613 Seminar: Antonia Gambacorta


Over the past decades, hyperspectral sensors have constantly improved their capacity for atmospheric soundings. The first infrared hyper spectral sounder, the Atmospheric InfraRed Sounder (AIRS), was launched by NASA on May 4th 2002 and opened a new era of high-resolution observations that enabled atmospheric sounding with unprecedented vertical resolution. The success of this mission has laid the foundation for a long-term strategy of hyper spectral missions, designed on the base of a worldwide partnership of research and operational agencies. The next decades of European and US hyperspectral satellite missions include the EUMETSAT MetOp series (2006, 2012, 2018), the Suomi NPOESS Preparatory Project (NPP), the Joint Polar Satellite Sounding Project (2017, 2022, 2027, 2032), the EUMETSAT Polar System – Second Generation (EPS SG) series (2020 – 2040) and Meteosat Third Generation (MTG, 2023). Equally promising missions are in the pipeline in China, India and Japan.
We are faced with a challenging program that calls for unprecedented computational efficiency and sophisticated inversion methods aimed at maximizing the utilization of large volumes of data, when it comes to real time weather and long-term climate applications. Developing a mathematically sound and globally applicable (land/ocean, day/night, all season, all sky, full column) retrieval product that can fully exploit all available satellite assets (infrared, microwave, visible), is essential to defining a modern, physical and independent data record of atmospheric variables, suitable for climate applications.
This talk provides an overview of the state of the art in atmospheric sounding theory, with a focus on the problematic aspects -mainly nonlinearity and rank deficiency- characterizing the practical implementation of a thermal inverse method. In this respect, this work proposes few guidelines aiming at indicating some possible steps that future missions and retrieval development efforts may want to consider, in order to maximize the future use of hyperspectral data for improved weather and climate applications.
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