Abstract. The increasing need of renewable energy fosters the expansion of wind turbine sites for power production throughout Europe with manifold effects, both on the positive and negative side. The latter concerns, among others, radar observations in the proximity of wind turbine (WT) sites. With the aim of better understanding the effects of large, moving scatterers like wind turbines on radar returns, MeteoSwiss performed two dedicated measurement campaigns with a mobile X-band Doppler polarimetric weather radar (METEOR 50DX) in the northeastern part of Switzerland in March 2019 and March 2020. Based on the usage of an X-band radar system, the performed campaigns are up to now unique. The main goal was to quantify the effects of wind turbines on the observed radar moments, to retrieve the radar cross-section (RCS) of the turbines themselves and to investigate the conditions leading to the occurrence of the largest RCS. Dedicated scan strategies, consisting of PPI (plan position indicator), RHI (range–height indicator) and fixed-pointing modes, were defined and used for observing a wind park consisting of three large wind turbines. During both campaigns, measurements were taken in 24/7 operation. The highest measured maxima of horizontal reflectivity (ZH) and RCS reached 78.5 dBZ and 44.1 dBsm, respectively. A wind turbine orientation (yawing) stratified statistical analysis shows no clear correlation with the received maximum returns. However, the median values and 99th percentiles of ZH show different enhancements for specific relative orientations. Some of them remain still for Doppler-filtered data, supporting the importance of the moving parts of the wind turbine for the radar returns. Further, we show, based on investigating correlations and an OLS (ordinary least square) model analysis, that the fast-changing rotor blade angle (pitch) is a key parameter, which strongly contributes to the variability in the observed returns.

Pyrad is a real-time data processing framework developed by MeteoSwiss. The framework is aimed at reading, processing and visualizing polar data from individual weather radars as well as composite Cartesian products both off-line and in real time. The processing flow is controlled by three simple configuration files. This allows the construction of reproducible data processing chains. In the off-line mode, data from multiple radars can be ingested. It is written in the Python programming language. Most of the signal processing and part of the data visualization is performed by a MeteoSwiss-developed version of the Py-ART radar toolkit, which contains enhanced features. Thanks to the broad types of input files accepted and its flexibility it can be easily adapted and used by any member of the weather radar community. The source code is available on GitHub. Compiled versions are also available on PyPI and conda-forge. They are distributed under a BSD license.

This letter illustrates the effects that the regular pattern of the metallic unions of a four-panel radome had on the polarimetric variables [differential reflectivity Z dr , copolar differential phase φ dp offset, and copolar correlation coefficient (ρ hv)] as well as on the sun measurements of a mobile X-band weather radar. In particular, we focus on the analysis of the spatial distribution of the biases and the temporal variability of the sun measurements. We show that the metallic unions result in a nonnegligible sinusoidal-like spatial variability of the estimated biases (on the order of 7 •-8 • for φ dp offset and 0.4-0.5 dB for Z dr bias), as well as a drop in ρ hv in rain and a large temporal variability in the power measured by sun scans. These effects are compared with the measurements collected without a radome and with the measurements collected with a seamless monoblock radome on the same radar system. It is shown that operating without radome, when possible, has a positive impact on the data quality, largely reducing the spatial variability of the biases and increasing the ρ hv in rain. Similar performances, without the inherent risks, can be obtained as well with a seamless radome. Nevertheless, regardless of the form of operation, we advocate for monitoring the data quality as accurately as possible if quantitative applications are desired.

<p>Hail is a major threat connected to severe thunderstorms and an estimation of the hail size is important to issue warnings for the public. Radar real-time 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 available within the Swiss hail network, can provide information about hail diameters observed on the ground. Unfortunately, due to the small size of these sensors (e.g. 0.2 m<sup>2</sup>) the estimation of the hail size distribution (HSD) can have large uncertainties. To overcome this issue, aerial drone-based 2D orthophotos can be analyzed by using state-of-the-art custom trained AI-object detection models to identify hail stones in the images and to estimate the HSD.</p><p>A large right moving supercell with a lifespan of more than 6 hours crossed the midlands of Switzerland from south west in the afternoon of 20<sup>th</sup> June 2021. The hail swath of this classical supercell was intercepted near Entlebuch and aerial images of the hail on the ground were taken by a DJI Matrice 300RTK drone immediately after the storm has passed. The drone was equipped with a 50 megapixels full frame camera. The average ground sampling distance is 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 level.</p><p>A 2D orthomosaic model of the survey area (soccer field) is created based on 116 captured images during the first drone mapping flight. The orthomosaic covers an area of about 750 m<sup>2</sup> and is then used to detect hail by using a region-based Convolutional Neural Network (Mask R-CNN) model. First, we characterize the hail sizes based on the individual hail segmentation masks resulting from the model detections and investigate the performance with respect to manual hail annotations from experts that are used as validation and test data sets. We present the final obtained HSD from more than 18000 hail stones (<em>D<sub>max</sub></em> = 39 mm, <em>D<sub>med</sub></em> = 9 mm) and compare it with nearby automatic hail sensor observations and weather radar based hail products like MESHS (Maximum Expected Severe Hail Size).  Furthermore, we provide first insights into hail melting processes that can be inferred from the information retrieved from a total of 5 subsequent flights performed with the drone within about 20 minutes after the passage of the supercell.</p>

Abstract. The increasing need of renewable energy fosters the expansion of wind turbine sites for power production throughout Europe with manifold effects, both on the positive and negative side. The latter concerns, among others, radar observations in the proximity of wind turbine (WT) sites. With the aim of better understanding the effects of large, moving scatterers like wind turbines on radar returns, MeteoSwiss performed two dedicated measurement campaigns with a mobile X-band Doppler polarimetric weather radar (METEOR 50DX) in the north-eastern part of Switzerland in March 2019 and March 2020. Based on the usage of a X-band radar system, the performed campaigns are up to now unique. The main goal was to quantify the effects of wind turbines on the observed radar moments, to retrieve the radar cross section (RCS) of the turbine themselves, and to investigate the conditions leading to the occurrence of the largest RCS. Dedicated scan strategies, consisting of PPI (Plan Position Indicator), RHI (Range-height Indicator) and fixed-pointing modes, were defined and used for observing a wind park consisting of three large wind turbines. During both campaigns, measurements were taken in 24/7 operation. The highest measured maxima of horizontal reflectivity (ZH) and RCS reached 78.5 dBZ respectively 44.1 dBsm. A wind turbine orientation (yawing) stratified statistical analysis shows no clear correlation with the received maximum returns. However, the median values and 99th percentiles of ZH and RCS show different enhancements for specific relative orientations. Further, we show, based on investigating correlations and an OLS (ordinary least square) model analyses, that the fast changing rotor blade angle (pitch) is a key parameter, which strongly contributes to the variability of the observed returns.