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Abstract The detection of multilayer clouds in the atmosphere can be particularly challenging from passive visible and infrared imaging radiometers since cloud boundary information is limited primarily to the topmost cloud layer. Yet detection of low clouds in the atmosphere is important for a number of applications, including aviation nowcasting and general weather forecasting. In this work, we develop pixel-based machine learning–based methods of detecting low clouds, with a focus on improving detection in multilayer cloud situations and specific attention given to improving the Cloud Cover Layers (CCL) product, which assigns cloudiness in a scene into vertical bins. The random forest (RF) and neural network (NN) implementations use inputs from a variety of sources, including GOES Advanced Baseline Imager (ABI) visible radiances, infrared brightness temperatures, auxiliary information about the underlying surface, and relative humidity (which holds some utility as a cloud proxy). Training and independent validation enlists near-global, actively sensed cloud boundaries from the radar and lidar systems on board theCloudSatandCALIPSOsatellites. We find that the RF and NN models have similar performances. The probability of detection (PoD) of low cloud increases from 0.685 to 0.815 when using the RF technique instead of the CCL methodology, while the false alarm ratio decreases. The improved PoD of low cloud is particularly notable for scenes that appear to be cirrus from an ABI perspective, increasing from 0.183 to 0.686. Various extensions of the model are discussed, including a nighttime-only algorithm and expansion to other satellite sensors. Significance StatementUsing satellites to detect the heights of clouds in the atmosphere is important for a variety of weather applications, including aviation weather forecasting. However, detecting low clouds can be challenging if there are other clouds above them. To address this, we have developed machine learning–based models that can be used with passive satellite instruments. These models use satellite observations at visible and infrared wavelengths, an estimate of relative humidity in the atmosphere, and geographic and surface-type information to predict whether low clouds are present. Our results show that these models have significant skill at predicting low clouds, even in the presence of higher cloud layers. 

Atmospheric gravity waves (AGWs) are among the important energy and momentum transfer mechanisms from the troposphere to the middle and upper atmosphere. Despite their understood importance in governing the structure and dynamics of these regions, mesospheric AGWs remain poorly measured globally, and largely unconstrained in numerical models. Since late 2011, the Suomi National Polar-orbiting Partnership (NPP) Visible/Infrared Imaging Radiometer Suite (VIIRS) day–night band (DNB) has observed global AGWs near the mesopause by virtue of its sensitivity to weak emissions of the OH* Meinel bands. The wave features, detectable at 0.75 km spatial resolution across its 3000 km imagery swath, are often confused by the upwelling emission of city lights and clouds reflecting downwelling nightglow. The Ionosphere, Mesosphere, upper Atmosphere and Plasmasphere (IMAP)/ Visible and near-Infrared Spectral Imager (VISI) O2 band, an independent measure of the AGW structures in nightglow based on the International Space Station (ISS) during 2012–2015, contains much less noise from the lower atmosphere. However, VISI offers much coarser resolution of 14–16 km and a narrower swath width of 600 km. Here, we present preliminary results of comparisons between VIIRS/DNB and VISI observations of AGWs, focusing on several concentric AGW events excited by the thunderstorms over Eastern Asia in August 2013. The comparisons point toward suggested improvements for future spaceborne airglow sensor designs targeting AGWs. 

Abstract During 30 September to 9 October 2016, Hurricane Matthew traversed the Caribbean Sea to the east coast of the United States. During its period of greatest intensity, in the central Caribbean, Matthew excited a large number of concentric gravity waves (GWs or CGWs). In this paper, we report on hurricane‐generated CGWs observed in both the stratosphere and mesosphere from spaceborne satellites and in the ionosphere by ground Global Positioning System receivers. We found CGWs with horizontal wavelengths of ~200–300 km in the stratosphere (height of ~30–40 km) and in the airglow layer of the mesopause (height of ~85–90 km), and we found concentric traveling ionospheric disturbances (TIDs or CTIDs) with horizontal wavelengths of ~250–350 km in the ionosphere (height of ~100–400 km). The observed TIDs lasted for more than several hours on 1, 2, and 7 October 2016. We also briefly discuss the vertical and horizontal propagation of the Hurricane Matthew‐induced GWs and TIDs. This study shows that Hurricane Matthew induced significant dynamical coupling between the troposphere and the entire middle and upper atmosphere via GWs. It is the first comprehensive satellite analysis of gravity wave propagation generated by hurricane event from the troposphere through the stratosphere and mesosphere into the ionosphere.