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Spatio-temporal consistency of cloud-microphysical parameter sensitivity in a warm-conveyor belt

Hieronymus, Maicon ; Oertel, Annika ORCID iD icon 1; Miltenberger, Annette K.; Brinkmann, André
1 Institut für Meteorologie und Klimaforschung Troposphärenforschung (IMKTRO), Karlsruher Institut für Technologie (KIT)

Abstract:

A good representation of clouds and precipitation processes is essential in numerical weather and climate models. Subgrid-scale processes, such as cloud physics, are parameterized and inherently introduce uncertainty into models. Traditionally, the sensitivities of the model state to specific uncertain parameters are quantified through perturbations to a few selected parameters, limited by computational resources. Algorithmic Differentiation (AD) enables the efficient and simultaneous estimation of sensitivities for a large number of parameters, thereby overcoming the previous limitations and significantly enhancing the efficiency of the analysis. This framework provides an objective way to identify processes where more precise representations have the largest impact on model accuracy. AD-estimated sensitivities can also address the underdispersiveness of perturbed ensemble simulations by guiding the parameter selection or the perturbation itself. In our study, we applied AD to 169 uncertain parameters identified in the two-moment microphysics scheme of the numerical weather prediction (NWP) model ICON of the German Weather Service. This application of AD allowed us to evaluate the sensitivities of specific humidity, latent heating, and latent cooling along several thousand warm conveyor belt trajectories. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000183653
Veröffentlicht am 08.08.2025
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung Troposphärenforschung (IMKTRO)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 07.2025
Sprache Englisch
Identifikator ISSN: 1877-7503, 1877-7511
KITopen-ID: 1000183653
HGF-Programm 12.11.34 (POF IV, LK 01) Improved predictions from weather to climate scales
Erschienen in Journal of Computational Science
Verlag Elsevier
Band 89
Seiten 102614
Nachgewiesen in OpenAlex
Web of Science
Dimensions
Scopus
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