The goal is to assess the feasibility of an ice crystal icing detection algorithm based exclusively on remote sensing data. Active measurements are used to train and validate a newly developed random forest algorithm that is applied to passive satellite imagery to estimate the ice crystal icing conditions probability. 83 % of ice crystal icing conditions are correctly detected, showing potential for an operational implementation to mitigate its negative effects on the fleet.

We introduce a helicopter-borne mass balance approach, utilizing the HELiPOD platform, to accurately quantify methane (CH₄) emissions from coal mining activities. The comparison of our top-down mass flux estimates (up to 3000 kg h⁻¹) against those from bottom-up in-mine CH₄ safety sensors revealed very good agreement. This approach also has a great potential in quantifying emission source strengths (down to 20 kg h⁻¹) from a wide range of other CH₄ emitters (e.g. landfills, oil & gas industry).

Solar irradiance monitoring is an integral part of climate research. But the major drawback was the larger uncertainties associated with the measurements. To minimize the uncertainties, improved calibration standards, measurement techniques and sensors were developed. In this study we are validating the a newly developed Bi-Tec sensor spectroradiometer with an average uncertainty of 0.53 % with world radiometric reference (WRR) and international system of units (SI).

This study focuses on aerosol particles, which critically influence the atmosphere by scattering and absorbing light. To understand these interactions, airborne field campaigns deploy instruments that can measure these particles’ directly or indirectly via remote sensing. We introduce the In Situ Aerosol Retrieval Algorithm (ISARA) to ensure consistency between aerosol measurements and show that the two data sets generally align, with some deviation caused by the presence of larger particles.

In this study, we developed a new way to produce and measure nitrous acid, an important atmospheric gas that can be difficult to synthesize. By carefully controlling temperatures and reaction conditions, we created high-purity (<96 %) samples and showed how they can be stored and released when needed. This advance makes it easier to study how nitrous acid contributes to air pollution and atmospheric interactions, while also improving tools for laboratory experiments and instrument testing.

A three-dimensionally printed filter holder is connected to a lightweight, high-performance pump. This sampling system allows for easy and cost-effective measurements of organic aerosols at different heights and locations. By elucidating the chemical composition of organic aerosol, sources and processing of the compounds can be identified. Measurements at different altitudes and times of the day provide insight into the chemical aging and daytime trends of the aerosol particles.

The vertical distribution of atmospheric ice impacts Earth's weather and climate. The Ice Cloud Imager (ICI) will measure at microwave and sub-millimetre frequencies, which are well suited to detect atmospheric ice. In this study, a machine learning model is trained on ICI simulations. Results show that the vertical distribution of ice can be derived from ICI observations, and that ICI could offer a valuable data source that complements existing radar- and lidar-based measurements.

This study evaluates dust particles, their identification, amount and variability in the atmosphere using Metop-satellite observations from 2007 to 2019. Results show reliable detection of dust and consistent spatial and seasonal patterns. Dust-dominated observations have moderate to good accuracy against ground-based measurements, though biases and outliers occur. Sampling density varies across regions, but satellites still provide valuable contributions to global dust monitoring.

Monitoring airborne microbes is crucial for health and ecosystems, but often slow and expensive. We adapted an existing instrument, Rapid-E, using Laser-Induced Fluorescence and machine learning, for rapid, field-deployable bacterial identification. Our system successfully detected bacteria and showed promise in distinguishing various species. This faster approach improves environmental monitoring and helps safeguard public health by quickly spotting potential microbial threats in the air.

We tested two satellite-based irradiance datasets against both high- and low-accuracy ground-based measurements. The dataset is unique: it includes irradiance measurements from a new research-grade monitoring station in a rare climate, along with new satellite data from China's Fengyun-4B geostationary satellite. Findings suggest that using low-accuracy measurements as a reference for validation can be risky.

We propose a novel method which is accurate in identification of micro-dynamics phase transition processes for ammonium sulfate aerosols by using two-dimensional correlation spectroscopy. we explore more sophisticated structural evolution patterns and the precise sequence of hydrogen-bonding rearrangements. These findings deepen the mechanistic understanding of aerosol phase transitions at the molecular scale, which could inspire new research directions in atmospheric heterogeneous chemistry.

We developed and validated a model to correct for water vapor diffusion in sampling bags, enabling reconstruction of the original isotopic composition. We quantified all major sources of error and their uncertainties and introduced permeability experiments as a necessary step before field sampling. This study improves the accuracy, reliability, and standardization of atmospheric isotope observations using air bags.

