The Ozone Monitoring Suite (OMS) launched in August 2023 is a new Chinese hyperspectral UV-VIS (Ultraviolet-Visible) instrument. This study used the OMS measurements and Differential Optical Absorption Spectroscopy (DOAS) inversion to for the first time retrieve global SO₂ columns from OMS. The results show that the OMS SO₂ retrievals exhibit good stability over clean oceanic regions, successfully capture volcanic SO₂ plumes, and effectively detect the elevated SO₂ columns from anthropogenic emissions.

The study present a method to estimate how much the radar signal is weakened as it passes through rain or clouds, designed to implement in the new EarthCARE satellite cloud profiling radar data. The approach builds on the method used in the CloudSat mission, with key improvements that make it robust under non-ideal instrument conditions in the early mission phase. This leads to more reliable retrieval of clouds and rainfall during initial satellite operations.

Sulfuric acid forms particles in the atmosphere, but the airborne sampling faces challenges due to vapor losses in inlet lines. An innovative aircraft sampling system to sample sulfuric acid from the sea surface into the lower stratosphere (0–15 km) is described and characterized. Our results challenge the widely held view that laminar core sampling is the best strategy to sample condensable vapors, and identify better strategies to sample condensable vapors.

We present a laser-based tomographic approach for dynamically quantifying and mapping greenhouse gas emissions at facility scale. It was applied during a campaign at a research farm in Eastern Finland, where methane emissions from typical manure-handling events were monitored. The results show that incorporating prior information on source locations improves the tolerance of the flux estimates with respect to environmental disturbances.

Continuous year-round measurements in Chiba, Japan, combining in‑situ CO₂ observations with MAX‑DOAS (Multi-Axis Differential Optical Absorption Spectroscopy) NO₂, showed that CO₂ levels decrease on days with low near-surface NO₂. CO₂ enhancements quantified using these low-NO₂ days correlated strongly with NO₂ and black carbon, indicating that this metric effectively tracks fossil-fuel-derived CO₂. The approach offers a simple, accurate method for monitoring urban CO₂ emissions in megacities.

A commercial dynamic chamber system is modified to measure reactive nitrogen and greenhouse gas fluxes of interest for climate and air quality. The system is optimized for high performance so that fluxes of surface-active gases like ammonia, or reactive gases like nitrogen dioxide, can be made robustly. Lab and field measurements demonstrate consistent fluxes with those from other in-situ agricultural soil reports, as these are expected hot spots of reactive nitrogen gas exchange.

We present an improved version of the TROPOMI/WFMD (TROPOspheric Monitoring Instrument/Weighting Function Modified Differential) algorithm for the simultaneous retrieval of atmospheric methane and carbon monoxide from satellite observations. The updated data product combines higher data yield with better precision and accuracy, expanding its suitability for a wider range of scientific applications. These substantial advances are mainly due to refined quality filtering, enabling more reliable identification of cloudy scenes and mitigating specific aerosol-related issues.

Satellite NO₂ data are essential for monitoring air pollution, but they are affected by several sources of uncertainty. This study evaluates the major error contributors in TROPOMI (TROPOspheric Monitoring Instrument) NO₂ retrievals by analyzing each retrieval step and comparing with ground-based observations. Key findings include biases in the separation of stratospheric and tropospheric NO₂, representation and smoothing errors, and substantial variability among the ground-based instruments used for validation.

We test the impact of a big increase in the number of radio occultation observations being used in the Met Office's weather forecast system, since idealised studies showed benefit even with very large increases. Initial tests indicated that this degraded forecast performance, due to biases in the modelling of the observations. Correcting those biases led to the system showing improved forecast performance with the extra observations. The largest improvements were in the southern extra-tropics.

We present a fast, interpretable machine learning method to retrieve key aerosol parameters from ground-based Sun-sky photometer measurements. Trained on simulated data covering diverse aerosol and atmospheric conditions, ensuring robustness and physical consistency. Applied to real observations, it agrees well with AERONET products and reduces computation time by orders of magnitude, offering a practical tool for monitoring aerosols and their effects on air quality and climate.

We use a statistical method called Empirical Orthogonal Function (EOF) analysis to analyze complex data, focusing on its strengths and limitations. This method is widely used in climate research, but its use in atmospheric chemistry is relatively new.  While EOF analysis can be powerful, it may not be suitable for datasets that do not follow specific statistical assumptions. Our research provides recommendations to improve how we use this technique in analyzing atmospheric chemistry data.

This study developed a numerical algorithm to simultaneously retrieve aerosol and surface parameters from multi-angle polarimetric observations from space. Our research shows that accurate single scattering albedo retrieval requires degree of linear polarization observation uncertainties below 0.01. The retrieval results agrees with ground-based measurements well, and successfully characterize regional pollution events. We also generate global maps of retrieved aerosol and surface parameters. 

In this study, we leverage the power of machine learning to develop classifiers using a comprehensive dataset of SPMS spectra. These classifiers enable automatic differentiation of aerosol particles based on their chemistry and size, facilitating more accurate and efficient aerosol classification. Our results show increased accuracy when including unlabeled data in a semi-supervised framework.

The Changing Atmosphere Infra-Red Tomography Explorer (CAIRT) is a satellite mission concept developed to observe, among other, the gradient of ozone and water vapour in the upper troposphere and lower stratosphere where these species have their largest radiative impact. By simulating CAIRT observations and measuring their constrain on an atmospheric model using data assimilation, this study shows that CAIRT specifications are adequate to fulfil this objective.

The surface structure of ice crystals in clouds influences how sunlight travels through the atmosphere and contributes to uncertainty in climate predictions. Because these features are difficult to observe, we present a practical approach for mapping ice crystal surface structure using an established capture technique. Ice crystals were produced in a laboratory cloud chamber and their surfaces were measured in detail. This approach enables reliable mapping of ice crystal surfaces.

We have deployed two spectrometers on a ship traveling along the coast of Japan. By that, we were able to repeatedly measure the greenhouse gas and air pollutant emissions of power plants, large industrial facilities, and cities. Using the ratios between the different gases, we are able to identify sources based on their unique signature. In addition, we are able to show that spectrometers can be operated on a ship, while still fulfilling the high standards of land-based observation networks.