Graphic showing the framework for MCSMIP.
A framework for Mesoscale Convective Systems tracking Method Intercomparison (MCSMIP) to comprehensively evaluate the representation of mesoscale convective systems (MCSs) in an ensemble of global kilometer-scale model simulations in the DYAMOND project. Using ten different feature trackers applied to the simulations and satellite observations, the authors identify robust metrics to evaluate model performance in key MCS characteristics and offer guidance for model development. Credit: Feng et al. [2025], Figure 1 (modified)
Editors’ Highlights are summaries of recent papers by AGU’s journal editors.
Source: Journal of Geophysical Research: Atmospheres

Large convective storms, known as mesoscale convective systems (MCSs), are the main drivers of extreme rainfall and severe weather. Accurately representing these storms in Earth system models is essential for predicting their variations and changes.

Feng et al. [2025] apply ten different feature tracking methods to assess MCSs in an ensemble of next-generation global kilometer-scale or storm-resolving simulations. Although different tracking methods produced somewhat different estimates of storm frequency and rainfall in observations, consistent patterns emerged when comparing model simulations with observations. While the models generally capture storm frequency well, they tend to underestimate the rainfall amount from these storms and their contribution to total precipitation, particularly over oceans. Most models predicted heavier MCS rainfall for a given amount of atmospheric water vapor compared to observations. Mesoscale Convective Systems tracking Method (MCSMIP) provides a framework for a more robust evaluation of model performance to guide future model development to improve predictions of storms and their attendant impacts.

Citation: Feng, Z., Prein, A. F., Kukulies, J., Fiolleau, T., Jones, W. K., Maybee, B., et al. (2025). Mesoscale convective systems tracking method intercomparison (MCSMIP): Application to DYAMOND global km-scale simulations. Journal of Geophysical Research: Atmospheres, 130, e2024JD042204. https://doi.org/10.1029/2024JD042204

—Rong Fu, Editor, JGR: Atmospheres

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