An Interpolation Method to Reduce the Computational Time in the Stochastic Lagrangian Particle Dispersion Modeling of Spatially Dense XCO<sub>2</sub> Retrievals

Roten, Dustin; Wu, Dien; Fasoli, Benjamin; Oda, T.; Lin, John C.

doi:10.1029/2020ea001343

Cited by 9 publications

(4 citation statements)

References 57 publications

(94 reference statements)

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“…The data in this study involved CEs and land use data. CEs data were sourced from the open-source data inventory (https://db.cger.nies.go.jp/dataset/ODIAC/, accessed on 7 May 2024) [41]; these CEs data successfully estimate the spatial distribution of fossil fuel CEs on a global scale by combining night-time lighting data and emission location profiles of individual power plants using an innovative emission modeling approach. The spatial resolution is 1000 m. The land use change data were downloaded from the Data Center for Resources and Environmental Sciences and the Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 7 May 2024).…”

Section: Data Sourcesmentioning

confidence: 99%

Impact of Urban Expansion on Carbon Emissions in the Urban Agglomerations of Yellow River Basin, China

Wang,

Zeng,

Wang

et al. 2024

Land

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Continued urban expansion (UE) has long been regarded as a huge challenge for climate change mitigation. However, much less is known about how UE affects carbon emissions (CEs), especially in the urban agglomerations of the Yellow River Basin (UAYRB), China. In this regard, this study introduced kernel density analysis, the Gini coefficient, and Markov chains to reveal the UE patterns and carbon emissions intensity (CEI) in the UAYRB at the county level, and explored the spatial heterogeneity of the impact of UE on CEI with the geographically and temporally weighted regression model. The results show that both CEI and UE in the UAYRB showed a steady growing trend during the study period. The kernel density of CEI and UE revealed that CEI in the UAYRB was weakening, while the UE rate continuously slowed down. The Gini coefficients of both CEI and UE in the UAYRB region were at high levels, indicating obvious spatial imbalance. The Markov transfer probability matrix for CEI with a time span of five years showed that CEI growth will still occur over the next five years, while that of UE was more obvious. Meanwhile, counties with a regression coefficient of UE on CEI higher than 0 covered the majority, and the distribution pattern remained quite stable. The regression coefficients of different urban landscape metrics on CEI in the UAYRB varied greatly; except for the landscape shape index, the regression coefficients of the aggregation index, interspersion and juxtaposition index, and patch density overall remained positive. These findings can advance the policy enlightenment of the high-quality development of the Yellow River Basin.

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Section: Data Sourcesmentioning

confidence: 99%

Impact of Urban Expansion on Carbon Emissions in the Urban Agglomerations of Yellow River Basin, China

Wang,

Zeng,

Wang

et al. 2024

Land

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“…However, the spatial resolution of these satellites is relatively rough [16,17], and there are certain limitations in local area inversion and point source scale monitoring of XCO 2 . Currently, some researchers have solved this problem through spatial interpolation [18,19] and multi-satellite spatiotemporal data fusion [20,21], but these methods perform better at large spatial scales.…”

Section: Introductionmentioning

confidence: 99%

Exploring Urban XCO2 Patterns Using PRISMA Satellite: A Case Study in Shanghai

Wu,

Xie,

Wang

2024

Atmosphere

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As global warming intensifies, monitoring carbon dioxide (CO2) has increasingly become a focal point of research. Investigating urban XCO2 emission systems holds paramount importance, given the pivotal role of cities as major contributors to carbon emissions. Consequently, this study centers on urban locales, employing Shanghai as a case study for a comprehensive evaluation of regional XCO2 levels. We utilized high spatial resolution imagery from the PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite to conduct an XCO2 assessment over the Baoshan District with a 30 m spatial resolution from April 2021 to October 2022. Our XCO2 analysis was conducted in two steps. Firstly, we conducted a sensitivity analysis on key parameters in the inversion process, where cloud cover severely interfered with inversion accuracy. Therefore, we developed the Fmask 4.0 cloud removal and iterative maximum a posteriori differential optical absorption spectroscopy (FIMAP-DOAS) algorithm. This novel integration eliminated cloud interference during the inversion process, achieving high-precision CO2 detection in the region. Secondly, we compared the XCO2 of the region with Level-2 data from carbon monitoring satellites such as OCO-2. The comparison results showed a strong consistency, with a root mean squared error (RMSE) of 0.75 ppm for Shanghai XCO2 data obtained from the PRISMA satellite compared to OCO-2 Level-2 data and an RMSE of 1.49 ppm compared to OCO-3. This study successfully established a high-accuracy and high-spatial-resolution XCO2 satellite monitoring system for the Shanghai area. The efficacy of the FIMAP-DOAS algorithm has been demonstrated in CO2 monitoring and inversion within urban environments, with potential applicability to other cities.

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“…Using such satellite observations is computationally prohibitive for most common inversion schemes, especially for inversions that use Lagrangian backward models to compute the sensitivity of observations to surface fluxes, as the satellite retrievals are not only numerous but also sensitive to the whole atmospheric column. The computational costs can be reduced using techniques that for example, calculate the observational sensitivity for only a subset of the soundings (e.g., Roten et al., 2021), but such methods are usually associated with their own limitations, such as not being applicable to broader regions or longer time periods.…”

Section: Introductionmentioning

confidence: 99%

Regional CO₂ Inversion Through Ensemble‐Based Simultaneous State and Parameter Estimation: TRACE Framework and Controlled Experiments

Chen

Zhang

Lauvaux

et al. 2023

J Adv Model Earth Syst

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An Interpolation Method to Reduce the Computational Time in the Stochastic Lagrangian Particle Dispersion Modeling of Spatially Dense XCO₂ Retrievals

Cited by 9 publications

References 57 publications

Impact of Urban Expansion on Carbon Emissions in the Urban Agglomerations of Yellow River Basin, China

Impact of Urban Expansion on Carbon Emissions in the Urban Agglomerations of Yellow River Basin, China

Exploring Urban XCO2 Patterns Using PRISMA Satellite: A Case Study in Shanghai

Regional CO₂ Inversion Through Ensemble‐Based Simultaneous State and Parameter Estimation: TRACE Framework and Controlled Experiments

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An Interpolation Method to Reduce the Computational Time in the Stochastic Lagrangian Particle Dispersion Modeling of Spatially Dense XCO2 Retrievals

Cited by 9 publications

References 57 publications

Impact of Urban Expansion on Carbon Emissions in the Urban Agglomerations of Yellow River Basin, China

Impact of Urban Expansion on Carbon Emissions in the Urban Agglomerations of Yellow River Basin, China

Exploring Urban XCO2 Patterns Using PRISMA Satellite: A Case Study in Shanghai

Regional CO2 Inversion Through Ensemble‐Based Simultaneous State and Parameter Estimation: TRACE Framework and Controlled Experiments

Contact Info

Product

Resources

About

An Interpolation Method to Reduce the Computational Time in the Stochastic Lagrangian Particle Dispersion Modeling of Spatially Dense XCO₂ Retrievals

Regional CO₂ Inversion Through Ensemble‐Based Simultaneous State and Parameter Estimation: TRACE Framework and Controlled Experiments