Water vapor is a strong greenhouse gas, and accurate measurements of its concentration in the upper atmosphere (~8–25 km altitude) are crucial for reliable climate predictions. We investigated the performance of four airborne hygrometers, deployed on aircraft or stratospheric balloon platforms and based on different techniques, in a climate simulation chamber. The results demonstrate the high accuracy and reliability of the involved sensors for atmospheric monitoring and research applications.

We present, to our knowledge, the first relaxed eddy accumulation system explicitly tailored to a radiocarbon (¹⁴C)-based partitioning of fossil and non-fossil urban CO₂ fluxes. Laboratory tests and in-depth quality and performance checks prove that the system meets the technical requirements. A pilot application on a tall tower in the city of Zurich, Switzerland, demonstrates the ability to separate fossil and non-fossil CO₂ components within the typical precision of ¹⁴C measurements.

Accurate quantification of methane emissions is crucial in reducing greenhouse gases. This paper presents a comprehensive evaluation of several multi-source emission quantification models using data from fixed-point continuous monitoring systems (CMSs), collected during a controlled-release study. This work offers a novel contribution by evaluating multi-source emissions quantification techniques, leveraging a fixed-point CMS to capture the complexities of overlapping plumes from simultaneous releases.

This study leverages machine learning models to classify cloud thermodynamic phases using multi-sensor remote sensing data collected at the Department of Energy Atmospheric Radiation Measurement North Slope of Alaska observatory. We evaluate model performance, feature importance, and application of the model to another observatory and quantify how the models respond to instrument outages.

This work develops an Neural-Network-based above cloud aerosol (ACA) detection and retrieval scheme for multi-angular polarimetric (MAP) instruments. On one year of the retrieval, the retrieved aerosol properties (aerosol optical thickness, AOT, Angstrom Exponent, AE, and Single Scattering Albedo, SSA) agree well with adjacent clear-sky aerosol retrievals. The seasonal global pattern of ACA events and above cloud AOT are also within expectation.

Our research compares three systems of continuous flow analysis coupled with cavity ring-down spectrometry (CFA-CRD) from Venice, Paris, and Grenoble laboratories for measuring water isotopes in ice cores, crucial for reconstructing past climate. We quantify each system’s mixing and measurement noise effects, which impact the achievable resolution of isotope continuous records. Our findings reveal specific configurations and procedures to enhance measurement accuracy, providing a framework to optimise water isotope analysis.

Surveillance cameras have emerged as a new low-cost, high-resolution Near Surface Precipitation Type observer. A novel deep learning approach is developed to classify rain, snow, and graupel, achieving 93 % accuracy in real-world observations. The model remains robust to camera parameter variations and maintains reliable performance at wind speeds below 5 m s⁻¹, demonstrating strong potential for large-scale practical applications.

The Global Ozone Monitoring Experiment (GOME)-type Ozone Profile Essential Climate Variable (GOP-ECV) data record provides monthly mean ozone profiles with global coverage from 1995 to 2021 at a spatial resolution of 5° × 5°. Measurements from five nadir-viewing satellite sensors are first harmonized and then merged into a coherent record. The long-term stability of the data record is further improved through scaling the profiles using the GOME-type Total Ozone Essential Climate Variable (GTO-ECV) data record as a reference.

In this work, we design two weather radar network consistency validation methods: the FY-3G Precipitation Radar Based Model (SGRCM) and the Ground-based Weather Radar Network Consistency Validation Model (AWRCM). They can provide a reliable reference for calibrating the reflectivity factor of weather radar in various frequency bands.

Fast temperature sensors are deployed on drones to accurately measure temperature changes and fluctuations in the atmosphere. Compared to standard sensors, these new sensors showed better accuracy, especially in rapidly changing temperatures. Over 100 drone flights confirmed the sensors' ability to measure temperature fast enough to compare to standard meteorological instruments. This new method provides valuable data for understanding turbulent heat fluxes in the atmospheric boundary layer.

Detection of methane point source emissions with satellite-based imaging spectrometers is assessed in offshore areas. After integrating simulations into data from 2 different satellite missions, we extract the probability of detection. We compare our results with real plumes, which shows the consistency of the models. Detection and monitoring efforts can be optimized by filtering data based on these results, reducing unnecessary searches and increasing the action taken on these emissions.

This study analyzes nitrogen dioxide available data in Catalonia, a region with diverse geography and climate, by comparing satellite- and ground-based data from 2022–2023. Analyses at various scales showed a moderate overall correlation – stronger in suburban areas and weaker in urban zones. Correlations peaked in winter and autumn, likely due to stable weather. The findings suggest that satellite data can complement ground monitoring but require adjustments for regional and climatic variations.

In order to correctly assimilate satellite radiance observations in data assimilation systems, the systematic observation biases must be corrected to conform to a Gaussian normal distribution with a mean of 0. In this paper, a two-step bias correction scheme is established based on radiation observations of HIRAS-II (Hyperspectral Infrared Atmospheric Sounder-II) carried on FY-3E.

We investigate how seasonal solar illumination affects the pointing accuracy of EarthCARE’s cloud profile radar (CPR) antenna and introduce a correction based on surface Doppler measurements. The correction improves measurement accuracy by reducing Doppler velocity biases to within 5 and 7 cm s⁻¹. Our results demonstrate the importance of continuous pointing characterization to maintain the scientific accuracy of EarthCARE’s CPR Doppler observations.

The Complete Data Fusion is an a posteriori method used to combine remote sensing products from independent observations of the same or proximate air masses. In this study, we extend the algorithm’s applicability to two-dimensional products, testing it with simulated ozone datasets from nadir and limb measurements. Our results show that the exploitation of the tomographic capabilities of future atmospheric sensors maximizes the information extracted from complementary datasets.