An algorithm to retrieve water vapor above clouds, based on shortwave infrared observations, is developed and tested with idealized and realistic atmospheric profiles. It aims to improve understanding of interactions between water vapor and clouds to enhance models. Integrated into the C³IEL space mission planned for 2028, it uses a Bayesian method and shows good accuracy, except for thin or low clouds.

An improved version (v.5.3) of Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) winds is now available. The wind speeds are determined by use of the Doppler effect. Using a better set of reference molecules to determine the Doppler effect as well as a new method for determining the heading angle (look direction) of the satellite has improved our wind speeds, which has been validated by other instruments. These winds can be used to improve atmospheric models.

Rare forms of oxygen, called isotopes, in carbon dioxide help scientists understand important atmopsheric processes, for example where CO₂ comes from. In this paper, we examined how to improve the accuracy of a laser-based method for measuring oxygen isotopes in CO₂, focusing on the rare mass-17 isotope. By testing instruments in two labs, we identified ways to make the results more reliable. Our findings provide practical guidance and support wider use of this method in environmental studies.

Ammonia, a toxic gas produced largely through agricultural emissions, is one of the key pollutants that can be monitored by satellite instruments orbiting the Earth. Satellites rely on accurate spectral line parameters to interpret their data, which are obtained through fitting data from lab-based measurements. This work performs such experiments on the ammonia ν₂ band, determining new line parameters which will provide a more accurate basis for modelling satellite measurements of ammonia.

This study uses information theory to enhance rainfall retrieval from attenuation data of commercial microwave links (CML). The framework enables evaluation of the performance of CMLs as rainfall sensors in a probabilistic manner and assessment of information content of an arbitrary variable for the rainfall retrieval, e.g. synoptic type. The study shows that using the information theory concept can also directly improve data processing of attenuation data, in this case dry-wet classification.

The 2019–2020 Australian wildfires triggered massive Pyro-cumulonimbus clouds, injecting smoke aerosols into the stratosphere and forming a self-sustaining vortex that reached 35 km altitude. This vortex created a transient ozone mini-hole. Using satellite and ground-based observations, we tracked a 30–40 % initial ozone depletion, which decayed to ~7 % within a month. These findings highlight the impact of extreme wildfires on stratospheric dynamics and ozone composition.

A powerful lidar system has been installed at the high-altitude observatory Schneefernerhaus (2575 m) to allow for atmospheric temperature measurements up to more than 80 km within just one hour. The temperature profiles are calibrated by values obtained from chemiluminscence of the hydroxyl radical around 86 km. The temperature profiles are successfully compared with satellite and lidar data.

NO₂ surface concentrations are obtained across North America using satellite data and machine learning, and compared to traditional approaches to determine surface NO₂ from satellite observations.

This research investigates a novel method for directly estimating wind velocity variances from averaged Doppler spectra in the frequency domain. Compared to the conventional time-domain approach, the proposed method offers a substantial improvement. Despite some limitations, this study marks a significant advancement in turbulence estimation using pulsed Doppler lidars, which presents promising potential for wind turbine load assessments.

This study introduces GEMINI-UK (Greenhouse gas Emissions Monitoring network to Inform Net-zero Initiatives for the UK), the first UK-wide network using ground-based instruments to monitor net fluxes of CO₂ and methane. By simulating its performance, we show that GEMINI-UK will significantly reduce uncertainties in these flux estimates, complementing data from existing tall towers and future satellite missions. The network will strengthen the UK's ability to track greenhouse gases, evaluate climate policies, and meet net-zero goals.

We developed an improved method to estimate the size and amount of aerosol particles using measurements from the world’s first spaceborne high spectral resolution lidar. Tests with different aerosol types show that the method can provide reliable results and improve the description of larger particles. This work demonstrates the potential of future satellite observations to deliver a clearer picture of global air pollution and its role in climate.

In this work, a new quantitative retrieval of cloud thermodynamic phase partitioning based on multi-angle polarimetric imaging is presented. The retrieval is validated using synthetic data for idealized and realistic cloud cases and applied to measurements of the airborne specMACS instrument during the HALO-(AC)³ campaign. It provides high spatial resolution information about phase partitioning at cloud top and allows for example to study phase transitions in Arctic mixed-phase clouds.