An innovative physical scheme to retrieve simultaneously surface temperature and emissivities using high spectral infrared observations from IASI

Paul, M.; Aires, Filipe; Prigent, Catherine; Trigo, Isabel F.; Bernardo, Frédéric

doi:10.1029/2011jd017296

Cited by 27 publications

(30 citation statements)

References 36 publications

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“…Over land, we use monthly mean spectral surface emissivity climatological values at a spatial resolution of 0.25 • × 0.25 • , retrieved from IASI measurements (Paul et al, 2012). For AIRS, these spectral surface emissivities have been interpolated to the AIRS wavelengths.…”

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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“…The solution of the radiative transfer equation requires the simultaneous knowledge of two unknowns: Tskin and the surface emissivity. This is generally solved with the assumption of a good initial guess to constrain the solution (Aires et al, 2001;Paul et al, 2012) and a rapid and accurate direct transfer model (Rodgers, 1976). Since the observed radiance spectra are affected by the surface properties, using it as input to the ANN takes emissivity knowledge into account.…”

Section: Validation Of the Tann Obtained From The Era5 Neural Networkmentioning

confidence: 99%

Skin temperature from the Thermal Infrared Sounder IASI

Safieddine

Parracho

George

et al. 2019

Preprint

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Abstract. Skin temperature (Tskin) derived from infrared sensors on board satellites provides a continuous view of Earth’s surface day and night and allows for the monitoring of global temperature changes relevant for climate trends. Tskin from the Infrared Atmospheric Sounding Interferometer (IASI) has not been properly exploited to date to assess its long-term spatio-temporal variability and no current homogenous Tskin record from IASI exists. In this study, we present a fast retrieval method of Tskin based on an artificial neural network from a set of IASI channels selected using the information theory/entropy reduction technique. We compare and validate our IASI Tskin product with that from EUMETSAT Level 2, ECMWF Reanalysis ERA5, SEVIRI land-surface temperature products, as well as ground measurements. Our results show good correlation between the IASI neural network product and the datasets used for validation, with a standard deviation between 1 and 4 °C. This method can be applied to other infrared measurements, and allows for the construction of a robust Tskin dataset, making it suitable for trend analysis.

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“…Indeed, without an accurate atmospheric correction, the artificial neural network (ANN) method [8,9], physical method [10][11][12][13], and multi-channel method [14] are commonly used to provide us with LST information for the satellite level. With accurate atmospheric correction, there are mainly two ways to solve the problem.…”

Section: Introductionmentioning

confidence: 99%

An Improved Linear Spectral Emissivity Constraint Method for Temperature and Emissivity Separation Using Hyperspectral Thermal Infrared Data

Lan

Zhao

et al. 2019

Sensors

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The linear spectral emissivity constraint (LSEC) method has been proposed to separate temperature and emissivity in hyperspectral thermal infrared data with an assumption that land surface emissivity (LSE) can be described by an equal interval piecewise linear function. This paper combines a pre-estimate shape method with the LSEC method to provide an initial-shape estimation of LSE which will create a new piecewise scheme for land surface temperature (LST) and LSE separation. This new scheme is designated as the pre-estimate shape (PES)-LSEC method. Comparisons with the LSEC method using simulated data sets show that the PES-LSEC method has better performance in terms of accuracy for both LSE and LST. With an at-ground error of 0.5 K, the root-mean-square errors (RMSEs) of LST and LSE are 0.07 K and 0.0045, respectively, and with the scale factor of moisture profile 0.8 and 1.2, the RMSEs of LST are 1.11 K and 1.14 K, respectively. The RMSEs of LSE in each channel are mostly below 0.02 and 0.04, respectively, which are better than for the LSEC method. In situ experimental data are adopted to validate our method: The results show that RMSE of LST is 0.9 K and the mean value of LSE accuracy is 0.01. The PES-LSEC method with fewer segments achieves better accuracy than that of LSEC and preserves most of the crest and trough information of emissivity.

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An innovative physical scheme to retrieve simultaneously surface temperature and emissivities using high spectral infrared observations from IASI

Cited by 27 publications

References 36 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

Skin temperature from the Thermal Infrared Sounder IASI

An Improved Linear Spectral Emissivity Constraint Method for Temperature and Emissivity Separation Using Hyperspectral Thermal Infrared Data

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