6 papers
Application of the latent twins approach for clear sky retrieval from IASI observations
Michele Martinazzo, Cristina Sgattoni, Marco Menarini +3
In recent years, data-driven approaches emerged as alternatives to traditional physics-based retrievals, taking advantage of machine learning techniques such as learnable pseudoinv…
Latent Twins: A Framework for Scene Recognition and Fast Radiative Transfer Inversion in FORUM All-Sky Observations
Cristina Sgattoni, Luca Sgheri, Matthias Chung +1
The FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) mission will provide, for the first time, systematic far-infrared spectral measurements of Earth's outgoing…
Machine Learning for Cloud Detection in IASI Measurements: A Data-Driven SVM Approach with Physical Constraints
Chiara Zugarini, Cristina Sgattoni, Luca Sgheri
Cloud detection is fundamental for the interpretation and operational exploitation of hyperspectral infrared sounders, yet the capability of infrared radiances alone to provide rel…
A physics-aware data-driven surrogate approach for fast atmospheric radiative transfer inversion
Cristina Sgattoni, Luca Sgheri, Matthias Chung
FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) was selected in 2019 as the ninth Earth Explorer mission by the European Space Agency (ESA). Its primary object…
Determination of emissivity profiles using a Bayesian data-driven approach
Luca Sgheri, Cristina Sgattoni, Chiara Zugarini
In this paper, we explore the determination of a spectral emissivity profile that closely matches real data, intended for use as an initial guess and/or a-priori information in a r…
Solving nonlinear systems of equations via spectral residual methods: stepsize selection and applications
Enrico Meli, Benedetta Morini, Margherita Porcelli +1
Spectral residual methods are derivative-free and low-cost per iteration procedures for solving nonlinear systems of equations. They are generally coupled with a nonmonotone linese…