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Inverse Modeling of PArameterized physics STochastic uncertainty using process-level Observations

Project description

Getting a better handle on what we do not know increases clarity about climate change

The ability to predict seasonal climate changes and the occurrence of severe weather events is important for preparation, mitigation and policy making. Mathematical models are invaluable to our predictions of processes as well as their evolutions and outcomes. However, all models and their predictions have underlying uncertainties that reflect a variety of unknowns or different pathways. Quantifying the underlying uncertainties is equally important to predicting local or regional climate changes and seasonal events. The EU-funded IMPASTO project is enhancing our ability to represent the uncertainty in certain physical parameters by incorporating observation data via an inverse stochastic modelling paradigm. The outcomes will address the need to quantify the prediction uncertainty at sub-regional and local scales to achieve high-resolution regional seasonal climate ensemble modelling.

Field of science

  • /social sciences/sociology/governance/public services

Call for proposal

H2020-WF-02-2019
See other projects for this call

Funding Scheme

MSCA-IF-EF-RI - RI – Reintegration panel

Coordinator

FACULTY OF PHYSICS OF THE UNIVERSITY OF BELGRADE
Address
Studentski Trg 12-16
11001 Belgrade
Serbia
Activity type
Higher or Secondary Education Establishments
EU contribution
€ 140 021,76