Spatial-temporal variations of extreme precipitation indices and their linkage with atmospheric circulation in the middle and lower reaches of the Yangtze River

Yang, Jing; Huang, Ya; Li, Zhenhua; Li, Yanping

doi:10.21203/rs.3.rs-2110924/v1

Cited by 1 publication

(1 citation statement)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the northern part of the region, rivers and lakes account for a large percentage of the total area. Wetlands, such as waterbodies, are also important sources of natural CH 4 emissions [42] and a large number of wetlands and biogenic sources, such as paddy fields, increase CH 4 concentrations during the process of growth [43]. Moreover, given the lower latitude in the southern region, the temperature is slightly greater than that in the northern region from a year−round perspective.…”

Section: Calculation Of the Spatial Distribution Of Ch 4 In The Yrd B...mentioning

confidence: 99%

Comparative Analysis and High−Precision Modeling of Tropospheric CH4 in the Yangtze River Delta of China Obtained from the TROPOMI and GOSAT

Cai,

Xiang

2024

Atmosphere

View full text Add to dashboard Cite

Remote sensing satellite monitoring involving the use of shortwave infrared (SWIR) solar backscatter radiation to measure atmospheric CH4 column concentrations provides wide−ranging and accurate data for quantitatively determining atmospheric CH4 emissions and is highly important for human studies of atmospheric composition and environmental protection. The ESA−launched Sentinel−2 satellite equipped with a tropospheric monitoring instrument (TROPOMI) can provide the concentration of CH4 columns in every piece of the global atmosphere every day. However, these data may be affected by surface albedo, SWIR, aerosols, cirrus cloud scattering, and other factors. The greenhouse gas observing satellite (GOSAT) launched by Japan has fairly accurate data that are minimally affected by the aforementioned factors; however, its data density is much less than that of the TROPOMI. In this study, we propose a CH4 model that combines the TROPOMI and GOSAT data. We construct the model by analyzing the data from the TROPOMI and GOSAT at the same location at the same time. Then, we apply the proposed model to a certain location at a certain time with TROPOMI data but without GOSAT data to obtain a large range of high−precision CH4 data. The most developed urban agglomeration in the Yangtze River Delta in China was selected for model construction and the correlations between the TROPOMI and GOSAT data and their spatial and temporal trends were analyzed. First, we analyzed the CH4 concentrations in the same area measured by both models. The results revealed a high degree of temporal and spatial correlation in the YRD region. The correlation coefficient reached 0.71 in the metropolitan area of the YRD. At the small−city scale, the correlation is much more significant, with the correlation reaching 0.80, 0.79, and 0.71 for Nanjing, Shanghai, and Ningbo, respectively. The most accurate model was screened through comparative construction to calibrate the TROPOMI data and high−precision and high−coverage CH4 concentration information was obtained for the study area. Five models (linear model, quadratic term model, cubic term model, lognormal model, and logistic model) were used to select the best−fitting model. The magnitudes of the differences in the CH4 concentrations calculated by each model were compared. The final results showed that the linear model, as the prediction model, had the highest accuracy, with a coefficient of determination (R22) of 0.542. To avoid the specificity of the constructed model, we used the same method in several simulations to validate. The coefficient of determination of the model constructed with different stochastic data was greater than 0.5. Subsequently, we used Nanjing as the study area and applied the same method to construct the model. The coefficient of determination of the model (R22) was approximately 0.601. The model constructed in this research can be used not only for data conversion between the same products from different sensors to obtain high−precision data products but also for calibrating newly developed satellite data products that utilize mature data products.

show abstract

Section: Calculation Of the Spatial Distribution Of Ch 4 In The Yrd B...mentioning

confidence: 99%

Comparative Analysis and High−Precision Modeling of Tropospheric CH4 in the Yangtze River Delta of China Obtained from the TROPOMI and GOSAT

Cai,

Xiang

2024

Atmosphere

View full text Add to dashboard Cite

show abstract

Spatial-temporal variations of extreme precipitation indices and their linkage with atmospheric circulation in the middle and lower reaches of the Yangtze River

Cited by 1 publication

References 55 publications

Comparative Analysis and High−Precision Modeling of Tropospheric CH4 in the Yangtze River Delta of China Obtained from the TROPOMI and GOSAT

Comparative Analysis and High−Precision Modeling of Tropospheric CH4 in the Yangtze River Delta of China Obtained from the TROPOMI and GOSAT

Contact Info

Product

Resources

About