Increases in agriculture, oil and gas, and waste management activities have contributed to the increase in atmospheric methane levels and resultant climate warming. In this paper, we explore the use of small uncrewed aircraft systems (sUASs) and AirCore technology to detect and quantify methane emissions. Results from field experiments demonstrate that sUASs and AirCore technology can be effective for detecting and quantifying methane emissions in near real time.

The three most widely used techniques for measuring black carbon (BC) have been deployed continuously for 2 years at a French high-altitude research station. Despite a similar temporal variation in the BC load, we found significant biases by up to a factor of 8 between the three instruments. This study raises questions about the relevance of using these instruments for specific background sites, as well as the processing of their data, which can vary according to the atmospheric conditions.

This study examined the transferability of machine learning calibration models among low-cost sensor units targeting NO₂ and NO. The global models were evaluated under similar and different emission conditions. To counter cross-sensitivity, the study proposed integrating O₃ measurements from nearby reference stations, in Switzerland. The models show substantial improvement when O₃ measurements are incorporated, which is more pronounced when in regions with elevated O₃ concentrations.

The Tropospheric Remotely Observed Profiling via Optimal Estimation physical retrieval is used to retrieve temperature and humidity profiles from various combinations of passive and active remote sensing instruments, surface platforms, and numerical weather prediction models. The retrieved profiles are assessed against collocated radiosonde in non-cloudy conditions to assess the sensitivity of the retrievals to different input combinations. Case studies with cloudy conditions are also inspected.

This study demonstrates a new machine learning approach to efficiently and accurately estimate atmospheric carbon dioxide levels from satellite data. Rather than using traditional complex physics-based retrieval methods, neural network models are trained on simulated data to rapidly predict CO₂ concentrations directly from satellite spectral measurements.

The Sentinel-5P satellite operated by the European Space Agency has carried the TROPOspheric Monitoring Instrument (TROPOMI) around the Earth since October 2017. This mission also produces atmospheric ozone profile data which are described in detail for May 2018 to April 2023. Independent validation using ground-based reference measurements demonstrates that the operational ozone profile product mostly fully and at least partially complies with all mission requirements.

In this paper, the relationship between observations from two instruments, a meteor radar and a field-widened Michelson interferometer (ERWIN) which provide complementary information on this region, is investigated. On average the ratio of ERWIN winds to meteor radar winds is ∼ 0.7. Differences between the wind observations may be caused by variations in the airglow brightness associated with dissipating gravity waves.

ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.

In the solar energy application field, it is key to simulate solar resources anywhere on the globe. We conceived the Solar Resource estimate (SolaRes) tool to provide precise and accurate estimates of solar resources for any solar plant technology. We present the validation of SolaRes by comparing estimates with measurements made on two ground-based platforms in northern France for 2 years at 1 min resolution. Validation is done in clear-sky conditions where aerosols are the main factors.

Accurately estimating biogenic volatile organic compound (BVOC) emissions in forest ecosystems has been challenging. This research presents a framework that utilizes drone-based lidar, photogrammetry, and image recognition technologies to identify plant species and estimate BVOC emissions. The largest cumulative isoprene emissions were found in the Myrtaceae family, while those of monoterpenes were from the Rubiaceae family.

The system presented here can accurately generate and reproduce a stable flow of gas mixtures of known concentrations over several days using ambient air as a dilution medium. In combination with the small size and low weight of the system, this enables the calibration of hydrogen sensors in the field, reducing the influence of matrix effects on the accuracy of the sensor. The system is inexpensive to assemble and easy to maintain, which is the key to reliable measurement results.

Boundary-layer height observations remain sparse in time and space. In this study we create a new fuzzy logic method for synergistically combining boundary-layer height estimates from a suite of instruments. These estimates generally compare well to those from radiosondes; plus, the approach offers near-continuous estimates through the entire diurnal cycle. Suspected reasons for discrepancies are discussed. The code for the newly presented fuzzy logic method is provided for the community to use.

Using data collected from three levels of a 62 m tower, we found that both the temperature variances and sensible heat flux obtained from sonic anemometers are consistently lower, by a few percent, compared to those from fine-wire thermocouples.

The squall line is a type of convective system. Squall lines are often associated with damaging weather, so identifying and tracking squall lines plays an important role in early meteorological disaster warnings. A clustering-based method is proposed in this article. It can identify the squall lines within the radar scanning range with an accuracy rate of 95.93 %. It can also provide the three-dimensional structure and movement tracking results for each squall line.

The paper presents a new method that categorizes atmospheric aerosols by analyzing their optical properties with a Mie–Raman–fluorescence lidar. The research specifically looks into understanding the presence of smoke, urban, and dust aerosols in the mixtures identified by this lidar. The reliability of the results is evaluated using the Monte Carlo technique. The effectiveness of this approach is successfully demonstrated through testing in ATOLL, an observatory influenced by diverse aerosols.

Knowledge of stratospheric aerosol characteristics is important for understanding chemical and climate aerosol feedbacks. Two particle size distribution parameters, the aerosol extinction coefficient and the effective radius, are obtained from SCIAMACHY limb observations. The aerosol characteristics show good agreement with independent data sets from balloon-borne and satellite observations. This data set expands the limited knowledge of stratospheric aerosol characteristics.

This study proposes an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. It is suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 μm. For cloud droplets, it is 1.0–10 μm. This algorithm can quickly obtain the microphysical parameters of atmospheric particles and has better robustness.