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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.

During the 2012 deployment of the NASA Hurricane and Severe Storm Sentinel (HS3) field campaign, several flights were dedicated to investigating Hurricane Nadine. Hurricane Nadine developed in close proximity to the dust-laden Saharan air layer and is the fourth-longest-lived Atlantic hurricane on record, experiencing two strengthening and weakening periods during its 22-day total life cycle as a tropical cyclone. In this study, the NASA GEOS-5 atmospheric general circulation model and data assimilation system was used to simulate the impacts of dust during the first intensification and weakening phases of Hurricane Nadine using a series of GEOS-5 forecasts initialized during Nadine’s intensification phase (12 September 2012). The forecasts explore a hierarchy of aerosol interactions within the model: no aerosol interaction, aerosol–radiation interactions, and aerosol–radiation and aerosol–cloud interactions simultaneously, as well as variations in assumed dust optical properties. When only aerosol–radiation interactions are included, Nadine’s track exhibits sensitivity to dust shortwave absorption, as a more absorbing dust introduces a shortwave temperature perturbation that impacts Nadine’s structure and steering flow, leading to a northward track divergence after 5 days of simulation time. When aerosol–cloud interactions are added, the track exhibits little sensitivity to dust optical properties. This result is attributed to enhanced longwave atmospheric cooling from clouds that counters shortwave atmospheric warming by dust surrounding Nadine, suggesting that aerosol–cloud interactions are a more significant influence on Nadine’s track than aerosol–radiation interactions. These findings demonstrate that tropical systems, specifically their track, can be impacted by dust interaction with the atmosphere.