Research Highlights

Collaborative capabilities were designed to enable unique measurements of aerosol optical properties, water uptake, cloud formation potential, and chemical composition to understand how sources, aging and mixing affect energy within earth systems. Three aerosol regimes were probed in depth during a summer campaign in Houston, Texas: urban, particle growth, and dust.

The ARM Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) applies a multi-sensor approach to classify thermodynamic cloud phase by integrating lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave radiometer-derived liquid water path, and radiosonde temperature measurements. Cloud Hydrometeors are classified into seven phase categories including: liquid, drizzle, liquid + drizzle (liq_driz), rain, ice, snow, and mixed-phase. In this study, we evaluated a machine learning (ML) method for thermodynamic cloud phase classification, trained on three years of THERMOCLDPHASE VAP observations.

Cold-air outbreaks over high-latitude oceans typically include mixed-phase clouds and precipitation—in particular supercooled liquid clouds that support snow and graupel through ice growth processes. Here, we present a machine learning approach to retrieve liquid water path (LWP) using passive microwave measurements combined with vertically integrated radar reflectivities. The approach is an extension of Cadeddu et al. (2009), with the novel addition of radar reflectivity. The machine learning models are trained using the Passive and Active Microwave Radiative Transfer (PAMTRA) code applied to output from numerical simulations of three independent cold-air outbreaks sampled during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) campaign. Brightness temperatures corresponding to the four sidebands of an upward-looking G-band (183 GHz) vapor radiometer, along with the vertically integrated reflectivity from a zenith-pointing 95-GHz Wyoming Cloud Radar, are simulated from the perspective of a near-surface aircraft track. The radar reflectivity helps discriminate the snow contribution to the brightness temperatures. The machine learning models are thereafter tested on a simulation of an independent cold-air outbreak during COMBLE and against measurements from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) observatory. This machine learning approach is shown to provide robust, computationally efficient, near-real-time measurements of LWP and water vapor path during the Cold-Air Outbreak Experiment in the Sub-Arctic Region (CAESAR) campaign in February-April 2024.