Javascript must be enabled to continue!
Radar-based Hail Detection and Hail Size Estimation at DWD
View through CrossRef
<p>Hail is a pronounced natural hazard in Germany. Nevertheless, major hail events are quite rare and there is a lack of information in hail occurrence and size and its spatiotemporal distribution. Measurement sensors that are able to detect hail (e.g. disdrometers) are in principle available in Germany, but the spatial density of those stations is far lower than the typical spatial extent of hail events. Furthermore, sensors for hail size estimation are still in evaluation stage and currently only located at a few selected places. Hail reports based on professional and particularly amateurish eyewitness become increasingly important. But besides a certain degree of subjectivity in the reported hail size, highly populated areas might be overrepresented compared to rural and sparsely populated areas. Areal information from weather radar networks can overcome this issue with a high spatiotemporal resolution. Because of the high update frequency and fast availability of radar data, an automatic hail detection and hail size estimation might provide valuable hints to forecasters and supports the warning decision process. &#160; &#160; &#160;&#160;<br />The Deutscher Wetterdienst (DWD) utilizes a C-Band dual-polarimetric weather radar network consisting of 17 radar stations that provide ten volume scans and a terrain-following low-elevation scan every five minutes. The operationally used hydrometeor classification algorithm HYMEC processes data of reflectivity, differential reflectivity and co-polar correlation coefficient to distinguish between hail and other hydrometeors. With this classification a hail distribution over Germany can already be derived. For the analysis of hail sizes, the Maximum Expected Size of Hail (MESH) and a method based on Vertical Integrated Ice (VII) are used. The latter method is motivated by a linear relation between maximum hail size and VII proposed by our forecasters based on their practical experience. &#160; &#160; &#160; &#160;<br />This contribution will give an overview on the statistics of hail occurrence and hail size using the aforementioned algorithms in Germany during the convective seasons 2021 and 2022. Also, selected case studies are discussed in more detail. The results are compared against hail observations from manned and automatic weather stations, reports from the European Severe Weather Database and user reports from DWD&#8217;s WarnWetter-App.</p>
Title: Radar-based Hail Detection and Hail Size Estimation at DWD
Description:
<p>Hail is a pronounced natural hazard in Germany.
Nevertheless, major hail events are quite rare and there is a lack of information in hail occurrence and size and its spatiotemporal distribution.
Measurement sensors that are able to detect hail (e.
g.
disdrometers) are in principle available in Germany, but the spatial density of those stations is far lower than the typical spatial extent of hail events.
Furthermore, sensors for hail size estimation are still in evaluation stage and currently only located at a few selected places.
Hail reports based on professional and particularly amateurish eyewitness become increasingly important.
But besides a certain degree of subjectivity in the reported hail size, highly populated areas might be overrepresented compared to rural and sparsely populated areas.
Areal information from weather radar networks can overcome this issue with a high spatiotemporal resolution.
Because of the high update frequency and fast availability of radar data, an automatic hail detection and hail size estimation might provide valuable hints to forecasters and supports the warning decision process.
&#160; &#160; &#160;&#160;<br />The Deutscher Wetterdienst (DWD) utilizes a C-Band dual-polarimetric weather radar network consisting of 17 radar stations that provide ten volume scans and a terrain-following low-elevation scan every five minutes.
The operationally used hydrometeor classification algorithm HYMEC processes data of reflectivity, differential reflectivity and co-polar correlation coefficient to distinguish between hail and other hydrometeors.
With this classification a hail distribution over Germany can already be derived.
For the analysis of hail sizes, the Maximum Expected Size of Hail (MESH) and a method based on Vertical Integrated Ice (VII) are used.
The latter method is motivated by a linear relation between maximum hail size and VII proposed by our forecasters based on their practical experience.
&#160; &#160; &#160; &#160;<br />This contribution will give an overview on the statistics of hail occurrence and hail size using the aforementioned algorithms in Germany during the convective seasons 2021 and 2022.
Also, selected case studies are discussed in more detail.
The results are compared against hail observations from manned and automatic weather stations, reports from the European Severe Weather Database and user reports from DWD&#8217;s WarnWetter-App.
</p>.
Related Results
On Flores Island, do "ape-men" still exist? https://www.sapiens.org/biology/flores-island-ape-men/
On Flores Island, do "ape-men" still exist? https://www.sapiens.org/biology/flores-island-ape-men/
<span style="font-size:11pt"><span style="background:#f9f9f4"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><b><spa...
Hubungan Perilaku Pola Makan dengan Kejadian Anak Obesitas
Hubungan Perilaku Pola Makan dengan Kejadian Anak Obesitas
<p><em><span style="font-size: 11.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-langua...
Do urban areas intensify hail?
Do urban areas intensify hail?
Abstract
Hail represents a significant meteorological hazard and a source of insured property losses in multiple countries. Hence, there is i...
Analyse und Kommunikation der beobachteten Klimatrends in Deutschland
Analyse und Kommunikation der beobachteten Klimatrends in Deutschland
<p>Eine der Aufgaben des Deutschen Wetterdienstes ist die Klima&#252;berwachung f&#252;r Deutschland. Dazu verwendet der DWD die Daten der Wetterstati...
DWD-Crowdsourcing: Are User Reports beneficial for Object-based Nowcasting?
DWD-Crowdsourcing: Are User Reports beneficial for Object-based Nowcasting?
Since July 2020 the DWD WarnWetter-App comprises the Crowdsourcing module “User Reports”. This module provides users the functionality to report observations ab...
A case study on severe hailstorm on 27 July 2019 in the province of Styria, Austria
A case study on severe hailstorm on 27 July 2019 in the province of Styria, Austria
<p>A severe hailstorm activity on 27<sup>th</sup> July 2019 created significant damage to crops in the province of Styria, Austria. The ha...
Kommunikation des beobachteten Klimawandels in Deutschland
Kommunikation des beobachteten Klimawandels in Deutschland
<p>Eine der Aufgaben des Deutschen Wetterdienstes ist die Klima&#252;berwachung f&#252;r Deutschland. Dazu verwendet der DWD die Daten der Wetterstati...
Hail: Mechanisms, Monitoring, Forecasting, Damages, Financial Compensation Systems, and Prevention
Hail: Mechanisms, Monitoring, Forecasting, Damages, Financial Compensation Systems, and Prevention
Hail has long caused extensive damage and economic loss in places inhabited by humans. Climate change is expected to lead to different types of damage due to the geographic charact...

