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| DOI | 10.1016/j.atmosenv.2020.118146 |
| Probabilistic merging and verification of monthly gridded aerosol products | |
| Yang D.; Gueymard C.A. | |
| 发表日期 | 2021 |
| ISSN | 1352-2310 |
| 卷号 | 247 |
| 英文摘要 | This paper aims at merging five gridded products of monthly aerosol optical depth (AOD), namely, MERRA-2, MISR, MODIS-Terra, MODIS-Aqua, and VIIRS, over a period of eight years, from 2012 March to 2020 February. Since these individual products offer alternative realizations of the same underlying AOD process, it is beneficial to study them collectively. To that effect, several parametric and nonparametric regressions, including ensemble model output statistics, quantile regression, quantile regression neural network, and quantile regression forest, are used to optimally combine these products. These regressions generate predictive distributions or quantiles, and thus can be thought of as probabilistic merging or fusion tools. As compared to traditional merging or fusion techniques, which only issue single-valued predictions of AOD, the present ones allow the final AOD product to carry a notion of probability, which is essential for uncertainty quantification. To assess the quality of the final AOD product with respect to that of the individual products, this study employs the important, yet often overlooked, distribution-oriented verification approach. In addition, the calibration and sharpness of the predictive distributions issued by different merging techniques are compared using two strictly proper scoring rules, as well as appropriate graphical tools. Overall, a significant reduction (13%) in root mean square error is achieved by the best merging method (quantile regression forest) compared to the best original dataset (MERRA-2). A significant reduction in bias is also achieved with respect to the MODIS and VIIRS databases, and even more so, to MISR's. © 2020 Elsevier Ltd |
| 关键词 | Aerosol optical depthEnsemble modelingMergingOptimal probabilistic fusionRemote sensingVerification |
| 语种 | 英语 |
| scopus关键词 | Aerosols; Forestry; Mean square error; Probability distributions; Radiometers; Aerosol optical depths; Ensemble model output statistics; Non-parametric regression; Predictive distributions; Proper scoring rules; Quantile regression; Root mean square errors; Uncertainty quantifications; Merging; aerosol; Aqua (satellite); artificial neural network; error analysis; MODIS; optical depth; probability; satellite altimetry; satellite data; VIIRS; aerosol; article; calibration; controlled study; forest; optical depth; prediction; probability; remote sensing; uncertainty |
| 来源期刊 | ATMOSPHERIC ENVIRONMENT
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| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/248582 |
| 作者单位 | Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research (A*STAR), Singapore; Solar Consulting Services, Colebrook, NH 03576, United States |
| 推荐引用方式 GB/T 7714 | Yang D.,Gueymard C.A.. Probabilistic merging and verification of monthly gridded aerosol products[J],2021,247. |
| APA | Yang D.,&Gueymard C.A..(2021).Probabilistic merging and verification of monthly gridded aerosol products.ATMOSPHERIC ENVIRONMENT,247. |
| MLA | Yang D.,et al."Probabilistic merging and verification of monthly gridded aerosol products".ATMOSPHERIC ENVIRONMENT 247(2021). |
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