The new high-time-resolution sampler STRAS has been designed, developed and tested. It enables automatic sequential sampling of up to 168 hourly samples of PM₁₀, PM_2.5 or PM₁. It has been conceived for subsequent elemental composition analysis (from Na to Pb) by particle-induced X-ray emission (PIXE), but optical techniques may also be applied to measure black and brown carbon. Its use combined with other high-temporal-resolution instrumentation can provide complete chemical speciation of aerosols on an hourly basis.

We tested a variety of scientific measurements and algorithms for distinguishing the amounts of air pollution that were emitted by a nearby polluter from background pollution that was already in the air. The results show that machine learning and other statistical algorithms produced accurate estimates of this background pollution. These findings help scientists and regulators to understand where pollution comes from and to improve measurements of pollution from sources like traffic.

Low-cost optical particle counters (OPCs) offer new opportunities to monitor dust particles from wind soil erosion. Their price, size, and power consumption are lower than those of traditional OPCs. We tested the ability of the low-cost OPC-N3 from Alphasense to estimate dust emission flux during erosion events in Jordan. N3 estimated the dust flux well, with differences of less than 30 % compared to a traditional OPC. Our results confirm the potential of low-cost OPCs for dust erosion research.

ESA's Aeolus mission, launched in 2018, has exceeded expectations, providing valuable global wind lidar data for nearly 5 years. Its data have improved weather forecasting, with Mie-cloudy winds proving to be especially precise. Challenges have emerged, such as unexpected misalignments in signal angles and reduced signal levels due to beam clipping and laser issues. Lessons from Aeolus highlight the need for better optical alignment and active control systems for future lidar missions.

A robust, automatic volatile organic compound (VOC) collection system was developed for vertical VOC sampling associated with the 2022 DOE ARM-program-led TRACER in Houston, Texas.  This modular sampler has been developed to measure vertical profiles of VOCs to improve near-surface characterization. This article helps fill the current lack of commercially available options for aerial VOC sampling and serves to support and encourage researchers to build and develop custom samplers. 

The SAMURAI-S system is an innovative measurement tool combining a high accuracy wind sensor with a multi-rotor drone to improve atmospheric turbulence observations. While traditional methods lack flexibility and accuracy in dynamic environments, SAMURAI-S provides high maneuverability and precise 3D wind measurements. The research demonstrated the system's ability to match the data quality of conventional methods, with a slight overestimation in vertical turbulence under higher wind conditions.

Our research introduces a novel deep-learning approach for classifying and segmenting ground-based infrared thermal images, a crucial step in cloud monitoring. Tests based on self-captured data showcase its excellent accuracy in distinguishing image types and in structure segmentation. With potential applications in astronomical observations, our work pioneers a robust solution for ground-based sky quality assessment, promising advancements in the photometric observation experiments.

We analyzed signal delays in the atmosphere, using eight co-located global navigation satellite system (GNSS) stations, and compared linear horizontal gradients with microwave radiometer data. A weak constraint applied when estimating gradients in the GNSS data processing, enhancing short-lived gradient tracking but increasing noise, which can be mitigated by averaging gradients from the eight stations. This approach may enhance GNSS-based atmospheric monitoring for use in weather nowcasting.

This paper presents a comprehensive calibration procedure for the first spaceborne high-spectral-resolution lidar with an iodine vapor absorption filter Aerosol and Carbon Detection Lidar (ACDL) on board DQ-1 by utilizing nighttime 532 nm multi-channel data. We analyzed the error sources of the multi-channel calibration coefficients and assessed the results. The results indicate that the uncertainty of the clear-air scattering ratio was within the anticipated range of 7.9 %.

Atmospheric stability indicates whether air vertical motion is dumped or amplified. This is important for wind energy applications as it affects wind turbine wakes and thus the yield of wind parks. The paper provides an assessment of stability metrics measured by ground-based microwave radiometers in different climatological conditions and instrument types, onshore and offshore. Results indicate that special precaution may be required offshore to achieve typical onshore performances.

Aerosol composition has a large influence on the climate system. This study uses realistic simulated scenarios to look at the information content of a combination of three satellite-based instruments (SLSTR, IASI and GOME-2). It shows that it is possible to retrieve 6 to 15 different aerosol components in addition to the aerosol optical depth (AOD) and different surface parameters. The results are used for the development of a synergistic multi-sensor retrieval algorithm.

Water is a precious resource, and it is essential to monitor and predict the current and future occurrence of precipitation-producing clouds. We investigate the cloud characteristics related to precipitation using several cloud cases in the United Arab Emirates with data from aircraft measurements, satellite observations, and weather radar observations. This study provides scientific support for the development of an applicable framework to examine cloud precipitation processes.

Long-term records of plant fluorescence offer vital insights into changing vegetation activity. The GOME-2A sensor provides extensive global observations but suffers from calibration and instrument degradation, which affects data consistency. This study presents the SIFTER v3 algorithm, which effectively resolves these issues and includes other improvements, resulting in robust, accurate, and consistent GOME-2A fluorescence measurements from 2007 to 2017.

