The Cloud‐Aerosol Transport System (CATS) is an elastic backscatter lidar that was launched on 10 January 2015 to the International Space Station (ISS). CATS provides both space‐based technology demonstrations for future Earth Science missions and operational science measurements. This paper outlines the CATS Level 1 data products and processing algorithms. Initial results and validation data demonstrate the ability to accurately detect optically thin atmospheric layers with 1064 nm nighttime backscatter as low as 5.0E−5 km⁻¹ sr⁻¹. This sensitivity, along with the orbital characteristics of the ISS, enables the use of CATS data for cloud and aerosol climate studies. The near‐real‐time downlinking and processing of CATS data are unprecedented capabilities and provide data that have applications such as forecasting of volcanic plume transport for aviation safety and aerosol vertical structure that will improve air quality health alerts globally.

Accurate knowledge of cloud optical properties, such as extinction-to-backscatter ratio and depolarization ratio, can have a significant impact on the quality of cloud extinction retrievals from lidar systems because parameterizations of these variables are often used in nonideal conditions to determine cloud phase and optical depth. Statistics and trends of these optical parameters are analyzed for 4 yr (2003–07) of cloud physics lidar data during five projects that occurred in varying geographic locations and meteorological seasons. Extinction-to-backscatter ratios (also called lidar ratios) are derived at 532 nm by calculating the transmission loss through the cloud layer and then applying it to the attenuated backscatter profile in the layer, while volume depolarization ratios are computed using the ratio of the parallel and perpendicular polarized 1064-nm channels. The majority of the cloud layers yields a lidar ratio between 10 and 40 sr, with the lidar ratio frequency distribution centered at 25 sr for ice clouds and 16 sr for altocumulus clouds. On average, for ice clouds the lidar ratio slightly decreases with decreasing temperature, while the volume depolarization ratio increases significantly as temperatures decrease. Trends for liquid water clouds (altocumulus clouds) are also observed. Ultimately, these observed trends in optical properties, as functions of temperature and geographic location, should help to improve current parameterizations of extinction-to-backscatter ratio, which in turn should yield increased accuracy in cloud optical depth and radiative forcing estimates.

From June to October, low‐level clouds in the southeast (SE) Atlantic often underlie seasonal aerosol layers transported from African continent. Previously, the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) 532 nm lidar observations have been used to estimate the relative vertical location of the above‐cloud aerosols (ACA) to the underlying clouds. Here we show new observations from NASA's Cloud‐Aerosol Transport System (CATS) lidar. Two seasons of CATS 1064 nm observations reveal that the bottom of the ACA layer is much lower than previously estimated based on CALIPSO 532 nm observations. For about 60% of CATS nighttime ACA scenes, the aerosol layer base is within 360 m distance to the top of the underlying cloud. Our results are important for future studies of the microphysical indirect and semidirect effects of ACA in the SE Atlantic region.

Smoke particles can be injected by pyrocumulonimbus (pyroCb) in the upper troposphere and lower stratosphere, but their effects on the radiative budget of the planet remain elusive. Here, by focusing on the record‐setting Pacific Northwest pyroCb event of August 2017, we show with satellite‐based estimates of pyroCb emissions and injection heights in a chemical transport model (GEOS‐Chem) that pyroCb smoke particles can result in radiative forcing of ∼0.02 W/m² at the top of the atmosphere averaged globally in the 2 months following the event and up to 0.9 K/day heating in the Arctic upper troposphere and lower stratosphere. The modeled aerosol distributions agree with observations from satellites (Earth Polychromatic Imaging Camera [EPIC], Cloud‐Aerosol Transport System [CATS], and Cloud‐Aerosol Lidar with Orthogonal Polarization [CALIOP]), showing the hemispheric transport of pyroCb smoke aerosols with a lifetime of 5 months. Hence, warming by pyroCb aerosols can have similar temporal duration but opposite sign to the well‐documented cooling of volcanic aerosols and be significant for climate prediction.

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.