A Python diagnostics package for evaluation of Madden–Julian Oscillation (MJO) teleconnections in subseasonal-to-seasonal (S2S) forecast systems.

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Bibliographic Details
Title: A Python diagnostics package for evaluation of Madden–Julian Oscillation (MJO) teleconnections in subseasonal-to-seasonal (S2S) forecast systems.
Authors: Stan, Cristiana, Kollapaneni, Saisri, Jenney, Andrea M., Wang, Jiabao, Wu, Zheng, Zheng, Cheng, Kim, Hyemi, Garfinkel, Chaim I., Singh, Ayush
Source: Geoscientific Model Development; 2025, Vol. 18 Issue 20, p7969-7985, 17p
Subject Terms: MADDEN-Julian oscillation, TELECONNECTIONS (Climatology), DATA management, FORECASTING, PYTHON programming language
Abstract: The Madden–Julian Oscillation (MJO) teleconnections diagnostics package is an open-source Python software package that provides process-level evaluation of MJO teleconnections predicted by subseasonal-to-seasonal (S2S) forecast systems. The package provides in-depth process-level evaluation of both tropospheric and stratospheric pathways defining the atmospheric teleconnections from the tropics to the extratropics on S2S time scales. The analyses include the comparison of a forecast model with a default verification dataset or user-provided verification data. The package consists of a user-friendly graphical user interface (GUI), which allows the package to be applied to both operational and research models. This approach allows for efficient data management and the reproducibility of the analysis. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:The Madden–Julian Oscillation (MJO) teleconnections diagnostics package is an open-source Python software package that provides process-level evaluation of MJO teleconnections predicted by subseasonal-to-seasonal (S2S) forecast systems. The package provides in-depth process-level evaluation of both tropospheric and stratospheric pathways defining the atmospheric teleconnections from the tropics to the extratropics on S2S time scales. The analyses include the comparison of a forecast model with a default verification dataset or user-provided verification data. The package consists of a user-friendly graphical user interface (GUI), which allows the package to be applied to both operational and research models. This approach allows for efficient data management and the reproducibility of the analysis. [ABSTRACT FROM AUTHOR]
ISSN:1991959X
DOI:10.5194/gmd-18-7969-2025