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. The three infrared atmospheric sounding interferometers(IASIs), launched in 2006, 2012, and 2018, are key instruments to weatherforecasting, and most meteorological centres assimilate IASI nadir radiancedata into atmospheric models to feed their forecasts. The European Organisation for the Exploitation ofMeteorological Satellites (EUMETSAT)recently released a reprocessed homogeneous radiance record for the wholeIASI observation period, from which 13 years (2008–2020) oftemperature profiles can be obtained. In this work, atmospheric temperaturesat different altitudes are retrieved from IASI radiances measured in thecarbon dioxide absorption bands (654–800 and 2250–2400 cm−1)by selecting the channels that are the most sensitive to the temperature atdifferent altitudes. We rely on an artificial neural network (ANN) toretrieve atmospheric temperatures from a selected set of IASI radiances. Wetrained the ANN with IASI radiances as input and the European Centre forMedium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5) asoutput. The retrieved temperatures were validated with ERA5, with in situradiosonde temperatures from the Analysed RadioSoundings Archive (ARSA)network and with EUMETSAT temperatures retrieved from IASI radiances using adifferent method. Between 750 and 7 hPa, where IASI is most sensitive totemperature, a good agreement is observed between the three datasets: thedifferences between IASI on one hand and ERA5, ARSA, or EUMETSAT on theother hand are usually less than 0.5 K at these altitudes. At 2 hPa, as theIASI sensitivity decreases, we found differences up to 2 K between IASI andthe three validation datasets. We then computed atmospheric temperaturelinear trends from atmospheric temperatures between 750 and 2 hPa. We foundthat in the past 13 years, there is a general warming trend of thetroposphere that is more important at the poles and at mid-latitudes (0.5 K/decade at mid-latitudes, 1 K/decade at the North Pole). The stratosphereis globally cooling on average, except at the South Pole as a result of theozone layer recovery and a sudden stratospheric warming in 2019. The coolingis most pronounced in the equatorial upper stratosphere (−1 K/decade). Thiswork shows that ANN can be a powerful and simple tool to retrieve IASItemperatures at different altitudes in the upper troposphere and in thestratosphere, allowing us to construct a homogeneous and consistenttemperature data record adapted to trend analysis.

atmospheric measurement techniques

Time evolution of temperature profiles retrieved from 13 years of infrared atmospheric sounding interferometer (IASI) data using an artificial neural network