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Clear-sky aerosol optical depth over East China estimated from visibility measurements and chemical transport modeling
https://orcid.org/0000-0002-2362-2940
Abstract Horizontal visibility measured at ground meteorological stations provides an under-exploited source of information for studying the interdecadal variation of aerosols and their climatic impacts. Here we propose to use a 3-hourly visibility dataset to infer aerosol optical depth (AOD) over East China, using the nested GEOS-Chem chemical transport model to interpret the spatiotemporally varying relations between columnar and near-surface aerosols. Our analysis is focused in 2006 under cloud-free conditions. We evaluate the visibility-inferred AOD using MODIS/Terra and MODIS/Aqua AOD datasets, after validating MODIS data against three ground AOD measurement networks (AERONET, CARSNET and CSHNET). We find that the two MODIS datasets agree with ground-based AOD measurements, with negative mean biases of 0.05–0.08 and Reduced Major Axis regression slopes around unity. Visibility-inferred AOD roughly capture the general spatiotemporal patterns of the two MODIS datasets with negligible mean differences. The inferred AOD reproduce the seasonal variability (correlation exceeds 0.9) and the slight AOD growth from the late morning to early afternoon shown in the MODIS datasets, suggesting the validity of our AOD inference method. Future research will extend the visibility-based AOD inference to study the long-term variability of AOD.
Highlights We use chemical transport modeling to improve conversion from visibility to AOD. We use MODIS and three ground AOD networks to validate visibility-inferred AOD. Seasonal and daytime variations of visibility-inferred AOD agree with MODIS AOD. Our visibility-based AOD inference can be used for multi-decadal aerosol studies.
Clear-sky aerosol optical depth over East China estimated from visibility measurements and chemical transport modeling
https://orcid.org/0000-0002-2362-2940
Abstract Horizontal visibility measured at ground meteorological stations provides an under-exploited source of information for studying the interdecadal variation of aerosols and their climatic impacts. Here we propose to use a 3-hourly visibility dataset to infer aerosol optical depth (AOD) over East China, using the nested GEOS-Chem chemical transport model to interpret the spatiotemporally varying relations between columnar and near-surface aerosols. Our analysis is focused in 2006 under cloud-free conditions. We evaluate the visibility-inferred AOD using MODIS/Terra and MODIS/Aqua AOD datasets, after validating MODIS data against three ground AOD measurement networks (AERONET, CARSNET and CSHNET). We find that the two MODIS datasets agree with ground-based AOD measurements, with negative mean biases of 0.05–0.08 and Reduced Major Axis regression slopes around unity. Visibility-inferred AOD roughly capture the general spatiotemporal patterns of the two MODIS datasets with negligible mean differences. The inferred AOD reproduce the seasonal variability (correlation exceeds 0.9) and the slight AOD growth from the late morning to early afternoon shown in the MODIS datasets, suggesting the validity of our AOD inference method. Future research will extend the visibility-based AOD inference to study the long-term variability of AOD.
Highlights We use chemical transport modeling to improve conversion from visibility to AOD. We use MODIS and three ground AOD networks to validate visibility-inferred AOD. Seasonal and daytime variations of visibility-inferred AOD agree with MODIS AOD. Our visibility-based AOD inference can be used for multi-decadal aerosol studies.
Clear-sky aerosol optical depth over East China estimated from visibility measurements and chemical transport modeling
https://orcid.org/0000-0002-2362-2940
Abstract Horizontal visibility measured at ground meteorological stations provides an under-exploited source of information for studying the interdecadal variation of aerosols and their climatic impacts. Here we propose to use a 3-hourly visibility dataset to infer aerosol optical depth (AOD) over East China, using the nested GEOS-Chem chemical transport model to interpret the spatiotemporally varying relations between columnar and near-surface aerosols. Our analysis is focused in 2006 under cloud-free conditions. We evaluate the visibility-inferred AOD using MODIS/Terra and MODIS/Aqua AOD datasets, after validating MODIS data against three ground AOD measurement networks (AERONET, CARSNET and CSHNET). We find that the two MODIS datasets agree with ground-based AOD measurements, with negative mean biases of 0.05–0.08 and Reduced Major Axis regression slopes around unity. Visibility-inferred AOD roughly capture the general spatiotemporal patterns of the two MODIS datasets with negligible mean differences. The inferred AOD reproduce the seasonal variability (correlation exceeds 0.9) and the slight AOD growth from the late morning to early afternoon shown in the MODIS datasets, suggesting the validity of our AOD inference method. Future research will extend the visibility-based AOD inference to study the long-term variability of AOD.
Highlights We use chemical transport modeling to improve conversion from visibility to AOD. We use MODIS and three ground AOD networks to validate visibility-inferred AOD. Seasonal and daytime variations of visibility-inferred AOD agree with MODIS AOD. Our visibility-based AOD inference can be used for multi-decadal aerosol studies.
Clear-sky aerosol optical depth over East China estimated from visibility measurements and chemical transport modeling
Lin, Jintai (author) / van Donkelaar, Aaron (author) / Xin, Jinyuan (author) / Che, Huizheng (author) / Wang, Yuesi (author)
Atmospheric Environment ; 95 ; 258-267
2014-06-19
10 pages
Article (Journal)
Electronic Resource
English
| Elsevier | 2014