The Data Assimilation Research Testbed: A Robust, Scalable Software Facility with Groundbreaking Capabilities for Model-Data Integration
Le banc d'essai de recherche sur l'assimilation de données : une plateforme logicielle robuste et évolutive dotée de capacités révolutionnaires pour l'intégration modèle-données
Gharamti, Mohamad El ; Kershaw, Helen ; Raeder, Kevin ; Raczka, Brett ; Johnson, Benjamin ; Smith, Marlena ; Anderson, Jeffrey ; Amrhein, Daniel ; Collins, Nancy ; Hoar, Timothy ; Gaubert, Benjamin ; Grooms, Ian ; Kugler, Lukas
Année de publication
2025
Data assimilation (DA) is a powerful computational technique that enhances the predictive capabilities of numerical models by integrating observational data. The Data Assimilation Research Testbed (DART) is a community facility for ensemble DA, developed and maintained at the National Science Foundation National Center for Atmospheric Research (NSF NCAR) by a collaborative team of DA experts, physical scientists, and software engineers. DART has been instrumental in providing ensemble DA solutions for the atmosphere, ocean, land, hydrosphere, cryosphere, and many other applications. Here, we present the latest advancements in DART, supported by over twenty years of scientific innovation. DART offers state-of-the-art ensemble DA algorithms, support for over 50 models, expanded observation types, access to publicly available reanalysis datasets, enhanced software capabilities, improved diagnostic tools, and enriched tutorial and educational resources. We discuss the improved prediction accuracy enabled by the new ensemble algorithms and describe DART's adaptable codebase and documentation, highlighting its functionality, efficiency, and broad user base. We also emphasize recent community engagement initiatives that support the educational goals of graduate and undergraduate students, early career scientists, and researchers from various fields. Finally, we demonstrate how DART's infrastructure can accelerate scientific research by enabling users to integrate their own models, observations, and problem-specific configurations.</div>
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