CLImate INTelligence: Extreme events detection, attribution and adaptation design using machine learning

Informations projet

CLINT

N° de convention de subvention: 101003876

DOI

10.3030/101003876

Date de signature de la CE 10 Mai 2021

Date de début 1 Juillet 2021

Date de fin 31 Octobre 2025

Financé au titre de

• SOCIETAL CHALLENGES - Climate action, Environment, Resource Efficiency and Raw Materials

Coût total

€ 6 067 719,98

Contribution de l’UE

€ 6 067 719,98

Coordonné par

POLITECNICO DI MILANO
Italy

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

Documents, reports (16)

Local Climate Services

Report from T7.1 integrating milestone MS2 and reporting the needs, as suggested by the stakeholders in dedicated meeting, for local CS in the different Climate Change Hotspots and the specifications of existing services, formulating any user-inspired EE variables and indices as input to WP3-5, and formulating the impact indicators for quantifying the value of AI-enhanced CS.

ML algorithms for EE forecasts and reconstruction

Report from T2.4-5 describing the ML algorithms to generate fully data-driven forecasts of EE, and of the ML algorithms generating spatial predictions or reconstruction of missing values. This report will integrate milestones MS17 and MS22.

Project Management Plan

Report from T1.1 describing the decision-making structures and procedures adopted in the process; the review quality procedures; the management processes to be implemented in steering each WP and task; the financial and resource use reporting guidelines.

Central data repository

Report from T8.2 updating deliverable D8.2 and describing the setup and maintenance workflow of the central data repositories

Communication and Dissemination plan - first update

The report from T9.3 will include an update of deliverable D9.2 with the activities carried out in the first reporting period.

Preliminary AI-enhanced Climate Services for local decision-making

Report from T7.2-4 integrating milestones MS12-14 and describing the preliminary analysis of the value of AI-enhanced CS in the different Climate Change Hotspots for different EE categories.

Extreme Events causation analysis

Report from T4.1 integrating milestones MS18 and MS24 and reviewing existing knowledge, data and models for physical causation analysis for different EE, including concurrent extremes.

Extreme Events detection

Report from T3.1-3.4 integrating milestones MS5-11 and reviewing existing knowledge, data and models for EE detection, and, for each category of EEs, identifying indices, datasets, and candidate drivers for ML based detection.

Preliminary report on AI-enhanced Climate Services for extreme impacts

The report integrates milestone MS15 and illustrates the preliminary analysis of AI-enhanced CS for European EE impacts for the water, energy, and food sectors.

Preliminary AI-enhanced Extreme Events detection

Report from T3.1-3.4 illustrating the preliminary applications of the ML algorithms developed in WP2 to EE detection.

Extreme Events attribution

The report from T5.1 expands milestone MS16 and reviews existing knowledge, data and models for attribution analysis of different EE.

Review of ML algorithms for Climate Science

Report describing state-of-the-art algorithms developed in the Machine Learning and Artificial Intelligence domain to support climate science in the detection, causation, and attribution of extreme events addressed in T2.1-3.

EU Climate Services on the impacts of Extreme Events

The report from T6.1-6.3 integrates milestones MS3-4 and reviews existing EU CS with a focus on EE impacts for the water, energy and food sectors. It will also report suggestions and requirements gathered from the stakeholders in dedicated meetings.

Communication and Dissemination plan - second update

The report from T9.3 will include an update of deliverable D9.4 with the activities carried out in the second reporting period, including a chapter reporting on the webinars/workshop organized in the second reporting period.

AI-enhanced attribution and projections of Extreme Events

The deliverable from T5.1-5.2-5.3 integrates milestones MS25-26-27 and reports on the applications of ML algorithms to EE attribution, the detection of observed trends and the construction of future storylines, outlining pros and cons for each category of EE.

Communication and Dissemination plan

Report from T9.1-9.2 describing the strategies that will be used to obtain the objectives of this WP. The plan will include a communication requirements analysis, identification of stakeholders and target audiences, and will outline dissemination activities and channels to be used and will display time management features for their implementation.

