Explore the vast range of titles available.

Ice-nucleating particles (INPs) produce ice from supercooled water droplets through heterogeneous freezing in the atmosphere. INPs have often 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 17 different locations and investigated the impact of altitude, terrain, time since last snowfall and depth upon freezing temperatures. The INP concentrations were measured using the home-built DRoplet Ice Nuclei Counter Zurich (DRINCZ) and were then compared to spatial, temporal and physicochemical parameters. Boxplots of the freezing temperatures showed large variability in INP occurrence, even for samples collected 10 m apart on a plain and 1 m apart in depth. Furthermore, undiluted samples had cumulative INP concentrations ranging between 1 and 200 INP mL−1 of snowmelt over a temperature range of −5 to −19 ∘C. From this field-collected dataset, we parameterized the cumulative INP concentrations per cubic meter of air as a function of temperature with the following equation cair*(T)=e-0.7T-7.05, comparing well with previously reported precipitation data presented in Petters and Wright (2015). When assuming (1) a snow precipitation origin of the INPs, (2) a cloud water content of 0.4 g m−3 and (3) 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 estimates in precipitation allow us to extrapolate the concentrations to a frozen cloud 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 frozen cloud fractions. In all, the generated parameterization for INP concentrations in snowmelt could help estimate cloud glaciation temperatures.

Saharan dust significantly influences hail occurrence in Europe. Using Copernicus Atmosphere Monitoring Service (CAMS) and reanalysis data, crowd-sourced hail reports, lightning data, and radar measurements, we find a strong correlation between elevated dust loading and hail events. Hail coverage exceeding 28 % of 1° × 1° grid cells only occurs when dust loading surpasses 2.4 mg m−2, while on hail days the median dust load is 1.82 times higher than on non-hail days (7σ difference). This effect is particularly strong along the Alpine crest, central France, eastern Germany, Austria, and Eastern Europe, where median dust loads more than double on hail days. By grouping data according to synoptic weather patterns, we confirm that hail days consistently exhibit higher dust concentrations regardless of prevailing synoptic conditions, supporting the robust link between dust and hail. Peak hail activity occurs at 38 mg m−2 or a dust optical depth of 0.033, suggesting enhanced cloud and ice nucleation. Above this range, hail frequency declines, likely due to microphysical or radiative constraints. Crowd-sourced reports show significantly more hail events on high-dust days, with up to 10 times more reports for hail >20 mm. Statistical hail models, including a logistic regression model (LRM) and a generalized additive model (GAM), rank dust as one of the top three predictors. Its inclusion increases the critical success index (CSI) by 5 % (LRM) and 12 % (GAM), and boosts explained variance in the GAM by 6 %. These findings identify Saharan dust as a key modulator of European hail activity, exerting both enhancing and inhibiting effects depending on dust concentration and the definition of hail events.

Hail-producing convective thunderstorms are a major threat to agriculture and infrastructure causing large financial losses. Remote sensing techniques such as dual-polarimetric weather radar can provide hail observations over large areas, but do not necessary reflect the situation on the ground. Current ground-based observations—such as automatic hail sensors, hail pads, and crowd-sourced reports—provide valuable information but exhibit limitations for validating radar products in terms of area coverage. Drone-based hail photogrammetry coupled with machine-learning (ML) techniques has the potential to close this observational gap by sampling thousands of hailstones within the hail core across large areas of hundreds of square meters and provide a hail size distribution estimation. However, the reliability of this new technique has not yet been assessed. In this study, we conducted experiments on different grass surfaces using synthetic hail objects of known sizes and quantity to assess the uncertainty of the ML-based hail size distribution retrievals. The findings of the experiments are then compared with a real hail event surveyed using drone-based hail photogrammetry. Using drone-based hail photogrammetry coupled with ML, 98% of the synthetic hail objects and 81% of hailstones were correctly detected. Additionally, sizes of the detected objects were retrieved with a minor underestimation of around −0.75 mm across all sizes for both synthetic hail objects (10–78 mm) and hailstones (3–24 mm). Hence, the high accuracy coupled with a large sampling area provides an estimation of representative hail size distributions on the ground. These reliable ground observations are a valuable basis for applications such as validation of weather radar hail estimates.

