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Challenge to retrieve a long term temperature record with IASI

Abstract : IASI measures Earth’s infrared radiance over 8461 channels between 645 and 2760 cm-1. Atmospheric temperatures at different altitudes can be retrieved from the radiances measured in the CO2 absorption bands (645-800 cm-1 and 2250-2400 cm-1) by selecting the channels that are the most sensitive to the temperature profile. The three IASI instruments on board of the Metop suite of satellites launched in 2006, 2012 and 2018, will provide a long time series for temperature, adequate for studying the long term evolution of atmospheric temperature. However, over the past 14 years, EUMETSAT has carried out several updates on the processing algorithms for both radiances and temperatures and, as a result, the time series are not homogeneous and this makes the computation of trends for temperature and atmospheric composition very difficult. In 2018, EUMETSAT has reprocessed the radiances with the most recent version of the algorithm and there is now a homogeneous radiance dataset available. Here, we evaluate the impact of EUMETSAT updates by comparing the old radiance dataset (non-homogeneous) with the reprocessed one (homogeneous) and IASI temperatures with ERA5 reanalysis temperatures. We find that some of the updates cause important changes of the temperature (several degrees). To obtain a homogeneous temperature dataset, we use an artificial neural network (NN) method to compute atmospheric temperatures from the reprocessed radiances. We train the NN with IASI radiances as input and ERA5 temperatures as output. We validate the results of the NN with ERA5 and with in-situ radiosonde temperatures from the ARSA (Analyzed RadioSoundings Archive) network. Between 500 and 10 hPa, where IASI has most of its sensitivity, a good agreement is observed between the three datasets. This work suggests that NN can be a powerful and simple tool to retrieve IASI temperatures at different altitudes in the upper troposphere and in the stratosphere, allowing us to construct a homogeneous and consistent temperature data record.
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https://hal-insu.archives-ouvertes.fr/insu-03649744
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Submitted on : Friday, April 22, 2022 - 8:32:27 PM
Last modification on : Friday, August 5, 2022 - 2:46:00 PM

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  • HAL Id : insu-03649744, version 1

Citation

Marie Bouillon, Sarah Safieddine, Simon Whitburn, Lieven Clarisse, Filipe Aires, et al.. Challenge to retrieve a long term temperature record with IASI. EUMETSAT, Meteorological Satellite Conference 2021, Sep 2021, Online, France. ⟨insu-03649744⟩

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