A comparison of radiative transfer models for simulating Atmospheric Infrared Sounder (AIRS) radiances

Saunders, Roger; Rayer, Peter; Brunel, Pascal; Engeln, Axel von; Bormann, Niels; Strow, L. L.; Hannon, S.; Heilliette, Sylvain; Liu, Xu; Miskolczi, F.; Han, Yong; Masiello, Guido; Moncet, Jean-Luc; Uymin, G.; Sherlock, V.; Turner, Duncan L.

doi:10.1029/2006jd007088

Cited by 83 publications

(65 citation statements)

References 25 publications

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“…In summary, PCRTM is much faster than the channelbased RTM since it predicts PC scores (or 'super channel' magnitudes) directly instead of channel radiance or transmittance individually. The parametrization of PCRTM is physical and can be trained to any desired accuracy relative to (LBL) codes Saunders et al, 2006). The radiance variation as a function of temperature, H 2 O, O 3 , CH 4 , N 2 O, CO, surface emissivity and observation geometry is captured via monochromatic radiative transfer calculations.…”

Section: Description Of the Pcrtm Forward Modelmentioning

confidence: 99%

Case-study of a principal-component-based radiative transfer forward model and retrieval algorithm using EAQUATE data

Liu

Zhou

Larar

et al. 2007

Q.J.R. Meteorol. Soc.

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ABSTRACT:The objective of the paper is to apply a novel radiative transfer model and a physical retrieval algorithm to hyperspectral data taken during the European Aqua Atmospheric Thermodynamics Experiment (EAQUATE) campaign. A principal-component-based radiative transfer model (PCRTM) is used to calculate projection coefficients of the radiance spectrum onto a set of predefined empirical orthogonal functions (EOFs) and associated derivatives with respect to the state vector. Instead of fitting channel radiances, the physical retrieval algorithm iteratively fits the principal component (PC) scores or the EOF projection coefficients of the observed radiance spectrum using the PCRTM as its forward model. Since the EOFs are orthonormal to each other, only a few PC scores are needed to capture the information content of the radiance spectrum, therefore reducing the computational time needed for running both the forward model and the inversion. This paper demonstrates the application of such a physical algorithm for retrieving atmospheric temperature, moisture and ozone profiles, and surface properties such as surface skin temperature and surface emissivity. The results have been compared with those obtained with a NAST-I channel-based physical retrieval algorithm and with those obtained from collocated radiosonde and LIDAR measurements.

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Section: Description Of the Pcrtm Forward Modelmentioning

confidence: 99%

Case-study of a principal-component-based radiative transfer forward model and retrieval algorithm using EAQUATE data

Liu

Zhou

Larar

et al. 2007

Q.J.R. Meteorol. Soc.

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“…[19] The atmospheric contribution to the observed spectra is modeled using the Optimal Spectral Sampling (OSS) method [Moncet et al, 2001;Eluszkiewicz et al, 2005b;Saunders et al, 2007]. The theoretical basis and implementation of the OSS method are described by Moncet et al [2008].…”

Section: A1 Atmospheric Forward Modelmentioning

confidence: 99%

Atmospheric and surface retrievals in the Mars polar regions from the Thermal Emission Spectrometer measurements

Eluszkiewicz¹,

Moncet²,

Shephard³

et al. 2008

J. Geophys. Res.

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[1] Retrievals of atmospheric temperatures, surface emissivities, and dust opacities in the Mars polar regions from the Thermal Emission Spectrometer (TES) spectra are presented. The retrievals correspond to two types of spectra, characterized by small and large band depths BD 25 in the 25-mm band of solid CO 2 . These two types of spectra have previously been identified with locations covered by slab ice and fluffy CO 2 frost, respectively. Above the first atmospheric scale height, there is a correlation between the degree of saturation in the retrieved atmospheric temperatures and the two types of surface, with the high BD 25 spectra (''cold spots'') showing larger supersaturations around 1 mbar. This supports the hypothesis that cold spots correspond to locations with potential or actual atmospheric precipitation. Furthermore, the retrieved temperature profiles exhibit a warming above 1 mbar (15 km), which appears real even when the limited number of independent pieces of information from the measurement ($3) and coarse vertical resolution of the TES instrument above 15 km are considered. The spectral shape of the retrieved surface emissivities in the cold spot locations is consistent with modeling results attributing high BD 25 to porosity. For the low BD 25 spectra, the retrieved emissivities are spectrally flat but significantly less than unity (0.8-0.9). The cause of these spectrally uniform deviations from blackbody behavior (which are not supported by modeling) remains to be investigated, with a noticeable reduction in the deviation from the blackbody behavior achieved through a zero-radiance-level correction to the TES spectra available from the Planetary Data System.

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“…One of them is to increase the computational speed of the radiative transfer model needed by the inversion process. There are lots of efforts devoted to the development of fast radiative transfer models for simulating hyper-spectral or ultra-spectral radiances (Strow et al, , 2006Saunders et al, 2000Saunders et al, , 2007Matricardi, 2003;Matricardi and Saunders, 1999;Moncet et al, 2001;Liu et al, 2003;Edwards et al, 2000;McMillin et al, 1995McMillin et al, , 1997Barnet et al, 2000). These models are orders of magnitude faster than line-by-line radiative transfer models.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of atmospheric profiles and cloud properties from IASI spectra using super-channels

et al. 2009

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Abstract. The Infrared Atmospheric Sounding Interferometer (IASI) is an ultra-spectral satellite sensor with 8461 spectral channels. IASI spectra contain high information content on atmospheric, cloud, and surface properties. The instrument presents a challenge for using thousands of spectral channels in a physical retrieval system or in a Numerical Weather Prediction (NWP) data assimilation system. In this paper we describe a method of simultaneously retrieving atmospheric temperature, moisture, and cloud properties using all available IASI channels without sacrificing computational speed. The essence of the method is to convert the IASI channel radiance spectra into super-channels by an Empirical Orthogonal Function (EOF) transformation. Studies show that about 100 super-channels are adequate to capture the information content of the radiance spectra. A Principal Component-based Radiative Transfer Model (PCRTM) is used to calculate both the super-channel magnitudes and derivatives with respect to atmospheric profiles and other properties. A physical retrieval algorithm then performs an inversion of atmospheric, cloud, and surface properties in the super channel domain directly therefore both reducing the computational need and preserving the information content of the IASI measurements. While no large-scale validation has been performed on any retrieval methodology presented in this paper, comparisons of the retrieved atmospheric profiles, sea surface temperatures, and surface emissivities with co-located ground-and aircraft-based measurements over four days in Spring 2007 over the South-Central United States indicate excellent agreement.Correspondence to: X. Liu (xu.liu-1@nasa.gov)

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A comparison of radiative transfer models for simulating Atmospheric Infrared Sounder (AIRS) radiances

Cited by 83 publications

References 25 publications

Case-study of a principal-component-based radiative transfer forward model and retrieval algorithm using EAQUATE data

Case-study of a principal-component-based radiative transfer forward model and retrieval algorithm using EAQUATE data

Atmospheric and surface retrievals in the Mars polar regions from the Thermal Emission Spectrometer measurements

Retrieval of atmospheric profiles and cloud properties from IASI spectra using super-channels

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