Spectral Signatures of Earth’s Climate Variability over 5 Years from IASI

Brindley, Helen E.; Bantges, R.; Russell, Jill; Murray, J. E.; Dancel, Christopher; Belotti, Claudio; Harries, J. E.

doi:10.1175/jcli-d-14-00431.1

Cited by 17 publications

(8 citation statements)

References 34 publications

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“…The interest in exploiting highly spectrally resolved IASI data to study climate variability has been previously highlighted [21][22][23][24]; preliminary T skin trends from Metop-A IASI measurements were presented at the SPIE conference [25]. Although the spectral signature of climate variability and T skin anomalies have been studied for similar instruments [26,27], relatively little has been done to generate systematic climate-data records for surface and atmospheric parameters with IASI measurements.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks to Retrieve Land and Sea Skin Temperature from IASI

Safieddine¹,

Parracho²,

George³

et al. 2020

Remote Sensing

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Surface skin temperature (Tskin) derived from infrared remote sensors mounted on board satellites provides a continuous observation of Earth’s surface and allows the monitoring of global temperature change relevant to climate trends. In this study, we present a fast retrieval method for retrieving Tskin based on an artificial neural network (ANN) from a set of spectral channels selected from the Infrared Atmospheric Sounding Interferometer (IASI) using the information theory/entropy reduction technique. Our IASI Tskin product (i.e., TANN) is evaluated against Tskin from EUMETSAT Level 2 product, ECMWF Reanalysis (ERA5), SEVIRI observations, and ground in situ measurements. Good correlations between IASI TANN and the Tskin from other datasets are shown by their statistic data, such as a mean bias and standard deviation (i.e., [bias, STDE]) of [0.55, 1.86 °C], [0.19, 2.10 °C], [−1.5, 3.56 °C], from EUMETSAT IASI L-2 product, ERA5, and SEVIRI. When compared to ground station data, we found that all datasets did not achieve the needed accuracy at several months of the year, and better results were achieved at nighttime. Therefore, comparison with ground-based measurements should be done with care to achieve the ±2 °C accuracy needed, by choosing, for example, a validation site near the station location. On average, this accuracy is achieved, in particular at night, leading to the ability to construct a robust Tskin dataset suitable for Tskin long-term spatio-temporal variability and trend analysis.

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Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks to Retrieve Land and Sea Skin Temperature from IASI

Safieddine¹,

Parracho²,

George³

et al. 2020

Remote Sensing

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“…In contrast, whitening is immediate, does not require an inverse model nor the ability of a forward model to account for all the observed features, and can be applied on spectral ranges of any size. Likewise, in a more traditional analysis of trends in high resolution infrared spectra (Brindley et al., 2015; Strow & DeSouza‐Machado, 2020), it is very challenging to deal properly with variations of O 3 , CH 4 , CO 2 , or H 2 O due to, for example, natural temporal variations, variations in temperature or sampling biases related to clouds; whereas these are dealt with automatically using the whitening technique.…”

Section: Discussionmentioning

confidence: 99%

Identification of Short and Long‐Lived Atmospheric Trace Gases From IASI Space Observations

Longueville

Clarisse

Franco

et al. 2021

Geophysical Research Letters

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In recent years, major progress has been made in measuring weakly absorbing atmospheric trace gases from high spectral resolution space observations. In this paper, we apply the so‐called whitening transformation on spectra of the Infrared Atmospheric Sounding Interferometer, and show that it allows removing most of the climatological background from spectra, leaving a residual that contains those spectral signatures that depart from normality. These can subsequently be attributed to changes in the abundance of trace species. This is illustrated for two diverging cases: (1) a biomass burning plume from the 2019/2020 Australian bushfires, leading to the unambiguous identification of nine reactive trace gases, including a first observation of glycolaldehyde; (2) spectra observed a decade apart, from which changes in eight long‐lived halogenated substances are identified; three of them never observed before by a nadir sounder.

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“…The instrument was designed for numerical weather prediction and atmospheric composition monitoring (Clerbaux, Boynard, et al., 2009; Collard et al., 2009; Hilton et al., 2012), but with more than 13 years of readily available data to date, with the prospect of having a similar instrument until 2040 with the IASI‐NG mission, IASI can also be used in climate research. Although interest in exploiting spectrally resolved data to study climate variability has been previously highlighted (Brindley et al., 2015; Clerbaux, Hadji‐Lazaro, et al., 2003; Smith et al., 2015), and the need to construct a climate data record is becoming increasingly evident, relatively little has been done so far to generate consistent records for climate variables with IASI (i.e., derived with a single algorithm from a single well‐calibrated instrument, with no substantial changes to the instrument characteristics over time). In order to be adequate for use in climate applications, SST data must be not only accurate, but also consistent over time (i.e., homogeneous).…”

Section: Introductionmentioning

confidence: 99%