Information on the viscosity of secondary organic aerosols is needed when making air quality, climate, and atmospheric chemistry predictions. Viscosity depends on temperature, so we developed a new method for measuring the temperature-dependent viscosity of small samples. As an application of the method, we measured the viscosity of farnesene secondary organic aerosol at different temperatures.

To retrieve tropospheric ozone column information, we subtract stratospheric column data of BASCOE from TROPOMI/S5P total ozone columns. The new S5P-BASCOE data agree well with existing tropospheric data like OMPS-MERRA-2. The data are also compared to ozone soundings. The tropospheric ozone columns show the expected temporal and spatial patterns. We will also apply the algorithm to future UV nadir missions like Sentinel 4 or 5 or to recent and ongoing missions like GOME_2 or OMI.

The quality control of sub-hourly rain gauge data is a challenging task due to the high variability and low spatial consistency of the data. We developed an innovative approach to the quality control of telemetric rain gauge data focused on assessing the reliability of individual observations. Our scheme employs weather radar data to detect erroneous rain gauge measurements and to assess the data reliability. The scheme is used operationally by the Polish meteorological and hydrological service.

Strong changes in pressure, temperature, and humidity occur when small scientific aircraft ascend through the atmosphere to measure carbon dioxide. These strong changes can produce errors in the carbon dioxide measurements. To avoid these errors, we present a low-cost and simple correction method. This low-complexity method allows more researchers to study atmospheric carbon dioxide, reducing entry barriers in this field.

Aerosol properties (size distributions, refractive indices) can be retrieved from in situ, angularly resolved light scattering measurements performed with polar nephelometers. We apply an established framework to assess the aerosol property retrieval potential for different instrument configurations, target applications, and assumed prior knowledge. We also demonstrate how a reductive greedy algorithm can be used to determine the optimal placements of the angular sensors in a polar nephelometer.

Integrated water vapour measurements are often compared for the calibration and validation of instruments or techniques. Measurements made at different altitudes must be corrected to account for the vertical variation of water vapour. This paper shows that the widely used empirical correction model has severe limitations that are overcome using the proposed model. The method is applied to the inter-comparison of GPS and satellite microwave radiometer data in a tropical mountainous area.

The aim of this work is to investigate the limitations of calculating long-term trends of a parameter that quantifies the overall effect of atmospheric aerosols on the solar radiation. A main finding is that even instruments with good agreement between their observations can show significantly different linear trends. By calculating time-varying trends, the trend agreement is shown to improve. We also show that different methods of trend estimation can result in significant trend differences.

This investigation presents adaptive thermal image velocimetry (A-TIV), a newly developed algorithm to spatially measure near-surface atmospheric velocities using an infrared camera mounted on uncrewed aerial vehicles. A validation and accuracy assessment of the retrieved velocity fields shows the successful application of the algorithm over short-cut grass and turf surfaces in dry conditions. This provides new opportunities for atmospheric scientists to study surface–atmosphere interactions.

Geostationary satellites continuously image a given location on Earth, a feature that satellites designed to characterize atmospheric ice lack. However, the relationship between geostationary images and atmospheric ice is complex. Machine learning is used here to leverage such images to characterize atmospheric ice throughout the day in a probabilistic manner. Using structural information from the image improves the characterization, and this approach compares favourably to traditional methods.

The ERICA is a specially designed aerosol particle mass spectrometer for in situ, real-time chemical composition analysis of aerosols. It can operate completely autonomously, in the absence of an instrument operator. Its design has enabled its operation under harsh conditions, like those experienced in the upper troposphere and lower stratosphere, aboard unpressurized high-altitude research aircraft. The instrument has successfully participated in several aircraft operations around the world.

In this study, we have investigated the impact of 3D clouds on the tropospheric NO₂ retrieval from UV–visible sensors. We applied standard NO₂ retrieval methods including cloud corrections to synthetic data generated by the 3D radiative transfer model. A sensitivity study was done for synthetic data, and dependencies on various parameters were investigated. Possible mitigation strategies were investigated and compared based on 3D simulations and observed data.

Precise and accurate measurements of vertical winds at the mesosphere and lower thermosphere are rare. Although meteor radars have been used for decades to observe horizontal winds, their ability to derive reliable vertical wind measurements was always questioned. In this article, we provide mathematical concepts to retrieve mathematically and physically consistent solutions, which are compared to the state-of-the-art non-hydrostatic model UA-ICON.

We show promising results for a new machine-learning based paradigm for processing field-acquired cloud droplet holograms. The approach is fast, scalable, and leverages GPUs and other heterogeneous computing platforms. It combines applications of transfer and active learning by using synthetic data for training and a small set of hand-labeled data for refinement and validation. Artificial noise applied during synthetic training enables optimized models for real-world situations.

This study develops an innovative approach for the detection and prediction of foehn winds. The approach uses products generated from GNSS (Global Navigation Satellite Systems) in combination with machine learning-based classification algorithms to detect and predict foehn winds at Altdorf, Switzerland. Results are encouraging and comparable to similar studies using meteorological data, which might qualify the method as an additional tool for short-term foehn forecasting in the future.

Accurately estimating greenhouse gas (GHG) emissions is essential to reaching net-zero goals to combat the climate crisis. Airborne box-flights are ideal for assessing regional GHG emissions, as they can attain small error. We compare two box-flight algorithms and found they produce similar results, but daily variability must be considered when deriving emissions inventories. Increasing the consistency and agreement between airborne methods moves us closer to achieving more accurate estimates.

Weather radars measure rainfall in altitude whereas hydro-meteorologists are mainly interested in rainfall at ground level. During their fall, drops are advected by the wind which affects the location of the measured field. Governing equation linking acceleration, gravity, buoyancy, and drag force is updated to account for oblateness of drops. Then multifractal wind is used as input to explore velocities and trajectories of drops. Finally consequence on radar rainfall estimation is discussed.