Preprints
https://doi.org/10.5194/gmd-2024-55
https://doi.org/10.5194/gmd-2024-55
Submitted as: development and technical paper
 | 
09 Apr 2024
Submitted as: development and technical paper |  | 09 Apr 2024
Status: this preprint is currently under review for the journal GMD.

The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1)

Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell

Abstract. The Modular and Integrated Data Assimilation System (MIDAS) software (version 3.9.1) is described in terms of its range of functionality, modular software design, parallelization strategy, and current uses within real-time operational and experimental systems. MIDAS is developed at Environment and Climate Change Canada for both operational and research applications, including all atmospheric data assimilation (DA) elements of the numerical weather prediction systems. The software is designed to be sufficiently general to enable other DA applications, including atmospheric constituents (e.g. ozone), sea ice, and sea surface temperature. In addition to describing the current MIDAS applications, a sample of the results from these systems is presented to demonstrate their performance in comparison with either systems from before the switch to using MIDAS software or similar systems at other NWP centres. The modular software design also allows the code that implements high-level components (e.g. observation operators, error covariance matrices, state vectors) to easily be used in many different ways depending on the application, such as for both variational and ensemble DA algorithms; for estimating the observation impact on short-term forecasts; and for performing various observation pre-processing procedures. The use of a single common DA software for multiple components of the Earth system provides both practical and scientific benefits, including the facilitation of future research on DA approaches that explicitly include the coupled connections between multiple Earth system components. To this end, work is currently underway to allow the use of MIDAS DA algorithms for initializing both deterministic and ensemble three-dimensional ocean model forecasts.

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Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell

Status: open (until 14 Jun 2024)

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  • RC1: 'Comment on gmd-2024-55', Anonymous Referee #1, 30 Apr 2024 reply
Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell
Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell

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Short summary
The Modular and Integrated Data Assimilation System (MIDAS) is described. The flexible design of MIDAS enables both deterministic and ensemble prediction applications for the atmosphere and several other Earth system components. It is currently used for all main operational weather prediction systems in Canada and also for sea ice and sea surface temperature analysis. The use of MIDAS for multiple Earth system components will facilitate future research on coupled data assimilation.