Other (1)

Climate Services Information Systems architecture

Detailed description of backend related software architecture. The report from T8.3 integrates milestone MS21 and will include the descriptions new developed and already available components to establish CSIS services providing ML codes as processes. The report will also include the appropriate code repositories.

Websites, patent fillings, videos etc. (1)

CLINT website, visual identity and logo

Initial package of the communication material, including the project logo, website, social media. This dissemination material from T9.2 will be continuously updated throughout the project.

Open Research Data Pilot (1)

Data Management Plan

Report from T8.1 describing the policy concerning the acquisition, storage and classification and management and distribution of project data. The report will include procedures for data collection, storage, protection, retention and destruction.

Publications

Peer reviewed articles (25)

Tropical Cyclone Genesis Potential Indices in a New High‐Resolution Climate Models Ensemble: Limitations and Way Forward

Auteurs: L. Cavicchia, E. Scoccimarro, G. Ascenso, A. Castelletti, M. Giuliani, S. Gualdi
Publié dans: Geophysical Research Letters, Numéro 50, 2024, ISSN 0094-8276
Éditeur: American Geophysical Union
DOI: 10.1029/2023gl103001

Nighttime heat waves in the Euro-Mediterranean region: definition, characterisation, and seasonal prediction

Auteurs: Verónica Torralba, Stefano Materia, Leone Cavicchia, M Carmen Álvarez-Castro, Chloé Prodhomme, Ronan McAdam, Enrico Scoccimarro, Silvio Gualdi
Publié dans: Environmental Research Letters, Numéro 19, 2024, Page(s) 034001, ISSN 1748-9326
Éditeur: Institute of Physics Publishing
DOI: 10.1088/1748-9326/ad24cf

Robustness of hydrometeorological extremes in surrogated seasonal forecasts

Auteurs: Katharina Klehmet, Peter Berg, Denica Bozhinova, Louise Crochemore, Yiheng Du, Ilias Pechlivanidis, Christiana Photiadou, Wei Yang
Publié dans: International Journal of Climatology, Numéro 44, 2024, Page(s) 1725-1738, ISSN 0899-8418
Éditeur: John Wiley & Sons Inc.
DOI: 10.1002/joc.8407

The New Max Planck Institute Grand Ensemble With CMIP6 Forcing and High‐Frequency Model Output

Auteurs: Dirk Olonscheck, Laura Suarez‐Gutierrez, Sebastian Milinski, Goratz Beobide‐Arsuaga, Johanna Baehr, Friederike Fröb, Tatiana Ilyina, Christopher Kadow, Daniel Krieger, Hongmei Li, Jochem Marotzke, Étienne Plésiat, Martin Schupfner, Fabian Wachsmann, Lara Wallberg, Karl‐Hermann Wieners, Sebastian Brune
Publié dans: Journal of Advances in Modeling Earth Systems, Numéro 15, 2023, ISSN 1942-2466
Éditeur: American Geophysical Union
DOI: 10.1029/2023ms003790

Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review

Auteurs: Sancho Salcedo-Sanz, Jorge Pérez-Aracil, Guido Ascenso, Javier Del Ser, David Casillas-Pérez, Christopher Kadow, Dušan Fister, David Barriopedro, Ricardo García-Herrera, Matteo Giuliani, Andrea Castelletti
Publié dans: Theoretical and Applied Climatology, Numéro 155, 2024, Page(s) 1-44, ISSN 0177-798X
Éditeur: Springer Verlag
DOI: 10.1007/s00704-023-04571-5

When it comes to Earth observations in AI for disaster risk reduction, is it feast or famine? A topical review

Auteurs: Monique M Kuglitsch, Arif Albayrak, Jürg Luterbacher, Allison Craddock, Andrea Toreti, Jackie Ma, Paula Padrino Vilela, Elena Xoplaki, Rui Kotani, Dominique Berod, Jon Cox, Ivanka Pelivan
Publié dans: Environmental Research Letters, Numéro 18, 2023, Page(s) 093004, ISSN 1748-9326
Éditeur: Institute of Physics Publishing
DOI: 10.1088/1748-9326/acf601

