proper group is derived at four zenith angles, respectively. Hence, there are 22 thresholds for each zenith angle. This is similar to the simple threshold method described in section 3.2. Again, linear fitting between thresholds and 1/cos θ of four points' data (four zenith angles) for each pair of adjacent groups are carried out to obtain the coefficients. Then, the dataset of thresholds for all zenith angles is derived. In other words, the linear fitting process for the lapse rate, described above, is repeated 22 times to set up a whole threshold database for different PW values at different zenith angles. According to this threshold database, a given Rad at θ can be properly mapped to the equivalent group at nadir-view. Then, the PW sub-intervals can be derived according to the look-up table shown in Fig. 2.

    Similarly, ECMWF profiles at different θ over tropical oceans in January, April, July and October 2002 are randomly selected to validate the accuracy of this classification method at multiple angles. The results are listed in Table 3. Compared to the results at nadir-view, the accuracy of the estimation varies little along zenith angle.

  • AIRS has no coverage at frequencies lower than 649.6 cm-1 or between 1613.9 and 2000 cm-1. There are also some gaps between 649.6 and 1613.9 cm-1. Since we want to derive spectral fluxes based only on AIRS radiances observations over the whole IR region, the spectral flux in each AIRS channel and AIRS gaps should both be handled.

    For each of the AIRS channels, more than 80 000 randomly selected ECMWF profiles over the ocean between 30°S and 30°N in January, April, July and October 2002 are selected and the anisotropic factors for zenith angles from 0° to 45° of each AIRS channels is obtained by feeding these profiles into MODTRAN5. The anisotropic factors and associated profiles are categorized into discrete intervals of PW, ∆ T and T s, as listed in Table 1. In the same discrete interval, the mean anisotropic factor is defined by the mean value from all samples. Then, the spectral fluxes in AIRS channels can be derived according to the spectrally dependent ADMs.

    To estimate spectral fluxes in the frequency gaps of AIRS instruments, the same scheme as in Huang et al. (2008, 2010) is authorized to apply here; see Huang et al. (2008, section 3.2) for more detail. Based on principal component analysis, the unknown information in the channels not covered by AIRS can be estimated by the nearest channels with similar spectral resolutions. By training ECMWF profiles, spectral fluxes over "filled-in channels" are estimated with a multi-regression scheme, which essentially finds the least-squares fit of the projections of spectral fluxes in AIRS channels onto the predefined principal components. This kind of solution has been used in other estimations of missing information (e.g., Mann et al., 1998).

4. Validation
  • As listed in Table 1, the PW sub-intervals described in section 3.1 are slightly different to those used in CERES ADMs. First, in section 4.1, the effect of the adjustment is evaluated. Validation of the whole algorithm includes theoretical validation and observational comparison. In section 4.2, synthetic AIRS spectra are combined with the radiance-based classification of discrete intervals to derive the spectral fluxes. Comparing between such spectral fluxes and those directly computed from MODTRAN5 can help evaluate the whole algorithm theoretically because the differences are only from this algorithm, while MODTRAN5 is used as a surrogate of radiative transfer in the real world. Comparison between the broadband OLR derived from the AIRS observations by this algorithm and those of collocated CERES measurements is described in section 4.3. This comparison includes more realistic uncertainties, such as those in spectroscopy, forward modeling and collocation strategies, to show the reliability of the whole algorithm for real observations.

  • To improve the accuracy of the PW look-up table method, the PW sub-intervals are adjusted to <1, 1-2, 2-5 and >5, while those in the CERES ADMs are <1, 1-3, 3-5 and >5. Statistical analysis of the ECMWF profiles in January 2006 over the ocean between 30°S and 30°N shows that the distribution patterns of sample number in all 14 discrete intervals are similar before and after adjustment, although the samples in discrete intervals 11 and 12 increase while those in discrete intervals 1-10 decrease.

    To evaluate the effect on the predicted fluxes caused by this adjustment, we randomly choose the ECMWF profiles in January 2005 and January 2006 in conjunction with MODTRAN5 to derive synthetic AIRS spectra. Then, predicted OLR and spectra from 10 to 2000 cm-1 can be generated by the spectral ADMs to compare with the directly computed OLR and spectra fluxes. In this validation, the discrete intervals are classified according to the true values of PW, T s and ∆ T from ECMWF profiles instead of the radiance-based methods described in sections 3.1 and 3.2. So, the error caused by the estimated method is excluded and the differences between the predicted and directly computed results in each discrete interval are only due to the adjustment of PW sub-intervals. The validation results show that the mean relative differences are within 0.5% and the standard deviations are no more than 1%. The statistical results show that the bias caused by PW subinterval adjustment is acceptable in most discrete intervals.

