Recent  

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.

The mesosphere, part of the upper atmosphere, contains small solid dust particles, mostly made up of material from interplanetary space. We are preparing an experiment to collect such particles during a rocket flight. A new instrument has been designed and numerical simulations have been performed to investigate the airflow nearby as well as its dust collection efficiency. The collected dust particles will be further analyzed in the laboratory in order to study their chemical composition.

The Swedish research satellite MATS (Mesospheric Airglow/Aerosol Tomography and Spectroscopy) is designed to study atmospheric waves in the mesosphere and lower thermosphere. These waves perturb the temperature field, and thus, by observing three-dimensional temperature fluctuations, their properties can be quantified. This pre-study uses synthetic MATS data generated from a general circulation model to investigate how well wave properties can be retrieved.

In this paper we present an overview of the QUANT project, which to our knowledge is one of the largest evaluations of commercial sensors to date. The objective was to evaluate the performance of a range of commercial products and also to nourish the different applications in which these technologies can offer relevant information.

A new hyperspectral radiometer (HSR1) was deployed and evaluated in the central United States (northern Oklahoma). The HSR1 total spectral irradiance agreed well with nearby existing instruments, but the diffuse spectral irradiance was slightly smaller. The HSR1-retrieved aerosol optical depth (AOD) also agreed well with other retrieved AODs. The HSR1 performance is encouraging: new hyperspectral knowledge is possible that could inform atmospheric process understanding and weather forecasting.

Geostationary satellite data have been used to measure the stratospheric aerosols from the explosive Hunga volcanic eruption by using the data in a novel way. The onboard imager views part of the Earth's limb and data from this region were analysed to generate vertical cross-sections of aerosols high in the atmosphere. The analyses show the hemispheric spread of the aerosols and their vertical structure in layers from 22–28 km in the stratosphere.

Accurate cloud quantification is critical for climate research. We developed a novel computer vision framework using deep neural networks and clustering algorithms for cloud classification and segmentation from ground-based all-sky images. After a full year of observational training, our model achieves over 95 % accuracy on four cloud types. The framework enhances quantitative analysis to support climate research by providing reliable cloud data.

Many types of halos appear in the sky. Each type of halo reflects the state of the atmosphere; therefore observing them from the ground greatly helps in understanding the state of the atmosphere. However, halos are easily obscured by the contrast of the cloud itself, making it difficult to observe them. This study describes the construction of a sky-color model for halos and a new effective algorithm to reveal halos in images.

It is crucial to accurately measure the mixing states of light-absorbing carbon particles from emission sources like wildfires and biomass combustion to decrease climate forcing uncertainties. This study compares methods that measure light-absorbing carbon in the atmosphere. The CPMA-SP2 method offers more accurate results than traditional light-scattering methods, such as the leading-edge-only (LEO) method, thereby enhancing the accuracy of measuring the mixing states of light-absorbing carbon.

A new modular and affordable instrument was developed to automatically collect wet deposition continuously with an off-grid solar top-up power package. Monthly collections were performed across the Newfoundland and Labrador Boreal Ecosystem Latitudinal Transect of experimental forest sites from 2015 to 2016. The proof-of-concept systems were validated with baseline measurements of pH and conductivity and then applied to dissolved organic carbon as an analyte of emerging biogeochemical interest. 

The number of cloud droplets per unit volume, N_d, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers.  We show that deriving N_d is very uncertain, although a synergistic algorithm seems to produce useful characterizations of N_d and effective particle size. 

Comparing different aerosol data sets for scientific studies often requires converting aerosol extinction data between different wavelengths. A common approximation for the spectral behavior of aerosol is the Ångström formula; however, this introduces biases. Using measurements across many different wavelengths from a single instrument, we derive an empirical relationship to both characterize this bias and offer a correction for other studies that may employ this analysis approach.

A new four-dimensional, multiyear, and near-global climate data record of the fine-mode (submicrometer diameter) and coarse-mode (supermicrometer diameter) components of atmospheric pure dust is presented. The dataset is considered unique with respect to a wide range of potential applications, including climatological, time series, and trend analysis over extensive geographical domains and temporal periods, validation of atmospheric dust models and datasets, and air quality.