Automatische Schmelzschichterkennung mit einem Mikro-Regen-Radar

2021 ◽  
Author(s):  
Finn Burgemeister ◽  
Piet Markmann ◽  
Hans-Jürgen Kirtzel
Keyword(s):  

<p class="western">An zwei deutschen Flughäfen werden vom Deutschen Wetterdienst seit 2018 Mikro-Regen-Radare (MRR) betrieben, um zu erproben, welcher Nutzen daraus für die meteorologische Sicherung des Luftverkehrs gezogen werden kann. Ein wichtiger Parameter ist die automatische Detektion und Höhenbestimmung der Schmelzschicht, die zum Beispiel für die Erkennung und Bewertung von Vereisungssituationen genutzt werden kann. Zusätzlich kann die Kenntnis der Schmelzschichthöhe helfen, die Qualität der Niederschlagsmessungen mit Wetterradaren zu verbessern.</p> <p class="western">Das MRR ist ein vertikal blickendes Doppler-Radar. Es liefert damit zwar nur eine lokale Messung der Schmelzschichthöhe, diese aber mit großer Zuverlässigkeit, da die Messbedingungen bei vertikaler Strahlrichtung besonders günstig sind. Auch komplexe Strukturen, wie doppelte Schmelzschichten, können so erkannt werden. Sie kommen zwar nur selten vor, sind aber Indikatoren für besonders gefährliche Vereisungssituationen. In dem erprobten Verfahren werden zusätzlich zur Reflektivität, die in der Schmelzschicht das bekannte Maximum aufweist, auch die Dopplergeschwindigkeit und die Breite des Dopplerspektrums zur Detektion herangezogen. Diese Variablen werden bei vertikaler Strahlrichtung maßgeblich durch die Fallgeschwindigkeit beziehungsweise die Fallgeschwindigkeitsverteilung der Hydrometeore bestimmt, und weisen beim Übergang von der festen zur flüssigen Phasen charakteristische Signaturen auf. Damit ist eine zuverlässige Schmelzschichterkennung auch in Regenereignissen möglich, in denen das Reflexionsmaximum durch die allgemeine Variabilität des Reflexionsprofils maskiert wird.</p> <p class="western">Hier wird ein einjähriger Datensatz mit Radiosonden-Aufstiegen verglichen, um die Zuverlässigkeit des im MRR implementierten automatischen Detektionsalgorithmus zu analysieren.</p>

Author(s):  
H. Järvinen ◽  
K. Salonen ◽  
M. Lindskog ◽  
A. Huuskonen ◽  
S. Niemelä ◽  
...  
Keyword(s):  

1971 ◽  
Author(s):  
H. W. Prinsen ◽  
R. H. Jarvis ◽  
S. G. Margolis

2018 ◽  
Vol 146 (8) ◽  
pp. 2483-2502 ◽  
Author(s):  
Howard B. Bluestein ◽  
Kyle J. Thiem ◽  
Jeffrey C. Snyder ◽  
Jana B. Houser

Abstract This study documents the formation and evolution of secondary vortices associated within a large, violent tornado in Oklahoma based on data from a close-range, mobile, polarimetric, rapid-scan, X-band Doppler radar. Secondary vortices were tracked relative to the parent circulation using data collected every 2 s. It was found that most long-lived vortices (those that could be tracked for ≥15 s) formed within the radius of maximum wind (RMW), mainly in the left-rear quadrant (with respect to parent tornado motion), passing around the center of the parent tornado and dissipating closer to the center in the right-forward and left-forward quadrants. Some secondary vortices persisted for at least 1 min. When a Burgers–Rott vortex is fit to the Doppler radar data, and the vortex is assumed to be axisymmetric, the secondary vortices propagated slowly against the mean azimuthal flow; if the vortex is not assumed to be axisymmetric as a result of a strong rear-flank gust front on one side of it, then the secondary vortices moved along approximately with the wind.


Author(s):  
Pallab Kumar Gogoi ◽  
Mrinal Kanti Mandal ◽  
Ayush Kumar ◽  
Tapas Chakravarty

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3588
Author(s):  
Yuki Iwata ◽  
Han Trong Thanh ◽  
Guanghao Sun ◽  
Koichiro Ishibashi

Heart rate measurement using a continuous wave Doppler radar sensor (CW-DRS) has been applied to cases where non-contact detection is required, such as the monitoring of vital signs in home healthcare. However, as a CW-DRS measures the speed of movement of the chest surface, which comprises cardiac and respiratory signals by body motion, extracting cardiac information from the superimposed signal is difficult. Therefore, it is challenging to extract cardiac information from superimposed signals. Herein, we propose a novel method based on a matched filter to solve this problem. The method comprises two processes: adaptive generation of a template via singular value decomposition of a trajectory matrix formed from the measurement signals, and reconstruction by convolution of the generated template and measurement signals. The method is validated using a dataset obtained in two different experiments, i.e., experiments involving supine and seated subject postures. Absolute errors in heart rate and standard deviation of heartbeat interval with references were calculated as 1.93±1.76bpm and 57.0±28.1s for the lying posture, and 9.72±7.86bpm and 81.3±24.3s for the sitting posture.


Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3937
Author(s):  
Seungeon Song ◽  
Bongseok Kim ◽  
Sangdong Kim ◽  
Jonghun Lee

Recently, Doppler radar-based foot gesture recognition has attracted attention as a hands-free tool. Doppler radar-based recognition for various foot gestures is still very challenging. So far, no studies have yet dealt deeply with recognition of various foot gestures based on Doppler radar and a deep learning model. In this paper, we propose a method of foot gesture recognition using a new high-compression radar signature image and deep learning. By means of a deep learning AlexNet model, a new high-compression radar signature is created by extracting dominant features via Singular Value Decomposition (SVD) processing; four different foot gestures including kicking, swinging, sliding, and tapping are recognized. Instead of using an original radar signature, the proposed method improves the memory efficiency required for deep learning training by using a high-compression radar signature. Original and reconstructed radar images with high compression values of 90%, 95%, and 99% were applied for the deep learning AlexNet model. As experimental results, movements of all four different foot gestures and of a rolling baseball were recognized with an accuracy of approximately 98.64%. In the future, due to the radar’s inherent robustness to the surrounding environment, this foot gesture recognition sensor using Doppler radar and deep learning will be widely useful in future automotive and smart home industry fields.


Sign in / Sign up

Export Citation Format

Share Document