Detection and Height Measurement of Tenuous Clouds and Blowing Snow in ICESat‐2 ATLAS Data. Issue 17 (6th September 2021)

• Record Type: Journal Article
• Authors: Herzfeld, Ute; Hayes, Adam; Palm, Stephen; Hancock, David; Vaughan, Mark; Barbieri, Kristine
• Is Part Of: Geophysical research letters. Volume 48:Issue 17(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-06
• Subjects: ICESat‐2 -- atmosphere -- algorithm -- climate -- tenuous clouds -- blowing snow; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2021GL093473
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: Tenuous atmospheric layers, such as high, thin cirrus clouds and blowing snow over Antarctica, play an important role in the climate system, affecting energy fluxes between the Earth and the atmosphere. Our knowledge of the structure of the atmosphere is largely derived from atmospheric satellite measurements. Yet tenuous layers can be hard to detect in satellite lidar data, especially in daylight data characterized by high solar background. In this study, we introduce an approach to detect tenuous atmospheric layers in the atmospheric lidar measurements of NASA's ICESat‐2. The density‐dimension algorithm for ICESat‐2 atmospheric data (DDA‐atmos) identifies atmospheric layers while automatically adapting to different background conditions of night, twilight, and daylight data. This capability, demonstrated for tenuous clouds and blowing snow, offers a data‐based solution to an important climate modeling problem. Atmospheric layer boundaries, detection confidence, and density fields resultant from the DDA‐atmos are reported in ICESat‐2 atmospheric data product ATL09. Plain Language Summary: Tenuous atmospheric layers, such as high, thin cirrus clouds and blowing snow over Antarctica, play an important role in the climate system, because they affect energy fluxes between the Earth and the atmosphere. Our knowledge of the structure of the atmosphere is largely derived from atmospheric satellite measurements. Yet tenuous layers can be hard to detect in satellite lidar data, especially in daylight data characterized by high solar background, which results in high noise. In this study, we introduce an approach to detect tenuous atmospheric layers in the atmospheric lidar measurements of NASA's Ice, Cloud and land Elevation Satellite ICESat‐2. To this end, we develop a mathematical algorithm, the density‐dimension algorithm for ICESat‐2 atmospheric data, that identifies atmospheric layers while automatically adapting to different background conditions of night, twilight, and daylight data. The resultant capability to detect thin clouds and blowing snow in ICESat‐2 data offers a data‐based solution to an important climate modeling problem. The results are publicly available on the ICESat‐2 atmospheric data product ATL09. Key Points: The atmospheric channel of ICESat‐2 measures tenuous clouds and blowing snow The density‐dimension algorithm for atmospheric data detects tenuous layers while automatically adapting to night and daylight conditions This capability offers a data‐based solution to including tenuous clouds in atmospheric climate models