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A data assimilation algorithm for predicting rain


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Janjić, Tijana ; Ruckstuhl, Yvonne ; Toint, Philippe L.:
A data assimilation algorithm for predicting rain.
In: Quarterly journal of the Royal Meteorological Society. 147 (2021) 736. - S. 1949-1963.
ISSN 1477-870x ; 0035-9009


Open Access
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Convective-scale data assimilation uses high-resolution numerical weather prediction models and temporally and spatially dense observations of relevant atmospheric variables. In addition, it requires a data assimilation algorithm that is able to provide initial conditions for a state vector of large size with one third or more of its components containing prognostic hydrometeors variables whose non-negativity needs to be preserved. The algorithm also needs to be fast as the state vector requires a high updating frequency in order to catch fast-changing convection. A computationally efficient algorithm for quadratic optimization (QO, or formerly QP) is presented here, which preserves physical properties in order to represent features of the real atmosphere. Crucially for its performance, it exploits the fact that the resulting linear constraints may be disjoint. Numerical results on a simple model designed for testing convective-scale data assimilation show accurate results and promising computational cost. In particular, if constraints on physical quantities are disjoint and their rank is small, further reduction in computational costs can be achieved.

Weitere Angaben

Schlagwörter:convective-scale predictions, data assimilation, disjoint linear constraints, quadratic optimization,preservation of non-negativity
Sprache des Eintrags:Englisch
Institutionen der Universität:Mathematisch-Geographische Fakultät > Mathematik > Heisenberg Professur für Datenassimilation
DOI / URN / ID:10.1002/qj.4004
Open Access: Freie Zugänglichkeit des Volltexts?:Ja
Die Zeitschrift ist nachgewiesen in:
Titel an der KU entstanden:Ja
Eingestellt am: 06. Dez 2021 18:40
Letzte Änderung: 31. Aug 2023 09:13
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