The greenhouse gases observing satellite (GOSAT) was launched in 2009 to measure the global concentrations of atmospheric CO2 and CH4. GOSAT is equipped with two earth-observing instruments : the thermal and near-infrared sensor for carbon observation Fourier transform spectrometer (TANSO-FTS), and the cloud and aerosol imager (TANSO-CAI). The presence —even partial— of clouds in the instantaneous field-of-view (IFOV) of the FTS can lead to incorrect estimates of CO2 or CH4 concentrations. Thus, it is necessary that cloud-contaminated FTS data be identified and rejected. A cloud-screening algorithm (CLAUDIA : cloud and aerosol unbiased decision intellectual algorithm) was developed for the CAI to identify cloud-free FTS data (CLAUDIA-CAI). The result is publicly available as the “CAI L2 cloud flag” product. We evaluated the accuracy of the GOSAT CAI L2 cloud flag product by comparing readings from the product with visual inspections of the same CAI images in the Amazon. We found the accuracy of the algorithm to be approximately 80 %, and the accuracy of obtaining cloud-free FTS data in the Amazon to be approximately 30 %. Various tests were then performed to increase cloud-free FTS data for the GOSAT-2. One experiment narrowed the IFOV of the FTS to increase the frequency. We also evaluated how the accuracy of obtaining cloud-free FTS data changed with a narrower IFOV. The result showed that a narrower IFOV of FTS was effective in increasing not only the frequency but also the accuracy of obtaining cloud-free data. However, it could also potentially increase underestimation of CO2 concentrations by overlooking small clouds. We could not confirm the difference in the dry air column-averaged mixing ratio of CO2 in the FTS data, which were judged cloud-contamination by visual inspection of CAI L1B+ images in IFOV of FTS, depending on the clouds.
The Mw 9.0 Tohoku Earthquake that occurred on March 11, 2011, off the Pacific coast of northeastern (Tohoku) Japan caused gigantic tsunamis, resulting in widespread devastation and crustal movements. In a previous study, we proposed a method for capturing the two-dimensional (2D) surface movements from pairs of temporal intensity images, based on the high orbit accuracy of the satellite TerraSAR-X (TSX). Detecting three-dimensional (3D) displacement from a single pair of TSX images is difficult, and thus in the present study we used three pairs of TSX images taken in the ascending and descending paths to estimate 3D crustal movements. First, the 2D crustal movements due to the 2011 Tohoku earthquake were detected from three respective pairs of images. We derived the relationship between the 3D displacement and the 2D converted movement in synthetic aperture radar (SAR) images according to the observation model and acquisition conditions of the SAR sensor. We then estimated the 3D movements by combining the detected 2D movements that occurred within a short time interval. We compared the results with the GEONET observation records and found that the results and records are consistent with each other.
To clarify the characteristics of various global sea surface wind speed (SSW) products, we evaluated SSW data in the products by using those observed by 103 moored buoys. From this evaluation, we found that the SSW data of Quick Scatterometer (QuikSCAT) without using rain flag and Japanese Ocean Flux Data sets with Use of Remote Sensing Observations version 2 (J-OFURO2) have relatively large positive bias compared with other products. Because the Cross-Calibrated Multi-Platform (CCMP) has the smallest root mean square (RMS) error and the largest correlation coefficient in this evaluation, we concluded that the CCMP gives the best performance. Thus, we carried out inter-comparisons between the CCMP and each of the remaining 14 products. SSW data of QuikSCAT and J-OFURO2 have large positive mean differences for the CCMP SSW data, and local maxima of RMS differences can be found over the inter-tropical convergence zone (ITCZ) and the South Pacific convergence zone (SPCZ). Moreover, we investigated the cause of overestimation by QuikSCAT and found the overestimation to be caused by rain contamination included in the QuikSCAT SSW data. However, if we carry out quality control for the QuikSCAT SSW data by using rain flag information, not only positive bias but also RMS error for QuikSCAT is effectively reduced. Therefore, the QuikSCAT SSW data without rain-contaminated data have relatively good accuracy. In addition, the CCMP properly represents an orographic effect in the coastal regions because of inclusion in the model-derived product, and the CCMP has the best accuracy in the coastal regions as well as in the open ocean.
Volcanic ash particles originating from the eruptions of Mt. Sakurajima (31.59°N, 130.66°E) were observed with Mie lidar at an altitude of 1.6-2.3 km over Saga (33.24°N, 130.29°E) on 21 and 22 August, 2013. The lidar data showed a high depolarization ratio (10-15 %) and a moderately low backscatter wavelength exponent (0.6-0.7), indicating the presence of supermicrometer-sized nonspherical particles. The aerosol optical thicknesses at 500 nm derived from the skyradiometer were 0.12 on 21 August (13 : 50 LT) and 0.40 on 22 August (12 : 50 LT). The Ångström exponent was 0.16-0.49 and the single scattering albedo was 0.73-0.91, indicating the predominance of supermicrometer-sized and moderately absorbing particles.