Stochastically Perturbed Weights: Ensembles from Deterministic Machine-Learning Weather Models

• URL: http://arxiv.org/abs/2609.08412v1
• Date: Tue, 08 Sep 2026 08:18:40 GMT
• Title: Stochastically Perturbed Weights: Ensembles from Deterministic Machine-Learning Weather Models
• Abstract summary: Many machine-learning weather models (MLWMs) are deterministic, producing a single forecast with no estimate of its own uncertainty.<n>A growing family of trained-probabilistic models generate calibrated ensembles directly, at the price of a dedicated training run.<n>We ask how much uncertainty can be extracted from a deterministic checkpoint that already exists, without retraining it.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine-learning weather models (MLWMs) now match or outperform operational numerical weather prediction (NWP) at global medium-range forecasting, at far lower inference cost. Many deployed MLWMs are deterministic, producing a single forecast with no estimate of its own uncertainty, whereas a growing family of trained-probabilistic models generate calibrated ensembles directly, at the price of a dedicated training run. We ask instead how much uncertainty can be extracted from a deterministic checkpoint that already exists, without retraining it. Where physical ensembles represent model uncertainty by stochastically perturbing parametrisation tendencies, we perturb the network's raw weight tensors at inference time, a scheme we call stochastically perturbed weights (SPW). We also ask whether it works, where and on which scales to inject the noise, and where it fails. A three-phase ablation across four deterministic backbones, Aurora, GraphCast, SFNO, and AIFS, selects one production baseline per model, benchmarked against the trained-probabilistic AIFS-ENS, FourCastNet 3 and Atlas as well as the operational ECMWF ensemble (IFS-ENS) over 112 initialisation times. At a 240 h (10-day) lead time the SPW ensembles reach continuous ranked probability skill scores (CRPSS) between 0.04 and 0.13 below the best trained-probabilistic baseline, at zero marginal training cost. No injection site works across models: the productive tensor group is architecture-specific, so SPW is at present a tuning procedure rather than a plug-and-play recipe. Its main failure mode is a coherent whole-field offset that overdisperses the domain mean, and restricting the noise to coarse scales or perturbing the initial conditions each repair part of it.

