Next Article in Journal
Assessment of Errors Caused by Forest Vegetation Structure in Airborne LiDAR-Derived DTMs
Next Article in Special Issue
Potential of Spaceborne Lidar Measurements of Carbon Dioxide and Methane Emissions from Strong Point Sources
Previous Article in Journal
Circa 2010 Land Cover of Canada: Local Optimization Methodology and Product Development
Previous Article in Special Issue
The Methane Isotopologues by Solar Occultation (MISO) Nanosatellite Mission: Spectral Channel Optimization and Early Performance Analysis
Article

A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering—Part 2: Application to XCO2 Retrievals from OCO-2

Institute of Environmental Physics, University of Bremen, P.O. Box 330440, 28334 Bremen, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(11), 1102; https://doi.org/10.3390/rs9111102
Received: 31 August 2017 / Revised: 19 October 2017 / Accepted: 24 October 2017 / Published: 28 October 2017
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)
Satellite retrievals of the atmospheric dry-air column-average mole fraction of CO 2 (XCO 2 ) based on hyperspectral measurements in appropriate near (NIR) and short wave infrared (SWIR) O 2 and CO 2 absorption bands can help to answer important questions about the carbon cycle but the precision and accuracy requirements for XCO 2 data products are demanding. Multiple scattering of light at aerosols and clouds can be a significant error source for XCO 2 retrievals. Therefore, so called full physics retrieval algorithms were developed aiming to minimize scattering related errors by explicitly fitting scattering related properties such as cloud water/ice content, aerosol optical thickness, cloud height, etc. However, the computational costs for multiple scattering radiative transfer (RT) calculations can be immense. Processing all data of the Orbiting Carbon Observatory-2 (OCO-2) can require up to thousands of CPU cores and the next generation of CO 2 monitoring satellites will produce at least an order of magnitude more data. For this reason, the Fast atmOspheric traCe gAs retrievaL FOCAL has been developed reducing the computational costs by orders of magnitude by approximating multiple scattering effects with an analytic solution of the RT problem of an isotropic scattering layer. Here we confront FOCAL for the first time with measured OCO-2 data and protocol the steps undertaken to transform the input data (most importantly, the OCO-2 radiances) into a validated XCO 2 data product. This includes preprocessing, adaptation of the noise model, zero level offset correction, post-filtering, bias correction, comparison with the CAMS (Copernicus Atmosphere Monitoring Service) greenhouse gas flux inversion model, comparison with NASA’s operational OCO-2 XCO 2 product, and validation with ground based Total Carbon Column Observing Network (TCCON) data. The systematic temporal and regional differences between FOCAL and the CAMS model have a standard deviation of 1.0 ppm. The standard deviation of the single sounding mismatches amounts to 1.1 ppm which agrees reasonably well with FOCAL’s average reported uncertainty of 1.2 ppm. The large scale XCO 2 patterns of FOCAL and NASA’s operational OCO-2 product are similar and the most prominent difference is that FOCAL has about three times less soundings due to the inherently poor throughput (11%) of the MODIS (moderate-resolution imaging spectroradiometer) based cloud screening used by FOCAL’s preprocessor. The standard deviation of the difference between both products is 1.1 ppm. The validation of one year (2015) of FOCAL XCO 2 data with co-located ground based TCCON observations results in a standard deviations of the site biases of 0.67 ppm (0.78 ppm without bias correction) and an average scatter relative to TCCON of 1.34 ppm (1.60 ppm without bias correction). View Full-Text
Keywords: CO2; Satellite; remote sensing; OCO-2; validation CO2; Satellite; remote sensing; OCO-2; validation
Show Figures

Figure 1

MDPI and ACS Style

Reuter, M.; Buchwitz, M.; Schneising, O.; Noël, S.; Bovensmann, H.; Burrows, J.P. A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering—Part 2: Application to XCO2 Retrievals from OCO-2. Remote Sens. 2017, 9, 1102. https://doi.org/10.3390/rs9111102

AMA Style

Reuter M, Buchwitz M, Schneising O, Noël S, Bovensmann H, Burrows JP. A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering—Part 2: Application to XCO2 Retrievals from OCO-2. Remote Sensing. 2017; 9(11):1102. https://doi.org/10.3390/rs9111102

Chicago/Turabian Style

Reuter, Maximilian; Buchwitz, Michael; Schneising, Oliver; Noël, Stefan; Bovensmann, Heinrich; Burrows, John P. 2017. "A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering—Part 2: Application to XCO2 Retrievals from OCO-2" Remote Sens. 9, no. 11: 1102. https://doi.org/10.3390/rs9111102

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop