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On the 28th of June 2021, a supercell traversed the complex topography of Switzerland. While propagating over a distance of 350 km and lasting 8 hours, the cell produced severe hail with diameters up to 9 cm. A case study is performed, aimed at understanding the development of the supercell environment, and the physical processes associated with the cell’s initiation, intensification, propagation, and dissipation. To this end, ensemble hindcast simulations are conducted with a convection-resolving numerical weather prediction model (COSMO-1E) coupled to a one-dimensional hail growth parameterization (HAILCAST). The simulation setup is identical to the operational ensemble forecasts performed at MeteoSwiss. Object-based analysis of the simulated hail cell is facilitated by a tracking algorithm, developed specifically to address the many challenges associated with tracking hail storms in output from numerical simulations. The hail cell tracks enable a storm-relative analysis of the storm environment and the temporal evolution of the storm structure during its phases of development. The inflow of air feeding the storm updraft is investigated with the aid of Lagrangian air parcel trajectories initiated along the hail track, giving novel insights into the low-level flow and moisture sources of the storm. All ensemble members produce thunderstorms comparable to the observed storm in terms of track position and intensity. However, the observations and the simulations differ with respect to initiation location and the lifetime of the cell. The analysis focuses on the intensification and dissipation stages of the storm, and especially how and why the cell tracks in the ensemble members differ from each other. more...

Aerosol–cloud interactions, including the ice nucleation of supercooled liquid water droplets caused by ice-nucleating particles (INPs) and macromolecules (INMs), are a source of uncertainty in predicting future climate. Because INPs and INMs have spatial and temporal heterogeneity in source, number, and composition, predicting their concentration and distribution is a challenge requiring apt analytical instrumentation. Here, we present the development of our drop Freezing Ice Nuclei Counter (FINC) for the estimation of INP and INM concentrations in the immersion freezing mode. FINC's design builds upon previous droplet freezing techniques (DFTs) and uses an ethanol bath to cool sample aliquots while detecting freezing using a camera. Specifically, FINC uses 288 sample wells of 5–60 µL volume, has a limit of detection of −25.4 ± 0.2 ∘C with 5 µL, and has an instrument temperature uncertainty of ± 0.5 ∘C. We further conducted freezing control experiments to quantify the nonhomogeneous behavior of our developed DFT, including the consideration of eight different sources of contamination. As part of the validation of FINC, an intercomparison campaign was conducted using an NX-illite suspension and an ambient aerosol sample from two other drop freezing instruments: ETH's DRoplet Ice Nuclei Counter Zurich (DRINCZ) and the University of Basel's LED-based Ice Nucleation Detection Apparatus (LINDA). We also tabulated an exhaustive list of peer-reviewed DFTs, to which we added our characterized and validated FINC. In addition, we propose herein the use of a water-soluble biopolymer, lignin, as a suitable ice-nucleating standard. An ideal INM standard should be inexpensive, accessible, reproducible, unaffected by sample preparation, and consistent across techniques. First, we compared lignin's freezing temperature across different drop freezing instruments, including on DRINCZ and LINDA, and then determined an empirical fit parameter for future drop freezing validations. Subsequently, we showed that commercial lignin has consistent ice-nucleating activity across product batches and demonstrated that the ice-nucleating ability of aqueous lignin solutions is stable over time. With these findings, we present lignin as a good immersion freezing standard for future DFT intercomparisons in the research field of atmospheric ice nucleation., Atmospheric Measurement Techniques, 14 (4), ISSN:1867-1381, ISSN:1867-8548 more...

Aerosol-cloud interactions, including the ice nucleation of supercooled liquid water droplets caused by ice nucleating particles (INPs) and macromolecules (INMs), are a source of uncertainty in predicting future climate. Because of INPs' and INMs' spatial and temporal heterogeneity in source, number, and composition, predicting their concentration and distribution is a challenge, requiring apt analytical instrumentation. Here, we present the development of our drop Freezing Ice Nucleation Counter (FINC), a droplet freezing technique (DFT), for the quantification of INP and INM concentrations in the immersion freezing mode. FINC's design builds upon previous DFTs and uses an ethanol bath to cool sample aliquots while detecting freezing using a camera. Specifically, FINC uses 288 sample wells of 5–60 µL volume, has a limit of detection of −25.37 ± 0.15 ˚C with 5 µL, and has an instrument temperature uncertainty of ± 0.5 ˚C. We further conducted freezing control experiments to quantify the non-homogeneous behavior of our developed DFT, including the consideration of eight different sources of contamination. As part of the validation of FINC, an intercomparison campaign was conducted using an NX-illite suspension and an ambient aerosol sample with two other drop-freezing instruments: ETH's DRoplet Ice Nuclei Counter Zurich (DRINCZ) and University of Basel’s LED-based ice nucleation detection apparatus (LINDA). We also tabulated an exhaustive list of peer-reviewed DFTs, to which we added our characterized and validated FINC. In addition, we propose herein the use of a water-soluble biopolymer, lignin, as a suitable ice nucleating standard. An ideal INM standard should be inexpensive, accessible, reproducible, unaffected by sample preparation, and consistent across techniques. First, we show that commercial lignin has a consistent ice nucleating activity across product batches. Second, we demonstrate that aqueous lignin solutions exhibit good solution stability over time. Third, we compare its freezing temperature across different drop-freezing instruments, including on DRINCZ, LINDA, and on the Weizmann Institute's Supercooled Droplets Observation on a Microarray (WISDOM) and determine an empirical fit parameter for future drop freezing validations. With these findings, we aim to show that lignin can be used as a good immersion freezing standard in future technique intercomparisons in the field of atmospheric ice nucleation. more...