Related papers

• Model-Aware Schedules Improve Generation via Fiberwise Optimal Transport [1.1014388269312267]
  Diffusion and flow-matching schedules control the signal and noise coefficients that mix data and noise along affine probability paths.<n>We introduce a model-aware schedule construction based on fiberwise optimal transport.<n>We evaluate DDPMs and flow matching across prediction targets, training configurations, risk-estimation checkpoints, datasets, and architectures.
  arXiv  Detail & Related papers  (2026-09-10T17:30:44Z)
• Scalable Lindblad Noise Learning via Stochastic Tensor-Network Simulation [48.887627688666974]
  Learning dissipation rates in large-scale open quantum systems is a major obstacle for near-term quantum technologies.<n>Here, we propose a scalable noise-learning framework for Lindblad dissipation rates that combines a simulation method, the Jump Method (TJM)<n>We demonstrate the approach on two noise models in the Ising model, in which independent dissipation rates are learned for each site up to $N_mathrmsite=16$.<n>This combination of scalable numerics and rigorous theoretical guarantees positions TJM-based noise learning as a practical foundation for characterizing dissipation in large quantum devices.
  arXiv  Detail & Related papers  (2026-08-25T15:07:11Z)
• Timestep-Conditioned Transformers for Global Weather Forecasting [1.7632505349721652]
  We present GEM-3, a probabilistic global weather model that addresses the trade-off through explicit multi-timestep inference.<n>With a single set of trained weights, the model timestep can be configured at inference time to balance predictability and usability across a broad forecast horizon.<n>We find that mixed-timestep training consistently improves rollout stability relative to timestep-specialist models.
  arXiv  Detail & Related papers  (2026-08-06T16:27:54Z)
• Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation [0.0]
  We propose the first application of split conformal prediction to neural operator-based physics simulation.<n>Our method achieves 89.1% empirical coverage at the target level of alpha=0.1.<n>Our method is implemented in an open-source platform with REST API endpoints and interactive 3D visualization.
  arXiv  Detail & Related papers  (2026-06-07T08:52:38Z)
• Federated Martingale Posterior Samping [28.76805404959501]
  Federated Bayesian neural networks require fixing a prior on the model parameters together with a likelihood.<n>Motivated by the rapid progress of predictive models, the martingale posterior, also known as predictive Bayes, replaces the prior--likelihood pair with a predictive distribution.<n>This letter proposes federated martingale posterior (FMP) sampling, a one-shot embarrassingly parallel protocol in which each client uploads a small set of trainable data embeddings and the server runs the predictive sampler centrally.
  arXiv  Detail & Related papers  (2026-05-18T15:33:18Z)
• Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification [10.816479922364097]
  We propose a general Amortized Predictability-aware Training Framework (APTF) for both time series forecasting (TSF) and time series classification (TSC)<n>APTF introduces two key designs that enable the model to focus on high-predictability samples while still learning appropriately from low-predictability ones.
  arXiv  Detail & Related papers  (2026-02-18T06:59:05Z)
• Controllable Probabilistic Forecasting with Stochastic Decomposition Layers [1.3995263206621]
  We introduce Decomposition Layers (SDL) for converting deterministic machine learning weather models into ensemble systems.<n>SDL applies learned perturbations at three decoder scales through latent-driven modulation, per-pixel noise, and channel scaling.<n>When applied to WXFormer via transfer learning, SDL requires less than 2% of the computational cost needed to train the baseline model.
  arXiv  Detail & Related papers  (2025-12-21T17:10:00Z)
• Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning [70.56067503630486]
  We argue that sixth-generation (6G) intelligence is not fluent token prediction but calibrated the capacity to imagine and choose.<n>We show that WM-MS3M cuts mean absolute error (MAE) by 1.69% versus MS3M with 32% fewer parameters and similar latency, and achieves 35-80% lower root mean squared error (RMSE) than attention/hybrid baselines with 2.3-4.1x faster inference.
  arXiv  Detail & Related papers  (2025-11-04T17:22:22Z)
• Impact of Noisy Supervision in Foundation Model Learning [91.56591923244943]
  This paper is the first work to comprehensively understand and analyze the nature of noise in pre-training datasets.<n>We propose a tuning method (NMTune) to affine the feature space to mitigate the malignant effect of noise and improve generalization.
  arXiv  Detail & Related papers  (2024-03-11T16:22:41Z)
• One More Step: A Versatile Plug-and-Play Module for Rectifying Diffusion Schedule Flaws and Enhancing Low-Frequency Controls [77.42510898755037]
  One More Step (OMS) is a compact network that incorporates an additional simple yet effective step during inference. OMS elevates image fidelity and harmonizes the dichotomy between training and inference, while preserving original model parameters. Once trained, various pre-trained diffusion models with the same latent domain can share the same OMS module.
  arXiv  Detail & Related papers  (2023-11-27T12:02:42Z)
• Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization [58.720142291102135]
  Recent has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of chaotic dynamical systems. In the absence of mitigating techniques, this technique can result in artificially rapid error growth, leading to inaccurate predictions and/or climate instability. We introduce Linearized Multi-Noise Training (LMNT), a regularization technique that deterministically approximates the effect of many small, independent noise realizations added to the model input during training.
  arXiv  Detail & Related papers  (2022-11-09T23:40:52Z)
• Uncertainty Estimation Using a Single Deep Deterministic Neural Network [66.26231423824089]
  We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. We scale training in these with a novel loss function and centroid updating scheme and match the accuracy of softmax models.
  arXiv  Detail & Related papers  (2020-03-04T12:27:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.