Hailstorms are among the most destructive weather events, posing significant threats to infrastructure, agriculture, and human life. This study applies hailstorm-tracking diagnostics to kilometer-scale, decade-long climate simulations over Europe using the COSMO v6 model driven by ERA5 reanalyses. Convection is treated explicitly, and hail is modeled online with the HAILCAST parameterization. Simulations represent current and future climate simulations, the latter corresponding to a +3 K global temperature increase implemented via a pseudo-global warming approach. We analyze high-frequency hail output at 5 min intervals, which enables tracking ∼40 000 hailstorms in Europe in current and future climate simulations separately. Storm track properties include length, duration, hail size, and spatial distribution, while three-dimensional environmental variables along these tracks yield storm-centered composites of hailstorm structure and allow for the examination of storm inflow environments. Our analysis reveals significant shifts in the characteristics of hailstorms under the future climate scenario. Notably, hail frequency trends vary across Europe, but the trends in hailstorm environments are comparatively uniform. The most striking results are as follows: (i) hail swath areas are projected to change in terms of both frequency and spatial extent, with a 2-fold increased frequency of storms producing ∼50 mm and larger hail diameters. Per-storm hail swath areas generally expand by 15 %–30 %, with swath area increases being more important for smaller hail, while frequency changes dominate for larger hail. (ii) The effect of increased hail melting due to the higher elevation of the 0 °C level on the storm maximum hail diameters is found to be minor. (iii) Precipitation and wind hazards accompanying hailstorms are expected to increase on average by 20 % and 5 %, respectively, whereas extreme hail–precipitation compound events, i.e., hail with a diameter of at least 30 mm followed by 50 mm h−1 of rainfall, are projected to be twice as frequent in the future.

Hail is a major threat associated with severe thunderstorms, and estimating the hail size is important for issuing warnings to the public. For the validation of existing operational, radar-derived hail estimates, ground-based observations are necessary. Automatic hail sensors, for example within the Swiss Hail Network, record the kinetic energy of hailstones to estimate the hail sizes. Due to the small size of the observational area of these sensors (0.2 m2), the full hail size distribution (HSD) cannot be retrieved. To address this issue, we apply a state-of-the-art custom trained deep learning object detection model to drone-based aerial photogrammetric data to identify hailstones and estimate the HSD. Photogrammetric data of hail on the ground were collected for one supercell thunderstorm crossing central Switzerland from southwest to northeast in the afternoon of 20 June 2021. The hail swath of this intense right-moving supercell was intercepted a few minutes after the passage at a soccer field near Entlebuch (canton of Lucerne, Switzerland) and aerial images were taken by a commercial DJI drone, equipped with a 45-megapixel full-frame camera system. The resulting images have a ground sampling distance (GSD) of 1.5 mm per pixel, defined by the focal length of 35 mm of the camera and a flight altitude of 12 m above the ground. A 2-dimensional orthomosaic model of the survey area (750.4 m2) is created based on 116 captured images during the first drone mapping flight. Hail is then detected using a region-based convolutional neural network (Mask R-CNN). We first characterize the hail sizes based on the individual hail segmentation masks resulting from the model detections and investigate the performance using manual hail annotations by experts to generate validation and test data sets. The final HSD, composed of 18 207 hailstones, is compared with nearby automatic hail sensor observations, the operational weather-radar-based hail product MESHS (Maximum Expected Severe Hail Size) and crowdsourced hail reports. Based on the retrieved data set, a statistical assessment of sampling errors of hail sensors is carried out. Furthermore, five repetitions of the drone-based photogrammetry mission within 18.65 min facilitate investigations into the hail-melting process on the ground.

