Emissivity of rough sea surface for 8–13 µm: modeling and verification

Wu, Xiangqian; Smith, William L.

doi:10.1364/ao.36.002609

Cited by 191 publications

(143 citation statements)

References 18 publications

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“…For AIRS, these spectral surface emissivities have been interpolated to the AIRS wavelengths. Over ocean, the surface emissivity is set to 0.99 for λ i < 10 µm and 0.98 for λ i ≥ 10 µm (Wu and Smith, 1997). Over snow and ice, the spectral surface emissivities are taken from Hori et al (2006) and, as they depend on the viewing zenith angle, they had to be corrected like in Smith et al (1996).…”

Section: Preparation and Comparison Of Atmospheric And Surface Ancillmentioning

confidence: 99%

Cloud climatologies from the infrared sounders AIRS and IASI: strengths and applications

et al. 2017

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Abstract. Global cloud climatologies have been built from 13 years of Atmospheric Infrared Sounder (AIRS) and 8 years of Infrared Atmospheric Sounding Interferometer (IASI) observations, using an updated Clouds from Infrared Sounders (CIRS) retrieval. The CIRS software can handle any infrared (IR) sounder data. Compared to the original retrieval, it uses improved radiative transfer modelling, accounts for atmospheric spectral transmissivity changes associated with CO 2 concentration and incorporates the latest ancillary data (atmospheric profiles, surface temperature and emissivities). The global cloud amount is estimated to be 0.67-0.70, for clouds with IR optical depth larger than about 0.1. The spread of 0.03 is associated with ancillary data. Cloud amount is partitioned into about 40 % high-level clouds, 40 % low-level clouds and 20 % mid-level clouds. The latter two categories are only detected in the absence of upper clouds. The A-Train active instruments, lidar and radar of the CALIPSO and CloudSat missions, provide a unique opportunity to evaluate the retrieved AIRS cloud properties. CIRS cloud height can be approximated either by the mean layer height (for optically thin clouds) or by the mean between cloud top and the height at which the cloud reaches opacity. This is valid for high-level as well as for low-level clouds identified by CIRS. IR sounders are particularly advantageous to retrieve upper-tropospheric cloud properties, with a reliable cirrus identification, day and night. These clouds are most abundant in the tropics, where high opaque clouds make up 7.5 %, thick cirrus 27.5 % and thin cirrus about 21.5 % of all clouds. The 5 % annual mean excess in high-level cloud amount in the Northern compared to the Southern Hemisphere has a pronounced seasonal cycle with a maximum of 25 % in boreal summer, in accordance with the moving of the ITCZ peak latitude, with annual mean of 4 • N, to a maximum of 12 • N. This suggests that this excess is mainly determined by the position of the ITCZ. Considering interannual variability, tropical cirrus are more frequent relative to all clouds when the global (or tropical) mean surface gets warmer. Changes in relative amount of tropical high opaque and thin cirrus with respect to mean surface temperature show different geographical patterns, suggesting that their response to climate change might differ.

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Section: Preparation and Comparison Of Atmospheric And Surface Ancillmentioning

confidence: 99%

Cloud climatologies from the infrared sounders AIRS and IASI: strengths and applications

et al. 2017

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“…Following the approach used by our regression algorithm (Zhou et al, 2002), we compress the surface emissivity into PC scores as well. The surface emissivity EOFs were generated from an ensemble of surface emissivities calculated using the Musuda ocean emissivity model (Musuda, 1988;Wu and Smith, 1997) and selected from the Salisbury emissivity library (Salisbury and D'Aria, 1992). The atmospheric temperature and moisture profile EOFs were generated from regional radiosondes.…”

Section: Description Of a Physical Retrieval Algorithm Based On Pcrtmmentioning

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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“…For water surfaces, we use the IRSSE model by van Delst and Wu (2000) to calculate the surface emissivity 10 in RemoTeC. This model is an update of Wu and Smith (1997) and the calculated emissivity is a function of sea-roughness as determined by the wind speed, viewing angle, and wavelength of the radiation. Furthermore, we consider atmospheric absorption by H 2 O, CH 4 , N 2 O, and CO 2 from the HITRAN 2008 database (Rothman et al, 2009) and describe atmospheric continuum absorption using the model by Mlawer et al (2012) to account for broad-band contributions from water, carbon dioxide, oxygen, nitrogen, and ozone.…”

Section: Forward Modelmentioning

confidence: 99%

“…A common approach to validate methane total column retrieval from shortwave infrared measurements is to compare the retrieved column to co-located ground-based measurements of the Total Carbon Column Observing Network (TCCON) (Wunch et al, 2011 atmospheric altitudes, leading to highly accurate estimates of the total column of methane rather than a profile. Because of the lack of other validation measurements, we decided to use these data for our product validation.…”

Section: Validation Approachmentioning

confidence: 99%

Methane profiles from GOSAT thermal infrared spectra

Lange¹,

Landgraf²

2017

Preprint

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Abstract. This paper discusses the retrieval of atmospheric methane profiles from the thermal infrared band of the Japanese Greenhouse Gases Observing Satellite (GOSAT) between 1210 and 1310 cm −1, using the RemoTeC analysis software. Approximately one degree of information on the vertical methane distribution is inferred from the measurements with the main sensitivity at about 9 km altitude but little sensitivity to methane in the lower troposphere. For verification, we compare the GOSAT methane abundance at measurement sites of the Total Carbon Column Observing Network (TCCON) to methane 5 profiles provided by the Monitoring Atmospheric Composition and Climate (MACC) model fields scaled to the total column observations at the sites. Without any radiometric corrections of GOSAT observations, differences between both data sets can be as large as 10%. To mitigate these differences, we developed a correction scheme using a principal component analysis of spectral fit residuals and airborne observations of methane during the HIAPER Pole-to-Pole Observations (HIPPO) campaign II and III. When the correction scheme is applied, the bias in the methane profile can be reduced to less than 2% over the whole 10 altitude range with respect to MACC model methane fields. Furthermore, we show that, with this correction, the retrievals result in smooth methane fields over land and ocean crossings and no differences are to be discerned between daytime and nighttime measurements. Finally, a cloud filter is developed for the nighttime and ocean measurements. This filter is rooted in the GOSAT-TIR measurements and is consistent with the cloud filter based on the GOSAT-SWIR measurements, despite the fact that the TIR-filter is less stringent.

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Emissivity of rough sea surface for 8–13 µm: modeling and verification

Cited by 191 publications

References 18 publications

Cloud climatologies from the infrared sounders AIRS and IASI: strengths and applications

Cloud climatologies from the infrared sounders AIRS and IASI: strengths and applications

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

Methane profiles from GOSAT thermal infrared spectra

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