A polarimetric radar operator to evaluate precipitation from the COSMO atmospheric model

Weather radars provide real-time measurements of precipitation at a high temporal and spatial resolution and over a large domain. A drawback, however, it that these measurements are indirect and require careful interpretation to yield relevant information about the mechanisms of precipitation. Radar observations are an invaluable asset for the numerical forecast of precipitation, both for data assimilation, parametrization of subscale phenomena and model verification. This thesis aims at investigating new uses for polarimetric radar data in numerical weather prediction. The first part of this work is devoted to the design of an algorithm able to automatically detect the location and extent of the melting layer of precipitation , an important feature of stratiform precipitation, from vertical radar scans. This algorithm is then used to provide a detailed characterization of the melting layer, in several climatological regions, providing thus relevant information for the parameterization of melting processes and the evaluation of simulated freezing level heights. The second part of this work uses a multi-scale approach based on the multifractal framework to evaluate precipitation fields simulated by the COSMO weather model with radar observations. A climatological analysis is first conducted to relate multifractal parameters to physical descriptors of precipitation. A short-term analysis, that focuses on three precipitation events over Switzerland, is then performed. The results indicate that the COSMO simulations exhibit spatial scaling breaks that are not present in the radar data. It is also shown that a more advanced microphysics parameterization generates larger extreme values, and more discontinuous precipitation fields, which agree better with radar observations. The last part of this thesis describes a new forward polarimetric radar operator, able to simulate realistic radar variables from outputs of the COSMO model, taking into account most physical aspects of beam propagation and scattering. An efficient numerical scheme is proposed to estimate the full Doppler spectrum, a type of measurement often performed by research radars, which provides rich information about the particle velocities and turbulence. The operator is evaluated with large datasets from various ground and spaceborne radars. This evaluation shows that the operator is able to simulate accurate Doppler variables and realistic distributions of polarimetric variables in the liquid phase. In the solid phase, the simulated reflectivities agree relatively well with radar observations, but the polarimetric variables tend to be underestimated. A detailed sensitivity analysis of the radar operator reveals that, in the liquid phase, the simulated radar variables depend very much on the hypothesis about drop geometry and drop size distributions. In the solid phase, the potential of more advanced scattering techniques is investigated, revealing that these methods could help to resolve the strong underestimation of polarimetric variables in snow and graupel

Detection and characterization of the melting layer based on polarimetric radar scans

Stratiform rain situations are generally associated with the presence of a melting layer characterized by a strong signature in polarimetric radar variables. This layer is an important feature as it indicates the transition from solid to liquid precipitation. The melting layer remains poorly characterized, particularly from a polarimetric radar point of view. In this work a new algorithm to automatically detect the melting layer on polarimetric RHI radar scans using gradients of reflectivity and copolar correlation is first proposed. The algorithm was applied to high-resolution X-band polarimetric radar data and validated by comparing the height of the detected layer with freezing-level heights obtained from radiosoundings and was shown to give both small errors and bias. The algorithm was then used on a large selection of precipitation events (more than 4000 RHI scans) from different seasons and climatic regions (South of France, Swiss Alps and plateau, and Iowa, USA) to characterize the geometric and polarimetric signatures of the melting layer. The melting layer is shown to have a very similar geometry on average, independent of the topography and climatic conditions. Variations in the thickness of the melting layer during and between precipitation events was shown to be strongly related to the presence of rimed particles, to the vertical velocity of hydrometeors and to the intensity of the bright band

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

Identification of snow precipitation mechanisms and accumulation patterns over complex terrain with very high resolution radar data and terrestrial laser scans

