Patrick A. Selmer

This study presents a feature-scale cross-validation of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) cloud–aerosol discrimination (CAD) products using 9289 globally collocated Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations acquired between October 2018 and June 2023. The operational ATL09 and a U-Net convolutional neural network (CNN) products are evaluated against CALIPSO on a pixel-by-pixel basis using class-specific metrics. Agreement improves with decreasing along-track comparison-window width, which limits spatial divergence from the ground-track crossing, whereas agreement varies weakly and non-monotonically across the actual 0–10 min inter-satellite time separation. Agreement also depends strongly on the CALIPSO integration size used for layer detection, which affects both feature detectability and effective spatial resolution. Restricting the analysis to ±60° latitude markedly increases aerosol agreement, consistent with reduced influence from high-latitude ambiguities associated with blowing snow, diamond dust, and larger crossing angles, while cloud metrics change more modestly. At a 1-s comparison width and 5-km maximum CALIPSO integration scale, CNN–CALIPSO F1 exceeds ATL09–CALIPSO F1 by 0.027–0.045 globally, with all paired 95% confidence intervals above zero across cloud and aerosol classes under daytime and nighttime conditions; however, some differences within ±60° are not distinguishable from zero. These results show that agreement is governed not only by retrieval algorithms but also by differences in instrument characteristics, effective spatial resolution, and sampling strategy. The proposed collocation framework provides a basis for evaluating current and future machine-learning approaches for spaceborne lidar CAD.

Laser-based remote sensing (lidar) is a proven technique for detecting atmospheric features such as clouds and aerosols as well as for determining their vertical distribution with high accuracy. Even simple elastic backscatter lidars can distinguish clouds from aerosols, and accurate knowledge of their vertical location is essential for air quality assessment, hazard avoidance, and operational decision-making. However, daytime lidar measurements suffer from reduced signal-to-noise ratio (SNR) due to solar background contamination. Conventional processing approaches mitigate this by applying horizontal and vertical averaging, which improves SNR at the expense of spatial resolution and feature detectability. This work presents a deep learning-based framework that enhances lidar SNR at native resolution and performs fast layer detection and cloud–aerosol discrimination. We apply this approach to ICESat-2 532 nm photon-counting data, using artificially noised nighttime profiles to generate simulated daytime observations for training and evaluation. Relative to the simulated daytime data, our method improves peak SNR by more than a factor of three while preserving structural similarity with true nighttime profiles. After recalibration, the denoised photon counts yield an order-of-magnitude reduction in mean absolute percentage error in calibrated attenuated backscatter compared with the simulated daytime data, when validated against real nighttime measurements. We further apply the trained model to a full month of real daytime ICESat-2 observations (April 2023) and demonstrate effective layer detection and cloud–aerosol discrimination, maintaining high recall for both clouds and aerosols and showing qualitative improvement relative to the standard ATL09 data products. As an alternative to traditional averaging-based workflows, this deep learning approach offers accurate, near real-time data processing at native resolution. A key implication is the potential to enable smaller, lower-power spaceborne lidar systems that perform as well as larger instruments.

Ice‐ and mixed‐phase clouds play an important role in the global radiative budget and hydrological cycle, yet the complexity of ice crystal shapes and the presence of supercooled liquid water (SCLW) present challenges for retrieving cloud properties from airborne and spaceborne remote sensing instruments. Airborne lidar measurements of the backscatter coefficient ( β ), color ratio ( χ ), and volume depolarization ratio ( δ ) provide additional spatial context for the cloud phase and to some extent the particle shapes both vertically and horizontally through clouds. Coordination between a NASA P‐3 and ER‐2 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast‐Threatening Snowstorms field campaign provided 3.3 hr of Cloud Physics Lidar observations from 16 events to be spatially related to the particle size and morphological properties from the Cloud Particle Imager, in addition to temperature and SCLW measurements obtained by the P‐3 aircraft. After the lidar data were matched to the P‐3 location, in situ microphysics data were mapped in β‐δ and triple‐wavelength frameworks involving χ . Compared to SCLW regions, regions dominated by ice typically exhibited lower β (−2 km ⁻¹ sr ⁻¹ ), lower 532/355‐nm χ (0.2) and were associated with particle sizes that were on average 105% larger and area ratios that were 40% lower. The relationships between cloud properties and lidar measurements established in this study have implications for future cloud phase and particle habit algorithms using airborne and spaceborne lidar data.

Space-based, photon-counting lidar instruments are effective tools for observing cloud and aerosol layers in the atmosphere. Cloud phases and several different kinds of aerosols are presently identified and typed using sophisticated, fine-tuned classification algorithms that operate using processed lidar data. We present a deep neural network semantic segmentation model that was trained using raw, uncalibrated photon count data and data products from the Cloud/Aerosol Transport System’s (CATS) 1064 nm lidar. Our approach successfully types layers in complex scenes using only raw photon counts, bin altitudes, and ground surface type at 14 to 171 times the spatial resolution of the CATS operational data product. We observe comparable cloud detection and phase determination to the CATS operational algorithm while also exhibiting a 15-point improvement in finding tenuous aerosol layers. Because the model is lightweight, does not rely upon ancillary information, and is optimized to leverage GPU computing, it has the potential to be deployed on-instrument to perform cloud and aerosol typing in real time.

