ALBERT

Description: Ground based in-situ measurements of carbon dioxide (CO2) and methane (CH4) at the dry lakebed at Railroad Valley (RRV) playa, Nevada, USA (38°30.234′ N, 115°41.604′ W, elevation 1437 m) were conducted over a five day period from 20–25 June 2010. The playa is a flat, desert site with virtually no vegetation, an overall size of 15 km × 15 km and is approximately 110 km south-west of the nearest city, Ely (elevation 1962 m, inhabitants 4000). The measurements were taken in support of the vicarious calibration experiment to validate column-averaged dry air mole fractions of CO2 and CH4 (XCO2 and XCH4) retrieved from the Greenhouse Gases Observing Satellite (GOSAT) which was launched in January 2009. This work reports on ground-based in-situ measurements of CO2 and CH4 from RRV playa and describes comparisons made between in-situ data and XCO2 and XCH4 from GOSAT.

Description: Ground based in-situ measurements of carbon dioxide (CO2) and methane (CH4) at the dry lakebed at Railroad Valley (RRV) playa, Nevada, USA (38°30.234′ N, 115°41.604′ W, elevation 1437 m) were conducted over a five day period from 20–25 June 2010. The playa is a flat, desert site with virtually no vegetation, an overall size of 15 km × 15 km and is approximately 110 km south-west of the nearest city, Ely (elevation 1962 m, inhabitants 4000). The measurements were taken in support of the vicarious calibration experiment to validate column-averaged dry air mole fractions of CO2 and CH4 (XCO2 and XCH4) retrieved from the Greenhouse Gases Observing Satellite (GOSAT) which was launched in January 2009. This work reports on ground-based in-situ measurements of CO2 and CH4 from RRV playa and describes comparisons made between in-situ data and XCO2 and XCH4 from GOSAT.

Description: The presence of aerosol has resulted in serious limitations in the data coverage and large uncertainties in retrieving carbon dioxide (CO2) amounts from satellite measurements. For this reason, an aerosol retrieval algorithm was developed for the Thermal and Near-infrared Sensor for carbon Observation-Cloud and Aerosol Imager (TANSO-CAI) launched in January 2009 on board the Greenhouse Gases Observing Satellite (GOSAT). The algorithm retrieves aerosol optical depth (AOD), aerosol size information, and aerosol type in 0.1° grid resolution by look-up tables constructed using inversion products from Aerosol Robotic NETwork (AERONET) sun-photometer observation over Northeast Asia as a priori information. To improve the accuracy of the TANSO-CAI aerosol algorithm, we consider both seasonal and annual estimated radiometric degradation factors of TANSO-CAI in this study. Surface reflectance is determined by the same 23-path composite method of Rayleigh and gas corrected reflectance to avoid the stripes of each band. To distinguish aerosol absorptivity, reflectance difference test between ultraviolet (band 1) and visible (band 2) wavelengths depending on AODs was used. To remove clouds in aerosol retrieval, the normalized difference vegetation index and ratio of reflectance between band 2 (0.674 μm) and band 3 (0.870 μm) threshold tests have been applied. To mask turbid water over ocean, a threshold test for the estimated surface reflectance at band 2 was also introduced. The TANSO-CAI aerosol algorithm provides aerosol properties such as AOD, size information and aerosol types from June 2009 to December 2013 in this study. Here, we focused on the algorithm improvement for AOD retrievals and their validation in this study. The retrieved AODs were compared with those from AERONET and the Aqua/MODerate resolution Imaging Sensor (MODIS) Collection 6 Level 2 dataset over land and ocean. Comparisons of AODs between AERONET and TANSO-CAI over Northeast Asia showed good agreement with correlation coefficient (R) 0.739 ± 0.046, root mean square error (RMSE) 0.232 ± 0.047, and linear regression line slope 0.960 ± 0.083 for the entire period. Over ocean, the comparisons between Aqua/MODIS and TANSO-CAI for the same period over Northeast Asia showed improved consistency, with correlation coefficient 0.830 ± 0.047, RMSE 0.140 ± 0.019, and linear regression line slope 1.226 ± 0.063 for the entire period. Over land, however, the comparisons between Aqua/MODIS and TANSO-CAI show relatively lower correlation (approximate R = 0.67, RMSE = 0.40, slope = 0.77) than those over ocean. In order to improve accuracy in retrieving CO2 amounts, the retrieved aerosol properties in this study have been provided as input for CO2 retrieval with GOSAT TANSO-Fourier Transform Spectrometer measurements.

Description: Remote Sensing, Vol. 9, Pages 1158: The Cross-Calibration of Spectral Radiances and Cross-Validation of CO2 Estimates from GOSAT and OCO-2 Remote Sensing doi: 10.3390/rs9111158 Authors: Fumie Kataoka David Crisp Thomas Taylor Chris O’Dell Akihiko Kuze Kei Shiomi Hiroshi Suto Carol Bruegge Florian Schwandner Robert Rosenberg Lars Chapsky Richard Lee The Greenhouse gases Observing SATellite (GOSAT) launched in January 2009 has provided radiance spectra with a Fourier Transform Spectrometer for more than eight years. The Orbiting Carbon Observatory 2 (OCO-2) launched in July 2014, collects radiance spectra using an imaging grating spectrometer. Both sensors observe sunlight reflected from Earth’s surface and retrieve atmospheric carbon dioxide (CO2) concentrations, but use different spectrometer technologies, observing geometries, and ground track repeat cycles. To demonstrate the effectiveness of satellite remote sensing for CO2 monitoring, the GOSAT and OCO-2 teams have worked together pre- and post-launch to cross-calibrate the instruments and cross-validate their retrieval algorithms and products. In this work, we first compare observed radiance spectra within three narrow bands centered at 0.76, 1.60 and 2.06 µm, at temporally coincident and spatially collocated points from September 2014 to March 2017. We reconciled the differences in observation footprints size, viewing geometry and associated differences in surface bidirectional reflectance distribution function (BRDF). We conclude that the spectral radiances measured by the two instruments agree within 5% for all bands. Second, we estimated mean bias and standard deviation of column-averaged CO2 dry air mole fraction (XCO2) retrieved from GOSAT and OCO-2 from September 2014 to May 2016. GOSAT retrievals used Build 7.3 (V7.3) of the Atmospheric CO2 Observations from Space (ACOS) algorithm while OCO-2 retrievals used Version 7 of the OCO-2 retrieval algorithm. The mean biases and standard deviations are −0.57 ± 3.33 ppm over land with high gain, −0.17 ± 1.48 ppm over ocean with high gain and −0.19 ± 2.79 ppm over land with medium gain. Finally, our study is complemented with an analysis of error sources: retrieved surface pressure (Psurf), aerosol optical depth (AOD), BRDF and surface albedo inhomogeneity. We found no change in XCO2 bias or standard deviation with time, demonstrating that both instruments are well calibrated.