Copertina

AI-driven weather forecasts enable anticipated attribution of extreme events to human-made climate change

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Jiménez-Esteve, Bernat, Barriopedro, David, Johnson, Juan Emmanuel, Garcia-Herrera, Ricardo
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Atmospheric and Oceanic Physics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

On the attribution of weather events to climate change using a fit to extreme value distributions
di: Sherman, Peter, et al.
Pubblicazione: (2023)

Harnessing AI data-driven global weather models for climate attribution: An analysis of the 2017 Oroville Dam extreme atmospheric river
di: Baño-Medina, Jorge, et al.
Pubblicazione: (2024)

The rise of data-driven weather forecasting
di: Ben-Bouallegue, Zied, et al.
Pubblicazione: (2023)

Robustness of AI-based weather forecasts in a changing climate
di: Rackow, Thomas, et al.
Pubblicazione: (2024)

BharatBench: Dataset for data-driven weather forecasting over India
di: Choudhury, Animesh, et al.
Pubblicazione: (2024)

"What is a realistic forecast?" Assessing data-driven weather forecasts, a journey from verification to falsification
di: Bouallègue, Zied Ben
Pubblicazione: (2026)

Cast3: Translating numerical weather prediction principles into data-driven forecasting
di: Nai, Congyi, et al.
Pubblicazione: (2026)

Aardvark weather: end-to-end data-driven weather forecasting
di: Vaughan, Anna, et al.
Pubblicazione: (2024)

A non-autonomous framework for climate change and extreme weather events increase in a stochastic energy balance model
di: Del Sarto, Gianmarco, et al.
Pubblicazione: (2024)

Impacts of extreme weather events on terrestrial carbon sequestration revealed by weather stations in the Northern Hemisphere
di: Shi, Haiyang
Pubblicazione: (2024)

AI-boosted rare event sampling to characterize extreme weather
di: Lancelin, Amaury, et al.
Pubblicazione: (2025)

Boosting weather forecast via generative superensemble
di: Nai, Congyi, et al.
Pubblicazione: (2024)

HealDA: Highlighting the importance of initial errors in end-to-end AI weather forecasts
di: Gupta, Aayush, et al.
Pubblicazione: (2026)

Hybrid physics-AI outperforms numerical weather prediction for extreme precipitation nowcasting
di: Das, Puja, et al.
Pubblicazione: (2024)

AIMIP Phase 1: systematic evaluations of AI weather and climate models
di: Henn, Brian, et al.
Pubblicazione: (2026)

Development of a Data-driven weather forecasting system over India with Pangu-Weather architecture and IMDAA reanalysis Data
di: Choudhury, Animesh, et al.
Pubblicazione: (2025)

An update to ECMWF's machine-learned weather forecast model AIFS
di: Moldovan, Gabriel, et al.
Pubblicazione: (2025)

Data driven weather forecasts trained and initialised directly from observations
di: McNally, Anthony, et al.
Pubblicazione: (2024)

Reviewing climate change attribution in UK natural hazards and their impacts
di: Mudhar, Regan, et al.
Pubblicazione: (2024)

Prediction of storm surge on evolving landscapes under climate change
di: Johnson, David R, et al.
Pubblicazione: (2023)

Multivariate post-processing of probabilistic sub-seasonal weather regime forecasts
di: Mockert, Fabian, et al.
Pubblicazione: (2024)

An ensemble of data-driven weather prediction models for operational sub-seasonal forecasting
di: Weyn, Jonathan A., et al.
Pubblicazione: (2024)

Windows of opportunity in subseasonal weather regime forecasting: A statistical-dynamical approach
di: Mockert, Fabian, et al.
Pubblicazione: (2025)

Hybrid weather prediction using spectral nudging toward machine-learning forecasts
di: Polichtchouk, I., et al.
Pubblicazione: (2026)

HRRRCast: a data-driven emulator for regional weather forecasting at convection allowing scales
di: Abdi, Daniel, et al.
Pubblicazione: (2025)

Leveraging deterministic weather forecasts for in-situ probabilistic temperature predictions via deep learning
di: Landry, David, et al.
Pubblicazione: (2024)

AIFS -- ECMWF's data-driven forecasting system
di: Lang, Simon, et al.
Pubblicazione: (2024)

The role of the surface evapotranspiration in regional climate modelling: Evaluation and near-term future changes
di: Ojeda, Matilde García-Valdecasas, et al.
Pubblicazione: (2024)

Supporting renewable energy planning and operation with data-driven high-resolution ensemble weather forecast
di: Wang, Jingnan, et al.
Pubblicazione: (2025)

A comparison of stretched-grid and limited-area modelling for data-driven regional weather forecasting
di: Wijnands, Jasper S., et al.
Pubblicazione: (2025)

Towards accurate extreme event likelihoods from diffusion model climate emulators
di: Manshausen, Peter, et al.
Pubblicazione: (2026)

Solar and anthropogenic climate drivers: an updated regression model and refined forecast
di: Stefani, Frank
Pubblicazione: (2026)

Using a rare event sampling technique to quantify extreme El Niño event statistics
di: Packman, Sarah, et al.
Pubblicazione: (2025)

Improving forecasts of precipitation extremes over Northern and Central Italy using machine learning
di: Grazzini, Federico, et al.
Pubblicazione: (2024)

Blending machine learning and physics-based approaches for weather and climate: a typology
di: Shipway, Benjamin J, et al.
Pubblicazione: (2026)

Interpolation of mountain weather forecasts by machine learning
di: Iwase, Kazuma, et al.
Pubblicazione: (2023)

Detecting extreme event-driven causality
di: Yu, Siyang, et al.
Pubblicazione: (2025)

On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates
di: Rampal, Neelesh, et al.
Pubblicazione: (2024)

Representing the Surface Ocean in ECMWF's data-driven forecasting system AIFS
di: Hahner, Sara, et al.
Pubblicazione: (2026)

Challenges in assessing Fire Weather changes in a warming climate
di: Matteo, Aurora, et al.
Pubblicazione: (2025)