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.

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.

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.

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.

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.

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 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.

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.

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.

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.