FEBRUARY 2018 H E N G S T EBECK ET AL. 299 Radar Network–Based Detection of Mesocyclones at the German Weather Service THOMAS HENGSTEBECK,KATHRIN WAPLER, AND DIRK HEIZENREDER Deutscher Wetterdienst, Offenbach, Germany PAUL JOE Environment and Climate Change Canada, Toronto, Ontario, Canada (Manuscript received 29 November 2016, in final form 23 October 2017) ABSTRACT The radar network of the German Weather Service [Deutscher Wetterdienst (DWD)] provides 3D Doppler data in high spatial and temporal resolution, supporting the identification and tracking of dynamic small-scale weather phenomena. The software framework Polarimetric Radar Algorithms (POLARA) has been developed at DWD to better exploit the capabilities of the existing remote sensing data. The data processing and quality assurance implemented in POLARA include a dual-PRF dealiasing algorithm with error correction. Azimuthal shear information is derived and processed in the mesocyclone detection algo- rithm (MCD). Low- and midlevel azimuthal shear and track products are available as composite (multiradar) products. Azimuthal shear may be considered as a proxy for rotation. The MCD results and azimuthal shear products are part of the severe weather detection algorithms of DWD and are provided to the forecaster on the NinJo meteorological workstation system. The forecaster analyzes potentially severe cells by combining near-storm environment data with the MCD product as well as with the instantaneous azimuthal shear products (mid- and low level) and their tracks. These products and tracks are used to diagnose threat potential by means of azimuthal shear intensity and track longevity. Feedback from forecasters has shown the utility of the algorithms to analyze and diagnose severe convective cells in Germany and in adjacent Europe. In this paper, the abovementioned algorithms and products are presented in detail and case studies illustrating us- ability and performance are shown. 1. Introduction high spatial and temporal resolution so that small-scale dynamic severe weather phenomena, such as mesocy- Severe weather associated with convective storms clones, can be resolved. poses a serious threat to life and property. The presence In the context of radar meteorology, observations of of storm-scale rotation, or mesocyclones, increases the mesocyclonic signatures in the form of storm-scale Ran- risk of heavy rain, severe hail, strong wind gusts, and kine vortices have been analyzed (Donaldson 1970; tornadoes within these storms. Thus, the real-time de- Burgess 1976) and several mesocyclone detection algo- tection of mesocyclonic shear gives valuable additional rithms have been developed in recent decades (Zrnic´ information for the meteorological warning management et al. 1985; Stumpf et al. 1998; Joe et al. 2004). The op- of a national weather service. The radar network of the erational mesocyclone detection algorithm (MCD) at German Weather Service [Deutscher Wetterdienst DWD uses a pattern vector approach as described in (DWD)] offers scanning of the lower atmosphere with Zrnic´ et al. (1985) to identify the regions of high shear within the center of the mesocyclonic rotation. An im- portant aspect of the DWD algorithm is the merging of Denotes content that is immediately available upon publica- 2D mesocyclone features from multiple network radars tion as open access. in overlapping regions to create a single 3D detection. A similar technique was demonstrated in the multiple-radar Corresponding author: Thomas Hengstebeck, thomas.hengstebeck@ severe storm analysis program for three neighboring dwd.de WSR-88D radar systems (Stumpf 2002). DOI: 10.1175/JTECH-D-16-0230.1 Ó 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 10/06/21 01:06 PM UTC 300 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 35 The mesocyclone objects are ranked and assigned a severity index using mesocyclone object properties like shear and rotational velocity. This is provided to the forecaster, as guidance, through the NinJo meteorologi- cal workstation system (Koppert et al. 2004). Meteorol- ogists then judge the significance of the mesocyclone detections using this severity metric, applying persistency, consistency checks (track of mesocyclone detections), and forecaster judgment (additional occurrences of se- vere weather features like hook echoes, interpretation of near-storm environment data, and large-scale forcing analysis). Further guidance is given by the azimuthal shear and corresponding track products. In this paper the scan strategy and the data acquisition within DWD’s radar network are outlined in section 2. Doppler wind data quality assurance issues of great im- portance for downstream applications are discussed in section 3. The mesocyclone detection and the rotation compositing are introduced in sections 4 and 5,re- spectively, as network-based algorithms. The operational applicability of the MCD and the rotation products and the advantages of the network approach are demon- FIG. 1. Radar