Investigating hail remote detection accuracy: A comprehensive verification of radar metrics with 150’000 crowdsourced observations over Switzerland.

Hail detection and sizing using radar is a common practice and radar-based algorithms have been developed and operationally deployed in several countries. Switzerland National Weather Service (MeteoSwiss) uses two radar hail metrics: the probability of hail at the ground (POH) to assess the presence of hail, and the maximum expected severe hailstone size (MESHS) to estimate the largest hailstone diameter. Radar-based hail metrics have the advantage of extended spatial coverage and high resolution, however they don’t measure hail directly on the ground. Therefore, they need to be calibrated and further verified with ground-based observations. Switzerland benefits from a large dataset of crowdsourced hail observations gathered through the reporting function of the MeteoSwiss app. Crowdsourced observations can contain wrong reports, both intended (jokes) or unintended (misuse), and have to be filtered before being used. Radar reflectivity is often used to remove reports where the maximum reflectivity is below a usual storm environment. However, this filtering method renders the observations dependent on the same radar signal used to compute hail metrics. Therefore, we test a spatio-temporal clustering method (ST-DBSCAN) based solely on the data to remove implausible reports. We then use the filtered dataset to make an extended verification of POH and MESHS in terms of Probability of Detection (POD), False Alarms Ratio (FAR), Critical Success Index (CSI) and Heidke Skill Score (HSS). We estimate the most skillful POH threshold to predict the presence of hail. We investigate the conditions leading to POH false alarms (radar signal without observation) and misses (observations without radar signal). We assess how good MESHS is compared to POH in discriminating > 2cm hailstones, and how good MESHS is in estimating the maximum hail size on the ground for thresholds of 3cm, 4cm, and 6cm. We found that POH has a good skill for hail detection with HSS reaching 0.8 (FAR 0.5)

How observations from automatic hail sensors in Switzerland shed light on local hailfall duration and compare with hailpads measurements

Measuring hailstorms is a difficult task due to the rarity and mainly small spatial extent of the events. Especially, hail observations from ground-based time-recording instruments are scarce. We present the first study of extended field observations made by a network of 80 automatic hail sensors from Switzerland. The main benefits of the sensors are the live recording of the hailstone kinetic energy and the precise timing of the impacts. Its potential limitations include a diameter dependent dead time which results in less than 5 % of missed impacts, and the possible recording of impacts not due to hail which can be filtered using a radar reflectivity filter. We assess the robustness of the sensors measurements by doing a statistical comparison of the sensor observations with hailpads observations and we show that despite their different measurement approaches, both devices measure the same hail size distributions. We then use the timing information to measure the local duration of hail events, the cumulative time distribution of impacts and the time of the largest hailstone during a hail event. We find that 75 % of local hailfalls last just a few minutes (from less than 4.4 min to less than 7.7 min, depending on a parameter to delineate the events) and that 75 % of impacts occurs in less than 3.3 min to less than 4.7 min. This time distribution suggests that most hailstones, including the largest, fall during a first phase of high hailstone density, while a few remaining and smaller hailstones fall in a second low density phase.</p

A novel method to identify sub-seasonal clustering episodes of extreme precipitation events and their contributions to large accumulation periods

Temporal (serial) clustering of extreme precipitation events on sub-seasonal time scales is a type of compound event. It can cause large precipitation accumulations and lead to floods. We present a novel, count-based procedure to identify episodes of sub-seasonal clustering of extreme precipitation. We introduce two metrics to characterise the frequency of sub-seasonal clustering episodes and their relevance for large precipitation accumulations. The procedure does not require the investigated variable (here precipitation) to satisfy any specific statistical properties. Applying this procedure to daily precipitation from the ERA5 reanalysis data set, we identify regions where sub-seasonal clustering occurs frequently and contributes substantially to large precipitation accumulations. The regions are the east and northeast of the Asian continent (north of Yellow Sea, in the Chinese provinces of Hebei, Jilin and Liaoning; North and South Korea; Siberia and east of Mongolia), central Canada and south of California, Afghanistan, Pakistan, the southwest of the Iberian Peninsula, and the north of Argentina and south of Bolivia. Our method is robust with respect to the parameters used to define the extreme events (the percentile threshold and the run length) and the length of the sub-seasonal time window (here 2–4 weeks). This procedure could also be used to identify temporal clustering of other variables (e.g. heat waves) and can be applied on different time scales (sub-seasonal to decadal). The code is available at the listed GitHub repository

