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  • 2020-2024  (2)
  • 1
    Publication Date: 2024-03-06
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉The usually short lifetime of convective storms and their rapid development during unstable weather conditions makes forecasting these storms challenging. It is necessary, therefore, to improve the procedures for estimating the storms' expected life cycles, including the storms' lifetime, size, and intensity development. We present an analysis of the life cycles of convective cells in Germany, focusing on the relevance of the prevailing atmospheric conditions. Using data from the radar‐based cell detection and tracking algorithm KONRAD of the German Weather Service, the life cycles of isolated convective storms are analysed for the summer half‐years from 2011 to 2016. In addition, numerous convection‐relevant atmospheric ambient variables (e.g., deep‐layer shear, convective available potential energy, lifted index), which were calculated using high‐resolution COSMO‐EU assimilation analyses (0.0625°), are combined with the life cycles. The statistical analyses of the life cycles reveal that rapid initial area growth supports wider horizontal expansion of a cell in the subsequent development and, indirectly, a longer lifetime. Specifically, the information about the initial horizontal cell area is the most important predictor for the lifetime and expected maximum cell area during the life cycle. However, its predictive skill turns out to be moderate at most, but still considerably higher than the skill of any ambient variable is. Of the latter, measures of midtropospheric mean wind and vertical wind shear are most suitable for distinguishing between convective cells with short lifetime and those with long lifetime. Higher thermal instability is associated with faster initial growth, thus favouring larger and longer living cells. A detailed objective correlation analysis between ambient variables, coupled with analyses discriminating groups of different lifetime and maximum cell area, makes it possible to gain new insights into their statistical connections. The results of this study provide guidance for predictor selection and advancements of nowcasting applications.〈/p〉
    Description: 〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Based on a combination of data of the cell tracking algorithm KONRAD of the German Weather Service and COSMO‐EU model analyses for the summer half‐years from 2011 to 2016, statistical relationships between storm attributes (lifetime and maximum horizontal area), and ambient variables as well as the storms' history are quantified. The initial growth of the cell area is a better indicator of the lifetime and maximum area than ambient variables are. Of the latter, measures of the midtropospheric wind and vertical wind shear, in particular, are most suitable for distinguishing between convective cells with short and long lifetimes, whereas higher convective instability favours larger cells. 〈boxed-text position="anchor" id="qj4505-blkfxd-0001" content-type="graphic" xml:lang="en"〉〈graphic position="anchor" id="jats-graphic-1" xlink:href="urn:x-wiley:00359009:media:qj4505:qj4505-toc-0001"〉 〈/graphic〉 〈/boxed-text〉〈/p〉
    Description: Bundesministerium für Digitales und Verkehr http://dx.doi.org/10.13039/100008383
    Keywords: ddc:551.6 ; convective storms ; life cycle ; multisource data ; nowcasting ; statistics ; weather prediction
    Language: English
    Type: doc-type:article
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  • 2
    Publication Date: 2023-02-08
    Description: Forecasting and early warning systems are important investments to protect lives, properties and livelihood. While early warning systems are frequently used to predict the magnitude, location and timing of potentially damaging events, these systems rarely provide impact estimates, such as the expected amount and distribution of physical damage, human consequences, disruption of services or financial loss. Complementing early warning systems with impact forecasts has a two‐fold advantage: it would provide decision makers with richer information to take informed decisions about emergency measures, and focus the attention of different disciplines on a common target. This would allow capitalizing on synergies between different disciplines and boosting the development of multi‐hazard early warning systems. This review discusses the state‐of‐the‐art in impact forecasting for a wide range of natural hazards. We outline the added value of impact‐based warnings compared to hazard forecasting for the emergency phase, indicate challenges and pitfalls, and synthesize the review results across hazard types most relevant for Europe. Plain language summary Forecasting and early warning systems are important investments to protect lives, properties and livelihood. While such systems are frequently used to predict the magnitude, location and timing of potentially damaging events, they rarely provide impact estimates, such as the expected physical damage, human consequences, disruption of services or financial loss. Extending hazard forecast systems to include impact estimates promises many benefits for the emergency phase, for instance, for organising evacuations. We review and compare the state‐of‐the‐art of impact forcasting across a wide range of natural hazards, and outline opportunities and key challenges for research and development of impact forecasting.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
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