Incorporating temporal variability to improve geostatistical analysis of satellite-observed CO2 in China

Baker, Ian; Lei, Liping; Guo, Dadong; Zhang, Li; Zhang, Bing

doi:10.1007/s11434-012-5652-7

Cited by 36 publications

(26 citation statements)

References 17 publications

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“…However, this evaluation has not been done for the spatio-temporal product-sum covariance model. Other studies that use a product-sum covariance model typically assume the validity of this covariance model on a sphere (e.g., Zeng et al, 2013Zeng et al, , 2016. Results from Huang et al (2011) explicitly validate the exponential covariance model on a sphere, as well as sums of the products of exponential covariance models and constants (provided that the constants are positive).…”

Section: Characterization Of Spatio-temporal Covariancementioning

confidence: 97%

“…As a result, kriging can account for a variable density of observations and can estimate uncertainties in the resulting maps. Various forms of kriging have recently been used to map satellite Earth observations, particularly for XCO 2 (e.g., Hammerling et al, 2012a, b;Tadić et al, 2015;Zeng et al, 2013Zeng et al, , 2016Guo et al, 2013). Hammerling et al (2012a, b) presented an approach to mapping Orbiting Carbon Observatory-2 (OCO-2) and GOSAT XCO 2 observations, respectively, with non-stationary properties.…”

Section: Introductionmentioning

confidence: 99%

“…These studies have either used various forms of Kalman smoothing (e.g., Cressie, 2011, 2012;Nguyen et al, 2014) or geostatistics (e.g., Guo et al, 2013;Zeng et al, 2013Zeng et al, , 2016. The former group of studies leverages Kalman smoothing to improve the computational tractability of mapping dense or abundant datasets, like OCO-2 and the Atmospheric Infrared Sounder (AIRS).…”

Section: Introductionmentioning

confidence: 99%

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Spatio-temporal approach to moving window block kriging of satellite data v1.0

et al. 2017

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Abstract. Numerous existing satellites observe physical or environmental properties of the Earth system. Many of these satellites provide global-scale observations, but these observations are often sparse and noisy. By contrast, contiguous, global maps are often most useful to the scientific community (i.e., Level 3 products). We develop a spatio-temporal moving window block kriging method to create contiguous maps from sparse and/or noisy satellite observations. This approach exhibits several advantages over existing methods: (1) it allows for flexibility in setting the spatial resolution of the Level 3 map, (2) it is applicable to observations with variable density, (3) it produces a rigorous uncertainty estimate, (4) it exploits both spatial and temporal correlations in the data, and (5) it facilitates estimation in real time. Moreover, this approach only requires the assumption that the observable quantity exhibits spatial and temporal correlations that are inferable from the data. We test this method by creating Level 3 products from satellite observations of CO 2 (XCO 2 ) from the Greenhouse Gases Observing Satellite (GOSAT), CH 4 (XCH 4 ) from the Infrared Atmospheric Sounding Interferometer (IASI) and solar-induced chlorophyll fluorescence (SIF) from the Global Ozone Monitoring Experiment-2 (GOME-2). We evaluate and analyze the difference in performance of spatio-temporal vs. recently developed spatial kriging methods.

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Section: Characterization Of Spatio-temporal Covariancementioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatio-temporal approach to moving window block kriging of satellite data v1.0

et al. 2017

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“…Satellite-based CO 2 observations of column-averaged dry air mole fraction (XCO 2 ) offer a new insight into the pattern of CO 2 mixing ratios and provide an additional constraint on the estimated CO 2 concentrations and fluxes of the atmospheric inversion method [16,[19][20][21][22]. These satellite-based measurements (e.g., GOSAT [23], AIRS [24], SCIAMACHY [25] and IASI [26]) allow for the quantification of large-scale temporal, spatial and seasonal variations in CO 2 .…”

Section: Introductionmentioning

confidence: 99%

Comparing simulated atmospheric carbon dioxide concentration with GOSAT retrievals

