Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, (1) increased the 1064 nm SNR by 75%, (2) increased the number of layers detected (any resolution) by 30%, and (3) enabled detection of 40% more atmospheric features during daytime operations at a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS operational data products. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime.

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.

Space-based atmospheric backscatter lidars provide critical information about the vertical distribution of clouds and aerosols, thereby improving our understanding of the climate system. They are additionally useful for detecting hazards to aviation and human health, such as volcanic plumes and man-made pollution events. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP, 2006–2023), Cloud-Aerosol Transport System (CATS, 2015–2017), and Advanced Topographic Laser Altimeter System (ATLAS 2018–present) are three such lidars that operated within the past 20 years. The signal-to-noise ratio (SNR) for these lidars is significantly lower in daytime data compared with nighttime data due to the solar background signal increasing the detector response noise. Averaging horizontally across profiles has been the standard way to increase SNR, but this comes at the expense of resolution. Modern, deep learning-based denoising algorithms can be applied to improve the SNR without coarsening resolution. This paper describes how one such model architecture, Dense Dense U-Net (DDUNet), was trained to denoise CATS 1064 nm raw signal data (photon counts) using artificially noised nighttime data. Simulated CATS daytime 1064 nm data were then created to assess the model’s performance. The denoised simulated data increased the daytime SNR by a factor of 2.5 (on average) and decreased minimum detectable backscatter (MDB) to ~$7.3\times {10}^{-4}$ km⁻¹sr⁻¹, which is lower than the CALIOP 1064 nm night MDB value of $8.6\times {10}^{-4}$ km⁻¹sr⁻¹. Layer detection was performed on simulated 2 km horizontal resolution denoised and 60 km averaged data. Despite the finer resolution input, the denoised layers had more true positives, fewer false positives, and an overall Jaccard Index of 0.54 versus 0.44 when compared to the layers detected on averaged data. Layer detection was also performed on a full month of denoised daytime CATS data (Aug. 2015) to detect layers for comparison with CATS standard Level 2 (L2) product layers. The detection on the denoised data yielded 2.33 times more, higher-quality bins within detected layers at 2.7–33 times finer resolution than the CATS L2 products.

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.

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.

Biomass burning in Africa provides a prolific source of aerosols that are transported from the source region to distant areas, as far away as South America and Australia. Models have long predicted the primary outflow and transport routes. Over time, field studies have validated the basic production and dynamics that underlie these transport patterns. In more recent years, the advancement of spaceborne active remote-sensing techniques has allowed for more detailed verification of the models and, importantly, verification of the vertical distribution of the aerosols in the transport regions, particularly with respect to westerly transport over the Atlantic Ocean. The Cloud-Aerosol Transport System (CATS) lidar on the International Space Station has detection sensitivity that provides observations that support long-held theories of aerosol transport from the African subcontinent over the remote Indian Ocean and as far downstream as Australia. Significance: Biomass burning in Africa can have impacts as far away as Australia. Flow of aerosols from Africa towards Australia has long been postulated by transport models, but has been poorly characterised due to a lack of measurements. The CATS instrument on the International Space Station has detection sensitivity that captures aerosol transport from Africa over the Indian Ocean to Australia. Open data set: https://cats.gsfc.nasa.gov/