AI-Based Classification of Marine Cloud Cell Patterns from ARM Radar

Science

Marine stratocumulus clouds often organize into mesoscale cellular convection (MCC), forming large-scale open or closed cell patterns. However, Earth System Models struggle to simulate these regimes due to limited observations and understanding of physical processes. In this study, researchers at Pacific Northwest National Laboratory applied an artificial intelligence/machine learning (AI/ML) approach to analyze long-term vertically pointing cloud radar data from the Atmospheric Radiation Measurement (ARM) User Facility’s Eastern North Atlantic (ENA) site. The method enables continuous time series of MCC classification, performs well, and reveals clear cloud and meteorological differences between the two MCC types. It also holds great potential for application to other ARM locations.

Impact

This work addresses a critical observational gap by enabling automated detection of MCC cloud regimes using ARM ground-based data. It also marks the first use of AI/ML to automatically detect and classify open- and closed-cell mesoscale cellular convection (MCC) from radar measurements. The resulting MCC events, available via the ARM Data Discovery Epochs interface (a collection of well-characterized, calibrated measurements focused on specific atmospheric phenomena), offer a valuable, easy-to-access resource for the research community. By providing continuous MCC classifications and showcasing real-world applications, this work offers a powerful tool for accelerating studies of marine boundary layer cloud processes, benefiting model evaluation, and expanding the scientific use of ARM data.

Summary

This study presents an automated method to detect and classify open- and closed-cell mesoscale cellular convection (MCC) using long-term ground-based radar observations from the ARM ENA site. While satellite-based approaches can identify MCC patterns, they are limited to snapshot views with coarse temporal resolution, often miss nighttime events, and lack information on vertical structure, making it difficult to fully capture the evolution and characteristics of MCC. When combined with other ARM datasets, the resulting classifications offer a valuable resource for advancing the understanding of marine cloud processes and improving the evaluation of Earth System Models.

Many cloud studies begin by identifying specific cloud events to analyze in detail, so sharing these well-characterized MCC events reduces the time and effort needed by ARM users. These detected MCC cases are also being used to support the Large Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO)-ENA scenario.

Beyond developing the classification method, two example applications illustrate its utility for ARM data users: (1) Aerosol–cloud interaction study, showing how droplet number and water content differ between open and closed cloud patterns, (2) meteorological analysis, combining ARM back-trajectory data and satellite imagery to see the change of MCC over time.