Zhang

Chen

et al. 2015

Science Bulletin

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Satellite observations of atmospheric carbon dioxide (CO 2 ) provide a useful way to improve the understanding of global carbon cycling. In this paper, we present a comparison between simulated CO 2 concentrations from an inversion model of the CarbonTracker Data Assimilation System (CTDAS) and satellite-based CO 2 measurements of column-averaged dry air mole fraction (denoted XCO 2 ) derived from version 3.3 Atmospheric CO 2 Observations from Space retrievals of the Greenhouse Gases Observing SATellite (ACOS-GOSAT) L2 data products. We examine the differences of CTDAS and GOSAT to provide important guidance for the further investigation of CTDAS in order to quantify the corresponding flux estimates with satellite-based CO 2 observations. We find that the mean point-by-point difference (CTDAS-GOSAT) between CTDAS and GOSAT XCO 2 is -0.11 ± 1.81 ppm, with a high agreement (correlation r = 0.77, P \ 0.05) over the studied period. The latitudinal zonal variations of CTDAS and GOSAT are in general agreement with clear seasonal fluctuations. The major exception occurs in the zonal band of 0°-15°N where the difference is approximately 4 ppm, indicating that large uncertainty may exist in the assimilated CO 2 for the lowlatitude region of the Northern Hemisphere (NH). Additionally, we find that the hemispherical/continental differences between CTDAS and GOSAT are typically less than 1 ppm, but obvious discrepancies occur in different hemispheres/continents, with high consistency (point-bypoint correlation r = 0.79, P \ 0.05) in the NH and a weak correlation (point-by-point correlation r = 0.65, P \ 0.05) in the Southern Hemisphere. Overall, the difference of CTDAS and GOSAT is small, and the comparison of CTDAS and GOSAT will further instruct the inverse modeling of CO 2 fluxes using GOSAT.

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“…Nevertheless, the fact is that these single satellite-based strategies could not ensure sufficient data for stable semivariogram estimation during Kriging interpolation because the number of valid data points is limited for a single satellite [13,18], which may cause large uncertainties. Rather than using L2 XCO 2 from a single dataset (e.g., GOSAT), as performed in existing literature, fusing available CO 2 measurements derived from various space-based data would facilitate the generation of highly reliable full coverage (L3) maps with high spatiotemporal resolution.…”

Section: Introductionmentioning

confidence: 99%

Mapping Global Atmospheric CO2 Concentration at High Spatiotemporal Resolution

et al. 2014

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Satellite measurements of the spatiotemporal distributions of atmospheric CO 2 concentrations are a key component for better understanding global carbon cycle characteristics. Currently, several satellite instruments such as the Greenhouse gases Observing SATellite (GOSAT), SCanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY), and Orbiting Carbon Observatory-2 can be used to measure CO 2 column-averaged dry air mole fractions. However, because of cloud effects, a single satellite can only provide limited CO 2 data, resulting in significant uncertainty in the characterization of the spatiotemporal distribution of atmospheric CO 2 concentrations. In this study, a new physical data fusion technique is proposed to combine the GOSAT and SCIAMACHY measurements. On the basis of the fused dataset, a gap-filling method developed by modeling the spatial correlation structures of CO 2 concentrations is presented with the goal of generating global land CO 2 distribution maps with high spatiotemporal resolution. The results show that, compared with the single satellite dataset (i.e., GOSAT or SCIAMACHY), the global spatial coverage of the fused dataset is significantly increased (reaching up to approximately 20%), and the temporal OPEN ACCESSAtmosphere 2014, 5 871 resolution is improved by two or three times. The spatial coverage and monthly variations of the generated global CO 2 distributions are also investigated. Comparisons with ground-based Total Carbon Column Observing Network (TCCON) measurements reveal that CO 2 distributions based on the gap-filling method show good agreement with TCCON records despite some biases. These results demonstrate that the fused dataset as well as the gap-filling method are rather effective to generate global CO 2 distribution with high accuracies and high spatiotemporal resolution.

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Incorporating temporal variability to improve geostatistical analysis of satellite-observed CO2 in China

Cited by 36 publications

References 17 publications

Spatio-temporal approach to moving window block kriging of satellite data v1.0

Spatio-temporal approach to moving window block kriging of satellite data v1.0

Comparing simulated atmospheric carbon dioxide concentration with GOSAT retrievals

Mapping Global Atmospheric CO2 Concentration at High Spatiotemporal Resolution

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