Mesoscale Cellular Convection Detection and Classification Using Convolutional Neural Networks: Insights From Long-Term Observations at ARM Eastern North Atlantic Site

Journal Article · 12 March 2025 · Journal of Geophysical Research. Machine Learning and Computation (Online)

DOI: https://doi.org/10.1029/2024jh000486 · OSTI ID:2530595

Tian, Jingjing  ^[1]; Comstock, Jennifer  ^[1]; Geiss, Andrew  ^[1]; Wu, Peng  ^[1]; Silber, Israel  ^[1]; Zhang, Damao  ^[1]; Kooloth, Parvathi  ^[1]; Feng, Ya‐ Chien  ^[1]

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1. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Marine boundary layer clouds are crucial in Earth's climate system. They frequently manifest as closed or open cell mesoscale cellular convection (MCC). MCC clouds are challenging to represent accurately in current climate models, highlighting the need for detailed observational data sets and in-depth analyses. This study utilizes over 8 years of observations from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility Eastern North Atlantic (ENA) site at Graciosa Island, Azores, to investigate these clouds. We first apply a convolutional neural network with a U-Net architecture to classify open and closed cells, marking the first application of such an approach for automatically detecting MCC patterns from ground-based radar measurements. This method addresses some observational gaps in satellite data related to low temporal resolution, nighttime challenges, and limited vertical structure capture. The analysis of the MCC cases shows clear differences between closed and open MCCs: Closed MCC clouds are characterized by lower cloud tops and bases, shallower cloud geometrical depth, weaker horizontal wind speeds, stronger atmospheric stability, and a more homogeneous liquid water path than open MCCs. Finally, we demonstrate two potential applications of our radar-based MCC classifications: (a) facilitating the investigation of aerosol-cloud interactions and (b) exploring meteorological factors along with MCC's evolution by integrating satellite imagery and back-trajectory analysis. The identified MCC cases offer a valuable resource for the scientific community to study MCC processes further and improve climate model accuracy.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER)
• Grant/Contract Number: AC05-76RL01830; AC05-00OR22725
• OSTI ID: 2530595
• Report Number(s): PNNL-SA--205367
• Journal Information: Journal of Geophysical Research. Machine Learning and Computation (Online), Journal Name: Journal of Geophysical Research. Machine Learning and Computation (Online) Journal Issue: 1 Vol. 2; ISSN 2993-5210
• Publisher: WileyCopyright Statement
• Country of Publication: United States
• Language: English