Preprints
https://doi.org/10.5194/egusphere-2025-4294
https://doi.org/10.5194/egusphere-2025-4294
15 Sep 2025
 | 15 Sep 2025
Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

SPREADS: From Research to Operational Open-Source Data Assimilation System

Carla Cardinali, Giovanni Conti, Marcelo Guatura, Sami Saarinen, Luis Gustavo Gonçalves De Gonçalves, Jeffrey Anderson, and Kevin Raeder

Abstract. The Scalable PaRallelised EArth Data Assimilation System (SPREADS) is a next-generation data assimilation system developed at CMCC-Foundation (Euro-Mediterranean Center on Climate Change) to support operational global forecasts. Built upon the Data Assimilation Research Testbed (DART), SPREADS incorporates key advancements such as First Guess at Appropriate Time (FGAT), enhanced observation handling via D4O (Database for Observations), and high-performance parallelisation to significantly improve computational efficiency and scalability. A major focus of SPREADS is the assimilation of a vastly increased number of asynchronous satellite-based radiances, which have been shown to substantially enhance analysis quality. Designed for coupled atmosphere-land-ocean-ice assimilation, SPREADS forms the core of the CMCC Earth SYstem Modelling and Data Assimilation (ESYDA division) operational forecast system. This paper presents the modifications made to DART, evaluates preliminary results, and outlines future developments toward fully coupled data assimilation.

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Carla Cardinali, Giovanni Conti, Marcelo Guatura, Sami Saarinen, Luis Gustavo Gonçalves De Gonçalves, Jeffrey Anderson, and Kevin Raeder

Status: open (until 10 Nov 2025)

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Carla Cardinali, Giovanni Conti, Marcelo Guatura, Sami Saarinen, Luis Gustavo Gonçalves De Gonçalves, Jeffrey Anderson, and Kevin Raeder
Carla Cardinali, Giovanni Conti, Marcelo Guatura, Sami Saarinen, Luis Gustavo Gonçalves De Gonçalves, Jeffrey Anderson, and Kevin Raeder
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Short summary

Scientists have developed research systems to test new ideas in data assimilation, but these often lack the efficiency and robustness needed for operational use. We addressed this gap with key innovations: a flexible observation database, first guess at the appropriate time, and modular, parallelised software enabling the assimilation of millions of observations.

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