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

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

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.

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.

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.

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.

Pubblicazioni

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

Autori: L. Cavicchia, E. Scoccimarro, G. Ascenso, A. Castelletti, M. Giuliani, S. Gualdi
Pubblicato in: Geophysical Research Letters, Numero 50, 2024, ISSN 0094-8276
Editore: American Geophysical Union
DOI: 10.1029/2023gl103001

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

Autori: Verónica Torralba, Stefano Materia, Leone Cavicchia, M Carmen Álvarez-Castro, Chloé Prodhomme, Ronan McAdam, Enrico Scoccimarro, Silvio Gualdi
Pubblicato in: Environmental Research Letters, Numero 19, 2024, Pagina/e 034001, ISSN 1748-9326
Editore: Institute of Physics Publishing
DOI: 10.1088/1748-9326/ad24cf

Robustness of hydrometeorological extremes in surrogated seasonal forecasts

Autori: Katharina Klehmet, Peter Berg, Denica Bozhinova, Louise Crochemore, Yiheng Du, Ilias Pechlivanidis, Christiana Photiadou, Wei Yang
Pubblicato in: International Journal of Climatology, Numero 44, 2024, Pagina/e 1725-1738, ISSN 0899-8418
Editore: John Wiley & Sons Inc.
DOI: 10.1002/joc.8407

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

Autori: 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
Pubblicato in: Journal of Advances in Modeling Earth Systems, Numero 15, 2023, ISSN 1942-2466
Editore: American Geophysical Union
DOI: 10.1029/2023ms003790

Autoencoder‐based flow‐analogue probabilistic reconstruction of heat waves from pressure fields

Autori: Jorge Pérez‐Aracil, Cosmin M. Marina, Eduardo Zorita, David Barriopedro, Pablo Zaninelli, Matteo Giuliani, Andrea Castelletti, Pedro A. Gutiérrez, Sancho Salcedo‐Sanz
Pubblicato in: Annals of the New York Academy of Sciences, Numero 1541, 2024, Pagina/e 230-242, ISSN 0077-8923
Editore: New York Academy of Sciences
DOI: 10.1111/nyas.15243

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

Autori: 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
Pubblicato in: Theoretical and Applied Climatology, Numero 155, 2024, Pagina/e 1-44, ISSN 0177-798X
Editore: Springer Verlag
DOI: 10.1007/s00704-023-04571-5

How good is my drought index? Evaluating predictability and ability to estimate impacts across Europe

Autori: A Shyrokaya, F Pappenberger, G Messori, I Pechlivanidis, H Cloke, G Di Baldassarre
Pubblicato in: Environmental Research Letters, Numero 20, 2025, Pagina/e 034051, ISSN 1748-9326
Editore: Institute of Physics Publishing
DOI: 10.1088/1748-9326/adb869

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

Autori: 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
Pubblicato in: Environmental Research Letters, Numero 18, 2023, Pagina/e 093004, ISSN 1748-9326
Editore: Institute of Physics Publishing
DOI: 10.1088/1748-9326/acf601

Hybrid approaches enhance hydrological model usability for local streamflow prediction

Autori: Yiheng Du, Ilias G. Pechlivanidis
Pubblicato in: Communications Earth & Environment, Numero 6, 2025, ISSN 2662-4435
Editore: Springer Science and Business Media LLC
DOI: 10.1038/s43247-025-02324-y

Skill-informed seamless communication of European S2S hydrological forecasts

Autori: Ilias G Pechlivanidis, Louise Crochemore
Pubblicato in: Environmental Research Letters, Numero 20, 2025, Pagina/e 074041, ISSN 1748-9326
Editore: Institute of Physics Publishing
DOI: 10.1088/1748-9326/ade0d7

Feature selection for data-driven seasonal forecasts of European heatwaves

Autori: Ronan McAdam, Jorge Pérez-Aracil, Antonello Squintu, César Peláez-Rodríguez, Felicitas Hansen, Verónica Torralba, Harilaos Loukos, Eduardo Zorita, Matteo Giuliani, Leone Cavicchia, Sancho Salcedo-Sanz, Enrico Scoccimarro
Pubblicato in: Communications Earth & Environment, Numero 6, 2025, ISSN 2662-4435
Editore: Springer Science and Business Media LLC
DOI: 10.1038/s43247-025-02863-4

