Carbon dioxide cover: carbon dioxide column concentration seamlessly distributed globally during 2009–2020
Abstract. For carbon dioxide concentration (XCO2) distribution, the improvement of spatial and temporal resolution is very important in some scientific studies (e.g., studies of the carbon cycle and assessment of carbon emissions based on top-down theory). However, carbon sniffing satellites based on passive theory (e.g., Gosat-2, OCO-2, and OCO-3) are susceptible to cloud and aerosol interference when the data are captured. Therefore, the data collected by carbon sniffing satellites have relatively low utilization, especially in some regions where data gaps exist. Here, we present the Carbon Dioxide Coverage (CDC) dataset, an innovative theory to obtain high spatial and temporal resolution maps of XCO2 distribution by combining spatial attributes and extracted temporal attributes from the GOSAT satellite series data. This theory is divided into the following three parts. Firstly, several background values in the raw GOSAT data were removed through data pre-processing, and for spatial attributes, GOSAT satellite data gap areas were filled by combining adjacent GOSAT data and empirical Bayesian kriging (EBK) theory in the study area. Secondly, for the temporal attributes, we constructed a time profile parameter library, based on the GOSAT data of the time series to extract the temporal parameters from a specific formula at each point of the study area. Finally, for the integration of temporal and spatial information, based on the GOSAT satellite data and the populated data based on spatial attributes, we assign the temporal parameter information from the time parameter library to each pixel location in the study area, combining the transfer component analysis (TCA) theory, and then combine the assigned parameters with specific formulas to complete the prediction of XCO2 distribution. For temporal resolution, both the GOSAT_FTS_L3_V2.95 and CDC datasets are monthly-averaged resolution datasets from 2010 to 2020. And for spatial resolution, the CDC dataset is 0.25° resolution with a significant improvement compared to GOSAT_FTS_L3_V2.95 which is 2.5° resolution. And the dataset contained 136 files. Besides, for the data validation part, we used OCO-2 satellite data from 2009 to 2020 and TCCON data at mid and low latitudes, respectively. This CDC dataset and the original data from the TCCON sites were compared on a monthly-averaged scale. And the results showed that R2 was 0.9686, and RMSE was 1.3811 ppm. We also derived statistical monthly averaged XCO2 from OCO-2 data and compared it with the data set from our theory. And our evaluation index R was greater than 0.7, by comparison with OCO-2 during 2014–2020. Finally, to assess the accuracy of the algorithm, we compared the predicted results with the input data for the period of 2009–2020. And the comparison results show that the mean value of R2 is 0.93 and the mean value of RMSE is 0.53 ppm during 2010–2020. Data gaps produced by sniffer satellites are disturbed by factors such as clouds and aerosols and can be filled by this mapping technique is mentioned in this paper. This technique improves the utilization of XCO2 and the accuracy and resolution of the CDC dataset is sufficient for scientific applications. And the CDC dataset is publicly available at https://doi.org/10.6084/m9.figshare.17826404.v4 (Zhang et al., 2022), which is of significance for a multitude of scientific carbon research.
This preprint has been withdrawn.
Haowei Zhang et al.
Haowei Zhang et al.
Haowei Zhang et al.
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Long-term and regional scale monitoring of CO2 from space is important for understanding climate changes. Satellite can cover globally but clear sky ratios vary much region by region. Spatial-temporal technique are useful. However, I found several critical issues in this paper. I recommend resubmission.
(1) GOSAT sampling pattern and CO2 density enhancement over large emission sources
The GOSAT sampling pattern consists of grid observation and target observation. The sampling pattern is not uniform. GOSAT is targeting global megacities which shows local enhancement. Over the ocean, GOSAT is tracking the specular reflection points of the sun, of which sampling pattern is not grid. Authors should describe in more detail how to use these data for analysis.
(2) GOSAT data source
I do not understand why authors use the NIES level 3 products.
Level 3 products are spatially interpolated already. As mentioned in (1), they are problematic. There are several Level 2 GOSAT products other than NIES such as ACOS, RemoTeC, University of Leicester, and JAXA. Why do authors use NIES products? There is no product defined as “official”.
(1) 2.2 validation data TCCON
When the authors use the muti-year data in TCCON comparison, the coefficient of determination becomes too good. The annual growth of global CO2 density should be removed for the analysis. The deviation and bias of matched up data should be presented.
(2) 2.2 Validation data: OCO-2 Level 2 product
The version of the OCO-2 level 2 products should be described. Older OCO-2 products have topography dependent bias. The difference in footprints of GOSAT and OCO-2 creates errors.
(1) Page 15 Table 1
Is the unit of RMSE ppm?
(2) page 19, Figure 2
The branching of left and right should be described.