Abstract
Abstract. An algorithm based on CO2 slicing, which has been used for cirrus cloud detection using thermal infrared data, was developed for high-resolution radiance spectra from satellites. The channels were reconstructed based on sensitivity height information of the original spectral channels to reduce the effects of measurement errors. Selection of the reconstructed channel pairs was optimized for several atmospheric profile patterns using simultaneous studies assuming a cloudy sky. That algorithm was applied to data by the Greenhouse gases Observing SATellite (GOSAT). Results were compared with those obtained from the space-borne lidar instrument on-board Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Monthly mean cloud amounts from the slicing generally agreed with those from CALIPSO observations despite some differences caused by surface temperature biases, optically very thin cirrus, multilayer structures of clouds, extremely low cloud tops, and specific atmospheric conditions. Comparison of coincident data showed good agreement, except for some cases, and revealed that the improved slicing method is more accurate than the traditional slicing method. Results also imply that improved slicing can detect low-level clouds with cloud top heights as low as approximately 1.5 km.
Highlights
Global warming is well known to have been caused by increasing greenhouse gas (GHG) emissions since the Industrial Revolution in the eighteenth century
A cloud detection algorithm based on a cirrus detection technique, the CO2 slicing method, was developed for highresolution thermal infrared (TIR) spectral data with channel reconstruction and channel optimization
For gases Observing SATellite (GOSAT) data analysis, optimal pairs of pseudo-channels were chosen from the indicators for each observation
Summary
Global warming is well known to have been caused by increasing greenhouse gas (GHG) emissions since the Industrial Revolution in the eighteenth century. Current cloud retrieval techniques used with TIR data from GOSAT discriminate clouds from the surface using brightness temperature contrast at the atmospheric window region near 10 μm (Imasu et al, 2010) This technique, called TIR threshold technique here, detects optically thin clouds or partly existing clouds in the IFOV only to a slight degree. This improved algorithm was applied to TIR spectra from GOSAT in Sect.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
