Drone-based photogrammetry combined with deep-learning to estimate hail size distributions and melting of hail on the ground

Hail is a major threat associated with severe thunderstorms and an estimation of the hail size is important for issuing warnings to the public. Operational radar products exist that estimate the size of the expected hail. For the verification of such products, ground based observations are necessary. Automatic hail sensors, as for example within the Swiss hail network, record the kinetic energy of hailstones and can estimate with this the hail diameters. However, due to the small size of the observational area of these sensors (0.2 m2) the estimation of the hail size distribution (HSD) can have large uncertainties. To overcome this issue, we combine drone-based aerial photogrammetry with a state-of-the-art custom trained deep-learning object detection model to identify hailstones in the images and estimate the HSD in a final step. This approach is applied to photogrammetric image data of hail on the ground from a supercell storm, that crossed central Switzerland from southwest to northeast in the afternoon of June 20, 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 Lucerne, Switzerland) and aerial images of the hail on the ground were taken by a commercial DJI drone, equipped with a 50 megapixels full frame camera system. The average ground sampling distance (GSD) that could be reached was 1.5 mm per pixel, which is set by the mounted camera objective with a focal length of 35 mm and a flight altitude of 12 m above ground. A 2D orthomosaic model of the survey area (750 m2) is created based on 116 captured images during the first drone mapping flight. Hail is then detected by 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 by using manual hail annotations by experts to generate validation and test data sets. The final HSD, composed of 18209 hailstones, is compared with nearby automatic hail sensor observations, the operational weather radar based hail product MESHS (Maximum Expected Severe Hail Size) and some crowdsourced hail reports. Based on the retrieved drone hail data set, a statistical assessment of sampling errors of hail sensors is carried out. Furthermore, five repetitions of the drone-based photogrammetry mission within about 18 min give the unique opportunity to investigate the hail melting process on the ground for this specific supercell hailstorm and location

Multiscale multifactorial approaches for engineering tendon substitutes

The physiology of tendons and the continuous strains experienced daily make tendons very prone to injury. Excessive and prolonged loading forces and aging also contribute to the onset and progression of tendon injuries, and conventional treatments have limited efficacy in restoring tendon biomechanics. Tissue engineering and regenerative medicine (TERM) approaches hold the promise to provide therapeutic solutions for injured or damaged tendons despite the challenging cues of tendon niche and the lack of tendon-specific factors to guide cellular responses and tackle regeneration. The roots of engineering tendon substitutes lay in multifactorial approaches from adequate stem cells sources and environmental stimuli to the construction of multiscale 3D scaffolding systems. To achieve such advanced tendon substitutes, incremental strategies have been pursued to more closely recreate the native tendon requirements providing structural as well as physical and chemical cues combined with biochemical and mechanical stimuli to instruct cell behavior in 3D architectures, pursuing mechanically competent constructs with adequate maturation before implantation.Authors acknowledge the project “Accelerating tissue engineering and personalized medicine discoveries by the integration of key enabling nanotechnologies, marinederived biomaterials and stem cells,” supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). Authors acknowledge the H2020 Achilles Twinning Project No. 810850, and also the European Research Council CoG MagTendon No. 772817, and the FCT Project MagTT PTDC/CTM-CTM/ 29930/2017 (POCI-01-0145-FEDER-29930

Spatial and temporal variability in the ice-nucleating ability of alpine snowmelt and extension to cloud frozen fraction

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 on freezing temperatures. The INP concentrations were measured using the homebuilt DRoplet Ice Nuclei Counter Zurich (DRINCZ) and were then compared to spatial, temporal and physiochemical 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 oC. From this field-collected dataset, we parameterized the cumulative INP concentrations per m-3 of air 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 (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

The effect of 3°C global warming on hail in Europe

Hail is a severe weather phenomenon in the Alpine region causing extensive damage to life and infrastructure. However, it is still unclear how hail events change in a future warmer climate. In the scClim project, we conducted convection-permitting regional climate simulations over Europe using the model COSMO with a ∼ 2.2 km horizontal resolution. The simulations encompass both present-day climate conditions for 2011–2021 and a climate scenario with a 3◦C global warming using a pseudo-global-warming approach. ERA5 reanalyses were used as boundary conditions and a CMIP6 simulation (MPI-ESM1-2-HR) for the large-scale climate-change signal. The simulations, with integrated online diagnostics for hail and lighting, provide total precipitation and maximum hail size estimates every 5 minutes, together with the maximum hourly lightning potential. This detailed model output allows for hail cell tracking in the climate simulations and the analysis of hail events in a warmer climate. The present-day simulation has been validated against observations of temperature, precipitation, hail and lightning. For hail in particular, the model validation with radar-based, station-based and crowd-sourced observations shows an overall good model performance in simulating hail on spatial, diurnal and seasonal scales. This allows further study of the climate signal of hail as simulated with the pseudo-global-warming approach. We plan to show first results of the simulation with a 3◦C global warming, namely, the changes in the spatial distribution and seasonal cycle of hail in Europe as well as the lifetime, storm area and location of hail cells

Drone-based photogrammetry combined with deep-learning to estimate hail size distributions and melting of hail on the ground

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, radarderived hail estimates, ground-based observations are necessary. Automatic hail sensors, as 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 (0.2m2), 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. We present the results of a single hail event on 20June2021. Thesurvey area suitable for hail detection within the created 2D orthomosaic model is 750m2. The final HSD, composed of 18’209 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 and five repetitions of the drone-based photogrammetry mission within 18.65min after the hail fall give the opportunity to investigate the hail melting process on the ground. Finally, we give an outlook to future plans and possible improvements of drone-based hail photogrammetry

Development of the drop Freezing Ice Nuclei Counter (FINC), intercomparison of droplet freezing techniques, and use of soluble lignin as an atmospheric ice nucleation standard

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.ISSN:1867-1381ISSN:1867-854

Development of the drop Freezing Ice Nuclei Counter (FINC), intercomparison of droplet freezing techniques, and use of soluble lignin as an atmospheric ice nucleation standard

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 mu L volume, has a limit of detection of − 25.4 ± 0.2 degrees C with 5 mu L, and has an instrument temperature uncertainty of ± 0.5 degrees 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

Development of the DRoplet Ice Nuclei Counter Zurich (DRINCZ): Validation and application to field-collected snow samples

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.ISSN:1867-1381ISSN:1867-854

The role of supervision in teacher development

This book aims to unpack one of the most difficult issues facing teacher education: What is the role of supervision in the development of teachers? The purpose of this chapter is to examine theory and research in supervision and evaluation and to place the contributions made by this book along a continuum of what we know about field supervision of student teachers