  • ECMWF profiles over the ocean between 30°S and 30°N in April and October 2006 are randomly selected and fed into MODTRAN5. The appropriate discrete intervals are classified by the radiance-based method instead of the true value. Then, synthetic AIRS spectra and longwave spectral fluxes are derived, as described in section 3.4. Taking nadir-view as an example, the differences between the spectral fluxes and the broadband OLR predicted from the synthetic AIRS spectra and the one directly computed from MODTRAN5 are examined. Figure 3 shows the differences of broadband OLR at 14 discrete intervals at nadir-view. Discrete intervals 3, 7 and 9 are not included due to the fact that there are not enough samples to give statistical results. For other discrete intervals, the mean OLR differences are between 0 and -2.2 W m-2 (a fraction of about 0.7%), with standard deviations of no more than 1.3 W m-2. The maximum differences from individual discrete intervals are within 3 W m-2. The OLR differences for other viewing zenith angles are similar to that shown in Fig. 3.

    The differences between the predicted and directly computed spectral fluxes are also examined and the results at nadir-view are shown in Fig. 4 as an example. For each discrete interval, the mean differences of spectral fluxes averaged for every 10 cm-1 from 10 to 2000 cm-1 are calculated at nadir-view. For the discrete intervals with sufficient samples, about 95% of all samples have a mean difference within 0.03 W( m2× 10\; cm-1)-1 and more than 98% of them have a mean difference within 0.05 W( m2× 10\; cm-1)-1. Proportionally, more than 99% of all samples have a mean relative difference less than 5%, while about 96% of all samples have a mean relative difference less than 3%. Although the distribution of samples is not well-proportioned in all discrete intervals due to the adjustments for PW sub-intervals, the comparisons still show good agreement for most situations. This indicates that, at least for theoretical comparisons, the algorithm is capable of obtaining spectral fluxes at 10 cm-1 intervals with sufficient confidence.

  • To evaluate the performance of the algorithm for real observations, broadband OLRs derived by this algorithm from AIRS spectra (OLR AIRS) are compared with collocated CERES OLR measurements (OLR CERES). Clear-sky observations in 2004 over tropical oceans (30°S-30°N) are used and the collocated strategy is very similar with that used in (Huang et al., 2008). An AIRS observation and a CERES measurement are considered as collocated only when (2) the time interval between two observations is within 6 s and (3) the distance between the center of an AIRS footprint and that of a CERES footprint on the surface is less than 3 km. Under these collocated criteria, about 1.061 million collocated clear-sky observations over tropical oceans in 2004 are selected. The clear-sky or cloudy scenes are determined from relative CERES products. Figure 5 shows a histogram of the differences between OLR AIRS and OLR CERES for all samples in 2004. The histogram approximates the Gaussian distribution and the mean differences are 0.19 W m-2 with a standard deviation of 1.23 W m-2.

    Figure 3.  The mean differences between the broadband OLR predicted from the synthetic AIRS spectra at nadir-view and directly computed OLR from MODTRAN5 for 14 discrete intervals classified by the solely radiance-based method. The input data are randomly chosen from ECMWF profiles over tropical oceans in April and October 2006. The dots show the mean differences, the error bars show the mean $\pm$ standard deviation, and the circles show the maximum and minimum relative differences for each discrete interval.

    Figure 4.  The mean differences between the predicted TOA spectral fluxes based on synthetic AIRS spectra at nadir-view and the directly computed TOA spectral fluxes from MODTRAN5 for each ADM discrete interval. The spectral flux is computed for every 10 cm$^-1$ interval from 10 to 2000 cm$^-1$. The units of the mean differences are W(m$^2\times 10$ cm$^-1$)$^-1$. The ordinate represents the 14 discrete intervals that are classified by the true input PW value. The input data are randomly chosen from ECMWF profiles over tropical oceans in April and October 2006.

    Figure 5.  Histogram of differences between AIRS-derived OLR and CERES OLR for all collocated AIRS and CERES clear-sky footprints over tropical oceans in 2004.

    Given that the CERES ADMs use a pair of slightly different anisotropic factors for daytime scenes and nighttime scenes, we further examine the comparison results in two groups: one in the ascending node and the other in the descending node. Figures 6a and b show the monthly mean OLR differences in 2004 of ascending and descending nodes, respectively. There is only a small fluctuation of OLR differences among different months due to the limitation of the training dataset for PW, T s and ∆ T estimation, especially for the ascending node. The mean OLR differences are within 0.36 W m-2, while the standard deviations are no more than 1.18 W m-2 for both nodes. The mean OLR differences for different discrete intervals are also examined and the results are shown in Fig. 7. The pattern is similar for both nodes and there is obvious variation among different discrete intervals. As mentioned above, the samples are not equally distributed in different discrete intervals during both the training and validation process. For this OLR comparison in 2004, the discrete intervals of 3, 4 and 7 have few samples, while the last four discrete intervals, 11-14, have more than 98% of all samples. For most discrete intervals except 1, 3 and 7, the mean OLR differences are within 2 W m-2, which is less than 1% of typical mean clear-sky OLR over tropical oceans.

