Improved slicing method for GOSAT cloud detection using TIR spectra

Abstract:

Greenhouse gas observing SATellite (GOSAT) is a polar orbiting satellite for greenhouse gas observations and the main sensor, TANSO-FTS, has three near-infrared bands and a thermal infrared (TIR) band which covers a wide spectral region of 5.56 – 14.3 mm with resolution of 0.2 cm^-1. The algorithm used in this study was based on a cirrus detection technique called CO₂ slicing method, modified as described below. The weighting functions which represent sensitivity profiles were calculated at each channel in the TIR band of GOSAT. The channels were reconstructed as sets of several spectral channels for each height level based on the peak heights of the weighting functions. Subsequently, the channel combinations were optimized based on simulation studies for several temperature profile patterns for each latitude and temperature at 500 hPa. The observed data in 2010 were analyzed using these optimized channels. Global cloud amounts and their seasonal variations were validated using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data. Results show some differences of cloud amounts and heights between GOSAT and CALIPSO, which might be caused by surface temperature biases, the inverse layer, the difference of sensitivity of sensors, and marine stratocumulus clouds. However, the characteristics of horizontal distributions derived using the slicing method were generally agreed with those obtained from CALIPSO and reported in previous researches. In addition, comparison of cloud top height using collocated data showed good agreement and clouds with optical thickness less than 0.1 are detectable using this method.