Team: Radiative Transfer and Spectroscopy
Satellite remote sensing is a powerful tool for investigating atmospheric processes, climate dynamics, and environmental changes. It enables global observations that, when combined with regional measurements from ground stations, provide a comprehensive understanding of atmospheric conditions. The analysis of remote sensing data requires a strong foundation in radiative transfer theory, retrieval algorithms, and computational techniques for modeling electromagnetic interactions and precise data on gaseous absorption.
Our research focuses on forth key areas that underpin atmospheric remote sensing:
- Theoretical aspects of radiative transfer – Understanding and modeling the propagation of radiation through the atmosphere, accounting for absorption, emission, and scattering.
- Atmospheric parameter retrieval methods – Developing robust numerical algorithms to extract geophysical variables from measured spectra.
- Laboratory Spectroscopy – Conducting high-precision spectroscopic measurements to improve molecular databases and refine atmospheric absorption and emission models.
- Computational methods in electromagnetic scattering – Advancing theoretical and numerical approaches for modeling the scattering of electromagnetic waves by atmospheric particles.
1. Theoretical aspects of radiative transfer
Radiative transfer modeling is a fundamental component of atmospheric remote sensing, allowing for the simulation of electromagnetic wave interactions with atmospheric constituents. However, radiative transfer is an inherently complex phenomenon that requires sophisticated numerical techniques to solve the radiative transfer equation efficiently.
Our research focuses on developing acceleration techniques for solving the radiative transfer equation, enabling the efficient modeling and near-real-time processing of hyperspectral measurements from atmospheric spectrometers. We developed a radiative transfer model in the ultraviolet, visible and near-infrared spectral ranges based on the discrete ordinate method with matrix exponential (DOME) and associated software package PyDOME, as well as infrared and microwave radiative transfer line-by-line model Py4CAtS. A key area of interest is the development of multidimensional radiative transfer models that account for horizontal inhomogeneities, such as variations in cloud structures and aerosol distributions. These models enhance the accuracy of remote sensing data interpretation by moving beyond traditional one-dimensional approximations, which often oversimplify real atmospheric conditions.
2. Atmospheric parameter retrieval methods
The inverse problem in remote sensing involves the retrieval of atmospheric properties – such as gas concentrations, temperature profiles, cloud and aerosol characteristics – from satellite measurements. Since the observed spectral data are typically nonlinear functions of the atmospheric state variables, retrieval algorithms must employ advanced numerical optimization and regularization techniques to achieve stable and accurate solutions.
We are actively working on enhancing classical retrieval approaches by improving optimization algorithms, refining regularization strategies, and increasing computational efficiency. In addition to these advancements, we are exploring machine learning-based methods to facilitate the retrieval of atmospheric parameters, particularly for the fast processing of large and complex measurements from modern hyperspectral instruments. These physics-based and data-driven approaches have the potential to significantly accelerate retrieval processes while maintaining high accuracy.
3. Laboratory Spectroscopy
Remote sensing for the precise determination of the amount and distribution of atmospheric trace gases relies on spectroscopic databases (e.g. HITRAN, SAO, JPL, etc…) containing parameters such as line position, line strengths, line broadening or absorption cross sections. Such databases are usually obtained by laboratory experiments and must be kept up to date in order to fulfil the requirements of satellite missions, which are often way ahead of current database quality. High-resolution spectroscopy is therefore necessary to extend the existing databases and to fill the gap for missing data.
In our laboratory we study gas-phase molecules of atmospheric interest by means of a Fourier-Transform (FT) absorption spectrometer. Such spectrometer and the corresponding measurement techniques have been enhanced continuously over the years to achieve high-resolution results for both pure gases and complex gas mixtures and it can employ different sources. The measured spectra are then analysed with the Multispectrum Fitting Software developed in our group, which delivers the final and accurate parameters for the databases.
4. Computational methods in electromagnetic scattering
Electromagnetic scattering plays a critical role in atmospheric remote sensing, particularly in the study of aerosols and cloud particles. Theoretical advancements in this area are essential for accurately describing how electromagnetic waves interact with atmospheric particles of varying shapes, compositions, and orientations.
Non-spherical particle scattering presents a significant challenge due to its high-dimensional nature and the computational demands required to achieve numerical convergence. To address these challenges, we develop numerical codes for scattering calculations, covering both spherical particles (using Mie Theory) and arbitrarily shaped particles (using advanced techniques such as T-Matrix and Discrete Dipole Approximation Methods). These computational tools are designed to improve the accuracy of aerosol and cloud characterization in remote sensing applications while optimizing computational performance for large-scale simulations.