    In brief, the results of both the theoretical validation and the AIRS-CERES comparisons show consistent performance in most situations, except some discrete intervals with limited samples. This indicates confidence of the algorithm in obtaining broadband OLR and spectral fluxes at 10 cm-1 or even larger spectral intervals.

5. Summary and discussion
  • In order to obtain spectral fluxes in the thermal-IR band, AIRS spectra are employed as an example to explore the possibility of developing an algorithm for clear skies over tropical oceans, based only on radiance measurements. The radiances and brightness temperatures in selected AIRS channels are applied to estimate PW, T s and ∆ T and determine the proper discrete intervals needed for the spectral ADMs. Then, the spectral fluxes and broadband OLRs can be converted from AIRS radiances by a spectrally dependent ADM. The solely radiance-based algorithm is validated against synthetic spectral fluxes as well as collocated CERES OLR observations, and show good consistency in most situations. This algorithm can be easily extended to other similar hyperspectral radiance measurements.

    To make the solely radiance-based algorithm completely applicable in practical observations, the classification of clear-sky and cloudy scenes based only on radiance is an essential precondition. However, it is still a challenging task to achieve precisely. The tri-spectrum method (Ackerman et al., 1990), which uses brightness temperatures of 8, 11 and 12 μm to distinguish clear-sky, water cloud and ice cloud is no longer effective due to the gap in AIRS radiances between 8.1 and 9 μm. Given that most transparent channels in the thermal-IR band are at 1231 cm-1, suggested by Aumann et al. (2003b, 2006), we also check the differences between the brightness temperatures of 1231 cm-1 (T B1231) and corresponding SST (T OI) from the NOAA's Optimum Interpolation (OI) SST V2 weekly mean products (Reynolds et al., 2002). NOAA-OI-SST analysis has been produced weekly on a 1°× 1° global grid from 1981 October to the present day. One month's AIRS measurements in January 2008 are used to compute Threshold1231, which is defined as \begin{equation} \label{eq3} { Threshold}_{1231}=T_{B1231}-T_{ OI} , (3)\end{equation} According to the collocated CERES scenes, there are 4646 clear-sky samples and 51 828 cloudy samples in this month. For example, Threshold1231 is set as 3.6 K, then samples with Threshold1231 over 3.6 K are clear-sky scenes and others are cloudy scenes. The accuracy of this method is 70.66% for CERES clear scenes and 89.16% for CERES cloudy scenes. For the CERES cloudy scenes misclassified as clear, 75% of misclassified samples are lower than 850 hPa, while 90% of them have a fraction less than 20%. A stricter Threshold1231 can improve the accuracy of clear-sky estimation, but it also largely excludes actual clear-sky samples, which cannot be determined exactly. It is still under investigation as to how to eliminate this kind of low and broken cloud based on radiances only.

    Figure 6.  (a) The monthly mean differences between the clear-sky OLR in 2004 over the tropical oceans estimated from AIRS spectra measured during daytime and that from the collocated CERES measurements. The dots show the mean differences and the error bars are $\pm$ standard deviation. (b) As in (a) but for nighttime.

    Figure 7.  (a) The mean differences in each discrete interval between the clear-sky OLR in 2004 over the tropical oceans estimated from AIRS spectra measured during daytime and that from the collocated CERES measurements. The dots show the mean differences and the error bars are $\pm$ standard deviation. (b) As in (a) but for nighttime.

    For the solely radiance-based algorithm itself, uncertainties in the derived spectral fluxes could originate from various sources. Due to the complexity of water vapor continuum absorption, the accuracy of the double-differential approach can still be improved. (Chen and Huang, 2014) developed a similar differential absorption method to improve the estimation of the clear-sky column water vapor, although different pairs of water absorption channels were applied and the contribution of the lapse rate was also considered. The adjustments of PW sub-intervals and the finite training dataset cause the non-uniform distribution among different discrete intervals for scene-type classification as well as spectral ADM construction. Meanwhile, errors in spectral fluxes over the entire thermal-IR spectral range exist in the multivariate regression schemes and also in forward radiative transfer modeling, especially in the far IR band, which is not covered by the AIRS instrument. Furthermore, as a first step for deriving TOA spectral fluxes for all skies based solely on radiance, more effort is needed to develop a similar algorithm for cloudy scenes.

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