Detection of NO₂ via cavity ring-down spectroscopy (CRDS) after thermal dissociation (TD) of alkyl and peroxy nitrates can be used to detect total atmospheric organic nitrates originating from the interaction between biogenic emissions of volatile organic compounds and nitrogen oxides of anthropogenic origin. Here we present an improved TD-CRDS technique that avoids systematic bias resulting from secondary chemistry in the heated inlets and that can be deployed in regions with strong biogenic emissions.

Fog and low stratus (FLS) are both persistent clouds close to the Earth's surface. This study introduces a new machine-learning-based algorithm developed for the Meteosat Second Generation geostationary satellites that can provide a coherent and detailed view of FLS development over large areas over the 24 h day cycle.

Traveling ionospheric disturbances (TIDs) are manifestations of atmospheric waves that are significant for the transfer of energy and momentum between atmospheric layers and regions. This work demonstrates that velocities and directions of TIDs can be measured by monitoring the tiny shift in frequency of AM radio signals when they reflect from a moving ionosphere and that this method can be scaled to use large numbers of radio receivers and transmitters to monitor TIDs on a continental scale.

CO₂ column-averaged dry-air mole fractions can be retrieved from space using spectrometers like OCO-2. However, nearby clouds induce spectral distortions that bias these retrievals beyond the accuracy needed for global CO₂ source and sink assessments. This study employs a physics-based linearization approach to represent 3D cloud effects and introduces radiance-level mitigation techniques for actual OCO-2 data, enabling the operational implementation of these corrections.

We characterize the optical properties of various aerosols using a compact dual-wavelength depolarization lidar (CIMEL CE376) at 532 and 808 nm. Through a modified two-wavelength Klett inversion method, we assess the vertical distribution and temporal evolution of Saharan dust, volcanic aerosols and wildfire smoke in the subtropical North Atlantic from August 2021 to August 2023. The study confirms the CE376 lidar's effectiveness in monitoring and characterizing atmospheric aerosols over time.

Long-term field testing across diverse climatic environments is conducted to identify the optimized calibration conditions for NO₂, NO, CO and O₃ electrochemical sensors. The results uncovered three factors that influence calibration performance: calibration period, concentration range and time averaging. We developed a comprehensive framework for the best sensor calibration practices, which serves as a valuable reference for calibrating various sensor types used in air quality monitoring.

CLIMCAPS extends the Aqua AIRS+AMSU record with retrievals from CrIS+ATMS on Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar Satellite System series (JPSS-1 to JPSS-4). With “continuous”, we mean a data record that is consistent in its characterization of natural variation despite changes in source instrumentation. Here we investigate how sounding continuity can improve across the full CLIMCAPS record (2002 to the present day), spanning multiple instruments and satellites.

This study analyzes three years of wind lidar measurements to explore the dynamics of the urban planetary boundary layer in Hefei, China. Results reveal that nocturnal low-level jets are most frequent in spring and intensify in summer, significantly enhancing turbulence and shear near the surface, particularly at night. Additionally, cloud cover raises the mixing layer height by approximately 100 m at night due to the greenhouse effect but reduces it by up to 200 m in the afternoon.

Using observations from the Visible Infrared Imaging Radiometer Suite day–night band, we developed a method for constructing gridded nighttime aerosol optical thickness (AOT) data based on the spatial derivative of measured top-of-atmosphere attenuated upwelling artificial lights at night. The gridded nighttime AOT retrievals, compared against Aerosol Robotic Network data, show reasonable skill levels for potential data assimilation, air quality, and climate studies of significant events.

This study revealed the existence of significant species-specific adsorptive and reactive losses of monoterpenes and sesquiterpenes in dynamic chambers. The deuterated α-pinene-d3 and β-caryophyllene-d2 were proven as effective surrogates in tracing these losses for some key monoterpenes and sesquiterpenes. The findings highlight the importance of selecting internal surrogates that closely match the adsorptive and reactive behaviors of target compounds for precise loss correction.

We evaluated how well we can infer air pollutant levels (ozone, carbon monoxide, and nitrogen oxides) between air quality stations throughout South Korea, finding good representation in most densely measured cities in spite of intense small-scale emission hotspots. Comparing observed air quality with gridded model output is desirable, and so we created gridded datasets over South Korea using air quality station measurements, which agreed with airborne measurements around Seoul.

Standard gases are essential for accurate greenhouse gas measurements. However, exchanging cylinders at remote sites presents logistical challenges, requiring systems that minimize gas consumption. We developed methods for calculating greenhouse gas mole fractions and uncertainties using our original system designed to reduce standard gas use. We validated its long-term stability through instrument comparisons. The system has proven effective for maintaining observations at remote sites.

This paper provides a comprehensive evaluation of the quality of radar-based precipitation estimation in mountainous areas and presents a method to mitigate the main shortcomings identified. It then compares three different ensemble analysis methods that combine radar-based precipitation estimates with forecasts from an ensemble numerical weather prediction model.