AIFS-COMPO: A Global Data-Driven Atmospheric Composition Forecasting System

• URL: http://arxiv.org/abs/2604.03300v1
• Date: Sun, 29 Mar 2026 11:16:49 GMT
• Title: AIFS-COMPO: A Global Data-Driven Atmospheric Composition Forecasting System
• Abstract summary: We introduce AIFS-COMPO, a skilful medium-range data-driven global forecasting system for aerosols and reactive gases.<n>Building on the ECMWF Artificial Intelligence Forecast System (AIFS), AIFS-COMPO employs a transformer-based encoder-processor-decoder architecture.<n>We evaluate AIFS-COMPO against a range of atmospheric composition observations and compare its performance with the operational CAMS global forecasting system IFS-COMPO.
• Score: 0.8074567926023328
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce AIFS-COMPO, a skilful medium-range data-driven global forecasting system for aerosols and reactive gases. Building on the ECMWF Artificial Intelligence Forecast System (AIFS), AIFS-COMPO employs a transformer-based encoder-processor-decoder architecture to jointly model meteorological and atmospheric composition variables. The model is trained on Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, analysis, and forecast data to learn the coupled dynamics of weather, emissions, transport, and atmospheric chemistry. We evaluate AIFS-COMPO against a range of atmospheric composition observations and compare its performance with the operational CAMS global forecasting system IFS-COMPO. The results show that AIFS-COMPO achieves comparable or improved forecast skill for several key species while requiring only a fraction of the computational resources. Furthermore, the efficiency of the approach enables forecasts beyond the current operational horizon, demonstrating the potential of AI-based systems for fast and accurate global atmospheric composition prediction.

Related papers

• Improving Ensemble CAPE Forecasts with a Diffusion Model Incorporating Aerosol Information [0.4014524824655106]
  Convective available potential energy (CAPE) is an important variable for forecasting severe weather.<n>The latest versions of the Global Forecast System (GFS) and related Global Ensemble Forecast System (GEFS) have exhibited a bias towards underestimating CAPE values during the summertime.<n>We train an artificial intelligence (AI) diffusion model to improve the skill and uncertainty of afternoon 6-hour lead time ensemble forecasts over the United States.
  arXiv  Detail & Related papers  (2026-05-19T18:32:55Z)
• Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting [52.53742727060265]
  Foundation models (FMs) for the Earth system learn statistical relationships between physical variables across massive datasets.<n>Here, we introduce Earth System Foundation Model (ESFM), a fully open model building on the 3Dwin UNet backbone of the pioneering model.<n>We present findings using dense gridded data (ERA5, CMIP6), regionally masked dense data, sparse gridded MODIS satellite data, and station data.<n>Results demonstrate competitive or superior performance relative to state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2026-04-20T11:40:39Z)
• FuXiWeather2: Learning accurate atmospheric state estimation for operational global weather forecasting [15.10103712842682]
  We present FuXiWeather2, a unified end-to-end neural framework for assimilation and forecasting.<n>FuXiWeather2 generates high-resolution ($0.25circ$) global analysis fields and 10-day forecasts within minutes.<n>These high-quality analysis fields drive deterministic forecasts that exceed the skill of the HRES system in 91% of evaluated metrics.
  arXiv  Detail & Related papers  (2026-03-16T14:36:47Z)
• DAWP: A framework for global observation forecasting via Data Assimilation and Weather Prediction in satellite observation space [60.729377189859]
  We propose our DAWP framework to enable AIWPs to operate in a complete observation space.<n>AIDA module applies a mask multi-modality autoencoder for assimilating irregular satellite observation tokens.<n>We show that AIDA significantly improves the roll out and efficiency of AIWP and holds promising potential to be applied in global precipitationresolution forecasting.
  arXiv  Detail & Related papers  (2025-10-13T03:13:35Z)
• Statistical post-processing yields accurate probabilistic forecasts from Artificial Intelligence weather models [0.0]
  We test the application of Bureau of Meteorology's statistical post-processing system, IMPROVER, to ECMWF's deterministic AIFS.<n>We show that blending AIFS with NWP models improves overall forecast skill, even when AIFS alone is not the most accurate component.
  arXiv  Detail & Related papers  (2025-04-17T06:05:10Z)
• Air Quality Prediction with A Meteorology-Guided Modality-Decoupled Spatio-Temporal Network [47.699409089023696]
  Air quality prediction plays a crucial role in public health and environmental protection.<n>Existing works underestimate the critical role atmospheric conditions in air quality prediction.<n> MDSTNet is an encoder framework explicitly that captures atmosphere-pollution dependencies for prediction.<n>ChinaAirNet is the first dataset combining air quality records with multi-pressure-level meteorological observations.
  arXiv  Detail & Related papers  (2025-04-14T09:18:11Z)
• Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method [66.80344502790231]
  We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset. Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors. Our proposed method outperforms other specially designed baseline models on multiple surface variables.
  arXiv  Detail & Related papers  (2024-01-22T14:02:56Z)
• Towards an end-to-end artificial intelligence driven global weather forecasting system [57.5191940978886]
  We present an AI-based data assimilation model, i.e., Adas, for global weather variables. We demonstrate that Adas can assimilate global observations to produce high-quality analysis, enabling the system operate stably for long term. We are the first to apply the methods to real-world scenarios, which is more challenging and has considerable practical application potential.
  arXiv  Detail & Related papers  (2023-12-18T09:05:28Z)
• Federated Prompt Learning for Weather Foundation Models on Devices [37.88417074427373]
  On-device intelligence for weather forecasting uses local deep learning models to analyze weather patterns without centralized cloud computing. This paper propose Federated Prompt Learning for Weather Foundation Models on Devices (FedPoD) FedPoD enables devices to obtain highly customized models while maintaining communication efficiency.
  arXiv  Detail & Related papers  (2023-05-23T16:59:20Z)
• Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms [53.38353133198842]
  Air quality significantly affects human health, it is increasingly important to accurately and timely predict the Air Quality Index (AQI) This paper proposes a new federated learning-based aerial-ground air quality sensing framework for fine-grained 3D air quality monitoring and forecasting. For ground sensing systems, we propose a Graph Convolutional neural network-based Long Short-Term Memory (GC-LSTM) model to achieve accurate, real-time and future AQI inference.
  arXiv  Detail & Related papers  (2020-07-23T13:32:47Z)

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.