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

Exploring the Use of European Weather Regimes for Improving User-Relevant Hydrological Forecasts at the Subseasonal Scale in Switzerland

Across the globe, there has been an increasing interest in improving the predictability of subseasonal hydrometeorological forecasts, as they play a valuable role in medium- to long-term planning in many sectors, such as agriculture, navigation, hydropower, and emergency management. However, these forecasts still have very limited skill at the monthly time scale; hence, this study explores the possibilities for improving forecasts through different pre- and postprocessing techniques at the interface with a Precipitationn–Runoff–Evapotranspiration Hydrological Response Unit Model (PREVAH). Specifically, this research aims to assess the benefit of European weather regime (WR) data within a hybrid forecasting setup, a combination of a traditional hydrological model and a machine learning (ML) algorithm, to improve the performance of subseasonal hydrometeorological forecasts in Switzerland. The WR data contain information about the large-scale atmospheric circulation in the North Atlantic–European region, and thus allow the hydrological model to exploit potential flow-dependent predictability. Four hydrological variables are investigated: total runoff, baseflow, soil moisture, and snowmelt. The improvements in the forecasts achieved with the pre- and postprocessing techniques vary with catchments, lead times, and variables. Adding WR data has clear benefits, but these benefits are not consistent across the study area or among the variables. The usefulness of WR data is generally observed for longer lead times, e.g., beyond the third week. Furthermore, a multimodel approach is applied to determine the “best practice” for each catchment and improve forecast skill over the entire study area. This study highlights the potential and limitations of using WR information to improve subseasonal hydrometeorological forecasts in a hybrid forecasting system in an operational mode

Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry.

BACKGROUND: The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. METHODS AND RESULTS: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012-2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3-2.3) versus 2.3 (IQR 1.8-2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32-9.35) vs 4.34 (4.16-4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. CONCLUSION: Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. CLINICAL TRIAL REGISTRATION-URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362

Towards operational subseasonal hydrometeorological ensemble predictions in mountainous catchments

Subseasonal hydrometeorological predictions have received increasing attention within the last decades. Significant advances in meteorological ensemble forecasting have led to skilful predictions of meteorological variables beyond the medium-range forecast horizon. Using these meteorological ensemble forecasts to force hydrological models can provide valuable probabilistic streamflow forecasts. Such predictions at the subseasonal time horizon can have a great impact for planning purposes in various economic and public sectors. Currently, the scientific basis for the predictability of subseasonal forecasts is being investigated and various meteorological organizations are running numerical weather predictions models for the subseasonal time-scale. In order to improve our knowledge to predict streamflows in small to medium sized mountainous catchments at the subseasonal time scales this PhD thesis focuses on the interface between the meteorological and the hydrological predictions and the assessment of the performance thereof. Thus, the current state of subseasonal hydrometeorological predictions is explored and statistical bias correction and downscaling methods are investigated to optimally combine meteorological and hydrological prediction models bridging the gap of application scales. Furthermore, a hydropower optimization system making use of the resulting streamflow predictions has been explored. In a first step the historical forecasts from subseasonal ECMWF Integrated Forecasting System have been analysed in terms of their performance to predict temperature and precipitation at 1637 measurement stations across Europe. Aside of the uncorrected direct model output, different post-processing techniques have been applied and their effect on the forecast performance is analysed. The results clearly demonstrate the need for post-processing the subseasonal predictions to achieve skilful subseasonal forecast for point observations. Post-processed forecasts indicate positive skill with respect to climatology for up to 19-25 days lead time (corresponding to forecast week 3) in case of weekly mean temperature. Forecast skills of weekly precipitation sums stay positive up to 5-11 days lead time (corresponding to forecast week 1) and clearly outperform the uncorrected forecasts. These statistically corrected subseasonal meteorological predictions with daily resolution are used to generate streamflow forecasts with the hydrological model PREAVH in small to medium size mountainous catchments (with areas between 80 and 1700 km2) of different hydroclimatic characteristics. Furthermore, the performance of the resulting streamflow forecasts is compared with the forecast performance of a traditional ensemble streamflow prediction (ESP) approach based on historical meteorological observations. The study clearly demonstrates the superiority of the subseasonal NWP-hydro prediction system. It is found that the benefits are most pronounced in the snow-dominated catchments and this underlines the importance of snow-related processes in subseasonal hydrometeorological predictions. In an additional analysis, the ensemble streamflow predictions were assessed in terms of their benefits for water resource management. To this end, the uncorrected meteorological predictions are used to generate streamflow forecasts and the effect of different hydrological post-processing techniques on the forecast performance is analysed. Besides the total runoff itself, multiple hydrologically relevant variables are analysed for 370 sub-catchments covering entire Switzerland: areal catchment precipitation, total baseflow and soil moisture storage. The results stress the importance of persistence and memory effects on the performance of streamflow forecasts. Subsurface processes were found to show a delayed response of one week in forecast performance. This can be of particular importance for applications in water resource management. Finally, the resulting subseasonal ensemble streamflow predictions were used in a hydropower optimization setup in the Swiss Alps to further assess the potential of the forecast in terms of their economic value. We found that the gain in forecast performance can indeed further translate into monetary benefits, i.e. economic value of a forecast. Depending on the optimization scheme used, the average additional gain of up to 4% could be expected in terms of revenues if such predictions were used in an operational manner instead of the climatological predictions (corresponding to an increase of 0.2 Mio EUR/yr in the Verzasca catchment investigated in this study)