Aerosol-cloud interactions are a source of high uncertainties in predicting future climate. One important aerosol-cloud interaction is ice nucleation of supercooled liquid water droplets caused by ice nucleating particles (INPs). Predicting the distribution and concentration of INPs is a challenge because of their spatial and temporal heterogeneity in source, number, and composition. Organic aerosols are particularly diverse and complex in chemical and physical composition and can be highly ice active to varying degrees. Here we present the development of our drop Freezing Ice Nucleation Chamber (FINC) for the quantification of INP concentration of aerosol in the immersion freezing mode. As part of the development and validation of FINC, we show results from an intercomparison using lignin as a comparison standard with three other drop-freezing instruments (ETH’s Drop Freezing Ice Nucleation counter Zurich (DRINCZ), University of Basel’s LED-based Ice Nucleation Detection Apparatus (LINDA), and Weizmann Institute’s Supercooled Droplet Observation of Microarray (WISDOM)). In addition, we present here preliminary findings of FINC’s application for determining predictors of the ice nucleating ability of organic matter, using several standards and field-collected samples of dissolved organic matter as a proxy for organic aerosol emitted from natural waters. These methods and results can aid in the community’s search for predictors and parameterizations of organic aerosol induced ice nucleation. more...

According to UNFCCC statistics, in 2015 Romania was the country in the European Union that reported the highest emissions of CH4 from the oil and gas sector to the atmosphere, in particular related to methane production and end use. Limiting these oil and gas-related emissions could provide an attractive greenhouse gas emission reduction target for the EU. However, the reported estimates are derived using standard emission factors and there are only very few observations which investigate whether the reported emissions are realistic. The ROMEO project was designed to provide experimental quantification of methane emissions from the oil and gas sector in Romania. This may strengthen the scientific basis for establishing effective emission mitigation measures. ROMEO is part of the international Climate and Clean Air Coalition's (CCAC's) Methane Science Studies. In August 2019, the first phase of ROMEO was a city campaign in Bucharest and Ploiesti, where methane emissions were quantified at the street level, using three vehicles. Source attribution was carried out by isotopic analysis and measurement of the ethane-methane ratio. The main ROMEO campaign took place in October 2019, using as campaign base the Strejnicu airfield near Ploiesti. Eight ground measurement teams visited more than 1000 individual facilities and performed methane measurements by stationary and mobile measurements from vehicles, using tracer release approaches and by plume mapping from drones. Very low wind speeds during the campaign period made emission quantification challenging, but about 200 quantifications were attempted. An optical gas imaging team visited many facilities in order to investigate the origin of the emissions at the component scale. Our project partner OMV-Petrom provided information on the facilities and site access where needed. Sites for emissions quantification were selected independent of the operator. To connect the facility scale to the regional scale, two research aircraft from INCAS and Scientific Aviation Inc. performed more than 20 research flights to identify and quantify methane emissions from individual facilities, facility clusters and extended regions. Ground-based in situ and total column measurements provide additional information on the background levels of CH4. Various models are used for emission quantification, from plume dispersion and mass balance models for individual facilities to atmospheric chemistry and transport models for interpretation of the larger scale aircraft measurements. The final goal of ROMEO is to provide a combined bottom-up and top-down approach to quantify CH4 emissions related to oil and gas exploration, natural gas distribution and gas use from Romania. I will present the overall setup of the ROMEO project, interesting examples from individual facilities and preliminary results from ground and airborne measurements. more...

