This paper introduces a framework for tracking sensor performance and comparison. We analyzed two decades of reflectance data from two well-known spectrometers over 20 desert sites, covering about 1300 spectral channels. The results lay the groundwork for cross-calibration of spectrometers by identifying which reference/constrained sensor and investigating the stability of invariant calibration sites.

This paper presents, for the first time, a method for cross-calibration of spectrometers using data from the Global Ozone Monitoring Experiment (GOME) and the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), which operated concurrently for 10 years. The method addresses key challenges compared with optical imagery calibration and integrates Polarization Monitoring Device data to enhance the accuracy and reliability of the cross-calibration process.

The Ascent-Drift-Descent Radiosonde System (ADDRS) represents an emerging cost-effective technology for upper-air measurements, Its 'Internet cloud + instrument terminal' design enables targeted observations for extreme weather research. Initial data application shows high potential for numerical weather prediction, The pilot application of ADDRS is list in the World Meteorological Organization Region II Coordination 2025-2027 demonstration project (No.RA II-18-I-DP-2).

In April 2024, we observed a fluorescent layer using fluorescence lidar at Nanping, South China. By combining multiple sources of information, we found that the long-range transported biomass burning aerosol (BBA) emitted by weak fire activity in the Indo-China Peninsula (ICP) was a major contributor to this layer. Our observations show that even weak fires in the ICP can affect South China, providing new insights into BBA transport in this region.

We present a new contrail detection algorithm for the geostationary Meteosat satellite, which outperforms other algorithms for this satellite. Contrails influence the climate but are hard to identify in geostationary satellite imagery with moderate spatial resolution. With this study, we enable the design and evaluation of contrail mitigation strategies, contributing to ongoing efforts in understanding, monitoring, and reducing the climate impact of aviation-induced cirrus.

Aviation affects the climate not only through carbon dioxide emissions but also by producing contrails that can alter the properties of natural cirrus clouds and influence the Earth's energy balance. In this study, we used detailed airborne laser measurements to detect contrails forming inside these clouds. We developed an automated method to identify and measure them, showing where they occur and providing a reliable way to monitor their climate impact without requiring flight data.

This study presents total ozone column (TOC) measurements over Thessaloniki, Greece, using a ground-based Ultraviolet-Visible (UV-VIS) instrument, by applying the direct sun Differential Optical Absorption Spectroscopy (DOAS) technique. The retrievals in both the UV (Huggins) and VIS (Chappuis) bands show very good agreement with collocated Brewer and Pandora observations. The results highlight the successful exploitation of the Chappuis bands for reliable ozone monitoring, providing a complementary approach to traditional UV-based TOC retrievals.

This research evaluates the potential of a cost-effective methane sensor for quantifying anthropogenic emissions. With active temperature control, the sensor performs comparably to the high-precision Active AirCore in estimating dairy-farm mass emissions, achieving results within 10% uncertainty. The uncertainty is mainly driven by wind and background variability, rather than by sensor precision. The results show that cost-effective sensors can improve monitoring networks.

We describe accurate measurement of particle formation and growth in the CERN CLOUD (European Council for Nuclear Research Cosmics Leaving OUtdoor Droplets) chamber, using a suite of gas- and particle-phase instruments. The interconnected measurements establish high accuracy in key particle properties and critically important gas-phase sulfuric acid. This is a template for accurate calibration of similar experiments and thus accurate determination of aerosol nucleation and growth rates, which are an important source of uncertainty in climate science.

We have developed a real-time reference system that can actively lock the wavelength of the detection laser, like a "navigation" system, overcoming the challenge of data inaccuracies caused by laser wavelength drift in previous measurements. This technological advancement will enable scientists to monitor the OH, a key component in the atmosphere, more reliably and over longer periods.

This paper integrates image recognition and semantic segmentation techniques into a dual-task deep learning model. A key innovation is the incorporation of physical characteristics of ice-covered transmission lines to physically constrain and refine the deep learning outputs. This framework not only achieves accurate identification of ice types on transmission lines but also significantly improves the computational accuracy of ice thickness estimation.

Our method evaluates how well automatic devices can classify pollen and other airborne particles in real time. Our goal is to compare different classification systems and understand their strengths and weaknesses. By developing this evaluation process, we aim to enhance the accuracy of bioaerosol forecasts. This research is essential for improving public health and helping people manage allergies more effectively.

We have developed and tested an instrument that quantitatively detects particulate nitrate. Gas-phase reactive nitrogen species are denuded using an active-carbon surface and particle nitrate is converted to NO₂, which is detected using cavity-ringdown-spectroscopy. Key to accurate measurement of particulate nitrate is drying of the sampled air. Excellent agreement with an aerosol-mass-spectrometer indicates that the instrument detects both organic and inorganic nitrate particle mass accurately.

Air pollution from fine and coarse particles is a major environmental issue in China, but their vertical and temporal changes are poorly understood due to insufficient long-term observations. This study shows that two major types of particles display clear differences with height and season, each having its own distinct characteristics using several years of continuous radar observations in Hefei. The radar data also indicate that these particles spread through the atmosphere differently.

We compared near real-time and benchtop XRF spectrometers measuring trace elements in airborne particles across three European cities. Results show filter material dictates accuracy: Teflon yielded strong inter-instrument agreement, while quartz caused systematic attenuation errors for light elements. Because empirical corrections left residual biases, using optimal substrates—preferably Teflon—is essential for accurately tracking pollution sources.

Matching geospatial data between datasets recorded on different coordinate systems requires choosing parameters that impact the subset of data in downstream analyses. We developed a framework to optimise the choice of parameters by maximising the mutual information between the data being compared. The optimised parameters vary spatially, and using the optimised parameters results in better comparisons between data than using fixed choices of parameters.

Since June 2019, an infrared camera has been scanning the nearly entire sky (diameter: 500 km) above DLR Oberpfaffenhofen (48.09° N, 11.28° E), Germany, every night providing images of the OH* airglow layer (height: 85–87 km), with a high spatial and temporal resolution (150 m, 2 min). We analysed three years of data for spatially confined small-scale wave structures with a machine learning approach. We derived seasonal variations and deduced that wave breaking is mostly observed in summer.

We present a new correction and calibration protocol to improve the accuracy of nitrous oxide isotope measurements obtained using laser spectroscopy. Through laboratory experiments, spectral simulations, and an automated MATLAB (Matrix Laboratory) reduction scheme, we identify and correct biases caused by matrix gas composition, spectral interferences and instrument variability. This protocol enables researchers to produce more reliable isotope data and supports standardised isotope analyses across laboratories.