WRaINfo: An Open Source Library for Weather Radar INformation for FURUNO Weather Radars Based on Wradlib

WRaINfo is a software for real-time weather radar data processing developed by the Helmholtz Innovation Lab FERN.Lab, a technology and innovation platform of the German Research Centre for Geosciences Potsdam (GFZ). WRaINfo is specifically designed for processing X-band weather radar data of FURUNO devices. The modules of the package allow to read and process raw data of the WR2120 and WR2100. For this purpose, many functions of the library wradlib are used and adapted. The processing is controlled by a configuration file, main functionalities include formatting, attenuation correction, clutter detection, georeferencing and gridding of the data. This allows the construction of reproducible, automatic data processing chains. The package is written in the Python programming language. The source code is publicly available on GitLab. Compiled versions are also available on PyPi. The package is distributed under the Apache 2.0 license

Skyrmion Hall Effect Revealed by Direct Time-Resolved X-Ray Microscopy

Magnetic skyrmions are highly promising candidates for future spintronic applications such as skyrmion racetrack memories and logic devices. They exhibit exotic and complex dynamics governed by topology and are less influenced by defects, such as edge roughness, than conventionally used domain walls. In particular, their finite topological charge leads to a predicted "skyrmion Hall effect", in which current-driven skyrmions acquire a transverse velocity component analogous to charged particles in the conventional Hall effect. Here, we present nanoscale pump-probe imaging that for the first time reveals the real-time dynamics of skyrmions driven by current-induced spin orbit torque (SOT). We find that skyrmions move at a well-defined angle {\Theta}_{SH} that can exceed 30{\deg} with respect to the current flow, but in contrast to theoretical expectations, {\Theta}_{SH} increases linearly with velocity up to at least 100 m/s. We explain our observation based on internal mode excitations in combination with a field-like SOT, showing that one must go beyond the usual rigid skyrmion description to unravel the dynamics.Comment: pdf document arxiv_v1.1. 24 pages (incl. 9 figures and supplementary information

wradlib/wradlib: wradlib v2.0.0

<h2>What's Changed</h2> <ul> <li>FIX: align earth radius in plot_scan_strategy for CG plots by @kmuehlbauer in https://github.com/wradlib/wradlib/pull/655</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/wradlib/wradlib/compare/2.0.2...2.0.3</p>If you use this software, please cite it using these metadata

openradar/xradar: xradar v0.4.0

What's Changed FIX: use datastore._group instead of variable["sweep_number"] by @kmuehlbauer in https://github.com/openradar/xradar/pull/123 use cmweather colormaps in xradar by @kmuehlbauer in https://github.com/openradar/xradar/pull/128 use crs_wkt instead of deprecated spatial_ref by @kmuehlbauer in https://github.com/openradar/xradar/pull/127 FIX: always read nodata and undetect attributes from ODIM file by @egouden in https://github.com/openradar/xradar/pull/125 Cf/Radial1 writer by @syedhamidali in https://github.com/openradar/xradar/pull/126 Release 0.4.0 by @kmuehlbauer in https://github.com/openradar/xradar/pull/131 New Contributors @syedhamidali made their first contribution in https://github.com/openradar/xradar/pull/126 Full Changelog: https://github.com/openradar/xradar/compare/0.3.0...0.4.0If you use this software, please cite it using these metadata

openradar/xradar: xradar v0.4.0

<h2>What's Changed</h2> <ul> <li>FIX: _FillValue + History in the cfradial1 exporter by @syedhamidali in https://github.com/openradar/xradar/pull/132</li> <li>FIX: fix sphinx-favicon, fix sphinx-extlinks to get readthedocs running by @kmuehlbauer in https://github.com/openradar/xradar/pull/140</li> <li>FIX: make coordinates consistent by @kmuehlbauer in https://github.com/openradar/xradar/pull/139</li> <li>FIX: prevent integer overflow when calculating azimuth in FURUNO scn files by @kmuehlbauer in https://github.com/openradar/xradar/pull/138</li> <li>Release 0.4.1 by @kmuehlbauer in https://github.com/openradar/xradar/pull/141</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/openradar/xradar/compare/0.4.0...0.4.1</p>If you use this software, please cite it using these metadata

openradar/xradar: xradar v0.4.0

<h2>What's Changed</h2> <ul> <li>FIX: Fix handling of sweep_mode attribiutes by @mgrover1 in https://github.com/openradar/xradar/pull/143</li> <li>FIX: explicitely check for "False" in get_crs() by @kmuehlbauer in https://github.com/openradar/xradar/pull/142</li> <li>Release 0.4.2 by @kmuehlbauer in https://github.com/openradar/xradar/pull/144</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/openradar/xradar/compare/0.4.1...0.4.2</p>If you use this software, please cite it using these metadata

