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Inverse Modeling of PArameterized physics STochastic uncertainty using process l...

Inverse Modeling of PArameterized physics STochastic uncertainty using process level Observations The emerging climate services in Europe and globally under the WMO GFCS critically depend on the science and application of Earth System (ES) modeling and prediction. An important aspect of the modelling and prediction challenge... ver más

30/04/2022

FMK

140K€

Presupuesto del proyecto: 140K€

Líder del proyecto

Faculty of Media and Communications No se ha especificado una descripción o un objeto social para esta compañía.

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2022-04-30

WF-02-2019: Widening Fellowships

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2 Participantes

FMK

140.02K€ | Lider

OESIA NETWORKS

745.06K€ | Participante

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Duración del proyecto: 24 meses Fecha Inicio: 2020-04-03
Fecha Fin: 2022-04-30

Líder del proyecto

Faculty of Media and Communications No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 140K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The emerging climate services in Europe and globally under the WMO GFCS critically depend on the science and application of Earth System (ES) modeling and prediction. An important aspect of the modelling and prediction challenge for the services is characterization of prediction uncertainty. Quantitative information about inherent uncertainty in the prediction is vital for risk assessments and decision support across public service and industry sectors. Over the past decades significant investments were made through the EC FPs into establishing the ES ensemble modeling and prediction capabilities that incorporate representation of sources of intrinsic uncertainty in the modeling systems and provide quantification of their impacts on seasonal to decadal climate prediction. The identified stakeholder priorities for the H2020 and beyond are in advancing of these prediction capabilities at regional level with high resolution models. This project is intended to contribute to the science of high resolution regional seasonal climate ensemble modeling, addressing the need for understanding and quantification of the prediction uncertainty at sub-regional and local scales. The research will focus on characterization of stochastic uncertainty associated with physical parameterizations in the regional ES models. The project will integrate the established ensemble modeling methodology from the foundational work with the global systems with a new approach to representing the stochastic uncertainty of physical parameterizations by means of inverse stochastic modeling using process-level observations. A long standing tradition in expert regional climate modeling at the hosting institution will be joined with the Researcher’s expertise and experience in geophysical modeling and cross-disciplinary methodology of data assimilation. The Researcher will be trained in the regional climate modeling and will apply the expertise to build a new research capacity at the hosting institution.

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