Natalia Roldán-Henao

The Goddard Lidar Observation and Validation Experiment (GLOVE) was a field campaign conducted from 27 January to 28 February 2025, at the NASA Armstrong Flight Research Center, situated at Edwards Air Force Base in California. One main objective of GLOVE was to validate atmospheric data products, including clouds and precipitation, obtained from the Earth Cloud, Aerosol, and Radiation Explorer (EarthCARE) satellite mission. Using the NASA high-altitude ER-2 aircraft, GLOVE deployed the Cloud Physics Lidar (CPL) to capture vertical profiles of aerosols and cloud features along EarthCARE's flight paths. This study validates the EarthCARE baselines BA and BD Atmospheric Lidar (ATLID) Level 2A Target Classification (A-TC) and Cloud Top Height (A-CTH) products against CPL observations from GLOVE. Results show substantial agreement between ATLID and CPL, with a better alignment under homogeneous than heterogeneous conditions, likely driven by ATLID's spatial averaging and multiple scattering effects. In addition, ATLID detects a higher fraction of cloud features than CPL, especially in baseline BA, with a mean probability of detection of 0.85, though at the expense of a higher false-positive rate, misclassifying about 7.6 % of clear-sky pixels as cloudy. Baseline BD has a smaller probability of detection (0.74) but reduces the false-positive rate to 4.8 %. Overall, cloud top height retrievals show good agreement with CPL. A-TC provides broader sample coverage with relatively good performance, while A-CTH retrieves fewer samples due to a stricter detection threshold, but achieves lower absolute errors and higher correlations. Results for baselines BA and BD show that the majority of samples in both A-TC and A-CTH meet the ATLID cloud top error requirement, with at least 76 % of cloud top estimates falling below the 0.3 km error threshold. The findings here highlight the role of spatial averaging, multiple scattering, and cloud heterogeneity in ATLID retrievals, factors that should be considered when using ATLID cloud products.

The Goddard Space Flight Center's Lidar Observation and Validation Experiment (GLOVE) was a field campaign conducted from 27 January to 28 February 2025, based out of NASA Armstrong Flight Research Center at Edwards Air Force Base in California. Its main goals were to validate atmospheric data products from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) and the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) satellite missions. The campaign utilized NASA's high-altitude Earth Resources-2 (ER-2) aircraft, equipped with four remote sensing instruments – including two lidars, a radar, and a spectrometer. GLOVE carried out eight flights totaling 40 flight hours and successfully captured seven ICESat-2 and six EarthCARE underflight segments of varying atmospheric conditions (i.e., aerosols, cirrus, and stratocumulus clouds) and surface types. The data collected during ICESat-2 underflights, especially of cirrus clouds and aerosols, offer valuable opportunities to assess the performance of both the operational and newer research-grade atmospheric ICESat-2 data products during daytime. Notably, the data from the Cloud Radar System (CRS), especially from snow-producing clouds, will play an important role in understanding the potential errors and uncertainties in EarthCARE Cloud Profiling Radar (CPR) Doppler data, the first-ever radar Doppler velocity measurements from space. All GLOVE data products are publicly accessible through a NASA Distributed Active Archive Center (DAAC) or other free, open-access repositories (please see Sect. 4, “Data availability”, for the DOIs and full data set citations). GLOVE serves as an example for conducting cost-effective and efficient airborne satellite validation campaigns.

Several mechanisms have been proposed for the aerosol invigoration effect. Although their principles are well established, their actual magnitudes and roles in cloud development remain uncertain and debatable. This uncertainty partly stems from observational‐based studies, in which it has been challenging to separate the co‐variability between aerosols and meteorology. Addressing this problem requires large data samples. To this end, this study employs the Atmospheric Radiation Measurement data set expanding to 16 years (some up to 17 years, compared to 10 years in previous work) in the U.S. Southern Great Plains. It also conducts a more careful and rigorous analysis to isolate the influences of convective available potential energy (CAPE) and synoptic patterns to address a previously raised concern. We incorporated a new key process affecting aerosol‐cloud interaction: cloud‐surface coupling. The state/degree of the coupling relationship turns out to play an important role in the invigoration effect. Our analysis reinforces earlier findings of a robust positive relationship between cloud thickness and aerosol loading across CAPE percentiles—but only under cloud‐surface coupled conditions. The increase in cloud thickness with aerosol loading is most pronounced in coupled clouds with high CAPE and bases below 1 km. Coupled clouds with bases below 1 km thicken between 1 and 4 km, depending on the CAPE percentile. Decoupled clouds show no such systematic changes. Synoptic patterns also lead to different strengths of the invigoration effect. Clean and polluted air masses are predominantly associated with northerly and southerly winds, respectively, with a stronger invigoration effect in cleaner air masses.

The Goddard Space Flight Center's Lidar Observation and Validation Experiment (GLOVE) was a field campaign conducted from January 27 to February 28, 2025, based out of NASA Armstrong Flight Research Center at Edwards Air Force Base in California. Its main goals were to validate atmospheric data products from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) and the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) satellite missions. The campaign utilized NASA's high-altitude Earth Resources-2 (ER-2) aircraft, equipped with four remote sensing instruments—including two lidars, a radar, and a spectrometer. GLOVE carried out eight flights totaling 40 flight hours and successfully captured seven ICESat-2 and six EarthCARE underflight segments of varying atmospheric conditions (i.e., aerosols, cirrus, and stratocumulus clouds) and surface types. The data collected during ICESat-2 underflights, especially of cirrus clouds and aerosols, offer valuable opportunities to assess the performance of both the operational and newer research-grade atmospheric ICESat-2 data products during daytime. Notably, the data from the Cloud Radar System (CRS), especially from snow-producing clouds, will play an important role in understanding the potential errors and uncertainties in EarthCARE Cloud Profiling Radar (CPR) Doppler data, the first-ever radar Doppler velocity measurements from space. All GLOVE data products are publicly accessible through a NASA Distributed Active Archive Center (DAAC) or other free, open-access repositories. GLOVE serves as an example for conducting cost-effective and efficient airborne satellite validation campaigns.

Land‐atmosphere interactions play a critical role in the evolution and formation of low‐level clouds. The different states of coupling between low‐level clouds and the surface are uncertain, primarily over continental regions, where complex thermodynamics complicates their investigation. This study uses observations from the Atmospheric Radiation Measurement User Facility to explore cloud‐surface coupling and perform a climatological analysis of this interaction in five countries across three continents. The results reveal consistent coupling thresholds and average percentages across the five sites, with coupled clouds accounting for 66% of the cases and decoupled clouds for 34%. Thermodynamic and dynamic evaluations show distinct differences between coupled and decoupled clouds. Coupled clouds are characterized by humid environments, in which vertical motions connect the surface and lower atmosphere to the cloud base, conditions that favor the formation of boundary layer clouds. Decoupled clouds prefer to occur in a drier and colder environment with vertical motions inside the boundary layer being detached from the cloud base, under which boundary layer clouds are hard to form. Coupled clouds peak during warmer hours and seasons, and vice versa for decoupled clouds. This study underscores the complexity of cloud‐land‐surface interactions and paves the way for further investigations into cloud formation and evolution under different atmospheric environments.

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.

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 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 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 minutes, 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.

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 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 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 minutes, 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.

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 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 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 minutes, 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.

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 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 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 minutes, 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.