Methane from facility-scale sources (e.g. landfills and oil and gas production facilities) are prone to leakage giving rise to highly uncertain emission flux estimates. To assess the overall impact of these sources, quantification from a representative set of individual sources – from which bottom-up inventories are generated - is necessary. An attractive approach to quantify emissions from diffusive and leaky sources involves deploying an unmanned aerial vehicle (UAV) equipped with a methane sensor which allows complete mapping of the spatial and temporal variability of emission plumes within a short period of time.Atmospheric methane concentrations were measured using a Quantum Cascade Laser Absorption Spectrometer (QCLAS) developed in-house. The spectrometer reaches in-flight precision of a few ppb at 1s time resolution, and its lightweight and compact footprint (~ 2.0 kg, ~ 15.0 x 45.0 x 25.0 cm) allows it to be mounted and flown on a commercial drone.We quantify methane emission fluxes from local sources by applying the mass balance method using the drone-based QCLAS system. The drone was flown downwind of a given source perpendicular to the main wind direction at different altitudes above ground, while geostatistical interpolation (Kriging) of the measured methane molar fractions was performed to spatially fill the gaps. The interpolated concentrations were multiplied by the cross-sectional area and the mean stream-wise wind profile obtained from a 3D sonic anemometer to get an emission flux.We report on the analysis of how well known emissions can be reproduced using this quantification setup based on controlled release experiments. Furthermore, we discuss the sensitivity of different measurement configurations, and provide recommendations for an optimal sampling and quantification strategy. We demonstrate the suitability and flexibility of the quantification method in investigating a wide range of facility-scale sources, which are not attainable with measurements from conventional ground-based sensors. more...

Ice nucleating particles (INPs) produce ice from supercooled water droplets through heterogeneous freezing in the atmosphere. Since the concentration of ice crystals affects the radiative properties of clouds as well as precipitation, constraining the liquid water to ice ratio could help reduce aerosol-cloud interaction uncertainties. INPs have been collected at the Jungfraujoch research station (at 3500 m a.s.l.) in central Switzerland; yet spatially diverse data on INP occurrence in the Swiss Alps are scarce and remain uncharacterized. We address this scarcity through our Swiss Alpine snow sample study which took place during the winter of 2018. We collected a total of 88 fallen snow samples across the Alps at different locations, altitudes, terrains, times since last snowfall and depths. The INP concentrations were measured using the homebuilt DRoplet Ice Nuclei Counter Zurich (DRINCZ) and were then compared to spatial, meteorological and physiochemical parameters. We also extend an alternative way of displaying frozen fraction (FF) versus temperature data through visualizing freezing temperatures as a boxplot to field collected samples. This plotting method displays the freezing temperature in one dimension, instead of the former two dimensions of FF vs temperature, allowing a condensed display of freezing temperature measurements. In the collected snow samples, large variability in INP occurrence was found, even for samples collected 10 m apart on a plain and 1 m apart in depth. Furthermore, undiluted samples had INP concentrations ranging between 1 and 100 INP ml−1 of snow water over a temperature range of −5 to −19 °C. From this field-collected data set, we parameterize the INP concentrations per milliliter of meltwater as a function of temperature with the following equation c*air (T)=e(−0.7T–7.05), comparing well with previously reported precipitation data presented in Petters and Wright, 2015. When assuming a cloud water content of 0.4 g−3 and a critical INP concentration for glaciation of 10 m−3, the majority of the snow precipitated from clouds with glaciation temperatures between −5 and −20 °C. Based on the observed variability in INP concentrations, we conclude that studies conducted at the high-altitude research station Jungfraujoch are representative for INP measurements in the Swiss Alps. Furthermore, the INP concentration precipitation estimates allow us to extrapolate the concentrations to a cloud frozen fraction. Indeed, this approach for estimating the liquid water to ice ratio in mixed phase clouds compares well with aircraft measurements, ground-based lidar and satellite retrievals of cloud frozen fractions. In all, the generated parameterization for INP concentrations in meltwater could help estimate cloud glaciation temperatures. more...

Ice formation in the atmosphere is important for regulating cloud lifetime, Earth's radiative balance and initiating precipitation. Due to the difference in the saturation vapor pressure over ice and water, in mixed-phase clouds (MPCs), ice will grow at the expense of supercooled cloud droplets. As such, MPCs, which contain both supercooled liquid and ice, are particularly susceptible to ice formation. However, measuring and quantifying the concentration of ice nucleating particles (INPs) responsible for ice formation at temperatures associated with MPCs is challenging due to their very low concentrations in the atmosphere (~ 1 in 105 at − 30 °C). Atmospheric INP concentrations vary over several orders of magnitude at a single temperature and strongly increase as temperature approaches the homogeneous freezing threshold of water. To further quantify the INP concentration in nature and perform systematic laboratory studies to increase the understanding of the properties responsible for ice nucleation, a new drop freezing instrument, the DRoplet Ice Nuclei Counter Zurich (DRINCZ) is developed. The instrument is based on the design of previous drop freezing assays and uses a USB camera to automatically detect freezing in a 96-well tray cooled in an ethanol chilled bath with an automated analysis procedure. Based on an in-depth characterization of DRINCZ, we develop a new method for quantifying and correcting temperature biases across drop freezing assays. DRINCZ is further validated performing NX illite experiments, which compare well with the literature. The temperature uncertainty in DRINCZ was determined to be ± 0.9 ˚C. Furthermore, we demonstrate the applicability of DRINCZ by measuring and analyzing field collected snow samples during an evolving synoptic situation in the Austrian Alps. The field samples fall within previously observed ranges for cumulative INP concentrations and show a dependence on air mass origin and upstream precipitation amount. more...