This study introduces a new approach to measure cloud cover from image data taken by ground-based sky observations. Our method used diverse sky images taken from various locations across the globe to train our machine learning model. We achieved a very high accuracy in detecting cloud cover, even in polluted areas. Our model surpasses traditional methods by running efficiently with minimal computational needs.

The newly developed Horizontal Cloud Condensation Nuclei Counter (HCCNC) is capable of generating supersaturation as low as 0.05 % at temperatures as low as 4 °C, allowing for the investigation of CCN activity of ammonium sulfate particles up to 200 nm diameter and ambient aerosol of larger diameter. The HCCNC could be used for low-temperature experiments where gas-phase partitioning into or out of the aerosol phase for semi-volatile compounds is relevant for CCN activity.

We present a global dataset of POlarization LIdar PHOtometer Networking (POLIPHON) dust conversion factors at 532 nm obtained using Aerosol RObotic NETwork (AERONET) observations at 137 sites for ice-nucleating particle (INP) and 123 sites for cloud condensation nucleation (CCN) calculations. We also conduct a comparison of dust CCN concentration profiles derived using both POLIPHON and the independent Optical Modelling of the CALIPSO Aerosol Microphysics (OMCAM) retrieval. 

Accurate methane quantification from oil and gas sites is critical for reliable reporting and climate assessments. This study tested downwind models using point sensors. A non-standard eddy covariance (EC) failed due to instrumentation issues, while the Gaussian plume inverse model (GPIM) and backward Lagrangian stochastic (bLs) model gave more reliable results. The bLs was the most accurate for single sources, with the best performance at 15 min averaging and 5° wind sectors.

We introduce a new, medium-sized metal chamber to observe how tiny aerosol particles behave in very dry conditions. We used the chamber to track how particles clump together over time. We found that although the particles behave differently at first, they eventually settle into similar patterns, with smoke clumping a bit more due to its unique shape. Overall, our work demonstrates that the chamber produces reliable results and can be adapted for future studies under more humid conditions.

The Cluster Ion Counter (CIC, Airel OÜ) is a novel instrument that simultaneously measures positive and negative ion concentrations below 5 nm. It provides high detection efficiency, low noise, a rapid time response, and excellent reliability, confirmed in lab experiments and at the CERN CLOUD chamber. Its design enables precise, robust long-term measurements, facilitating versatile applications in diverse environments, from laboratory settings to field and potential airborne research.

We developed a new method to automatically identify types of particles in the air, such as smoke, dust, or pollution, using a specialized laser system. This helps monitor air quality more efficiently and in greater detail. Our method uses real data collected over 3 years in northern France and can detect changes caused by weather conditions. It offers a faster and more accurate way to understand what is in the air we breathe.

In this numerical study, we assess the feasibility of a satellite-borne instrument for measuring the atomic oxygen concentration and temperature in the upper atmosphere by detecting its emission in the terahertz range. We simulate the measurement scenario considering technological feasibility and state-of-the-art atmosphere models. We find that both atomic oxygen and temperature could be accurately measured from an altitude of 100 km up to at least 200 km with such an instrument.

Global climate models, commonly used for climate predictions, struggle at capturing local-scale variations in air quality. We have used measurements of ultrafine particles (UFPs), a less understood air pollutant with potentially significant health implications, for training machine learning models that can substantially reduce the inaccuracy in UFP concentrations predicted by a climate model. This approach could aid epidemiological studies of ultrafine particles by extending exposure records.

A global AOD product from Particulate Observing Scanning Polarimeter (POSP) has been proposed and validated using Aerosol Robotic Network (AERONET). Results show a high accuracy, with correlation coefficients (R) of 0.914, a root mean square error (RMSE) of 0.085, outperforming Moderate Resolution Imaging Spectroradiometer (MODIS). Error analysis reveals seasonal variation with lower accuracy in autumn/winter, and increased uncertainty with lower Normalized Difference Vegetation Index NDVI.

A neural-network-based fast forward operator for polarized 3D radiative transfer is presented. The forward operator uses the new InDEpendent column local halF-sphere ApproXimation (IDEFAX). Polarized radiances simulated with IDEFAX and the forward operator were validated against full 3D radiative transfer simulations with MYSTIC and show a significantly improved representation of 3D radiative effects compared to the plane-parallel independent column approximation at comparable computation times.

Understanding how ice forms in clouds is crucial for predicting weather and climate; however, accurately measuring the ice-nucleating particles that trigger ice formation remains challenging. We developed an advanced instrument called the Freezing Ice Nucleation Detection Analyzer. By refining temperature control, automating freezing detection, and rigorously testing, we demonstrated that this instrument can reliably measure immersion mode ice-nucleating particles across diverse conditions.

This study presents a new method combining ground-based measurements and lidar to track how aerosols are distributed at different heights in the atmosphere. By correcting for humidity, which causes aerosols to grow and intensify the lidar signal, the method provides more accurate aerosol vertical profiles. Our results show that aerosol profiles can vary significantly over short distances. This technique can help improve understanding of aerosol-cloud interactions.

We developed a portable system to collect biogenic volatile organic compounds (BVOCs) emitted by trees, which can affect air quality and climate. Our system minimizes interference from contact with tree branches and enables sample collections from several trees in a single day. Field testing with Japanese cedar showed the system's robust performance, capturing BVOC variability and diurnal patterns, highlighting its value for advancing our understanding of BVOC dynamics in diverse ecosystems.