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

DOI:10.3390/rs9111102 期刊:Remote Sensing 出版年份:2017 更新时间:2025-09-09 09:28:46
摘要: Satellite retrievals of the atmospheric dry-air column-average mole fraction of CO2 (XCO2) based on hyperspectral measurements in appropriate near (NIR) and short wave infrared (SWIR) O2 and CO2 absorption bands can help to answer important questions about the carbon cycle but the precision and accuracy requirements for XCO2 data products are demanding. Multiple scattering of light at aerosols and clouds can be a significant error source for XCO2 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 CO2 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 XCO2 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 XCO2 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 XCO2 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 XCO2 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).
作者: Maximilian Reuter,Michael Buchwitz,Oliver Schneising,Stefan No?l,Heinrich Bovensmann,John P. Burrows
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To develop and validate a fast atmospheric trace gas retrieval method (FOCAL) for hyperspectral instruments approximating multiple scattering, specifically applied to XCO2 retrievals from OCO-2, aiming to reduce computational costs while maintaining accuracy and precision in XCO2 data products.

The FOCAL algorithm successfully reduces computational costs by orders of magnitude while producing XCO2 retrievals with accuracy and precision comparable to more computationally intensive methods. Validation against CAMS, NASA's OCO-2 product, and TCCON data shows good agreement, with systematic differences and single sounding mismatches within expected uncertainty ranges. The study highlights the potential of FOCAL for processing large datasets from current and future satellite missions.

The study acknowledges the inherently poor throughput (11%) of the MODIS-based cloud screening used by FOCAL’s preprocessor, which results in fewer soundings compared to NASA's operational product. Additionally, the noise model and bias correction rely on assumptions that may not capture all sources of error, and the validation is limited by the availability and distribution of TCCON ground-based observations.

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