Commercial Global Navigation Satellite System (GNSS) radio occultation (RO) satellites are generally cheap and can generate a large volume of globally distributed observations in a short period of time. To evaluate the practical application value of these data, we must assess their quality. We evaluate the quality of YUNYAO RO data. By using the “three-cornered hat” method and comparing with data from Metop-C and COSMIC-2, it was found that the YUNYAO GNSS-RO data are of high quality. 

A Doppler lidar was placed in a highly built-up area in London to measure wakes from tall buildings during a period of 1 year. We were able to detect wakes and assess their dependence on wind speed, wind direction, and atmospheric stability.

Waves are important as main drivers of different stratispheric patterns (streamers). We analyse changes in waves and infrasound characteristics related to streamers using continuous Doppler soundings and arrays of microbarometers in Czechia. Ground measurements using infrasound arrays showed that gravity wave propagation azimuths were more random during streamers than during calm conditions. Measurements in the ionosphere during streamers did not differ from those expected for the given time.

This study explores the potential of two newly launched commercial Global Navigation Satellite System (GNSS) radio occultation (RO) satellite missions for advancing Arctic lower-atmospheric studies. The products have a good sampling of the lower Arctic atmosphere and are useful to derive the planetary boundary layer (PBL) height during winter months. This research is a step towards closing the observation gap in polar regions due to the decomissioning of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-1) GNSS RO mission and the lack of high-latitude coverage by its successor (COSMIC-2).

A dual-field, integrated lidar system has been developed to detect atmospheric temperatures from the near surface to 60 km over Qingdao, China. Temperature profiles from the near surface to 60 km are derived by pure rotational Raman and Rayleigh scattering techniques using a single integrated lidar system. The results prove the system can make fine measurements of the atmospheric temperature profiles from the near surface to 60 km.

Multi-layer aerosol optical depth (AOD) is retrieved using the geostationary Spinning Enhanced Visible and Infrared Imager (SEVIRI) and machine learning, trained on Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data. The model provides AOD at a 3 km × 3 km spatial and 15 min temporal resolution over Europe. It accurately captured multi-layer AOD dynamics during Saharan dust transport and the Mount Etna eruption, demonstrating consistent physical accuracy.

The drone-based Flying Laboratory FLab was developed to simultaneously measure aerosol (number concentration, size distribution, and black carbon), trace gas (O₃, CO₂), and meteorological variables. FLab was characterized in the field regarding limitations and biases due to different flight maneuvers. Two application cases are presented: analysis of the development of the lowermost troposphere (up to 300 m) and successfully bridging ground-based and aircraft- and radiosonde-based measurements.

Our study presents a new method, the solar background calibration method, which improves temperature determinations in rotational Raman lidar systems. By utilizing background solar radiation, this technique offers more continuous and reliable temperatures independent of external measuring instruments. This new method enhances our ability to monitor and understand atmospheric trends and their association with climate change with greater accuracy.

Air pollutants such as particulate matter with diameters of 10 µm and 2.5 µm or less (PM₁₀ and PM_2.5) can cause adverse public health and environment effects; therefore their regular monitoring is crucial to keep pollutant concentrations under control. Our study demonstrates the potential of high-resolution aerosol optical depth (AOD) data from the Geostationary Environment Monitoring Spectrometer (GEMS) satellite to estimate ground-level PM concentrations using machine learning models. 

Sonic anemometers measure wind velocity in three dimensions. They are used worldwide to help assess the trace gas exchange, critical to understanding climate change. However, their physical framework interferes with the flow they measure. We present a new way of correcting measurements from sonic anemometers of several types. The method uses measurements of vertical wind velocity and other turbulent velocities, compares their ratios, and from this yields correction factors for sonic anemometers.

This study demonstrates the ability of a new method delivering the vertical distribution of particle shape to highlight riming and aggregation processes, identifying graupel and aggregates, respectively, as isometric particles. The distinction between these processes can be achieved using lidar or spectral techniques, as demonstrated in the case studies. The capability of the new method to identify rimed particles and aggregates without differentiating them can simplify statistical work.

We wanted to know how well the Dräger Tube method (DTM) is able to measure ammonia in agricultural experiments on small plots. We therefore compared the accuracy and sensitivity of Dräger Tubes in laboratory tests with more advanced analyzers. Dräger Tubes had a detection limit 3 to 4 times higher than expected. Since there are areas where the use of advanced analyzers is not feasible, the DTM should be improved, or simple and cost-effective measuring methods should be developed.