Long-term temperature prediction with hybrid autoencoder algorithms

Autori: J. Pérez-Aracil, D. Fister, C.M. Marina, C. Peláez-Rodríguez, L. Cornejo-Bueno, P.A. Gutiérrez, M. Giuliani, A. Castelleti, S. Salcedo-Sanz
Pubblicato in: Applied Computing and Geosciences, Numero 23, 2024, Pagina/e 100185, ISSN 2590-1974
Editore: elsevier
DOI: 10.1016/j.acags.2024.100185

Freddy: breaking record for Tropical Cyclone precipitation?

Autori: Enrico Scoccimarro, Paolo Lanteri, Leone Cavicchia
Pubblicato in: Environmental Research Letters 19 064013, 2024, ISSN 1748-9326
Editore: Institute of Physics Publishing
DOI: 10.5194/egusphere-egu24-5958

Interpretable linear dimensionality reduction based on bias-variance analysis

Autori: Paolo Bonetti, Alberto Maria Metelli, Marcello Restelli
Pubblicato in: Data Mining and Knowledge Discovery, Numero 38, 2024, Pagina/e 1713-1781, ISSN 1384-5810
Editore: Kluwer Academic Publishers
DOI: 10.1007/s10618-024-01015-0

A Systematic Framework for Data Augmentation for Tropical Cyclone Intensity Estimation Using Deep Learning

Autori: Guido Ascenso, Giulio Palcic, Enrico Scoccimarro, Andrea Castelletti
Pubblicato in: Journal of Geophysical Research: Machine Learning and Computation, Numero 1, 2024, ISSN 2993-5210
Editore: American Geophysical Union (AGU)
DOI: 10.1029/2024jh000206

Optimisation-based refinement of genesis indices for tropical cyclones

Autori: Ascenso, Guido Cavicchia, Leone Scoccimarro, Enrico Castelletti, Andrea
Pubblicato in: Environmental Research Communications, 2023, ISSN 2515-7620
Editore: IOP Publishing
DOI: 10.1088/2515-7620/acb52a

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

Autori: Enrico Scoccimarro, Oreste Cattaneo, Silvio Gualdi, Francesco Mattion, Alexandre Bizeul, Arnau Martin Risquez, Roberta Quadrelli
Pubblicato in: Communications Earth & Environment, Numero 4, 2023, ISSN 2662-4435
Editore: 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

Autori: 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
Pubblicato in: Natural Hazards and Earth System Sciences, Numero 23, 2023, Pagina/e 1699-1718, ISSN 1684-9981
Editore: EGU Journal
DOI: 10.5194/nhess-23-1699-2023

Identifying key drivers of heatwaves: A novel spatio-temporal framework for extreme event detection

Autori: J. Pérez-Aracil, C. Peláez-Rodríguez, Ronan McAdam, Antonello Squintu, Cosmin M. Marina, Eugenio Lorente-Ramos, Niklas Luther, Verónica Torralba, Enrico Scoccimarro, Leone Cavicchia, Matteo Giuliani, Eduardo Zorita, Felicitas Hansen, David Barriopedro, Ricardo García-Herrera, Pedro A. Gutiérrez, Jürg Luterbacher, Elena Xoplaki, Andrea Castelletti, S. Salcedo-Sanz
Pubblicato in: Weather and Climate Extremes, Numero 49, 2025, Pagina/e 100792, ISSN 2212-0947
Editore: Elsevier BV
DOI: 10.1016/j.wace.2025.100792

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

Autori: Anastasiya Shyrokaya, Gabriele Messori, Ilias Pechlivanidis, Florian Pappenberger, Hannah L Cloke, Giuliano Di Baldassarre
Pubblicato in: Environmental Research Letters, Numero 19, 2023, Pagina/e 014037, ISSN 1748-9326
Editore: Institute of Physics Publishing
DOI: 10.1088/1748-9326/ad10d9

Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives

Autori: Stefano Materia, Lluís Palma García, Chiem van Straaten, Sungmin O, Antonios Mamalakis, Leone Cavicchia, Dim Coumou, Paolo de Luca, Marlene Kretschmer, Markus Donat
Pubblicato in: WIREs Climate Change, Numero 15, 2024, ISSN 1757-7780
Editore: John Wiley & Sons Inc.
DOI: 10.1002/wcc.914

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

Autori: Jorge Pérez-Aracil, Carlos Camacho-Gómez, Eugenio Lorente-Ramos, Cosmin M. Marina, Laura M. Cornejo-Bueno, Sancho Salcedo-Sanz
Pubblicato in: Mathematics, Numero 11, 2023, Pagina/e 1666, ISSN 2227-7390
Editore: mdpi
DOI: 10.3390/math11071666

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

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

Atmospheric patterns drive marine heatwaves in the North Atlantic and Mediterranean Sea during summer 2023

Autori: Lorine Behr, Elena Xoplaki, Niklas Luther, Simon A Josey, Jürg Luterbacher
Pubblicato in: Environmental Research Letters, Numero 20, 2025, Pagina/e 104020, ISSN 1748-9326
Editore: Institute of Physics Publishing
DOI: 10.1088/1748-9326/ae0055

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

Autori: Anastasiya Shyrokaya, Florian Pappenberger, Ilias Pechlivanidis, Gabriele Messori, Sina Khatami, Maurizio Mazzoleni, Giuliano Di Baldassarre
Pubblicato in: WIREs Water, Numero 11, 2024, ISSN 2049-1948
Editore: WIREs
DOI: 10.1002/wat2.1698

Artificial intelligence reveals past climate extremes by reconstructing historical records

Autori: Étienne Plésiat, Robert J. H. Dunn, Markus G. Donat, Christopher Kadow
Pubblicato in: Nature Communications, Numero 15, 2024, ISSN 2041-1723
Editore: Nature Publishing Group
DOI: 10.1038/s41467-024-53464-2

Reinforcement Learning of Multi‐Timescale Forecast Information for Designing Operating Policies of Multi‐Purpose Reservoirs

Autori: D. Zanutto, A. Ficchì, M. Giuliani, A. Castelletti
Pubblicato in: Water Resources Research, Numero 61, 2025, ISSN 0043-1397
Editore: American Geophysical Union
DOI: 10.1029/2023wr036724

Enhancing Research-to-Operations in Hydrological Forecasting: Innovations across Scales and Horizons

Autori: Ilias G. Pechlivanidis, Yiheng Du, James Bennett, Marie-Amélie Boucher, Annie Y. Y. Chang, Louise Crochemore, Antara Dasgupta, Giuliano Di Baldassarre, Jürg Luterbacher, Florian Pappenberger, Maria-Helena Ramos, Louise Slater, Stefan Uhlenbrook, Fredrik Wetterhall, Andrew Wood, Waldo Lavado-Casimiro, Kei Yoshimura, Ruben Imhoff, Peter J. van Oevelen, Carolina Cantone, Céline Cattoën, Rafael Pi
Pubblicato in: Bulletin of the American Meteorological Society, Numero 106, 2025, Pagina/e E894-E919, ISSN 0003-0007
Editore: American Meteorological Society
DOI: 10.1175/bams-d-24-0322.1

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

Autori: Fister, Dušan; Pérez-Aracil, Jorge; Peláez-Rodríguez, César; Del Ser, Javier; Salcedo-Sanz, Sancho
Pubblicato in: arXiv preprint arXiv:2209.15424, Numero 2, 2022, ISSN 2331-8422
Editore: Arxiv
DOI: 10.48550/arxiv.2209.15424

Increasing heat and rainfall extremes now far outside the historical climate

Autori: Alexander Robinson, Jascha Lehmann, David Barriopedro, Stefan Rahmstorf, Dim Coumou
Pubblicato in: npj Climate and Atmospheric Science, Numero 4, 2022, ISSN 2397-3722
Editore: nature
DOI: 10.1038/s41612-021-00202-w

Artificial intelligence for modeling and understanding extreme weather and climate events