How observations from automatic hail sensors in Switzerland shed light on local hailfall duration and compare with hailpad measurements

Measuring the properties of hailstorms is a difficult task due to the rarity and mainly small spatial extent of the events. Especially, hail observations from ground-based time-recording instruments are scarce. We present the first study of extended field observations made by a network of 80 automatic hail sensors from Switzerland. The main benefits of the sensors are the live recording of the hailstone kinetic energy and the precise timing of the impacts. Its potential limitations include a diameter-dependent dead time, which results in less than 5 % of missed impacts, and the possible recording of impacts that are not due to hail, which can be filtered using a radar reflectivity filter. We assess the robustness of the sensors' measurements by doing a statistical comparison of the sensor observations with hailpad observations, and we show that, despite their different measurement approaches, both devices measure the same hail size distributions. We then use the timing information to measure the local duration of hail events, the cumulative time distribution of impacts, and the time of the largest hailstone during a hail event. We find that 75 % of local hailfalls last just a few minutes (from less than 4.4 min to less than 7.7 min, depending on a parameter to delineate the events) and that 75 % of the impacts occur in less than 3.3 min to less than 4.7 min. This time distribution suggests that most hailstones, including the largest, fall during a first phase of high hailstone density, while a few remaining and smaller hailstones fall in a second low-density phase

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

Genetic polymorphisms of MMP1, MMP3 and MMP7 gene promoter and risk of colorectal adenoma

BACKGROUND: Matrix metalloproteinases (MMP) have been shown to play a role in colorectal cancer (CRC). More recently, MMP1, MMP3 and MMP7 functional gene promoter polymorphisms have been found to be associated with CRC occurrence and prognosis. To document the role of MMP polymorphisms in the early step of colorectal carcinogenesis, we investigated their association with colorectal adenoma risk in a case-control study comprising 295 patients with large adenomas (LA), 302 patients with small adenomas (SA) and 568 polyp-free (PF) controls. METHODS: Patients were genotyped using automated fragment analysis for MMP1 -1607 ins/del G and MMP3 -1612 ins/delA (MMP3.1) polymorphisms and allelic discrimination assay for MMP3 -709 A/G (MMP3.2) and MMP7 -181 A/G polymorphisms. Association between MMP genotypes and colorectal adenomas was first tested for each polymorphism separately and then for combined genotypes using the combination test. Adjustment on relevant variables and estimation of odds ratios were performed using unconditional logistic regression. RESULTS: No association was observed between the polymorphisms and LA when compared to PF or SA. When comparing SA to PF controls, analysis revealed a significant association between MMP3 -1612 ins/delA polymorphism and SA with an increased risk associated with the 6A/6A genotype (OR = 1.67, 95%CI: 1.20–2.34). Using the combination test, the best association was found for MMP3.1-MMP1 (p = 0.001) with an OR of 1.88 (95%CI: 1.08–3.28) for the combined genotype 2G/2G-6A/6A estimated by logistic regression. CONCLUSION: These data show a relation between MMP1 -1607 ins/del G and MMP3 -1612 ins/delA combined polymorphisms and risk of SA, suggesting their potential role in the early steps of colorectal carcinogenesis

Shear-wave velocity structure beneath the Dinarides from the inversion of Rayleigh-wave dispersion

Highlights • Rayleigh-wave phase velocity in the wider Dinarides region using the two-station method. • Uppermost mantle shear-wave velocity model of the Dinarides-Adriatic Sea region. • Velocity model reveals a robust high-velocity anomaly present under the whole Dinarides. • High-velocity anomaly reaches depth of 160 km in the northern Dinarides to more than 200 km under southern Dinarides. • New structural model incorporating delamination as one of the processes controlling the continental collision in the Dinarides. The interaction between the Adriatic microplate (Adria) and Eurasia is the main driving factor in the central Mediterranean tectonics. Their interplay has shaped the geodynamics of the whole region and formed several mountain belts including Alps, Dinarides and Apennines. Among these, Dinarides are the least investigated and little is known about the underlying geodynamic processes. There are numerous open questions about the current state of interaction between Adria and Eurasia under the Dinaric domain. One of the most interesting is the nature of lithospheric underthrusting of Adriatic plate, e.g. length of the slab or varying slab disposition along the orogen. Previous investigations have found a low-velocity zone in the uppermost mantle under the northern-central Dinarides which was interpreted as a slab gap. Conversely, several newer studies have indicated the presence of the continuous slab under the Dinarides with no trace of the low velocity zone. Thus, to investigate the Dinaric mantle structure further, we use regional-to-teleseismic surface-wave records from 98 seismic stations in the wider Dinarides region to create a 3D shear-wave velocity model. More precisely, a two-station method is used to extract Rayleigh-wave phase velocity while tomography and 1D inversion of the phase velocity are employed to map the depth dependent shear-wave velocity. Resulting velocity model reveals a robust high-velocity anomaly present under the whole Dinarides, reaching the depths of 160 km in the north to more than 200 km under southern Dinarides. These results do not agree with most of the previous investigations and show continuous underthrusting of the Adriatic lithosphere under Europe along the whole Dinaric region. The geometry of the down-going slab varies from the deeper slab in the north and south to the shallower underthrusting in the center. On-top of both north and south slabs there is a low-velocity wedge indicating lithospheric delamination which could explain the 200 km deep high-velocity body existing under the southern Dinarides

