Cover Image

Ensemble reliability and the signal-to-noise paradox in large-ensemble subseasonal forecasts

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Roberts, Christopher David, Vitart, Frederic
Format:	Preprint
Published:	2024
Subjects:	Atmospheric and Oceanic Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Ensemble reliability and the signal‐to‐noise paradox in ECMWF subseasonal forecasts
by: Christopher D. Roberts, et al.
Published: (2026)

The role of the oceans for subseasonal prediction: insights from eddy-permitting and eddy-rich coupled forecast systems
by: Roberts, Christopher David, et al.
Published: (2026)

Unbiased calculation, evaluation, and calibration of ensemble forecast anomalies
by: Roberts, Christopher D., et al.
Published: (2024)

A hypothesis on ergodicity and the signal-to-noise paradox
by: Brener, Daniel J.
Published: (2023)

Are we misdiagnosing ensemble forecast reliability? On the insufficiency of Spread-Error and rank-based reliability metrics
by: Dirkson, Arlan, et al.
Published: (2025)

Ensemble size effects on conditional reliability estimates: slope attenuation bias and correction methods
by: Spaeth, Jonas, et al.
Published: (2026)

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

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

Ensemble-size-dependence of deep-learning post-processing methods that minimize an (un)fair score: motivating examples and a proof-of-concept solution
by: Roberts, Christopher David
Published: (2026)

AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score
by: Lang, Simon, et al.
Published: (2024)

ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses
by: Watt-Meyer, Oliver, et al.
Published: (2024)

Generating ensembles of spatially-coherent in-situ forecasts using flow matching
by: Landry, David, et al.
Published: (2025)

Improving ensemble extreme precipitation forecasts using generative artificial intelligence
by: Sha, Yingkai, et al.
Published: (2024)

FuXi-S2S: A machine learning model that outperforms conventional global subseasonal forecast models
by: Chen, Lei, et al.
Published: (2023)

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere
by: Ling, Fenghua, et al.
Published: (2024)

GenCast: Diffusion-based ensemble forecasting for medium-range weather
by: Price, Ilan, et al.
Published: (2023)

Hybrid ensemble forecasting combining physics-based and machine-learning predictions through spectral nudging
by: Polichtchouk, Inna, et al.
Published: (2026)

Experimental investigations of underwater and airborne noises produced by a large hovercraft in Ural River estuary
by: Vedenev, A. I., et al.
Published: (2024)

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

A generalisation of the signal-to-noise ratio using proper scoring rules
by: Bröcker, Jochen, et al.
Published: (2025)

Learning low-dimensional representations of ensemble forecast fields using autoencoder-based methods
by: Chen, Jieyu, et al.
Published: (2025)

AI-informed model-analogs for understanding subseasonal-to-seasonal jet stream and North American temperature predictability
by: Landsberg, Jacob B., et al.
Published: (2025)

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

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

Spatial verification of global precipitation forecasts
by: Skok, Gregor, et al.
Published: (2024)

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

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

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

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

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

Advancing global aerosol forecasting with artificial intelligence
by: Gui, Ke, et al.
Published: (2024)

Climate network and complexity based ENSO forecast for 2026
by: Ludescher, Josef, et al.
Published: (2026)

AI-driven weather forecasts enable anticipated attribution of extreme events to human-made climate change
by: Jiménez-Esteve, Bernat, et al.
Published: (2024)

Rainfall forecasts in daily use over East Africa improved by machine learning
by: Cooper, Fenwick C., et al.
Published: (2025)

On the impact of initial conditions in the forecast of Hurricane Leslie extratropical transition
by: López-Reyes, Mauricio, et al.
Published: (2024)

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

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

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

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

Seasonal forecasting using the GenCast probabilistic machine learning model
by: Antonio, Bobby, et al.
Published: (2025)