Continuous data assimilation for global numerical weather prediction Article Swipe
Peter Lean
,
Elías Hólm
,
Massimo Bonavita
,
Niels Bormann
,
A. P. McNally
,
Heikki Järvinen
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1002/qj.3917
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1002/qj.3917
A new configuration of the European Centre for Medium‐Range Weather Forecasts (ECMWF) incremental 4D‐Var data assimilation (DA) system is introduced which builds upon the quasi‐continuous DA concept proposed in the mid‐1990s. Rather than working with a fixed set of observations, the new 4D‐Var configuration exploits the near‐continuous stream of incoming observations by introducing recently arrived observations at each outer loop iteration of the assimilation. This allows the analysis to benefit from more recent observations. Additionally, by decoupling the start time of the DA calculations from the observational data cut‐off time, real‐time forecasting applications can benefit from more expensive analysis configurations that previously could not have been considered. In this work we present results of a systematic comparison of the performance of a Continuous DA system against that of two more traditional baseline 4D‐Var configurations. We show that the quality of the analysis produced by the new, more continuous configuration is comparable to that of a conventional baseline that has access to all of the observations in each of the outer loops, which is a configuration not feasible in real‐time operational numerical weather prediction. For real‐time forecasting applications, the Continuous DA framework allows configurations which clearly outperform the best available affordable non‐continuous configuration. Continuous DA became operational at ECMWF in June 2019 and led to significant 2 to 3% reductions in medium‐range forecast root mean square errors, which is roughly equivalent to 2–3 hr of additional predictive skill.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/qj.3917
- OA Status
- green
- Cited By
- 24
- References
- 22
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W3088099721Canonical identifier for this work in OpenAlex
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https://doi.org/10.1002/qj.3917Digital Object Identifier
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Continuous data assimilation for global numerical weather predictionWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2020Year of publication
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2020-09-27Full publication date if available
- Authors
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Peter Lean, Elías Hólm, Massimo Bonavita, Niels Bormann, A. P. McNally, Heikki JärvinenList of authors in order
- Landing page
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https://doi.org/10.1002/qj.3917Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://hdl.handle.net/10138/334617Direct OA link when available
- Concepts
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Numerical weather prediction, Data assimilation, Computer science, Decoupling (probability), Forecast verification, Forecast skill, Range (aeronautics), Weather forecasting, Baseline (sea), Meteorology, Environmental science, Geology, Geography, Aerospace engineering, Control engineering, Oceanography, EngineeringTop concepts (fields/topics) attached by OpenAlex
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24Total citation count in OpenAlex
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2025: 10, 2024: 6, 2023: 3, 2022: 2, 2021: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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