Multiscale Validation and Trend Evolution of Global Aerosol Reanalysis Datasets: A Comprehensive Comparative Study of CAMS and MERRA-2.
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| Title: | Multiscale Validation and Trend Evolution of Global Aerosol Reanalysis Datasets: A Comprehensive Comparative Study of CAMS and MERRA-2. |
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| Authors: | Wang, Ping1 (AUTHOR), Ding, Jianli1,2 (AUTHOR) dingjl@xju.edu.cn, Wang, Jinjie1 (AUTHOR), Guo, Yitu1,2 (AUTHOR), Liu, Fangqing1 (AUTHOR), Zhao, Shuang1 (AUTHOR), Han, Haiyan1 (AUTHOR), Yuan, Shiyi1 (AUTHOR), Ma, Wen1 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1569. 22p. |
| Subjects: | Atmospheric aerosol measurement, Model validation, Data assimilation, Trend analysis, Climate research |
| Abstract: | Highlights: What are the main findings? CAMS AOD shows a higher global correlation, while MERRA-2 AOD is more robust; both perform best in low–mid latitudes, and MERRA-2 AE is overall superior to CAMS AE. Spatiotemporal validation reveals strong seasonal and hourly discrepancies between the two reanalysis datasets; 2003–2023 global AOD declines monotonically, whereas MERRA-2 AE shows an increasing trend after EEMD. What are the implications of the main findings? CAMS reanalysis is recommended for short-term, real-time, and urban/biomass burning applications, while MERRA-2 is more suitable for coarse-mode aerosol and long-term climate studies. The identified biases and performance gaps provide clear directions for improving aerosol emission inventories, parameterization schemes, and data assimilation in future reanalysis systems. Aerosol optical depth (AOD) and Ångström exponent (AE) are critical parameters for characterizing atmospheric aerosols, playing a pivotal role in atmospheric environmental monitoring and climate change studies. This study addressed the imperative need for a systematic evaluation of mainstream reanalysis products by conducting a comprehensive multi-scale assessment of the CAMS and MERRA-2 datasets (2003–2023), encompassing data quality verification, spatiotemporal pattern analysis, and trend evolution investigation. The following key findings emerge: (1) Both AOD data exhibited the best performance observed in low–mid latitudes. CAMS AOD (AODC) showed a slightly better correlation, while MERRA-2 AOD (AODM) demonstrated superior robustness. Both AE data performed similarly, and MERRA-2 AE (AEM) was superior. Both AE data performed better in low latitudes and near Europe. (2) CAMS and MERRA-2 showed good performance in annual and seasonal variations, with significant fluctuations and biases in the annual cycle. Both models achieved the highest AE performance in summer. MERRA-2 AOD demonstrated better hourly performance during daytime. The hourly stability of AE was slightly worse than AOD, with notably degraded performance during midday hours. (3) The distribution and trends of AOD over land showed spatial consistency. The distribution of AEM was generally lower than AEC's. After ensemble empirical mode decomposition (EEMD), all datasets showed monotonically decreasing trends except for AEM. This study provides valuable insights into the strengths and limitations for CAMS and MERRA-2 and suggests possible areas for improvement in future data assimilation and parameterization. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? CAMS AOD shows a higher global correlation, while MERRA-2 AOD is more robust; both perform best in low–mid latitudes, and MERRA-2 AE is overall superior to CAMS AE. Spatiotemporal validation reveals strong seasonal and hourly discrepancies between the two reanalysis datasets; 2003–2023 global AOD declines monotonically, whereas MERRA-2 AE shows an increasing trend after EEMD. What are the implications of the main findings? CAMS reanalysis is recommended for short-term, real-time, and urban/biomass burning applications, while MERRA-2 is more suitable for coarse-mode aerosol and long-term climate studies. The identified biases and performance gaps provide clear directions for improving aerosol emission inventories, parameterization schemes, and data assimilation in future reanalysis systems. Aerosol optical depth (AOD) and Ångström exponent (AE) are critical parameters for characterizing atmospheric aerosols, playing a pivotal role in atmospheric environmental monitoring and climate change studies. This study addressed the imperative need for a systematic evaluation of mainstream reanalysis products by conducting a comprehensive multi-scale assessment of the CAMS and MERRA-2 datasets (2003–2023), encompassing data quality verification, spatiotemporal pattern analysis, and trend evolution investigation. The following key findings emerge: (1) Both AOD data exhibited the best performance observed in low–mid latitudes. CAMS AOD (AODC) showed a slightly better correlation, while MERRA-2 AOD (AODM) demonstrated superior robustness. Both AE data performed similarly, and MERRA-2 AE (AEM) was superior. Both AE data performed better in low latitudes and near Europe. (2) CAMS and MERRA-2 showed good performance in annual and seasonal variations, with significant fluctuations and biases in the annual cycle. Both models achieved the highest AE performance in summer. MERRA-2 AOD demonstrated better hourly performance during daytime. The hourly stability of AE was slightly worse than AOD, with notably degraded performance during midday hours. (3) The distribution and trends of AOD over land showed spatial consistency. The distribution of AEM was generally lower than AEC's. After ensemble empirical mode decomposition (EEMD), all datasets showed monotonically decreasing trends except for AEM. This study provides valuable insights into the strengths and limitations for CAMS and MERRA-2 and suggests possible areas for improvement in future data assimilation and parameterization. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101569 |