Long-term temperature prediction with hybrid autoencoder algorithms

Auteurs: J. Pérez-Aracil, D. Fister, C.M. Marina, C. Peláez-Rodríguez, L. Cornejo-Bueno, P.A. Gutiérrez, M. Giuliani, A. Castelleti, S. Salcedo-Sanz
Publié dans: Applied Computing and Geosciences, Numéro 23, 2024, Page(s) 100185, ISSN 2590-1974
Éditeur: elsevier
DOI: 10.1016/j.acags.2024.100185

Freddy: breaking record for Tropical Cyclone precipitation?

Auteurs: Enrico Scoccimarro, Paolo Lanteri, Leone Cavicchia
Publié dans: Environmental Research Letters 19 064013, 2024, ISSN 1748-9326
Éditeur: Institute of Physics Publishing
DOI: 10.5194/egusphere-egu24-5958

Interpretable linear dimensionality reduction based on bias-variance analysis

Auteurs: Paolo Bonetti, Alberto Maria Metelli, Marcello Restelli
Publié dans: Data Mining and Knowledge Discovery, Numéro 38, 2024, Page(s) 1713-1781, ISSN 1384-5810
Éditeur: Kluwer Academic Publishers
DOI: 10.1007/s10618-024-01015-0

Optimisation-based refinement of genesis indices for tropical cyclones

Auteurs: Ascenso, Guido Cavicchia, Leone Scoccimarro, Enrico Castelletti, Andrea
Publié dans: Environmental Research Communications, 2023, ISSN 2515-7620
Éditeur: IOP Publishing
DOI: 10.1088/2515-7620/acb52a

Country-level energy demand for cooling has increased over the past two decades

Auteurs: Enrico Scoccimarro, Oreste Cattaneo, Silvio Gualdi, Francesco Mattion, Alexandre Bizeul, Arnau Martin Risquez, Roberta Quadrelli
Publié dans: Communications Earth & Environment, Numéro 4, 2023, ISSN 2662-4435
Éditeur: nature
DOI: 10.1038/s43247-023-00878-3

The extremely hot and dry 2018 summer in central and northern Europe from a multi-faceted weather and climate perspective

Auteurs: Efi Rousi, Andreas H. Fink, Lauren S. Andersen, Florian N. Becker, Goratz Beobide-Arsuaga, Marcus Breil, Giacomo Cozzi, Jens Heinke, Lisa Jach, Deborah Niermann, Dragan Petrovic, Andy Richling, Johannes Riebold, Stella Steidl, Laura Suarez-Gutierrez, Jordis S. Tradowsky, Dim Coumou, André Düsterhus, Florian Ellsäßer, Georgios Fragkoulidis, Daniel Gliksman, Dörthe Handorf, Karsten Haustein, Ka
Publié dans: Natural Hazards and Earth System Sciences, Numéro 23, 2023, Page(s) 1699-1718, ISSN 1684-9981
Éditeur: EGU Journal
DOI: 10.5194/nhess-23-1699-2023

Significant relationships between drought indicators and impacts for the 2018–2019 drought in Germany

Auteurs: Anastasiya Shyrokaya, Gabriele Messori, Ilias Pechlivanidis, Florian Pappenberger, Hannah L Cloke, Giuliano Di Baldassarre
Publié dans: Environmental Research Letters, Numéro 19, 2023, Page(s) 014037, ISSN 1748-9326
Éditeur: Institute of Physics Publishing
DOI: 10.1088/1748-9326/ad10d9

New Probabilistic, Dynamic Multi-Method Ensembles for Optimization Based on the CRO-SL