network of DWD. Seventeen C-band weather radar strated in section 6 using case studies. Comparative sta- systems cover Germany completely. There are 16 dual-polarimetric tistical analyses of the MCD (through comparison of the systems and one single-polarimetric radar in Emden (emd). Maxi- MCD with previous studies and through comparison of mum unambiguous range of the lowermost elevation in the volume scan, which corresponds to a range of 180 km (dashed lines). the MCD network and single-site modes) and studies showing correlations between the cell lifetime and the cell’s rotational characteristics are presented in section 7. demanding dual-PRF ratios or single PRFs are used, al- The main conclusions are summarized in section 8. ways keeping the maximum unambiguous velocity at 2 32 m s 1 (see Table 1). Strong quality control of the radial velocity data is 2. Data needed for the mesocyclone detection and azimuthal DWD operates a network of 17 weather radar systems shear algorithms. The first step in quality control is delivering 2D and 3D radar data in 5-min intervals achieved by means of a zero Doppler velocity filter to (Helmert et al. 2014;seeFig. 1). Sixteen radar sites are remove stationary clutter and a set of data quality equipped with dual-polarimetric systems. In Emden threshold filters, including clutter correction (CCOR) (northwestern Germany) a single-polarimetric radar sys- and signal quality index (SQI) (Seltmann 2000). It is tem is in operation. The volume scan is composed of 10 notable that each pulse volume is composed of uniform elevation scans with elevation angles ranging from 0.58 to PRF samples. The clutter filter process is not compli- 258. This scan is configured such that both an adequate cated by nonuniform samples, which is the case for the maximum unambiguous Doppler velocity interval and a staggered PRT technique (Torres et al. 2004). maximum unambiguous range are achieved (Seltmann The volume scan data from all DWD radar stations et al. 2013). To obtain sufficient overlap between the radar are gathered at the central office of DWD in real time sites located at a mean nearest-neighbor distance of ap- by means of an automated file distribution system (AFD; proximately 140 km, a maximum range of 180 km was Deutscher Wetterdienst 2016a) and are available for determined to be required for the six lowermost elevation downstream applications. To adequately exploit the possi- scans. This is realized by implementing a 4:3 dual-PRF bilities of the polarimetric data, a software framework dealiasing scheme (Dazhang et al. 1984)withaso-called called Polarimetric Radar Algorithms (POLARA) was extended maximum unambiguous velocity yNyq,e of developed (Rathmann and Mott 2012). POLARA contains 2 32 m s 1. Since true Doppler velocities do not typically various algorithms, such as quality control, hydrometeor 2 exceed 64 m s 1, extended aliasing effects will be at classification, and quantitative precipitation estimation. most a single fold. For the higher elevations, shorter As part of quality control, the correction of dual-PRF ranges are sufficient for scanning the troposphere and less dealiasing errors is performed to eliminate spurious Unauthenticated | Downloaded 10/06/21 01:06 PM UTC FEBRUARY 2018 H E N G S T EBECK ET AL. 301 TABLE 1. DWD volume scan. Elevation(s) PRF(s) PRF ratio Max unambiguous range/velocity 2 0.58–5.58 in 18 steps 800 Hz: 600 Hz 4:3 180 km/32 m s 1 2 8.08 1200 Hz: 800 Hz 3:2 125 km/32 m s 1 2 12.08, 17.08,258 1200 Hz: 800 Hz 3:2 125 km/32 m s 1 (radar Emden) 2 2410 Hz — 60 km/32 m s 1 (other radars) velocity errors in high shear regions. This is an essential of a detection–correction filtering algorithm. Within the prerequisite for the mesocyclone detection and azimuthal POLARA quality assurance module (Werner 2014), a shear algorithms. The quality-controlled radar elevation scan Laplacian filter based on Joe and May (2003) is used. The data are henceforth called base data. These base data are Laplacian filter basically uses a k 3 k pixel window, with k stored at the central office of the DWD in a long-term archive being an odd integer, to detect and then to calculate a system (Deutscher Wetterdienst 2016b). The radar base data correction value ycorr for the window’s central pixel’s ra- and the derived products are visualized in the radar and dial velocity value yc. The correction uses the related Storm Cell Identification and Tracking (SCIT) layers (Joe maximum unambiguous velocity yNyq,c, which is either et al. 2005) of the NinJo workstation (Koppert et al. 2004). yNyq,1 or yNyq,2. It is performed by comparing a phase- averaged mean value ymean, calculated within the ex- 3. Quality assurance of Doppler wind data at DWD tended unambiguous velocity interval from the central y0 pixel’s neighbors, to all possible corrections corr(n)with In this section one of the most critical problems in the n being the integer fold number, quality assurance of Doppler wind data at DWD is y y0 (n) 5 y 6 2n Á y , n # Nyq,e.