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Abstract. Remote sensing measurements have been widely used to estimate the planetary boundary layer height (PBLHT). Each remote sensing approach offers unique strengths and faces different limitations. In this study, we use machine learning (ML) methods to produce a best-estimate PBLHT (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory. Three ML models – random forest (RF) classifier, RF regressor, and light gradient-boosting machine (LightGBM) – were trained on a dataset from 2017 to 2023 that included radiosonde, various remote sensing PBLHT estimates, and atmospheric meteorological conditions. Evaluations indicated that PBLHT-BE-ML from all three models improved alignment with the PBLHT derived from radiosonde data (PBLHT-SONDE), with LightGBM demonstrating the highest accuracy under both stable and unstable boundary layer conditions. Feature analysis revealed that the most influential input features at the SGP site were the PBLHT estimates derived from (a) potential temperature profiles retrieved using Raman lidar (RL) and atmospheric emitted radiance interferometer (AERI) measurements (PBLHT-THERMO), (b) vertical velocity variance profiles from Doppler lidar (PBLHT-DL), and (c) aerosol backscatter profiles from micropulse lidar (PBLHT-MPL). The trained models were then used to predict PBLHT-BE-ML at a temporal resolution of 10 min, effectively capturing the diurnal evolution of PBLHT and its significant seasonal variations, with the largest diurnal variation observed over summer at the SGP site. We applied these trained models to data from the ARM Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) field campaign (EPC), where the PBLHT-BE-ML, particularly with the LightGBM model, demonstrated improved accuracy against PBLHT-SONDE. Analyses of model performance at both the SGP and EPC sites suggest that expanding the training dataset to include various surface types, such as ocean and ice-covered areas, could further enhance ML model performance for PBLHT estimation across varied geographic regions. 

Abstract In this study, we conduct sensitivity experiments with the Community Atmosphere Model version 5 to understand the impact of representing heterogeneous distribution between cloud liquid and ice on the phase partitioning in mixed‐phase clouds through different perturbations on the Wegener‐Bergeron‐Findeisen (WBF) process. In two experiments, perturbation factors that are based on assumptions of pocket structure and the partial homogeneous cloud volume derived from the High‐performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole‐to‐Pole Observation (HIPPO) campaign are utilized. Alternately, a mass‐weighted assumption is used in the calculation of WBF process to mimic the appearance of unsaturated area in mixed‐phase clouds as the result of heterogeneous distribution. Model experiments are tested in both single column and weather forecast modes and evaluated against data from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program's Mixed‐Phase Arctic Cloud Experiment (M‐PACE) field campaign and long‐term ground‐based multisensor measurements. Model results indicate that perturbations on the WBF process can significantly modify simulated microphysical properties of Arctic mixed‐phase clouds. The improvement of simulated cloud water phase partitioning tends to be linearly proportional to the perturbation magnitude that is applied in the three different sensitivity experiments. Cloud macrophysical properties such as cloud fraction and frequency of occurrence of low‐level mixed‐phase clouds are less sensitive to the perturbation magnitude than cloud microphysical properties. Moreover, this study indicates that heterogeneous distribution between cloud hydrometeors should be treated consistently for all cloud microphysical processes. The model vertical resolution is also important for liquid water maintenance in mixed‐phase clouds.