Autori: Gustau Camps-Valls, Miguel-Ángel Fernández-Torres, Kai-Hendrik Cohrs, Adrian Höhl, Andrea Castelletti, Aytac Pacal, Claire Robin, Francesco Martinuzzi, Ioannis Papoutsis, Ioannis Prapas, Jorge Pérez-Aracil, Katja Weigel, Maria Gonzalez-Calabuig, Markus Reichstein, Martin Rabel, Matteo Giuliani, Miguel D. Mahecha, Oana-Iuliana Popescu, Oscar J. Pellicer-Valero, Said Ouala, Sancho Salcedo-Sanz,
Pubblicato in: Nature Communications, Numero 16, 2025, ISSN 2041-1723
Editore: Nature Publishing Group
DOI: 10.1038/s41467-025-56573-8

Detection and attribution of heat waves with the Multivariate Autoencoder Flow-Analogue Method (MvAE-AM)

Autori: Cosmin M. Marina, Jorge Pérez-Aracil, Ronan McAdam, Eugenio Lorente-Ramos, Niklas Luther, Eduardo Zorita, Enrico Scoccimarro, Jürg Luterbacher, Elena Xoplaki, Sancho Salcedo-Sanz
Pubblicato in: Atmospheric Research, Numero 328, 2025, Pagina/e 108409, ISSN 0169-8095
Editore: Elsevier BV
DOI: 10.1016/j.atmosres.2025.108409

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

Autori: Andrea Castelletti, Andrea Ficchì, Andrea Cominola, Pablo Segovia, Matteo Giuliani, Wenyan Wu, Sergio Lucia, Carlos Ocampo-Martinez, Bart De Schutter, José María Maestre
Pubblicato in: Annual Reviews in Control, Numero 55, 2024, Pagina/e 442-465, ISSN 1367-5788
Editore: 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

Autori: Sonia Jerez, David Barriopedro, Alejandro García‐López, Raquel Lorente‐Plazas, Andrés M. Somoza, Marco Turco, Judit Carrillo, Ricardo M. Trigo
Pubblicato in: Earth's Future, Numero 11, 2024, ISSN 2328-4277
Editore: Earth's Future
DOI: 10.1029/2022ef003332

The concept of spectrally nudged storylines for extreme event attribution

Autori: Frauke Feser, Theodore G. Shepherd
Pubblicato in: Communications Earth & Environment, Numero 6, 2025, ISSN 2662-4435
Editore: Springer Science and Business Media LLC
DOI: 10.1038/s43247-025-02659-6

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

Autori: Peláez-Rodríguez, C., Pérez-Aracil, J., Fister, D., Prieto-Godino, L., Deo, R.C., Salcedo-Sanz, S.
Pubblicato in: Renewable Energy, 2022, ISSN 0960-1481
Editore: 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

Autori: C. Peláez-Rodríguez, J. Pérez-Aracil, C.M. Marina, L. Prieto-Godino, C. Casanova-Mateo, P.A. Gutiérrez, S. Salcedo-Sanz
Pubblicato in: Knowledge-Based Systems, Numero 291, 2024, Pagina/e 111556, ISSN 0950-7051
Editore: Elsevier BV
DOI: 10.1016/j.knosys.2024.111556

The Summer Heatwave 2022 over Western Europe: An Attribution to Anthropogenic Climate Change

Autori: Frauke Feser, Linda van Garderen, Felicitas Hansen
Pubblicato in: Bulletin of the American Meteorological Society, Numero 105, 2025, Pagina/e E2175-E2179, ISSN 0003-0007
Editore: American Meteorological Society
DOI: 10.1175/bams-d-24-0017.1

Downscaling, bias correction, and spatial adjustment of extreme tropical cyclone rainfall in ERA5 using deep learning

Autori: Guido Ascenso, Andrea Ficchì, Matteo Giuliani, Enrico Scoccimarro, Andrea Castelletti
Pubblicato in: Weather and Climate Extremes, Numero 46, 2024, Pagina/e 100724, ISSN 2212-0947
Editore: Elsevier BV
DOI: 10.1016/j.wace.2024.100724

Storylines of projected summer warming in Iberia using atmospheric circulation, soil moisture and sea surface temperature as drivers of uncertainty

