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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. 

While existing hierarchical text classification (HTC) methods attempt to capture label hierarchies for model training, they either make local decisions regarding each label or completely ignore the hierarchy information during inference. To solve the mismatch between training and inference as well as modeling label dependencies in a more principled way, we formulate HTC as a Markov decision process and propose to learn a Label Assignment Policy via deep reinforcement learning to determine where to place an object and when to stop the assignment process. The proposed method, HiLAP, explores the hierarchy during both training and inference time in a consistent manner and makes inter-dependent decisions. As a general framework, HiLAP can incorporate different neural encoders as base models for end-to-end training. Experiments on five public datasets and four base models show that HiLAP yields an average improvement of 33.4% in Macro-F1 over flat classifiers and outperforms state-of-the-art HTC methods by a large margin. 

Abstract The profiles of marine boundary layer (MBL) cloud and drizzle microphysical properties are important for studying the cloud‐to‐rain conversion and growth processes in MBL clouds. However, it is challenging to simultaneously retrieve both cloud and drizzle microphysical properties within an MBL cloud layer using ground‐based observations. In this study, methods were developed to first decompose drizzle and cloud reflectivity in MBL clouds from Atmospheric Radiation Measurement cloud radar reflectivity measurements and then simultaneously retrieve cloud and drizzle microphysical properties during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE‐ENA) campaign. These retrieved microphysical properties, such as cloud and drizzle particle size (r_candr_m,d), their number concentration (N_candN_d) and liquid water content (LWC_candLWC_d), have been validated by aircraft in situ measurements during ACE‐ENA (~158 hr of aircraft data). The mean surface retrieved (in situ measured)r_c,N_c, andLWC_care 10.9 μm (11.8 μm), 70 cm⁻³(60 cm⁻³), and 0.21 g m⁻³(0.22 g m⁻³), respectively. For drizzle microphysical properties, the retrieved (in situ measured)r_d,N_d, andLWC_dare 44.9 μm (45.1 μm), 0.07 cm⁻³(0.08 cm⁻³), and 0.052 g m⁻³(0.066 g m⁻³), respectively. Treating the aircraft in situ measurements as truth, the estimated median retrieval errors are ~15% forr_c, ~35% forN_c, ~30% forLWC_candr_d, and ~50% forN_dandLWC_d. The findings from this study will provide insightful information for improving our understanding of warm rain processes, as well as for improving model simulations. More studies are required over other climatic regions.