This study presents a global aerosol climatology derived from six years (October 2018–October 2024) of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) observations, using a U-Net Convolutional Neural Network (CNN) machine learning algorithm for Cloud–Aerosol Discrimination (CAD). Despite ICESat-2’s design primarily as an altimetry mission with a single-wavelength, low-power, high-repetition-rate laser, ICESat-2 effectively captures global aerosol distribution patterns and can provide valuable insights to bridge the observational gap between the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) missions to support future spaceborne lidar mission design. The machine learning approach outperforms traditional thresholding methods, particularly in complex conditions of cloud embedded in aerosol, owing to a finer spatiotemporal resolution. Our results show that annually, between 60°S and 60°N, 78.4%, 17.0%, and 4.5% of aerosols are located within the 0–2 km, 2–4 km, and 4–6 km altitude ranges, respectively. Regional analyses cover the Arabian Sea (ARS), Arabian Peninsula (ARP), South Asia (SAS), East Asia (EAS), Southeast Asia (SEA), the Americas, and tropical oceans. Vertical aerosol structures reveal strong trans-Atlantic dust transport from the Sahara in summer and biomass burning smoke transport from the Savanna during dry seasons. Marine aerosol belts are most prominent in the tropics, contrasting with earlier reports of the Southern Ocean maxima. This work highlights the importance of vertical aerosol distributions needed for more accurate quantification of the aerosol–cloud interaction influence on radiative forcing for improving global climate models.

The Planetary Boundary Layer Height (PBLH) significantly impacts weather, climate, and air quality. Understanding the global diurnal variation of the PBLH is particularly challenging due to the necessity of extensive observations and suitable retrieval algorithms that can adapt to diverse thermodynamic and dynamic conditions. This study utilized data from the Cloud-Aerosol Transport System (CATS) to analyze the diurnal variation of PBLH in both continental and marine regions. By leveraging CATS data and a modified version of the Different Thermo-Dynamics Stability (DTDS) algorithm, along with machine learning denoising, the study determined the diurnal variation of the PBLH in continental mid-latitude and marine regions. The CATS DTDS-PBLH closely matches ground-based lidar and radiosonde measurements at the continental sites, with correlation coefficients above 0.6 and well-aligned diurnal variability, although slightly overestimated at nighttime. In contrast, PBLH at the marine site was consistently overestimated due to the viewing geometry of CATS and complex cloud structures. The study emphasizes the importance of integrating meteorological data with lidar signals for accurate and robust PBLH estimations, which are essential for effective boundary layer assessment from satellite observations.

NASA’s Ice, Cloud, and land Elevation Satellite (ICESat-2), designed for surface altimetry, plays a pivotal role in providing precise ice sheet elevation measurements. While its primary focus is altimetry, ICESat-2 also offers valuable atmospheric data. Current conventional processing methods for producing atmospheric data products encounter challenges, particularly in conditions with low signal or high background noise. The thresholding technique traditionally used for atmospheric feature detection in lidar data uses a threshold value to accept signals while rejecting noise, which may result in signal loss or false detection in the presence of excessive noise. Traditional approaches for improving feature detection, such as averaging, lead to a trade-off between detection resolution and accuracy. In addition, the discrimination of cloud from aerosol in the identified features is difficult given ICESat-2’s single wavelength and lack of depolarization measurement capability. To address these challenges, we demonstrate atmospheric feature detection and cloud–aerosol discrimination using deep learning-based semantic segmentation by a convolutional neural network (CNN). The key findings from our research are the effectiveness of a deep learning model for feature detection and cloud–aerosol classification in ICESat-2 atmospheric data and the model’s surprising capability to detect complex atmospheric features at a finer resolution than is currently possible with traditional processing techniques. We identify several examples where the traditional feature detection and cloud–aerosol discrimination algorithms struggle, like in scenarios with several layers of vertically stacked clouds, or in the presence of clouds embedded within aerosol, and demonstrate the ability of the CNN model to detect such features, resolving the boundaries between adjacent layers and detecting clouds hidden within aerosol layers at a fine resolution.

Concentrations of particulate aerosols and their vertical placement in the atmosphere determine their interaction with the Earth system and their impact on air quality. Space-based lidar, such as the Cloud–Aerosol Transport System (CATS) technology demonstration instrument, is well-suited for determining the vertical structure of these aerosols and their diurnal cycle. Through the implementation of aerosol-typing algorithms, vertical layers of aerosols are assigned a type, such as marine, dust, and smoke, and a corresponding extinction-to-backscatter (lidar) ratio. With updates to the previous aerosol-typing algorithms, we find that CATS, even as a technology demonstration, observed the documented seasonal cycle of aerosols, comparing favorably with the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) space-based lidar and the NASA Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) model reanalysis. By leveraging the unique orbit of the International Space Station, we find that CATS can additionally resolve the diurnal cycle of aerosol altitude as observed by ground-based instruments over the Maritime Continent of Southeast Asia.