<p>An operational, single-polarized X-band weather radar provides measurements in Hamburg&#8217;s city center for almost eight years. This weather radar operates at an elevation angle (~3.5&#176;) with a high temporal (30 s), range (60 m), and sampling (1&#176;) resolution resulting<span> in a</span> high information density within <span>the</span> 20 km <span>scan radius</span>. <span>Studies on short time periods (several months) proofs the performance of this low-cost local area weather radar. </span><span>For example, a</span><span> case study on a tornado in a rain event demonstrates its refined resolution </span><span>compared to</span><span> the German nationwide C-band radars. </span><span>Now, we aim for a eight-year precipitation climatology with 100 m resolution. This data set will enable reliable studies on urban extreme precipitation. This presentation will describe h</span><span>ow we </span><span>can</span><span> infer a precipitation estimate based on multi-</span><span>year</span><span> weather radar observations in the urban area of Hamburg.</span></p><p>The single-polarization and <span>small</span> <span>wavelength</span> <span>comes along with</span> high resolution <span>but at the same time</span> high uncertainties. We address several sources of errors affecting th<span>e</span> radar-based <span>precipitation</span> estimate, like the radar calibration, alignment, attenuation, noise, non-meteorologial echoes, <span>and </span><span><em>Z</em></span><span>-</span><span><em>R</em></span><span> relation. The deployment of additional vertically pointing micro rain radars yields drop size distributions at relevant heights reducing errors effectively concerning the radar calibration and required statistical relations (</span><span><em>k</em></span><span>-</span><span><em>Z</em></span><span> and </span><span><em>Z</em></span><span>-</span><span><em>R</em></span><span> relation). We outline the performance of the correction methods for long time periods and discuss open issues and limitations.</span></p><p><span>With this high-quality and -resolution weather radar product, refined studies on the spatial and temporal scale of </span><span>urban </span><span>precipitation will be possible. </span><span>This data set will be used for</span><span> further hydrological research in an urban area </span><span>within the project <em>Sustainable Adaption Scenarios for Urban Areas &#8211; Water from Four Sides</em> of the</span><span> Cluster of Excellence <em>Climate Climatic Change, and Society</em> (CliCCS).</span></p>
<p class="western">An zwei deutschen Flugh&#228;fen werden vom Deutschen Wetterdienst seit 2018 Mikro-Regen-Radare (MRR) betrieben, um zu erproben, welcher Nutzen daraus f&#252;r die meteorologische Sicherung des Luftverkehrs gezogen werden kann. Ein wichtiger Parameter ist die automatische Detektion und H&#246;henbestimmung der Schmelzschicht, die zum Beispiel f&#252;r die Erkennung und Bewertung von Vereisungssituationen genutzt werden kann. Zus&#228;tzlich kann die Kenntnis der Schmelzschichth&#246;he helfen, die Qualit&#228;t 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 Schmelzschichth&#246;he, diese aber mit gro&#223;er Zuverl&#228;ssigkeit, da die Messbedingungen bei vertikaler Strahlrichtung besonders g&#252;nstig sind. Auch komplexe Strukturen, wie doppelte Schmelzschichten, k&#246;nnen so erkannt werden. Sie kommen zwar nur selten vor, sind aber Indikatoren f&#252;r besonders gef&#228;hrliche Vereisungssituationen. In dem erprobten Verfahren werden zus&#228;tzlich zur Reflektivit&#228;t, 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 ma&#223;geblich durch die Fallgeschwindigkeit beziehungsweise die Fallgeschwindigkeitsverteilung der Hydrometeore bestimmt, und weisen beim &#220;bergang von der festen zur fl&#252;ssigen Phasen charakteristische Signaturen auf. Damit ist eine zuverl&#228;ssige Schmelzschichterkennung auch in Regenereignissen m&#246;glich, in denen das Reflexionsmaximum durch die allgemeine Variabilit&#228;t des Reflexionsprofils maskiert wird.</p> <p class="western">Hier wird ein einj&#228;hriger Datensatz mit Radiosonden-Aufstiegen verglichen, um die Zuverl&#228;ssigkeit des im MRR implementierten automatischen Detektionsalgorithmus zu analysieren.</p>
<p>The University of Hamburg operates a single-polarized X-band weather radar to investigate small scale precipitation in Hamburg&#8217;s center since 2013. This weather radar provides a temporal resolution of 30 s, a range resolution of 60 m, and a sampling resolution of 1&#176; within a 20 km radius. The X-band observations refine the coarse measurements of the German nationwide C-band radars. On the one hand, the resolution enables new capabilities in research and detection of extreme events, e.g. flash floods or tornadoes in rain events. On the other hand, with the single polarization and small wavelength, attenuation, noise, and non-meteorological echoes become a challenging issue. How can we derive products from disturbed weather radar observations?</p><p>We demonstrate new methods to process X-band weather radar observations effectively using synthetic and real data. Firstly, we present our python package for local weather radars. This package combines all steps of processing our measurements and includes well-established algorithms of image processing and radar meteorology. Secondly, we study machine learning as a new and potential method for our weather radar products. The developed neural network uses raw reflectivity measurements as input and results in data, which is free of noise and non-meteorological echoes. We outline assets and drawbacks of both methods and show possible connections.</p><p>Further X-band weather radar systems are planned for 2020 to monitor precipitation for the Hamburg metropolitan region in a networked environment. The high-quality and -resolution weather radar products will be provided for urban hydrology research within the Cluster of Excellence CLICCS - Climate, Climatic Change, and Society.</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.