Subseasonal hydrometeorological ensemble predictions in small- and medium-sized mountainous catchments: Benefits of the NWP approach

Traditional ensemble streamflow prediction (ESP) systems are known to provide a valuable baseline to predict streamflows at the subseasonal to seasonal timescale. They exploit a combination of initial conditions and past meteorological observations, and can often provide useful forecasts of the expected streamflow in the upcoming month. In recent years, numerical weather prediction (NWP) models for subseasonal to seasonal timescales have made large progress and can provide added value to such a traditional ESP approach. Before using such meteorological predictions two major problems need to be solved: the correction of biases, and downscaling to increase the spatial resolution. Various methods exist to overcome these problems, but the potential of using NWP information and the relative merit of the different statistical and modelling steps remain open. To address this question, we compare a traditional ESP system with a subseasonal hydrometeorological ensemble prediction system in three alpine catchments with varying hydroclimatic conditions and areas between 80 and 1700 km2. Uncorrected and corrected (pre-processed) temperature and precipitation reforecasts from the ECMWF subseasonal NWP model are used to run the hydrological simulations and the performance of the resulting streamflow predictions is assessed with commonly used verification scores characterizing different aspects of the forecasts (ensemble mean and spread). Our results indicate that the NWP-based approach can provide superior prediction to the ESP approach, especially at shorter lead times. In snow-dominated catchments the pre-processing of the meteorological input further improves the performance of the predictions. This is most pronounced in late winter and spring when snow melting occurs. Moreover, our results highlight the importance of snow-related processes for subseasonal streamflow predictions in mountainous regions.ISSN:1027-5606ISSN:1607-793

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

Exploring the use of European weather regimes for improving user-relevant hydrological forecasts at the sub-seasonal scale in Switzerland

Across the globe, there has been an increasing interest in improving the predictability of sub-seasonal hydro-meteorological forecasts as they play a valuable role in medium- to long-term planning in many sectors such as agriculture, navigation, hydropower, and emergency management. However, these forecasts still have very limited skill at the monthly time scale; hence this study explores the possibilities for improving forecasts through different pre- and post-processing techniques at the interface with a hydrological model (PREVAH). Specifically, this research aims to assess the benefit from European Weather Regime (WR) data into a hybrid forecasting setup, a combination of a traditional hydrological model and a machine learning (ML) algorithm, to improve the performance of sub-seasonal hydro-meteorological forecasts in Switzerland. The WR data contains information about the large-scale atmospheric circulation in the North-Atlantic European region, and thus allows the hydrological model to exploit potential flow-dependent predictability. Four hydrological variables are investigated: total runoff, baseflow, soil moisture, and snowmelt. The improvements in the forecasts achieved with the pre- and post-processing techniques vary with catchments, lead times, and variables. Adding WR data has clear benefits, but these benefits are not consistent across the study area or among the variables. The usefulness of WR data is generally observed for longer lead times, e.g., beyond the third week. Furthermore, a multi-model approach is applied to determine the “best practice” for each catchment and improve forecast skill over the entire study area. This study highlights the potential and limitations of using WR information to improve sub-seasonal hydro-meteorological forecasts in a hybrid forecasting system in an operational mode.ISSN:1525-755XISSN:1525-754

How different de-biasing techniques contribute to improve forecast quality and value for hydropower operations

ISSN:1029-7006ISSN:1607-796

Analysis of Outcomes in Ischemic vs Nonischemic Cardiomyopathy in Patients With Atrial Fibrillation A Report From the GARFIELD-AF Registry

IMPORTANCE Congestive heart failure (CHF) is commonly associated with nonvalvular atrial fibrillation (AF), and their combination may affect treatment strategies and outcomes