This paper presents the motivation for, and initial results from, the Multiple Altimeter Beam Experimental lidar (MABEL) instrument. The MABEL instrument provides a new capability for airborne altimetry measurements and serves as a prototype and simulator for the upcoming NASA second-generation Ice, Cloud, and Land Elevation Satellite (ICESat-2) mission. Designed to be highly flexible in measurement capability, MABEL serves as both a demonstration of measurement capability and a science tool for cryospheric and biospheric remote sensing. It is important to document the instrument specifications and essential background information to provide a suitable reference for the detailed MABEL results and science investigation publications that will be forthcoming.

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.

The ACT‐America project is a NASA Earth Venture Suborbital‐2 mission designed to study the transport and fluxes of greenhouse gases. The open and freely available ACT‐America data sets provide airborne in situ measurements of atmospheric carbon dioxide, methane, trace gases, aerosols, clouds, and meteorological properties, airborne remote sensing measurements of aerosol backscatter, atmospheric boundary layer height and columnar content of atmospheric carbon dioxide, tower‐based measurements, and modeled atmospheric mole fractions and regional carbon fluxes of greenhouse gases over the Central and Eastern United States. We conducted 121 research flights during five campaigns in four seasons during 2016–2019 over three regions of the US (Mid‐Atlantic, Midwest and South) using two NASA research aircraft (B‐200 and C‐130). We performed three flight patterns (fair weather, frontal crossings, and OCO‐2 underflights) and collected more than 1,140 h of airborne measurements via level‐leg flights in the atmospheric boundary layer, lower, and upper free troposphere and vertical profiles spanning these altitudes. We also merged various airborne in situ measurements onto a common standard sampling interval, which brings coherence to the data, creates geolocated data products, and makes it much easier for the users to perform holistic analysis of the ACT‐America data products. Here, we report on detailed information of data sets collected, the workflow for data sets including storage and processing of the quality controlled and quality assured harmonized observations, and their archival and formatting for users. Finally, we provide some important information on the dissemination of data products including metadata and highlights of applications of ACT‐America data sets.

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.