Abstract

The three IASI instruments, launched in 2006, 2012, and 2018, are key instruments to weather forecasting, and most meteorological centers assimilate IASI nadir radiance data into atmospheric models to feed their forecasts. The EUropean organisation for the exploitation of METeorological SATellites (EUMETSAT) recently released a reprocessed homogeneous radiance record for the whole IASI observation period, from which thirteen years (2008–2020) of temperature profiles can be obtained. In this work, atmospheric temperatures at different altitudes are retrieved from IASI radiances measured in the carbon dioxide absorption bands (654–800 cm−1 and 2250–2400 cm−1) by selecting the channels that are the most sensitive to the temperature at different altitudes. We rely on an Artificial Neural Network (ANN) to retrieve atmospheric temperatures from a selected set of IASI radiances. We trained the ANN with IASI radiances as input and the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5) as output. The retrieved temperatures were validated with ERA5, with in-situ radiosonde temperatures from the Analysed RadioSoundings Archive (ARSA) network and with EUMETSAT temperatures retrieved from IASI radiances using a different method. Between 750 and 7 hPa, where IASI is most sensitive to temperature, a good agreement is observed between the three datasets: the differences between IASI on one hand, and ERA5, ARSA or EUMETSAT on the other hand are usually less than 0.5 K at these altitudes. At 2 hPa, as the IASI sensitivity decreases, we found differences up to 2 K between IASI and the three validation datasets. We then computed atmospheric temperature linear trends from atmospheric temperatures between 750 and 2 hPa. We found that in the past thirteen years, there is a general warming trend of the troposphere, that is more important at the poles than at the equator (0.7 K/decade at the equator, 1 K/decade at the North Pole). The stratosphere is globally cooling on average, except at the South Pole as a result of the ozone layer recovery. The cooling is most pronounced in the equatorial upper stratosphere (−1 K/decade). This work shows that ANN 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 adapted to trend analyis.

Highlights

  • Atmospheric temperatures are a key component of Earth’s climate

  • We present a new atmospheric temperature product derived from the homogeneous Infrared Atmospheric Sounding Interferometer (IASI) radiance dataset, using an Artificial Neural Network (ANN) technique, in order to derive a homogeneous temperature data record

  • Validation of the IASI ANN product with ERA5, Analysed RadioSoundings Archive (ARSA) and EUMETSAT reprocessed temperatures shows a good agreement between the four datasets especially between 7 and 750

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Summary

Introduction

Atmospheric temperatures are a key component of Earth’s climate. Long term atmospheric temperature records are obtained from in situ measurements (lidars and radio soundings). These 45 observations are generally of excellent quality, they are sparse and unevenly distributed around the globe. Satellite observations have a better spatial coverage but the construction of a long temperature record from these observations usually requires merging several different instruments, and corrections and adjustments between the observations are needed to obtain a homogeneous dataset (Zou et al, 2014; Seidel et al, 2016). IASI measures radiance spectra from which surface and atmospheric temperatures (Hilton et al, 2012; Safieddine et al, 2020a) and trace gas concentrations can be retrieved (Clerbaux et al, 2009; Clarisse et al, 2011). Since IASI is planned to fly for at least 18 years, with the three instruments built at the same time and flying in constellation, continuity and stability are insured, and the potential of constructing a long-term climate data record at a range of altitudes is becoming evident

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