Application of Machine Learning to Severe Weather Prediction from Storm Top Indicators

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10513144" target="_blank" >RIV/00216208:11320/25:10513144 - isvavai.cz</a>

Výsledek na webu

<a href="https://physics.mff.cuni.cz/wds/proc/pdf25/WDS25_11_f8_Dolezalova.pdf" target="_blank" >https://physics.mff.cuni.cz/wds/proc/pdf25/WDS25_11_f8_Dolezalova.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Application of Machine Learning to Severe Weather Prediction from Storm Top Indicators

Popis výsledku v původním jazyce

This case study explores the practical application of a machine learning modelfor detecting overshooting tops (OTs) from high-resolution visible satellite imagery,without relying on infrared data. The model was applied to a severe convective weatherevent, and its OT detections were compared with ground-based reports of hazardousweather, including hail, wind gusts, and intense precipitation. The results demonstratethat the model successfully identified several regions of intense convection, showing goodspatial agreement with areas of reported severe weather. However, the correlation variedbetween individual events - while some hazardous occurrences aligned closely withpredicted OTs, others showed weaker correspondence. These findings highlight boththe potential and the limitations of OT-based storm severity assessment and support theuse of such models as complementary tools in real-time meteorological monitoring andnowcasting.

Název v anglickém jazyce

Application of Machine Learning to Severe Weather Prediction from Storm Top Indicators

Popis výsledku anglicky

This case study explores the practical application of a machine learning modelfor detecting overshooting tops (OTs) from high-resolution visible satellite imagery,without relying on infrared data. The model was applied to a severe convective weatherevent, and its OT detections were compared with ground-based reports of hazardousweather, including hail, wind gusts, and intense precipitation. The results demonstratethat the model successfully identified several regions of intense convection, showing goodspatial agreement with areas of reported severe weather. However, the correlation variedbetween individual events - while some hazardous occurrences aligned closely withpredicted OTs, others showed weaker correspondence. These findings highlight boththe potential and the limitations of OT-based storm severity assessment and support theuse of such models as complementary tools in real-time meteorological monitoring andnowcasting.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10509 - Meteorology and atmospheric sciences

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

WDS&apos;25 Proceedings of Contributed Papers - Physics

ISBN

978-80-7378-532-1

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

94-101

Název nakladatele

Matfyzpress

Místo vydání

Prague

Místo konání akce

Praha

Datum konání akce

3. 6. 2025

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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