Forecasting Large Hail and Lightning using Additive Logistic Regression Models and the ECMWF Reforecasts

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: Forecasting Large Hail and Lightning using Additive Logistic Regression Models and the ECMWF Reforecasts ; day:15 ; month:05 ; year:2023 ; pages:1-22 ; extent:22
Natural hazards and earth system sciences discussions ; (15.05.2023), 1-22 (gesamt 22)

Creator: Battaglioli, Francesco
Groenemeijer, Pieter
Tsonevsky, Ivan
Púčik, Tomàš

DOI: 10.5194/nhess-2023-40

URN: urn:nbn:de:101:1-2023051804272633604582

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:55 AM CEST

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Battaglioli, Francesco
Groenemeijer, Pieter
Tsonevsky, Ivan
Púčik, Tomàš

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Forecasting Large Hail and Lightning using Additive Logistic Regression Models and the ECMWF Reforecasts

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Other Objects (12)

Tropospheric chemistry in the Integrated Forecasting System of ECMWF

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Verification of ECMWF System 4 for seasonal hydrological forecasting in a northern climate

Assessment of seasonal forecasting errors of the ECMWF system in the eastern Indian Ocean

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Hail, Otto

Hail, Eugen

Hail, Otto

Hail! Russia!

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Hail, Columba!

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C-IFS-CB05-BASCOE: stratospheric chemistry in the Integrated Forecasting System of ECMWF

Verification of ECMWF System 4 for seasonal hydrological forecasting in a northern climate

Assessment of seasonal forecasting errors of the ECMWF system in the eastern Indian Ocean

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Climate model configurations of the ECMWF Integrated Forecasting System (ECMWF-IFS cycle 43r1) for HighResMIP

C-IFS-CB05-BASCOE: stratospheric chemistry in the Integrated Forecasting System of ECMWF

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Hail, Adolf

Hail, Otto

Hail, Eugen

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Hail, Columba!

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