Huracan: A skillful end-to-end data-driven system for ensemble data assimilation and weather prediction

• URL: http://arxiv.org/abs/2508.18486v1
• Date: Mon, 25 Aug 2025 20:55:46 GMT
• Title: Huracan: A skillful end-to-end data-driven system for ensemble data assimilation and weather prediction
• Authors: Zekun Ni, Jonathan Weyn, Hang Zhang, Yanfei Xiang, Jiang Bian, Weixin Jin, Kit Thambiratnam, Qi Zhang, Haiyu Dong, Hongyu Sun,
• Abstract summary: Huracan is an observation-driven weather forecasting system which combines an ensemble data assimilation model with a forecast model.<n>It is the first end-to-end system to achieve an accuracy comparable with that of ECMWF ENS, the state-of-the-art NWP competitor.<n>Our work is a major step forward in end-to-end data-driven weather prediction and opens up opportunities for further improving and revolutionizing operational weather forecasting.
• Score: 13.276021120809508
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Over the past few years, machine learning-based data-driven weather prediction has been transforming operational weather forecasting by providing more accurate forecasts while using a mere fraction of computing power compared to traditional numerical weather prediction (NWP). However, those models still rely on initial conditions from NWP, putting an upper limit on their forecast abilities. A few end-to-end systems have since been proposed, but they have yet to match the forecast skill of state-of-the-art NWP competitors. In this work, we propose Huracan, an observation-driven weather forecasting system which combines an ensemble data assimilation model with a forecast model to produce highly accurate forecasts relying only on observations as inputs. Huracan is not only the first to provide ensemble initial conditions and end-to-end ensemble weather forecasts, but also the first end-to-end system to achieve an accuracy comparable with that of ECMWF ENS, the state-of-the-art NWP competitor, despite using a smaller amount of available observation data. Notably, Huracan matches or exceeds the continuous ranked probability score of ECMWF ENS on 75.4% of the variable and lead time combinations. Our work is a major step forward in end-to-end data-driven weather prediction and opens up opportunities for further improving and revolutionizing operational weather forecasting.

Related papers

• OneForecast: A Universal Framework for Global and Regional Weather Forecasting [67.61381313555091]
  We propose a global-regional nested weather forecasting framework (OneForecast) based on graph neural networks.<n>By combining a dynamic system perspective with multi-grid theory, we construct a multi-scale graph structure and densify the target region.<n>We introduce an adaptive messaging mechanism, using dynamic gating units, to deeply integrate node and edge features for more accurate extreme event forecasting.
  arXiv  Detail & Related papers  (2025-02-01T06:49:16Z)
• Hybrid Forecasting of Geopolitical Events [71.73737011120103]
  SAGE is a hybrid forecasting system that combines human and machine generated forecasts.<n>The system aggregates human and machine forecasts weighting both for propinquity and based on assessed skill.<n>We show that skilled forecasters who had access to machine-generated forecasts outperformed those who only viewed historical data.
  arXiv  Detail & Related papers  (2024-12-14T22:09:45Z)
• DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models [0.0]
  We propose DeepMedcast, a deep learning method that generates intermediate forecasts between two or more NWP outputs.<n>Unlike averaging, DeepMedcast provides predictions in which meteorologically significant features align with the arithmetic mean.<n>We demonstrate the capability of DeepMedcast through case studies and verification results.
  arXiv  Detail & Related papers  (2024-11-15T07:42:16Z)
• FuXi Weather: A data-to-forecast machine learning system for global weather [13.052716094161886]
  FuXi Weather is a machine learning weather forecasting system that assimilates data from multiple satellites. FuXi Weather consistently outperforms ECMWF HRES in observation-sparse regions, such as central Africa.
  arXiv  Detail & Related papers  (2024-08-10T07:42:01Z)
• Data driven weather forecasts trained and initialised directly from observations [1.44556167750856]
  Skilful Machine Learned weather forecasts have challenged our approach to numerical weather prediction. Data-driven systems have been trained to forecast future weather by learning from long historical records of past weather. We propose a new approach, training a neural network to predict future weather purely from historical observations.
  arXiv  Detail & Related papers  (2024-07-22T12:23:26Z)
• Aardvark weather: end-to-end data-driven weather forecasting [30.219727555662267]
  Aardvark Weather is an end-to-end data-driven weather prediction system. It ingests raw observations and outputs global gridded forecasts and local station forecasts. It can be optimised end-to-end to maximise performance over quantities of interest.
  arXiv  Detail & Related papers  (2024-03-30T16:41:24Z)
• Uncertainty quantification for data-driven weather models [0.0]
  We study and compare uncertainty quantification methods to generate probabilistic weather forecasts from a state-of-the-art deterministic data-driven weather model, Pangu-Weather.<n>Specifically, we compare approaches for quantifying forecast uncertainty based on generating ensemble forecasts via perturbations to the initial conditions.<n>In a case study on medium-range forecasts of selected weather variables over Europe, the probabilistic forecasts obtained by using the Pangu-Weather model in concert with uncertainty quantification methods show promising results.
  arXiv  Detail & Related papers  (2024-03-20T10:07:51Z)
• ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [57.6987191099507]
  We introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast. We also introduce ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples. Our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
  arXiv  Detail & Related papers  (2024-02-02T10:34:13Z)
• GraphCast: Learning skillful medium-range global weather forecasting [107.40054095223779]
  We introduce a machine learning-based method called "GraphCast", which can be trained directly from reanalysis data. It predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute. We show that GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets.
  arXiv  Detail & Related papers  (2022-12-24T18:15:39Z)
• Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast [91.9372563527801]
  We present Pangu-Weather, a deep learning based system for fast and accurate global weather forecast. For the first time, an AI-based method outperforms state-of-the-art numerical weather prediction (NWP) methods in terms of accuracy. Pangu-Weather supports a wide range of downstream forecast scenarios, including extreme weather forecast and large-member ensemble forecast in real-time.
  arXiv  Detail & Related papers  (2022-11-03T17:19:43Z)
• A generative adversarial network approach to (ensemble) weather prediction [91.3755431537592]
  We use a conditional deep convolutional generative adversarial network to predict the geopotential height of the 500 hPa pressure level, the two-meter temperature and the total precipitation for the next 24 hours over Europe. The proposed models are trained on 4 years of ERA5 reanalysis data from 2015-2018 with the goal to predict the associated meteorological fields in 2019.
  arXiv  Detail & Related papers  (2020-06-13T20:53:17Z)

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.