Demystifying Data-Driven Probabilistic Medium-Range Weather Forecasting

• URL: http://arxiv.org/abs/2601.18111v1
• Date: Mon, 26 Jan 2026 03:52:16 GMT
• Title: Demystifying Data-Driven Probabilistic Medium-Range Weather Forecasting
• Authors: Jean Kossaifi, Nikola Kovachki, Morteza Mardani, Daniel Leibovici, Suman Ravuri, Ira Shokar, Edoardo Calvello, Mohammad Shoaib Abbas, Peter Harrington, Ashay Subramaniam, Noah Brenowitz, Boris Bonev, Wonmin Byeon, Karsten Kreis, Dale Durran, Arash Vahdat, Mike Pritchard, Jan Kautz,
• Abstract summary: We demonstrate that state-of-the-art probabilistic skill requires neither intricate architectural constraints nor specialized trainings.<n>We introduce a scalable framework for learning multi-scale atmospheric dynamics by combining a directly downsampled latent space with a history-conditioned local projector.<n>We find that our framework design is robust to the choice of probabilistic estimators, seamlessly supporting interpolants, diffusion models, and CRPS-based ensemble training.
• Score: 63.8116386935854
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recent revolution in data-driven methods for weather forecasting has lead to a fragmented landscape of complex, bespoke architectures and training strategies, obscuring the fundamental drivers of forecast accuracy. Here, we demonstrate that state-of-the-art probabilistic skill requires neither intricate architectural constraints nor specialized training heuristics. We introduce a scalable framework for learning multi-scale atmospheric dynamics by combining a directly downsampled latent space with a history-conditioned local projector that resolves high-resolution physics. We find that our framework design is robust to the choice of probabilistic estimator, seamlessly supporting stochastic interpolants, diffusion models, and CRPS-based ensemble training. Validated against the Integrated Forecasting System and the deep learning probabilistic model GenCast, our framework achieves statistically significant improvements on most of the variables. These results suggest scaling a general-purpose model is sufficient for state-of-the-art medium-range prediction, eliminating the need for tailored training recipes and proving effective across the full spectrum of probabilistic frameworks.

Related papers

• GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction [35.36975133932852]
  Trajectory prediction for surrounding agents is a challenging task in autonomous driving.<n>We introduce a novel Graph-oriented Inverse Reinforcement Learning framework, which is an IRL-based predictor equipped with vectorized context representations.<n>Our approach achieves state-of-the-art performance on the large-scale Argoverse & nuScenes motion forecasting benchmarks.
  arXiv  Detail & Related papers  (2025-06-26T09:46:53Z)
• Elucidated Rolling Diffusion Models for Probabilistic Weather Forecasting [52.6508222408558]
  We introduce Elucidated Rolling Diffusion Models (ERDM)<n>ERDM is the first framework to unify a rolling forecast structure with the principled, performant design of Elucidated Diffusion Models (EDM)<n>On 2D Navier-Stokes simulations and ERA5 global weather forecasting at 1.5circ resolution, ERDM consistently outperforms key diffusion-based baselines.
  arXiv  Detail & Related papers  (2025-06-24T21:44:31Z)
• Federated Dynamic Modeling and Learning for Spatiotemporal Data Forecasting [1.492694722803554]
  This paper presents an advanced Federated Learning (FL) framework for forecasting complextemporal data.<n>We replace the Gated Recurrent Unit (RU) module with a Long Short-Term Memory (LSTM) network.<n>The resulting architecture significantly improves the model's capacity to handle complex temporal patterns in diverse forecasting applications.
  arXiv  Detail & Related papers  (2025-03-06T15:16:57Z)
• Collaborative Deterministic-Probabilistic Forecasting for Diverse Spatiotemporal Systems [16.028948493876126]
  CoST achieves 25% over state-of-the-art baselines, while significantly reducing computational cost.<n>Trials across climate, energy communication, and urban systems show gains of 25% over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-02-16T06:35:26Z)
• Federated Learning with Projected Trajectory Regularization [65.6266768678291]
  Federated learning enables joint training of machine learning models from distributed clients without sharing their local data. One key challenge in federated learning is to handle non-identically distributed data across the clients. We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
  arXiv  Detail & Related papers  (2023-12-22T02:12:08Z)
• Learning Robust Precipitation Forecaster by Temporal Frame Interpolation [65.5045412005064]
  We develop a robust precipitation forecasting model that demonstrates resilience against spatial-temporal discrepancies. Our approach has led to significant improvements in forecasting precision, culminating in our model securing textit1st place in the transfer learning leaderboard of the textitWeather4cast'23 competition.
  arXiv  Detail & Related papers  (2023-11-30T08:22:08Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2023-10-17T20:30:16Z)
• $\clubsuit$ CLOVER $\clubsuit$: Probabilistic Forecasting with Coherent Learning Objective Reparameterization [42.215158938066054]
  We augment an MQForecaster neural network architecture with a modified multivariate Gaussian factor model that achieves coherence by construction.<n>We call our method the Coherent Learning Objective Reparametrization Neural Network (CLOVER)<n>In comparison to state-of-the-art coherent forecasting methods, CLOVER achieves significant improvements in scaled CRPS forecast accuracy, with average gains of 15%.
  arXiv  Detail & Related papers  (2023-07-19T07:31:37Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2022-06-16T06:13:53Z)
• PDC-Net+: Enhanced Probabilistic Dense Correspondence Network [161.76275845530964]
  Enhanced Probabilistic Dense Correspondence Network, PDC-Net+, capable of estimating accurate dense correspondences. We develop an architecture and an enhanced training strategy tailored for robust and generalizable uncertainty prediction. Our approach obtains state-of-the-art results on multiple challenging geometric matching and optical flow datasets.
  arXiv  Detail & Related papers  (2021-09-28T17:56:41Z)
• A Clustering-aided Ensemble Method for Predicting Ridesourcing Demand in Chicago [0.0]
  This study proposes a Clustering-aided Ensemble Method (CEM) to forecast the zone-to-zone travel demand for ridesourcing services. We implement and test the proposed methodology by using the ridesourcing-trip data in Chicago.
  arXiv  Detail & Related papers  (2021-09-08T04:58:29Z)
• A framework for probabilistic weather forecast post-processing across models and lead times using machine learning [3.1542695050861544]
  We show how to bridge the gap between sets of separate forecasts from NWP models and the 'ideal' forecast for decision support. We use Quantile Regression Forests to learn the error profile of each numerical model, and use these to apply empirically-derived probability distributions to forecasts. Second, we combine these probabilistic forecasts using quantile averaging. Third, we interpolate between the aggregate quantiles in order to generate a full predictive distribution.
  arXiv  Detail & Related papers  (2020-05-06T16:46:02Z)

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.