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Thomas F. Lee
,
Richard L. Bankert
, and
Cristian Mitrescu

NASA A-Train vertical profilers provide detailed observations of atmospheric features not seen in traditional imagery from other weather satellite data. CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) profiles vividly depict the vertical dimension of otherwise two-dimensional features shown in mapped products. However, most forecasters have never seen these profiles and do not appreciate their capacity to convey fundamental information about cloud and precipitation systems. Here, these profiles are accompanied by weather satellite images and explained in the context of various meteorological regimes. Profile examples are shown over frontal systems, marine stratocumulus, orographic barriers, tropical cyclones, and a severe thunderstorm.

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Jeffrey D. Hawkins
,
Jeremy E. Solbrig
,
Steven D. Miller
,
Melinda Surratt
,
Thomas F. Lee
,
Richard L. Bankert
, and
Kim Richardson

Abstract

Global monitoring of tropical cyclones (TC) is enhanced by the unique capabilities provided by the day–night band (DNB), a sensor included on the Visible Infrared Imaging Radiometer Suite (VIIRS) flying on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite. The DNB, a low-light visible–near-infrared-band passive radiometer, can leverage unconventional (i.e., nonsolar) sources of visible light illumination such as moonlight to infer storm structure at night. The DNB provides an unprecedented capability to resolve moonlit clouds at high resolution, offering numerous potential benefits to both operational TC analysts and researchers developing new methods of monitoring TCs occurring within the largely data-void tropical oceanic basins. DNB digital data provide significant enhancements over older nighttime visible data from the Defense Meteorological Satellite Program’s (DMSP) Operational Linescan System (OLS) by leveraging accurate calibration, high sensitivity, and sub-kilometer-scale imagery that covers 2–3 times the moon’s lunar cycle than the OLS. By leveraging these attributes, DNB data can enable the use of automated objective applications instead of subjective image interpretation. Here, the authors detail ways in which critical information about TC structure, location, intensity changes, shear environment, lightning, and other characteristics can be extracted when the DNB data are used in isolation or in a multichannel approach with coincident infrared (IR) channels.

Open access
Arunas Kuciauskas
,
Jeremy Solbrig
,
Tom Lee
,
Jeff Hawkins
,
Steven Miller
,
Mindy Surratt
,
Kim Richardson
,
Richard Bankert
, and
John Kent
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