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DTSTAMP:20241120T082409Z
LOCATION:HG F 1
DTSTART;TZID=Europe/Stockholm:20240603T150000
DTEND;TZID=Europe/Stockholm:20240603T153000
UID:submissions.pasc-conference.org_PASC24_sess155_msa253@linklings.com
SUMMARY:AIFS – ECMWF’s Data-Driven Probabilistic Forecasting System
DESCRIPTION:Minisymposium\n\nMihai Alexe (ECMWF)\n\nRecent developments in
  machine learning for weather forecasting have led to data-driven models t
 hat are comparable in skill to leading physics-based NWP systems. Over the
  past year, ECMWF has been developing its own data-driven forecasting syst
 em, the AIFS. By leveraging both data and model parallelism, AIFS can be t
 rained across O(100) GPUs; the latest version runs at a resolution of ca. 
 0.25-degrees. We give an overview of AIFS and ai-models, the pipeline that
  has been developed by ECMWF to produce data-driven weather forecasts, and
  runs daily – with open data delivery - on ECMWF's HPC. In addition, we sh
 owcase early results of ongoing research efforts at ECMWF, including data-
 driven probabilistic ensemble forecasting, and direct observation predicti
 on - a task that aims to produce a weather forecast solely from observatio
 nal data.\n\nDomain: Climate, Weather, and Earth Sciences, Computational M
 ethods and Applied Mathematics\n\nSession Chair: Karthik Kashinath (NVIDIA
  Inc., Lawrence Berkeley National Laboratory)
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