We used a Bruker EM27/SUN spectrometer within an automated weatherproof enclosure to measure greenhouse gas column concentrations over a 3-month period in Jinja, Uganda. The portability of the EM27/SUN allows us to evaluate satellite and model data in locations not covered by traditional validation networks. This is of particular value in tropical Africa, where extensive terrestrial ecosystems are a significant store of carbon and play a key role in the atmospheric budgets of CO₂ and CH₄.

The Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) is a new Earth-observing instrument that provides highly accurate measurements of the atmosphere and surface. Using a physics-based calibration technique, we show that AirHARP achieves high measurement accuracy in laboratory and field environments and exceeds a benchmark accuracy requirement for modern aerosol and cloud climate observations. Therefore, the HARP design is highly attractive for upcoming NASA climate missions.

The ozone sink into oceans and marine clouds is seldom studied and highly uncertain. Calculations suggest O₃ destruction at aqueous surfaces (ocean, droplets) may be strongly accelerated, but field evidence is missing. Here we compare three fast airborne O₃ instruments to measure eddy covariance fluxes of O₃ over the remote ocean, in clear and cloudy air. We find O₃ fluxes below clouds are consistently directed into clouds, while O₃ fluxes into oceans are much smaller and spatially variable.

This study focuses on improving the accuracy of satellite-based PM_2.5 retrieval, crucial for monitoring air quality and its impact on health. It employs machine learning to correct the AOD-to-PM_2.5 conversion ratio using various data sources. The approach produces high-resolution PM_2.5 estimates with improved accuracy. The method is flexible and can incorporate additional training data from different sources, making it a valuable tool for air quality monitoring and epidemiological studies.

Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.

We investigate the capabilities of a proposed satellite mission, CAIRT, for observing gravity waves throughout the middle atmosphere and present the necessary methodology for in-depth wave analysis. Our findings suggest that such a satellite mission is highly capable of resolving individual wave parameters and could give new insights into the role of gravity waves in general atmospheric circulation and atmospheric processes.

We have designed and deployed a mid-cost medium-precision CO₂ sensor monitoring network in Paris since July 2020. The data are automatically calibrated by a newly implemented data processing system. The accuracies of the mid-cost instruments vary from 1.0 to 2.4 ppm for hourly afternoon measurements. Our model–data analyses highlight prospects for integrating mid-cost instrument data with high-precision measurements to improve fine-scale CO₂ emission quantification in urban areas.

This article describes the current status of a lidar installed at ALOMAR in northern Norway. It has investigated the Arctic middle atmosphere on a climatological basis for 30 years. We discuss major upgrades of the system implemented during recent years, including methods for reliable remote operation of this complex lidar. We also show examples that illustrate the performance of the lidar during measurements at different altitude ranges and timescales.

A combination of data analysis techniques is introduced to separate local and regional influences on observed levels of carbon dioxide, carbon monoxide, and methane from an established ground-based remote sensing network. We take advantage of the covariations in these trace gases to identify the dominant type of sources driving these levels. Applying these methods in conjunction with existing approaches to other datasets can better address uncertainties in identifying sources and sinks.

We present a newly designed reduced-pressure chemical ionization reactor for detection of gas-phase organic and inorganic species. The system operates through the combined use of vacuum ultraviolet ionization and photosensitizers to generate numerous adduct ionization schemes. As a result, it offers the ability to simultaneously measure a wide variety of organic and inorganic species in terms of compound volatility and functionality, while being largely independent of changes in sample humidity.

We have developed a portable, optically based instrument that measures NO₂. It consumes less than 6 W of power, so it can easily run off a small battery. This instrument has made both balloon and UAV flights. NO₂ measurement results compare favorably with other known NO₂ instruments. We find this instrument to be stable with repeatable results compared with calibration sources. Material cost to build a single instrument is around USD 4000. This could be lowered with economies of scale.

Atmospheric bromine destroys ozone, impacts oxidation capacity, and oxidizes mercury into its toxic form. We constrain bromine by remote sensing of BrO from a mountaintop. Previous measurements retrieved two to three pieces of information vertically; we apply new methods to get five and a half vertically and two more in time. We compare with aircraft measurements to validate the methods and look at variations in BrO over the Pacific.

We describe the new feature mask (AEL-FM) and aerosol profile retrieval (AEL-PRO) algorithms developed for Aeolus lidar and present the evaluation of the Aeolus products using CALIPSO data for dust aerosols over Africa. We have found that Aeolus and CALIPSO show similar aerosol patterns in the collocated orbits and have good agreement for the extinction coefficients for the dust aerosols, especially for the cloud-free scenes. The finding is applicable to Aeolus L2A product Baseline 17.

The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.