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)

Lightning-jumps in convective cells tracked by radar as a nowcasting tool in complex orography

Presentación realizada en la 3rd European Nowcasting Conference, celebrada en la sede central de AEMET en Madrid del 24 al 26 de abril de 2019

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

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

Ensemble radar precipitation estimation for nowcasting and hydrology in the Alps

This paper explores the novel idea of generating ensables of radar precipitation estimates.Peer ReviewedPostprint (author’s final draft

Experiences with >50,000 Crowdsourced Hail Reports in Switzerland

Crowdsourcing is an observational method that has gained increasing popularity in recent years. In hail research, crowdsourced reports bridge the gap between heuristically defined radar hail algorithms, which are automatic and spatially and temporally widespread, and hail sensors, which provide precise hail measurements at fewer locations. We report on experiences with and first results from a hail size reporting function in the app of the Swiss National Weather Service. App users can report the presence and size of hail by choosing a predefined size category. Since May 2015, the app has gathered >50,000 hail reports from the Swiss population. This is an unprecedented wealth of data on the presence and approximate size of hail on the ground. The reports are filtered automatically for plausibility. The filters require a minimum radar reflectivity value in a neighborhood of a report, remove duplicate reports and obviously artificial patterns, and limit the time difference between the event and the report submission time. Except for the largest size category, the filters seem to be successful. After filtering, 48% of all reports remain, which we compare against two operationally used radar hail detection and size estimation algorithms, probability of hail (POH) and maximum expected severe hail size (MESHS). The comparison suggests that POH and MESHS are defined too restrictively and that some hail events are missed by the algorithms. Although there is significant variability between size categories, we found a positive correlation between the reported hail size and the radar-based size estimates

Object-based analysis of simulated thunderstorms in Switzerland: application and validation of automated thunderstorm tracking with simulation data

We present a feasibility study for an object-based method to characterise thunderstorm properties in simulation data from convection-permitting weather models. An existing thunderstorm tracker, the Thunderstorm Identification, Tracking, Analysis and Nowcasting (TITAN) algorithm, was applied to thunderstorms simulated by the Advanced Research Weather Research and Forecasting (AR-WRF) weather model at convection-permitting resolution for a domain centred on Switzerland. Three WRF microphysics parameterisations were tested. The results are compared to independent radar-based observations of thunderstorms derived using the MeteoSwiss Thunderstorms Radar Tracking (TRT) algorithm. TRT was specifically designed to track thunderstorms over the complex Alpine topography of Switzerland. The object-based approach produces statistics on the simulated thunderstorms that can be compared to object-based observation data. The results indicate that the simulations underestimated the occurrence of severe and very large hail compared to the observations. Other properties, including the number of storm cells per day, geographical storm hotspots, thunderstorm diurnal cycles, and storm movement directions and velocities, provide a reasonable match to the observations, which shows the feasibility of the technique for characterisation of simulated thunderstorms over complex terrain

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

A 15-year hail streak climatology for the Alpine region

In this study, we present a unique 15-year hail streak climatology for Switzerland based on volumetric radar reflectivity. Two radar-based hail detection products and an automatic thunderstorm-tracking algorithm were reprocessed for the Extended convective season (April–September) between 2002 and 2016. More than 1.1 Million convective cells were automatically tracked over the full radar domain, and over 191,000 storms and 31,000 hail streaks in the considered subdomain were selected for analysis following consistency and robustness tests. The year-to-year variability in t h e number of hailstorms reveals two types of convective seasons: (a) a few seasons with hail frequency far above the average, and (b) all other years with an average number of hailstorms. A high number of hailstorms in a particular year is not correlated with a higher number of convective storms in general, but is related to a greater fraction of severe storms. Convection initiation, hail initiation, and hail frequency maxima are located along the southern and northern foothills over the pre-Alpine area and over th e Jura mountains. Few hail streaks are present over the Alpine main ridge. Hail streak frequency and location is found to be strongly dependent on the synoptic-scale weather regimes. This is important for monthly and seasonal outlooks, as well as for climate modelling. Analysis of storm life cycles shows that: (a) the majority of hail swaths contain only a single hail streak, (b) severe storms follow a more rapid evolution during their initial stages than do less severe storms, and (c) severe storms produce more spatially extended hail streaks. Finally, significant seasonal and diurnal cycles are present in most of the considered storm characteristics

Nowcasting of thunderstorm severity with Machine Learning in the Alpine Region

Presentación realizada en la 3rd European Nowcasting Conference, celebrada en la sede central de AEMET en Madrid del 24 al 26 de abril de 2019