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.

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), 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 a user-friendly and fully 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.

Supercell thunderstorms are the most hazardous thunderstorm category and particularly impactful to society. Their monitoring is challenging and often confined to the radar networks of single countries. By exploiting kilometer-scale climate simulations, a first-of-its-kind characterization of supercell occurrence in Europe is derived for the current and a warmer climate. Despite previous notions of supercells being uncommon in Europe, the model shows ~700 supercells per convective season. Occurrence peaks are co-located with complex topography e.g. the Alps. The absolute frequency maximum lies along the southern Alps with minima over the oceans and flat areas. Contrasting a current-climate simulation with a pseudo-global-warming +3\\(^\\)C global warming scenario, the future climate simulation shows an average increase of supercell occurrence by 11 %. However, there is a spatial dipole of change with strong increases in supercell frequencies in central and eastern Europe and a decrease in frequency over the Iberian Peninsula and southwestern France.

Multiple procedural techniques can be used to obtain tissue to create a formalin-fixed, paraffin-embedded specimen for comprehensive genomic profiling (CGP) in lung cancer. The literature is mixed on whether the procedure affects CGP success. To examine whether biopsy procedure affects lung cancer CGP success. This was a cross-sectional study of all patients with lung cancer whose specimens were submitted for CGP between January and February 2020. Multiple quality control metrics were used to determine whether cases were successfully profiled. In all, 3312 samples were identified. Overall, 67.5% (2236 of 3312) of samples were obtained from biopsies, 13.0% (432 of 3312) from fine-needle aspirations (FNAs), 9.7% (321 of 3312) from resections, 5.3% (174 of 3312) from fluid cytology cell blocks, and 4.5% (149 of 3312) from bone biopsies. Overall, 70.1% (2321 of 3312) of cases passed CGP, 15.4% (510 of 3312) of cases were released as qualified reports, and 14.5% (481 of 3312) of cases failed CGP. Resection samples were the most likely to be successfully sequenced, failing in only 2.8% (9 of 321) of instances, while fluid cytology specimens were the least likely, failing in 23.0% (40 of 174) of instances. Biopsy (14.5% [324 of 2236]), FNA (18.5% [80 of 432]), and bone biopsy (18.8% [28 of 149]) specimens failed at intermediate frequencies. On multivariate logistic regression analysis of CGP success on specimen type, fluid cytology (odds ratio [OR], 0.08; 95% CI, 0.03-0.19), biopsy (OR, 0.25; 95% CI, 0.11-0.52), FNA (OR, 0.14; 95% CI, 0.06-0.32), and bone biopsy (OR, 0.07; 95% CI, 0.03-0.17) specimens had decreased odds of CGP success relative to resection samples. Among patients with successfully sequenced samples, 48.0% were eligible for at least 1 therapy, based on a companion diagnostic or National Comprehensive Cancer Network biomarker. The method of tissue acquisition was an important preanalytic factor that determined whether a sample would be successfully sequenced and whether a clinically actionable genomic alteration would be detected.

Covalent probes can display unmatched potency, selectivity and duration of action, however, their discovery is challenging. In principle, fragments that can irreversibly bind their target can overcome the low affinity that limits reversible fragment screening. Such electrophilic fragments were considered non-selective and were rarely screened. We hypothesized that mild electrophiles might overcome the selectivity challenge, and constructed a library of 993 mildly electrophilic fragments. We characterized this library by a new high-throughput thiol-reactivity assay and screened them against ten cysteine-containing proteins. Highly reactive and promiscuous fragments were rare and could be easily eliminated. By contrast, we found selective hits for most targets. Combination with high-throughput crystallography allowed rapid progression to potent and selective probes for two enzymes, the deubiquitinase OTUB2, and the pyrophosphatase NUDT7. No inhibitors were previously known for either. This study highlights the potential of electrophile fragment screening as a practical and efficient tool for covalent ligand discovery.