Crustal Thinning From Orogen to Back-Arc Basin: The Structure of the Pannonian Basin Region Revealed by P-to-S Converted Seismic Waves

We present the results of P-to-S receiver function analysis to improve the 3D image of the sedimentary layer, the upper crust, and lower crust in the Pannonian Basin area. The Pannonian Basin hosts deep sedimentary depocentres superimposed on a complex basement structure and it is surrounded by mountain belts. We processed waveforms from 221 three-component broadband seismological stations. As a result of the dense station coverage, we were able to achieve so far unprecedented spatial resolution in determining the velocity structure of the crust. We applied a three-fold quality control process; the first two being applied to the observed waveforms and the third to the calculated radial receiver functions. This work is the first comprehensive receiver function study of the entire region. To prepare the inversions, we performed station-wise H-Vp/Vs grid search, as well as Common Conversion Point migration. Our main focus was then the S-wave velocity structure of the area, which we determined by the Neighborhood Algorithm inversion method at each station, where data were sub-divided into back-azimuthal bundles based on similar Ps delay times. The 1D, nonlinear inversions provided the depth of the discontinuities, shear-wave velocities and Vp/Vs ratios of each layer per bundle, and we calculated uncertainty values for each of these parameters. We then developed a 3D interpolation method based on natural neighbor interpolation to obtain the 3D crustal structure from the local inversion results. We present the sedimentary thickness map, the first Conrad depth map and an improved, detailed Moho map, as well as the first upper and lower crustal thickness maps obtained from receiver function analysis. The velocity jump across the Conrad discontinuity is estimated at less than 0.2 km/s over most of the investigated area. We also compare the new Moho map from our approach to simple grid search results and prior knowledge from other techniques. Our Moho depth map presents local variations in the investigated area: the crust-mantle boundary is at 20–26 km beneath the sedimentary basins, while it is situated deeper below the Apuseni Mountains, Transdanubian and North Hungarian Ranges (28–33 km), and it is the deepest beneath the Eastern Alps and the Southern Carpathians (40–45 km). These values reflect well the Neogene evolution of the region, such as crustal thinning of the Pannonian Basin and orogenic thickening in the neighboring mountain belts

A comprehensive verification of the weather radar-based hail metrics POH and MESHS and a recalibration of POH using dense crowdsourced observations from Switzerland

Remote hail detection and hail size estimation using weather radar observations has the advantage of wide spatial coverage and high spatial and temporal resolution. Switzerland National Weather Service (MeteoSwiss) uses two radar-based hail metrics: the probability of hail at the ground (POH) to assess the presence of hail, and the maximum expected severe hailstone size (MESHS) to estimate the largest hailstone diameter. However, radar-based metrics are not direct measurements of hail and have to be calibrated with and verified against ground-based observations of hail, such as crowdsourced hail reports. Switzerland benefits from a particularly rich and dense dataset of crowdsourced hail reports from the MeteoSwiss app. We combine a new spatiotemporal clustering method (ST-DBSCAN) with radar reflectivity to filter the reports and use the filtered reports to verify POH and MESHS in terms of the Hit Rate, False Alarms Ratio (FAR), Critical Success Index (CSI), and Heidke Skill Score (HSS). Using a 4 km × 4 km maximum upscaling approach, we find FAR values between 0.3 and 0.7 for POH and FAR > 0.6 for MESHS. For POH, the highest CSI (0.37) and HSS (0.52) are obtained for a 60 % threshold, while for MESHS the highest CSI (0.25) and HSS (0.4) are obtained for a 2 cm threshold. We find that the current calibration of POH does not correspond to a probability and suggest a recalibration based on the filtered reports