PACE A Precipitationshed Approach to Changing Extremes

Summary

Characterizing the frequency of future precipitation extremes is a fundamental task in order to understand and mitigate their effects under future climate conditions. However, predicting these changes is a challenging task as intense rainfall is poorly represented in climate models, and extrapolations based on historical data are highly uncertain. The objective of the PACE project is bridging this disconnection by developing an innovative physically-informed statistical model for rainfall extremes. The proposed methodology explicitly accounts for the interannual variability of atmospheric moisture fluxes and their future predicted changes. PACE will achieve this objective by using an innovative precipitationshed perspective. Precipitationsheds - the regions of Earth from which most precipitable water originates before reaching a target site – will be computed using a moisture tracking technique. By including in the statistical representation of rainfall extreme key physical information integrated over a precipitationshed, PACE will provide a novel representation of how dynamic and thermodynamic changing conditions in upwind areas affect the frequency and probability distribution of downwind precipitation. By learning rainfall statistical properties from historical data and relying on climate model predictions only for large scale climate features, the PACE action will improve probabilistic rainfall predictions under future climate scenarios. The Candidate, recently graduated from Duke University (Durham, NC) will join the Host at TU Delft (Delft, The Netherlands) to achieve this research objective, and to continue his postgraduate professional development aimed at creating an independent research program in hydroclimatology.

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More information & hyperlinks

Web resources:	https://cordis.europa.eu/project/id/101067138
Start date:	01-09-2023
End date:	31-08-2025
Total budget - Public funding:	- 187 624,00 Euro

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Original description

Characterizing the frequency of future precipitation extremes is a fundamental task in order to understand and mitigate their effects under future climate conditions. However, predicting these changes is a challenging task as intense rainfall is poorly represented in climate models, and extrapolations based on historical data are highly uncertain. The objective of the PACE project is bridging this disconnection by developing an innovative physically-informed statistical model for rainfall extremes. The proposed methodology explicitly accounts for the interannual variability of atmospheric moisture fluxes and their future predicted changes. PACE will achieve this objective by using an innovative precipitationshed perspective. Precipitationsheds - the regions of Earth from which most precipitable water originates before reaching a target site will be computed using a moisture tracking technique. By including in the statistical representation of rainfall extreme key physical information integrated over a precipitationshed, PACE will provide a novel representation of how dynamic and thermodynamic changing conditions in upwind areas affect the frequency and probability distribution of downwind precipitation. By learning rainfall statistical properties from historical data and relying on climate model predictions only for large scale climate features, the PACE action will improve probabilistic rainfall predictions under future climate scenarios. The Candidate, recently graduated from Duke University (Durham, NC) will join the Host at TU Delft (Delft, The Netherlands) to achieve this research objective, and to continue his postgraduate professional development aimed at creating an independent research program in hydroclimatology.