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Domino: A framework for improving extreme event predictability using flow precursors

Urheber*innen

Dorrington,  Joshua
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Grazzini,  Federico
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Grams,  Christian
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Magnusson,  Linus
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Vitart,  Frederic
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Ferranti,  Laura
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Zitation

Dorrington, J., Grazzini, F., Grams, C., Magnusson, L., Vitart, F., Ferranti, L. (2023): Domino: A framework for improving extreme event predictability using flow precursors, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-0076


Zitierlink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5017016
Zusammenfassung
While models often struggle to directly predict extreme events in the subseasonal range, especially those characterised by small spatial scales such as extreme precipitation, prospects are better for predicting large-scale circulation patterns. Fortunately, many extreme events are closely coupled to the large-scale flow: for precipitation, large-scale wave activity determines moisture transport and availability, especially in the mid-latitudes where frontal rainfall is dominant. We present a framework for identifying the precursor patterns that ‘set the scene’ for extreme events, and using them to augment direct model output to produce more skilful hybrid forecasts. Implementation and extension of this framework is supported by a new open-access Python package, Domino, which reduces such analyses to only a few lines of code. Specifically we consider the predictability of European regional daily rainfall extremes at lead times of several weeks. We will discuss how using information about large-scale precursors, in combination with direct model output, allows extra skill to be extracted from our existing models, and allows us to make the most of the high dimensionality and high data volumes produced by modern forecast systems. We will discuss the potential of this flexible framework to be applied to other geographical regions, different kinds of extreme events and to different time-scales, and plans for a semi-operational implementation of this approach using the IFS forecast model.