论文--中国科学院地理科学与资源研究所

论文

论文题目：	Variability, predictability, and uncertainty in global aerosols inferred from gap-filled satellite observations and an econometric modeling approach
第一作者：	Li Xueke, Liu Kai, Tian Jing, Wang Menghua
联系作者：
发表年度：	2021
摘　　要：
英文摘要：	Time series analyses and stochastic modeling assessments of aerosols are critical for climate change and human health studies. However, the precise characterization of the aerosol optical depth (AOD), its variability, trends, and predictability, and its associated uncertainty at the global scale is largely unexplored. In addition, gaps in satellite-retrieved AODs across space and time remain an obstacle to accurately revealing aerosol properties. This study uses MODIS Collection 6 AOD retrievals for a time series analysis and modeling of global aerosols from 2003 to 2015. Random forest (RF) regression is first applied to replace the missing data in the satellite AOD retrievals. AOD variations and trends are then investigated and future values are predicted using a Mann-Kendall analysis approach and an autoregressive integrated moving average (ARIMA) model, respectively. The results indicate that the developed RF model enhances the AOD data coverage significantly, with the root mean square error and mean absolute error statistical metrics well below 0.13 and 0.08, respectively. High AOD loadings are found over East, South, and Southwest Asia, West and Central Africa, and northern South America. Prominent discrepancies are shown in aerosol variations and trends likely because of dust emissions, biomass burning, fossil fuel combustion, and socioeconomic practices, which has significant implications for climate systems and mitigation policy-making. The ARIMA model delineates AOD features with clear annual and seasonal variations and with high accuracy over most regions. The performance of the model is jointly impacted by the data quality and data values. Overall, our study suggests the feasibility and applicability of the RF model in reconstructing area-scale satellite missing AOD retrievals, as well as the ability of the stochastic ARIMA model to accurately depict and forecast AOD profiles. The globally simulated and predicted aerosols will improve evaluations of aerosol effects for climate and epidemiological studies.
刊物名称：	REMOTE SENSING OF ENVIRONMENT
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论文类别：	SCI