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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. The selections of the reconstructed channel pairs were optimized for several atmospheric profile patterns using simultaneous studies assuming cloudy sky. That algorithm was applied to data by the Greenhouse gases Observing SATellite (GOSAT). The results were compared with those obtained from space-borne lidar instrument onboard 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 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.

The Greenhouse Gases Observing Satellite (GOSAT) was launched on January 23, 2009, to monitor global atmospheric levels of CO2 and CH4 from space. GOSAT started initial operation of its instruments after an initial satellite system check. Although the radiant data obtained by the GOSAT instruments are currently in the preliminary stages of calibration and validation, the spectral absorption features of CO2 and CH4 are clearly identifiable. An initial retrieval of these gaseous concentrations was performed for measurement scenes of cloud-free conditions over land. These results showed that column-averaged dry air mole fractions of both CO2 and CH4 in the northern hemisphere were higher than those in the southern hemisphere. These latitudinal differences agree with data obtained from ground-based sources and other satellite observations; however, the absolute values of the gaseous concentrations from GOSAT data seem to have been underestimated. Calibrations as well as validation should be conducted to improve the quality of GOSAT retrievals.

[1] The Greenhouse Gases Observing Satellite (GOSAT) was successfully launched in January 2009, with the aim of providing global observations of greenhouse gases. We developed an algorithm to retrieve CO2 vertical profiles from the terrestrial radiation spectra at 700–800 cm−1 and assessed its validity. For this purpose, we first computed GOSAT pseudomeasurement spectra and then performed CO2 retrieval simulations using the maximum a posteriori (MAP) method, with analytical data for temperature information. Our simulations with no uncertainty in the estimates of atmospheric conditions such as surface temperature, surface emissivity, and profiles of temperature, water vapor, and ozone showed that the retrieved CO2 profiles had an accuracy of 1% above 800 hPa, with little dependence on the a priori profiles. Introducing correlations between layers in an a priori error covariance matrix was important for CO2 retrieval especially above 200 hPa. Enhancing the correlations below 800 hPa was important for CO2 retrieval there. Selecting 100 channels based on CO2 information content for all layers, 10 channels for the region above 55 hPa, and 50 channels for the region below 800 hPa was sufficient to achieve CO2 retrieval with 1% accuracy from the troposphere through the stratosphere. Our simulations with possible errors in the atmospheric conditions showed that 1% accuracy was also achieved at 600–100 hPa in every latitude region, although the retrieved CO2 concentrations probably included up to 4% positive and negative biases at 30°S–30°N above 100 hPa and at mid- and high latitudes below 600 hPa, respectively.

Analytical procedure to derive the ozone concentration profile from the analysis of the high-resolution spectrum data as observed by a space FTS sensor such as Interferometric Monitor for Greenhouse gases (IMG) onboard Advanced Earth Observing Satellite (ADEOS) has been presented. In the procedure, Jacobian matrices were re-calculated for each spectrum considering the cloud top temperature in the instantaneous field of view (IFOV) of the FTS sensor. That was because these cloud parameters seriously affect the elements of the Jacobian matrices sometimes causing the change of the sign of the elements. This method has been applied to the analysis of IMG data that were observed during the northern hemispheric ozone hole like event occurred in 1997 spring. It has been found that the usage of the precise instrumental line shape (ILS) of the sensor is very important to improve the vertical resolution of retrieval by loosening the retrieval constrains.