Auteurs: Jorge Pérez-Aracil, Carlos Camacho-Gómez, Eugenio Lorente-Ramos, Cosmin M. Marina, Laura M. Cornejo-Bueno, Sancho Salcedo-Sanz
Publié dans: Mathematics, Numéro 11, 2023, Page(s) 1666, ISSN 2227-7390
Éditeur: mdpi
DOI: 10.3390/math11071666

One month in advance prediction of air temperature from Reanalysis data with eXplainable Artificial Intelligence techniques

Auteurs: Gómez-Orellana, Antonio Manuel Guijo-Rubio, David Pérez-Aracil, Jorge Gutiérrez, Pedro Antonio Salcedo-Sanz, Sancho Hervás-Martínez, César
Publié dans: Atmospheric Research, 2023, ISSN 0169-8095
Éditeur: Elsevier BV
DOI: 10.1016/j.atmosres.2023.106608

Advances and gaps in the science and practice of impact‐based forecasting of droughts

Auteurs: Anastasiya Shyrokaya, Florian Pappenberger, Ilias Pechlivanidis, Gabriele Messori, Sina Khatami, Maurizio Mazzoleni, Giuliano Di Baldassarre
Publié dans: WIREs Water, Numéro 11, 2024, ISSN 2049-1948
Éditeur: WIREs
DOI: 10.1002/wat2.1698

Accurate Long-term Air Temperature Prediction with a Fusion of Artificial Intelligence and Data Reduction Techniques

Auteurs: Fister, Dušan; Pérez-Aracil, Jorge; Peláez-Rodríguez, César; Del Ser, Javier; Salcedo-Sanz, Sancho
Publié dans: arXiv preprint arXiv:2209.15424, Numéro 2, 2022, ISSN 2331-8422
Éditeur: Arxiv
DOI: 10.48550/arxiv.2209.15424

Increasing heat and rainfall extremes now far outside the historical climate

Auteurs: Alexander Robinson, Jascha Lehmann, David Barriopedro, Stefan Rahmstorf, Dim Coumou
Publié dans: npj Climate and Atmospheric Science, Numéro 4, 2022, ISSN 2397-3722
Éditeur: nature
DOI: 10.1038/s41612-021-00202-w

Model Predictive Control of water resources systems: A review and research agenda

Auteurs: Andrea Castelletti, Andrea Ficchì, Andrea Cominola, Pablo Segovia, Matteo Giuliani, Wenyan Wu, Sergio Lucia, Carlos Ocampo-Martinez, Bart De Schutter, José María Maestre
Publié dans: Annual Reviews in Control, Numéro 55, 2024, Page(s) 442-465, ISSN 1367-5788
Éditeur: Pergamon Press Ltd.
DOI: 10.1016/j.arcontrol.2023.03.013

An Action‐Oriented Approach to Make the Most of the Wind and Solar Power Complementarity

Auteurs: Sonia Jerez, David Barriopedro, Alejandro García‐López, Raquel Lorente‐Plazas, Andrés M. Somoza, Marco Turco, Judit Carrillo, Ricardo M. Trigo
Publié dans: Earth's Future, Numéro 11, 2024, ISSN 2328-4277
Éditeur: Earth's Future
DOI: 10.1029/2022ef003332

A hierarchical classification/regression algorithm for improving extreme wind speed events prediction

Auteurs: Peláez-Rodríguez, C., Pérez-Aracil, J., Fister, D., Prieto-Godino, L., Deo, R.C., Salcedo-Sanz, S.
Publié dans: Renewable Energy, 2022, ISSN 0960-1481
Éditeur: Pergamon Press Ltd.
DOI: 10.1016/j.renene.2022.11.042

A general explicable forecasting framework for weather events based on ordinal classification and inductive rules combined with fuzzy logic

Auteurs: C. Peláez-Rodríguez, J. Pérez-Aracil, C.M. Marina, L. Prieto-Godino, C. Casanova-Mateo, P.A. Gutiérrez, S. Salcedo-Sanz
Publié dans: Knowledge-Based Systems, Numéro 291, 2024, Page(s) 111556, ISSN 0950-7051
Éditeur: Elsevier BV
DOI: 10.1016/j.knosys.2024.111556

Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop

Auteurs: Antara Dasgupta, Louise Arnal, Rebecca Emerton, Shaun Harrigan, Gwyneth Matthews, Ameer Muhammad, Karen O'Regan, Teresa Pérez-Ciria, Emixi Valdez, Bart van Osnabrugge, Micha Werner, Carlo Buontempo, Hannah Cloke, Florian Pappenberger, Ilias G. Pechlivanidis, Christel Prudhomme, Maria-Helena Ramos, Peter Salamon
Publié dans: Journal of Flood and Risk Management, 2022, ISSN 1753-318X
Éditeur: Blackwell Publishing
DOI: 10.1111/jfr3.12880

Hydrological regimes explain the seasonal predictability of streamflow extremes

Auteurs: Yiheng Du, Ilaria Clemenzi, Ilias G Pechlivanidis
Publié dans: Environmental Research Letters, Numéro 18, 2024, Page(s) 094060, ISSN 1748-9326
Éditeur: Institute of Physics Publishing
DOI: 10.1088/1748-9326/acf678

Heat Waves: Physical Understanding and Scientific Challenges

Auteurs: D. Barriopedro, R. García‐Herrera, C. Ordóñez, D. G. Miralles, S. Salcedo‐Sanz
Publié dans: Reviews of Geophysics, Numéro 61, 2023, ISSN 8755-1209
Éditeur: American Geophysical Union
DOI: 10.1029/2022rg000780

Conference proceedings (5)

Ensemble Forecasts with Blocked K-Fold Cross-Validation in Multi-Objective Water Systems Control

Auteurs: Davide Spinelli, Matteo Giuliani, Andrea Castelletti
Publié dans: 2024 European Control Conference (ECC), 2024, Page(s) 493-498
Éditeur: IEEE
DOI: 10.23919/ecc64448.2024.10591306

Interpretable target-feature aggregation for multi-task learning based on bias-variance analysis.

Auteurs: P Bonetti, AM Metelli, and M Restelli
Publié dans: 2024
Éditeur: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
DOI: 10.48550/arxiv.2406.07991

DEPLOYMENT OF AI-ENHANCED SERVICES IN CLIMATE RESILIENCE INFORMATION SYSTEMS

Auteurs: N. Hempelmann, C. Ehbrecht, E. Plesiat, G. Hobona, J. Simoes, D. Huard, T. J. Smith, U. S. McKnight, I. G. Pechlivanidis, and C. Alvarez-Castro
Publié dans: 2022
Éditeur: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.,
DOI: 10.5194/isprs-archives-xlviii-4-w1-2022-187-2022

Compound events in Germany in 2018: drivers and case studies

Auteurs: Elena Xoplaki, Florian Ellsäßer, Jens Grieger, Katrin M. Nissen, Joaquim Pinto, Markus Augenstein, Ting-Chen Chen, Hendrik Feldmann, Petra Friederichs, Daniel Gliksman, Laura Goulier, Karsten Haustein, Jens Heinke, Lisa Jach, Florian Knutzen, Stefan Kollet, Jürg Luterbacher, Niklas Luther, Susanna Mohr, Christoph Mudersbach, Christoph Müller, Efi Rousi, Felix Simon, Laura Suarez-Gutierrez, Sve
Publié dans: 2024
Éditeur: EGU
DOI: 10.5194/egusphere-2023-1460

Causal feature selection via transfer entropy.

Auteurs: P Bonetti, AM Metelli, and M Restelli
Publié dans: International Joint Conference on Neural Networks, 2024
Éditeur: IEEE
DOI: 10.48550/arxiv.2310.11059

Other (1)

Nonlinear Feature Aggregation: Two Algorithms driven by Theory

Auteurs: Bonetti, Paolo; Metelli, Alberto Maria; Restelli, Marcello
Publié dans: Numéro 1, 2023
Éditeur: arXiv
DOI: 10.48550/arxiv.2306.11143