Mapping Global Atmospheric CO2 Concentration at High Spatiotemporal Resolution

Jing, Yingying; Shi, Jiancheng; Wang, Tianxing; Sussmann, Ralf

doi:10.3390/atmos5040870

Cited by 20 publications

(20 citation statements)

References 33 publications

(57 reference statements)

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“…A large variety of statistical techniques has been proposed to produce Level 3 maps from satellite remote sensing Level 2 retrievals. These include spatial kriging (e.g., [3,9]), spatial block kriging [10] for i = 1, . .…”

Section: Introductionmentioning

confidence: 99%

On Statistical Approaches to Generate Level 3 Products from Satellite Remote Sensing Retrievals

2018

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Satellite remote sensing of trace gases such as carbon dioxide (CO 2 ) has increased our ability to observe and understand Earth's climate. However, these remote sensing data, specifically Level 2 retrievals, tend to be irregular in space and time, and hence, spatio-temporal prediction is required to infer values at any location and time point. Such inferences are not only required to answer important questions about our climate, but they are also needed for validating the satellite instrument, since Level 2 retrievals are generally not co-located with ground-based remote sensing instruments. Here, we discuss statistical approaches to construct Level 3 products from Level 2 retrievals, placing particular emphasis on the strengths and potential pitfalls when using statistical prediction in this context. Following this discussion, we use a spatio-temporal statistical modelling framework known as fixed rank kriging (FRK) to obtain global predictions and prediction standard errors of column-averaged carbon dioxide based on Version 7r and Version 8r retrievals from the Orbiting Carbon Observatory-2 (OCO-2) satellite. The FRK predictions allow us to validate statistically the Level 2 retrievals globally even though the data are at locations and at time points that do not coincide with validation data. Importantly, the validation takes into account the prediction uncertainty, which is dependent both on the temporally-varying density of observations around the ground-based measurement sites and on the spatio-temporal high-frequency components of the trace gas field that are not explicitly modelled. Here, for validation of remotely-sensed CO 2 data, we use observations from the Total Carbon Column Observing Network. We demonstrate that the resulting FRK product based on Version 8r compares better with TCCON data than that based on Version 7r, in terms of both prediction accuracy and uncertainty quantification.

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Section: Introductionmentioning

confidence: 99%

On Statistical Approaches to Generate Level 3 Products from Satellite Remote Sensing Retrievals

2018

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“…In eqn (2), the first part of the inequality is always negative because T switch g is always larger than T L . This also forces T g to be chosen in eqn (1). When the temperature value T is higher than T switch g , the middle part of the inequality will be positive, I g is then forced to be 0 in eqn (2) so that the inequality is valid.…”

Section: Disjunctive Programming For Discretisation Of Continuous Varmentioning

confidence: 99%

“…If an IL in T 1 is determined to be the optimal IL among all combinations in both temperature ranges, T 1 will be chosen as T 1 chosen according to eqn (1). This result will then be compared with all possible ILs in T 2 .…”

Section: Optimal Molecular Designmentioning

confidence: 99%

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A systematic approach to design task-specific ionic liquids and their optimal operating conditions

Chong

Foo

Eljack

et al. 2016

Mol. Syst. Des. Eng.

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Carbon capture and storage (CCS) has gained great interest in recent years as a potential technology to mitigate industrial carbon dioxide (CO 2 ) emissions. Ionic liquids (ILs) were identified as potential CO 2 capturing solvents, due to their negligible vapour pressure, high thermal stability, and wide range of thermophysical properties. However, determining a task-specific IL merely through experimental studies is tedious and costly, as there are about a million possible combinations of cations and anions that may make up the ILs. This work presents a systematic approach to design an optimal IL for the purpose of carbon capture. The significant contribution of the presented approach in this work is the introduction of disjunctive programming to identify optimal operating conditions of the process involved while solving the IL synthesis problem. As studies show, the performance of ILs changes with the operating conditions, which in turn affects overall performance of the carbon capture process. Hence, the presented approach will determine the optimal IL by considering the effect of system operating conditions, and simultaneously determining optimal conditions of the carbon capture process. Operating conditions of the process are modelled as continuous variables; disjunctive programming can discretise these variables and reduce search space for results. Since most of the ILs to be designed are novel solvents, their thermophysical properties are estimated using the group contribution (GC) method. Appropriate structural constraints are defined to ensure the structure of the synthesised IL is feasible. An illustrative case study is solved to demonstrate the proposed approach.

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“…As a result, there is an opportunity to generate a long time series of global XCO 2 dataset starting from 2003 using XCO 2 retrievals from these satellites with careful integration and gap-filling.Gap-filling satellite-observed XCO 2 has been investigated in several studies from different perspectives [31][32][33]. Geostatistical approaches, especially kriging, were widely used for GOSAT observed XCO 2 Level 3 product production [17,34,35]. Spatial-only geostatistical methods do not take into account the temporal correlation structure of CO 2 data [19], which may provide extra information.…”

mentioning

confidence: 99%

Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method

Lei

Zhang

et al. 2020

Remote Sensing

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Column-averaged dry air mole fraction of atmospheric CO2 (XCO2), obtained by multiple satellite observations since 2003 such as ENVISAT/SCIAMACHY, GOSAT, and OCO-2 satellite, is valuable for understanding the spatio-temporal variations of atmospheric CO2 concentrations which are related to carbon uptake and emissions. In order to construct long-term spatio-temporal continuous XCO2 from multiple satellites with different temporal and spatial periods of observations, we developed a precision-weighted spatio-temporal kriging method for integrating and mapping multi-satellite observed XCO2. The approach integrated XCO2 from different sensors considering differences in vertical sensitivity, overpass time, the field of view, repeat cycle and measurement precision. We produced globally mapped XCO2 (GM-XCO2) with spatial/temporal resolution of 1 × 1 degree every eight days from 2003 to 2016 with corresponding data precision and interpolation uncertainty in each grid. The predicted GM-XCO2 precision improved in most grids compared with conventional spatio-temporal kriging results, especially during the satellites overlapping period (0.3–0.5 ppm). The method showed good reliability with R2 of 0.97 from cross-validation. GM-XCO2 showed good accuracy with a standard deviation of bias from total carbon column observing network (TCCON) measurements of 1.05 ppm. This method has potential applications for integrating and mapping XCO2 or other similar datasets observed from multiple satellite sensors. The resulting GM-XCO2 product may be also used in different carbon cycle research applications with different precision requirements.

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Mapping Global Atmospheric CO2 Concentration at High Spatiotemporal Resolution

Cited by 20 publications

References 33 publications

On Statistical Approaches to Generate Level 3 Products from Satellite Remote Sensing Retrievals

On Statistical Approaches to Generate Level 3 Products from Satellite Remote Sensing Retrievals

A systematic approach to design task-specific ionic liquids and their optimal operating conditions

Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method

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