Cover Image

Learning low-dimensional representations of ensemble forecast fields using autoencoder-based methods

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Jieyu, Höhlein, Kevin, Lerch, Sebastian
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Atmospheric and Oceanic Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Postprocessing of Ensemble Weather Forecasts Using Permutation-invariant Neural Networks
by: Höhlein, Kevin, et al.
Published: (2023)

Generative machine learning methods for multivariate ensemble post-processing
by: Chen, Jieyu, et al.
Published: (2022)

Graph Neural Networks and Spatial Information Learning for Post-Processing Ensemble Weather Forecasts
by: Feik, Moritz, et al.
Published: (2024)

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

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

Ensemble Kalman filter in latent space using a variational autoencoder pair
by: Pasmans, Ivo, et al.
Published: (2025)

Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning
by: Horat, Nina, 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)

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

Uncertainty quantification for data-driven weather models
by: Bülte, Christopher, et al.
Published: (2024)

Topographic Visualization of Near-surface Temperatures for Improved Lapse Rate Estimation
by: Höhlein, Kevin, et al.
Published: (2024)

Global atmospheric data assimilation with multi-modal masked autoencoders
by: Vandal, Thomas J., et al.
Published: (2024)

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

Improvements to the post-processing of weather forecasts using machine learning and feature selection
by: Iwase, Kazuma, et al.
Published: (2026)

WindDragon: Enhancing wind power forecasting with Automated Deep Learning
by: Keisler, Julie, et al.
Published: (2024)

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

Decomposing weather forecasting into advection and convection with neural networks
by: Chen, Mengxuan, et al.
Published: (2024)

FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting
by: Zhong, Xiaohui, et al.
Published: (2024)

Refined climatologies of future precipitation over High Mountain Asia using probabilistic ensemble learning
by: Tazi, Kenza, et al.
Published: (2025)

CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting
by: Xiong, Tianyi, et al.
Published: (2025)

Dynamical-generative downscaling of climate model ensembles
by: Lopez-Gomez, Ignacio, et al.
Published: (2024)

Analysis, forecasting and system identification of a floating offshore wind turbine using dynamic mode decomposition
by: Palma, Giorgio, et al.
Published: (2024)

Machine learning models for daily rainfall forecasting in Northern Tropical Africa using tropical wave predictors
by: Satheesh, Athul Rasheeda, et al.
Published: (2024)

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

Probabilistic measures afford fair comparisons of AIWP and NWP model output
by: Gneiting, Tilmann, et al.
Published: (2025)

Skillful joint probabilistic weather forecasting from marginals
by: Alet, Ferran, et al.
Published: (2025)

FuXi-2.0: Advancing machine learning weather forecasting model for practical applications
by: Zhong, Xiaohui, et al.
Published: (2024)

Skillful high-resolution weather forecasting independent of physical models
by: Zhao, Pengcheng, et al.
Published: (2026)

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

FuXi Weather: A data-to-forecast machine learning system for global weather
by: Sun, Xiuyu, et al.
Published: (2024)

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

Do AI models produce better weather forecasts than physics-based models? A quantitative evaluation case study of Storm Ciarán
by: Charlton-Perez, Andrew J., et al.
Published: (2023)

Applying ranking techniques for estimating influence of Earth variables on temperature forecast error
by: Flores, M. Julia, et al.
Published: (2024)

DIRESA, a distance-preserving nonlinear dimension reduction technique based on regularized autoencoders
by: De Paepe, Geert, et al.
Published: (2024)

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

Improving sub-seasonal wind-speed forecasts in Europe with a non-linear model
by: Tian, Ganglin, et al.
Published: (2024)

Generating realistic global precipitation fields from modelled atmospheric circulation
by: Aich, Michael, et al.
Published: (2025)

Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning
by: Nath, Pritthijit, et al.
Published: (2026)

Operational convection-permitting COSMO/ICON ensemble predictions at observation sites (CIENS)
by: Lerch, Sebastian, et al.
Published: (2025)

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