Autori: Jose M. Garrido-Perez, David Barriopedro, Ricardo M. Trigo, Pedro M.M. Soares, Giuseppe Zappa, M. Carmen Álvarez-Castro, Ricardo García-Herrera
Pubblicato in: Atmospheric Research, Numero 311, 2024, Pagina/e 107677, ISSN 0169-8095
Editore: Elsevier BV
DOI: 10.1016/j.atmosres.2024.107677

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

Autori: 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
Pubblicato in: Journal of Flood and Risk Management, 2022, ISSN 1753-318X
Editore: Blackwell Publishing
DOI: 10.1111/jfr3.12880

Hydrological regimes explain the seasonal predictability of streamflow extremes

Autori: Yiheng Du, Ilaria Clemenzi, Ilias G Pechlivanidis
Pubblicato in: Environmental Research Letters, Numero 18, 2024, Pagina/e 094060, ISSN 1748-9326
Editore: Institute of Physics Publishing
DOI: 10.1088/1748-9326/acf678

Moisture-modulated heatwaves: tracking 3D evolution patterns and projected changes

Autori: Qian Zhang, Jiabo Yin, Pan Liu, Wenbin Zhu, Xihui Gu, Lei Gu
Pubblicato in: Environmental Research Letters, Numero 20, 2025, Pagina/e 044037, ISSN 1748-9326
Editore: Institute of Physics Publishing
DOI: 10.1088/1748-9326/adc1e5

Heat Waves: Physical Understanding and Scientific Challenges

Autori: D. Barriopedro, R. García‐Herrera, C. Ordóñez, D. G. Miralles, S. Salcedo‐Sanz
Pubblicato in: Reviews of Geophysics, Numero 61, 2023, ISSN 8755-1209
Editore: American Geophysical Union
DOI: 10.1029/2022rg000780

AI‐Driven Weather Forecasts to Accelerate Climate Change Attribution of Heatwaves

Autori: B. Jiménez‐Esteve, D. Barriopedro, J. E. Johnson, R. García‐Herrera
Pubblicato in: Earth's Future, Numero 13, 2025, ISSN 2328-4277
Editore: American Geophysical Union (AGU)
DOI: 10.1029/2025ef006453

Leveraging GCM-based forecasts for enhanced seasonal streamflow prediction in diverse hydrological regimes

Autori: M. Girons Lopez, T. Bosshard, L. Crochemore, I.G. Pechlivanidis
Pubblicato in: Journal of Hydrology, Numero 650, 2025, Pagina/e 132504, ISSN 0022-1694
Editore: Elsevier BV
DOI: 10.1016/j.jhydrol.2024.132504

A multi-method attribution analysis of the Spain’s 2024 extreme precipitation event

Autori: David Barriopedro, Bernat Jiménez-Esteve, Soledad Collazo, Jose M. Garrido-Perez, Juan Emmanuel Johnson, Ricardo García-Herrera
Pubblicato in: Bulletin of the American Meteorological Society, 2025, ISSN 0003-0007
Editore: American Meteorological Society
DOI: 10.1175/bams-d-25-0049.1

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

Autori: Davide Spinelli, Matteo Giuliani, Andrea Castelletti
Pubblicato in: 2024 European Control Conference (ECC), 2024, Pagina/e 493-498
Editore: IEEE
DOI: 10.23919/ecc64448.2024.10591306

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

Autori: P Bonetti, AM Metelli, and M Restelli
Pubblicato in: 2024
Editore: 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

Autori: 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
Pubblicato in: 2022
Editore: 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

Autori: 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
Pubblicato in: 2024
Editore: EGU
DOI: 10.5194/egusphere-2023-1460

Causal feature selection via transfer entropy.

Autori: P Bonetti, AM Metelli, and M Restelli
Pubblicato in: International Joint Conference on Neural Networks, 2024
Editore: IEEE
DOI: 10.48550/arxiv.2310.11059

Nonlinear Feature Aggregation: Two Algorithms driven by Theory

Autori: Bonetti, Paolo; Metelli, Alberto Maria; Restelli, Marcello
Pubblicato in: Numero 1, 2023
Editore: arXiv
DOI: 10.48550/arxiv.2306.11143

È in corso la ricerca di dati su OpenAIRE...

Risultati finali

Pubblicazioni

Condividi questa pagina Condividi questa pagina sui social network

Scarica Scarica il contenuto della pagina