To retrieve surface and atmospheric temperature profiles, together with trace species concentrations is a fundamental challenge in numerical weather prediction and Earth monitoring. Over the last 20 years, the development of high-resolution infrared sensors on board Earth observation satellites has opened new remote sensing opportunities, providing an unprecedented source of information. However, infrared sensors cannot probe into thick cloud layers, rendering their observations insensitive to surface under cloudy conditions. This results in spatial fields flagged with missing data, disrupting the continuity of inferred information and hindering accurate modeling of energy fluxes between the surface and the atmosphere. Consequently, advanced interpolation techniques and spatial statistics are essential to process the available (very large) data sets and produce satellite products on a regular grid mesh. This paper reviews and presents the physical modeling of radiative transfer in the atmosphere and the related mathematics of inversion, tailored for high spectral-resolution infrared sensors.

Retrieving LST from infrared spectral observations is challenging because it needs separation from emissivity in surface radiation emission, which is feasible only when the state of the surface-atmosphere system is known. Thanks to its high spectral resolution, the Infrared Atmospheric Sounding Interferometer (IASI) instrument onboard Metop polar-orbiting satellites is the only sensor that can simultaneously retrieve LST, the emissivity spectrum, and atmospheric composition. Still, it cannot penetrate thick cloud layers, making observations blind to surface emissions under cloudy conditions, with surface and atmospheric parameters being flagged as voids. The present paper aims to discuss a downscaling-fusion methodology to retrieve LST missing values on a spatial field retrieved from spatially scattered IASI observations to yield level 3, regularly gridded data, using as proxy data LST from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) flying on Meteosat Second Generation (MSG) platform, a geostationary instrument, and from the Advanced Very High-Resolution Radiometer (AVHRR) onboard Metop polar-orbiting satellites. We address this problem by using machine learning techniques, i.e., Gradient Boosting, Random Forest, Gaussian Process Regression, Neural Network, and Stacked Regression. We applied the methodology over the Po Valley region, a very heterogeneous area that allows addressing the trained models' robustness. Overall, the methods significantly enhanced spatial sampling, keeping errors in terms of Root Mean Square Error (RMSE) and bias (Mean Absolute Error, MAE) very low. Although we demonstrate and assess the results primarily using IASI data, the paper is also intended for applications to the IASI follow-on, that is, IASI Next Generation (IASI-NG), and much more to the Infrared Sounder (IRS), which is planned to fly this year, 2025, on the Meteosat Third Generation platform (MTG).

During March 2025, three intrusions of Saharan dust affected southern Italy, with observable effects on atmospheric composition and, in particular, on greenhouse gases. A recent study conducted by the Institute of Methodologies for Environmental Analysis of the National Research Council of Italy (CNR-IMAA) documented these events through integrated in situ and remote sensing observations. Significant variations in CH4 and CO2 concentrations were detected in correspondence with the dust transport episodes. In this work, we propose an approach based on Physics-Informed Neural Networks (PINNs) to retrieve the vertical profile of CH4. The results are evaluated against high-precision ground-based measurements from CNR-IMAA, in order to assess the model’s predictive accuracy and its sensitivity to atmospheric variations associated with the presence of mineral aerosols.

2023 has replaced 2016 as the warmest year on record since 1850, bringing us closer to the 1.5 oC limit set by the Paris Agreement. High temperatures increase the likelihood of extreme events, with heatwaves and drought being prominent among them. Climate change has led to a rise in the frequency of droughts, affecting countries that never experienced them. Assessing drought events is crucial and satellite data can provide significant assistance due to its large spatial coverage and continuous data supply. Based on the Infrared Atmospheric Sounder Interferometer (IASI), we designed a new Water Deficit Index (wdi) that we have already proven useful in detecting drought events. Unfortunately, infrared sensors such as IASI cannot penetrate thick cloud layers, so observations are blinded to surface emissions under cloudiness bringing sparse and not homogeneous distributed data over a given spatial region. To reconstruct a model of the field of interest for the entire surface on a regular grid mesh, interpolation techniques, and spatial statistics to deal with huge data sets are mandatory. In this paper, we exploited the capability of two machine learning algorithms, i.e. gradient boosting and random forest, in converting IASI L2 scattered data to a regular L3 grid. Specifically, we trained a model that can predict the wdi index over a 0.05o regular grid, using data from other sensors as a proxy together with vegetational products, soil indices, and territorial and geographic information as covariates. We applied the methodology over the Po Valley region, which experienced an intense drought in the last three years causing high vegetation and soil water stress. Overall, we found that these methods can yield good results and allow simultaneous regular grid conversion and downscaling.

The Mediterranean basin is one of those areas where the impact of climate change is showing its most alarming consequences. Many regions in this area, both woodlands and croplands, have been suffering from droughts and water deficits due to the intense summer heatwaves of the last decades. Monitoring these phenomena is key to understanding how they are evolving and what could be done to mitigate their effects. Emissivity is a useful parameter in identifying the presence (or absence) of water. Surface and dew point temperatures are extremely useful not only in measuring the intensity of the heatwave but also in accounting for how much water content the surface is losing as humidity to the atmosphere. This paper presents a climatological study of Southern Italy’s water loss for the period 2015-2023 based on daily observations acquired by the Infrared Atmospheric Sounding Interferometer (IASI), mounted on top of EUMETSAT’s MetOp satellites. The Water Deficit Index (WDI) and the Emissivity Contrast Index (ECI) were estimated: monthly averages of each quantity were produced for the period of interest. Moreover, a validation with in situ measurements was conducted to better understand how these heatwave-induced droughts have been impacting the surface on different types of land covers.

Summary: ADViSELipidomics is a novel Shiny app for preprocessing, analyzing and visualizing lipidomics data. Ithandles the outputs from LipidSearch and LIQUID for lipid identification and quantification and the data fromthe Metabolomics Workbench. ADViSELipidomics extracts information by parsing lipid species (using LIPID MAPSclassification) and, together with information available on the samples, performs several exploratory and statisticalanalyses. When the experiment includes internal lipid standards, ADViSELipidomics can normalize the data matrix,providing normalized concentration values per lipids and samples. Moreover, it identifies differentially abundantlipids in simple and complex experimental designs, dealing with batch effect correction. Finally, ADViSELipidomicshas a user-friendly graphical user interface and supports an extensive series of interactive graphics.