Knowledge on changes in seasonal mountain snow water resources are essential e.g. for hydropower companies. Both, snow accumulation and ablation need to be investigated to make precise predictions of water stored in a seasonal snow cover. Only if the processes behind snow accumulation and ablation are understood with sufficient quantitative accuracy, the evolution of snow water resources under a changing climate can be addressed. It is known that different snow precipitation processes and snow redistribution are responsible for snow accumulation patterns in alpine terrain. In a small-scale analysis of radar data in the region of Davos, Mott et al. (2014) could identify different snow deposition patterns for homogeneous precipitation, seeder-feeder mechanism, preferential deposition and a combination of the seeder-feeder mechanism and preferential deposition. In addition to the snow precipitation mechanisms, snow redistribution due to snow-atmosphere interaction is essential to characterize snow accumulation patterns at small scales (Scipión et al., 2013). In this study we investigate small-scale patterns of precipitation for an extended area over the Dischma valley (Davos, CH) for the winter season 2014/2015. An X-band polarimetric radar was installed on a slope facing the Dischma valley and it conducted plane position indicator (PPI) scans at elevation angles of 7° and 10° (minimum distance to the ground is about 300m and 500m, respectively) and three range height indicator (RHI) scans along the Dischma valley and along the Landwasser valley (i.e. along Davos). These radar products are available with horizontal and vertical resolution of 75 meters and a high temporal resolution of 5 minutes. The specific spatial patterns revealed by the radar measurements allow to characterize the different types of winter precipitation as well as to identify cloud microphysical and dynamical processes that govern the precipitation distribution. The continuous radar measurements are also used to analyze the frequency of certain types of hydrometeors and precipitation genesis processes as well as snow precipitation patters, which are related to specific atmospheric situations. For a few snowfall events, we additionally analyze terrestrial laser scans (TLS) of steep rock faces with different orientations that were performed before and after the snow precipitation events. The results allow us to relate identified accumulation patterns to the identified precipitation patterns and confirm the importance of redistribution processes for accumulation in steep terrain

Bacterial but no SARS-CoV-2 contamination after terminal disinfection of tertiary care intensive care units treating COVID-19 patients

BACKGROUND In intensive care units (ICUs) treating patients with Coronavirus disease 2019 (COVID-19) invasive ventilation poses a high risk for aerosol and droplet formation. Surface contamination of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) or bacteria can result in nosocomial transmission. METHODS Two tertiary care COVID-19 intensive care units treating 53 patients for 870 patient days were sampled after terminal cleaning and preparation for regular use to treat non-COVID-19 patients. RESULTS A total of 176 swabs were sampled of defined locations covering both ICUs. No SARS-CoV-2 ribonucleic acid (RNA) was detected. Gram-negative bacterial contamination was mainly linked to sinks and siphons. Skin flora was isolated from most swabbed areas and Enterococcus faecium was detected on two keyboards. CONCLUSIONS After basic cleaning with standard disinfection measures no remaining SARS-CoV-2 RNA was detected. Bacterial contamination was low and mainly localised in sinks and siphons

wolfidan/cosmo_pol: cosmo_pol

First release of cosmo_pol a polarimetric radar operator for the COSMO NWP mode

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

RainForest: a random forest algorithm for quantitative precipitation estimation over Switzerland

Quantitative precipitation estimation (QPE) is a difficult task, particularly in complex topography, and requires the adjustment of empirical relations between radar observables and precipitation quantities, as well as methods to transform observations aloft to estimations at the ground level. In this work, we tackle this classical problem with a new twist, by training a random forest (RF) regression to learn a QPE model directly from a large database comprising 4 years of combined gauge and polarimetric radar observations. This algorithm is carefully fine-tuned by optimizing its hyperparameters and then compared with MeteoSwiss' current operational non-polarimetric QPE method. The evaluation shows that the RF algorithm is able to significantly reduce the error and the bias of the predicted precipitation intensities, especially for large and solid or mixed precipitation. In weak precipitation, however, and despite a posteriori bias correction, the RF method has a tendency to overestimate. The trained RF is then adapted to run in a quasi-operational setup providing 5 min QPE estimates on a Cartesian grid, using a simple temporal disaggregation scheme. A series of six case studies reveal that the RF method creates realistic precipitation fields, with no visible radar artifacts, that appear less smooth than the original non-polarimetric QPE and offers an improved performance for five out of six events