Comparison between precipitation estimates of ground-based weather radar composites and GPM’s DPR rainfall product over Germany

We compare more than three years (between 2014 and 2018) of precipitation estimates over Germany from the Dual-frequency Precipitation Radar (DPR) operating on the core satellite of the Global Precipitation Mission (GPM) with the radar-derived precipitation product RADOLAN RY provided by the German national meteorological service (DWD, Deutscher Wetterdienst). Incomplete overlap between the observation volumes due to the different scan geometries and inconsistencies related to the mutually assumed hydrometeor phases lead to large differences, when directly comparing DPR's near surface product (DPRns) with RADOLAN RY. We improve the correspondence between both data sets via two steps. First, we derive an adjusted DPR near surface product (DPRans) extracted from the DPR vertical profiles, that best fits to the scans height and beam width of the surface radar observations. Second, the data is classified into liquid, solid and mixed phases by adjusting hydrometeor phase classification (aHPC) to the RADOLAN scan geometry. With these steps the correlation between both data sets increases from r = 0.49 to r = 0.61 and the RMSD is reduced from 2.94 mm/h to 1.83 mm/h for the commonly observed precipitation, exceeding most of the results found in previous studies. The agreement is best in stratiform precipitation (r = 0.68, RMSD = 1.4 mm/h), for only stratiform and summer season (r = 0.7, RMSD = 1.59 mm/h), and for stratiform with only liquid precipitation (r = 0.67, RMSD = 1.58 mm/h). Unlike the the standard DPRns, the new DPRans product exhibits almost no seasonal differences in the capability of detection; for all seasons the CSI is 0.94 and the FAR/IFAR are 0.04/0.02

Towards nowcasting of winter precipitation: The Black Ice Event in Berlin 2014

Prediction of winter precipitation is challenging because besides its amount also its variable phase might have a strong impact on people, transportation, and infrastructure in general. We combine a bulk microphysics numerical weather prediction with a 1D spectral bin microphysical model, which explicitely treats the processes of melting, ice nucleation and refreezing as a first step towards a potential nowcasting application. Polarimetric weather radar observations from the German national meteorological service (DWD) are used to evaluate the approach. The potential of the strategy is demonstrated by its application to the black ice event occurring in Berlin, Germany, on 20 January 2014. The methodology is able to clearly identify the classical mechanism leading to freezing rain at the surface, which might be exploited in future nowcasting algorithms

The TRIple-frequency and Polarimetric radar Experiment for improving process observations of winter precipitation

This paper describes a 2-month dataset of ground-based triple-frequency (X, Ka, and W band) Doppler radar observations during the winter season obtained at the Jülich ObservatorY for Cloud Evolution Core Facility (JOYCE-CF), Germany. All relevant post-processing steps, such as re-gridding and offset and attenuation correction, as well as quality flagging, are described. The dataset contains all necessary information required to recover data at intermediate processing steps for user-specific applications and corrections (https://doi.org/10.5281/zenodo.1341389; Dias Neto et al., 2019). The large number of ice clouds included in the dataset allows for a first statistical analysis of their multifrequency radar signatures. The reflectivity differences quantified by dual-wavelength ratios (DWRs) reveal temperature regimes where aggregation seems to be triggered. Overall, the aggregation signatures found in the triple-frequency space agree with and corroborate conclusions from previous studies. The combination of DWRs with mean Doppler velocity and linear depolarization ratio enables us to distinguish signatures of rimed particles and melting snowflakes. The riming signatures in the DWRs agree well with results found in previous triple-frequency studies. Close to the melting layer, however, we find very large DWRs (up to 20 dB), which have not been reported before. A combined analysis of these extreme DWR with mean Doppler velocity and a linear depolarization ratio allows this signature to be separated, which is most likely related to strong aggregation, from the triple-frequency characteristics of melting particles