Geostationary satellites have the capability to offer AOD products with a higher frequency of observation within a given period, thus improving the geographical coverage of averaged AOD products compared to polar satellites. Moreover, the averaged AOD of geostationary satellites is more reflective of the average aerosol load as compared to AOD products derived from polar orbit satellites. Despite this, there is still an absence of comprehensive research on the comparative representativeness of AOD mean products from geostationary and polar orbit satellites, and most current research only focuses on retrieval accuracy. This paper compares the geographical coverage of averaged AOD products released by the Advanced Baseline Imager (ABI) sensor on the Geostationary Operational Environmental Satellite-R (GOES-R) (09:00 UTC-23:00 UTC) with AOD products released by the Moderate-resolution Imaging Spectroradiometer (MODIS) (16:00 UTC and 19:00 UTC) on hourly, daily, semi-monthly and monthly scales. Moreover, the Aerosol Robotic Network datasets were used to evaluate the accuracy of ABI AOD and to propose usage suggestions. In terms of daily AOD products, the AOD daily mean generated by the geostationary satellite GOES-R/ABI consistently outperforms the AOD daily mean generated by MODIS AOD in terms of spatial coverage. However, on monthly scales, the difference is no longer significant. With regard to accuracy, it is proved that when the time scale of averaging is gradually expanded, root mean square error (RMSE) and mean absolute error (MAE) gradually decrease. On the season scale, ABI AOD exhibits the highest level of accuracy during the autumn season (September, October, November); on the spatial scale, ABI AOD exhibits the best accuracy in North America. Therefore, if the daily, semi-monthly or monthly averaged ABI AOD datasets are used, the recommended time is autumn, and the study area is North America

Geographical coverage analysis and usage suggestions of temporal averaged aerosol optical depth product from GOES-R satellite data

Calvello M.;Pavese G.;
2024

Abstract

Geostationary satellites have the capability to offer AOD products with a higher frequency of observation within a given period, thus improving the geographical coverage of averaged AOD products compared to polar satellites. Moreover, the averaged AOD of geostationary satellites is more reflective of the average aerosol load as compared to AOD products derived from polar orbit satellites. Despite this, there is still an absence of comprehensive research on the comparative representativeness of AOD mean products from geostationary and polar orbit satellites, and most current research only focuses on retrieval accuracy. This paper compares the geographical coverage of averaged AOD products released by the Advanced Baseline Imager (ABI) sensor on the Geostationary Operational Environmental Satellite-R (GOES-R) (09:00 UTC-23:00 UTC) with AOD products released by the Moderate-resolution Imaging Spectroradiometer (MODIS) (16:00 UTC and 19:00 UTC) on hourly, daily, semi-monthly and monthly scales. Moreover, the Aerosol Robotic Network datasets were used to evaluate the accuracy of ABI AOD and to propose usage suggestions. In terms of daily AOD products, the AOD daily mean generated by the geostationary satellite GOES-R/ABI consistently outperforms the AOD daily mean generated by MODIS AOD in terms of spatial coverage. However, on monthly scales, the difference is no longer significant. With regard to accuracy, it is proved that when the time scale of averaging is gradually expanded, root mean square error (RMSE) and mean absolute error (MAE) gradually decrease. On the season scale, ABI AOD exhibits the highest level of accuracy during the autumn season (September, October, November); on the spatial scale, ABI AOD exhibits the best accuracy in North America. Therefore, if the daily, semi-monthly or monthly averaged ABI AOD datasets are used, the recommended time is autumn, and the study area is North America
2024
Istituto di Metodologie per l'Analisi Ambientale - IMAA
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/480023
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