Alfonso Ladino-Rincon, who attended ARM's 2024 summer school, works on projects to support the growth of the open science community.

A new phased approach for artificial intelligence (AI) prioritizes transparent governance, AI‑ready infrastructure, and user engagement to accelerate discovery across the ARM community.

A recent paper uses observations from ARM and other field campaigns to evaluate simulations from the U.S. Department of Energy's Energy Exascale Earth System Model at kilometer-scale resolution.

These newly released data from the Ka-Band ARM Zenith Radar (KAZR) have undergone calibration, correction, and quality control processes beyond ARM’s standard quality checks and corrections.

The product provides an automated cloud type classification for the Coast-Urban-Rural Atmospheric Gradient Experiment (CoURAGE) in Baltimore, Maryland, and ARM's Bankhead National Forest (BNF) atmospheric observatory in Alabama.

This machine learning value-added product for the 2024–2025 Coast-Urban-Rural Atmospheric Gradient Experiment (CoURAGE) provides key insights into the variability of large-scale circulations over the Baltimore, Maryland, region.

While a non-monotonic (“inverted-V”) cloud response to aerosol perturbation—initial cloud thickening via precipitation suppression followed by enhanced evaporative dissipation—has been previously reported, its meteorological conditioning remains unresolved. Using a deep-learning framework to objectively classify Eastern North Atlantic synoptic regimes, we show that the cloud response is strongly regime-dependent and that the U.S. Department of Energy's earth system model (E3SMv2) systematically overestimates liquid water loss, particularly in dynamically complex, precipitating environments with strong vertical motion. These biases are linked to uncertainties in models representing drizzle, entrainment, and turbulent processes.

Earth’s radiant energy budget represents the balance between the energy Earth receives from the sun and the energy Earth sends back into space. The radiant energy budget drives planetary dynamics, thermodynamics, and the water cycle. Researchers often use satellite data to estimate how much sunlight and heat move through the atmosphere. In this study, researchers compared radiative flux estimates calculated from satellite data and those calculated from ground-based sensors at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility's Eastern North Atlantic (ENA) site. They found that satellite measurements overestimate surface downwelling fluxes. On the other hand, fluxes based on observed cloud and thermodynamic data from ground-based sensors showed no such bias. The overestimation of satellite fluxes was attributed to inaccuracies in variables like temperature and cloud properties within the retrieval algorithm.

Warm rain formation is a critical factor for cloud modulation and the response of precipitation to aerosol loading. However, it remains challenging to accurately represent in atmospheric models across scales because the driving process, droplet collision-coalescence, involves interactions at micro‐scales that interact with larger scales. The current study, using observations and a state-of-the-art cloud model, investigates the role of small-scale turbulence in modulating the precipitation response to aerosol loading through drop coalescence.