Improving the Estimation of Daily Aerosol Optical Depth and Aerosol Radiative Effect Using an Optimized Artificial Neural Network

Aerosols can absorb and scatter surface solar radiation (SSR), which is called the aerosol radiative forcing effect (ARF). Great efforts have been made for the estimation of the aerosol optical depth (AOD), SSR and ARF using meteorological measurements and satellite observations. However, the accura...

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Published in:Remote Sensing
Main Authors: Wenmin Qin, Lunche Wang, Aiwen Lin, Ming Zhang, Muhammad Bilal
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2018
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Online Access:https://doi.org/10.3390/rs10071022
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spelling ftmdpi:oai:mdpi.com:/2072-4292/10/7/1022/ 2023-08-20T03:59:13+02:00 Improving the Estimation of Daily Aerosol Optical Depth and Aerosol Radiative Effect Using an Optimized Artificial Neural Network Wenmin Qin Lunche Wang Aiwen Lin Ming Zhang Muhammad Bilal agris 2018-06-27 application/pdf https://doi.org/10.3390/rs10071022 EN eng Multidisciplinary Digital Publishing Institute Atmospheric Remote Sensing https://dx.doi.org/10.3390/rs10071022 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 10; Issue 7; Pages: 1022 Genetic_BP aerosol optical depth surface solar radiation aerosol radiative forcing effect Text 2018 ftmdpi https://doi.org/10.3390/rs10071022 2023-07-31T21:35:50Z Aerosols can absorb and scatter surface solar radiation (SSR), which is called the aerosol radiative forcing effect (ARF). Great efforts have been made for the estimation of the aerosol optical depth (AOD), SSR and ARF using meteorological measurements and satellite observations. However, the accuracy, and spatial and temporal resolutions of these existing AOD, SSR and ARF models should be improved to meet the application requirements, due to the uncertainties and gaps of input parameters. In this study, an optimized back propagation (BP) artificial neural network (Genetic_BP) was developed for improving the estimation of the AOD values. The retrieved AOD values using the Genetic_BP model and meteorological measurements at China Meteorological Administration (CMA) stations were used to calculate SSR and bottom of the atmosphere (BOA) ARF (ARFB) using Yang’s Hybrid model (YHM). The result show that the Genetic_BP could be used for estimating AOD values with high accuracy (R = 0.866 for CASNET (China Aerosol Remote Sensing Network) stations and R = 0.865 for AERONET (Aerosol Robotic Network) stations). The estimated SSR also showed a good agreement with SSR measurements at 96 CMA radiation stations, with RMSE, MAE, R and R2 of 29.27%, 23.77%, 0.948, and 0.899, respectively. The estimated ARFB values are also highly correlated with the AERONET ARFB ones with RMSE, MAE, R and R2 of −35.47%, −25.33%, 0.843, and 0.711, respectively. Finally, the spatial and temporal variations of AOD, SSR, and ARFB values over Mainland China were investigated. Both AOD and SSR values are generally higher in summer than in other seasons. The ARFB are generally stronger in spring and summer than in other seasons. The ranges for the monthly mean AOD, SSR and ARFB values over Mainland China are 0.183–0.333, 10.218–24.196 MJ m−2day−1 and −2.986 to −1.244 MJ m−2day−1, respectively. The Qinghai-Tibetan Plateau has always been an area with the highest SSR, the lowest AOD and the weakest ARFB. In contrast, the Sichuan Basin has always been an ... Text Aerosol Robotic Network MDPI Open Access Publishing Boa ENVELOPE(15.532,15.532,66.822,66.822) Remote Sensing 10 7 1022
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic Genetic_BP
aerosol optical depth
surface solar radiation
aerosol radiative forcing effect
spellingShingle Genetic_BP
aerosol optical depth
surface solar radiation
aerosol radiative forcing effect
Wenmin Qin
Lunche Wang
Aiwen Lin
Ming Zhang
Muhammad Bilal
Improving the Estimation of Daily Aerosol Optical Depth and Aerosol Radiative Effect Using an Optimized Artificial Neural Network
topic_facet Genetic_BP
aerosol optical depth
surface solar radiation
aerosol radiative forcing effect
description Aerosols can absorb and scatter surface solar radiation (SSR), which is called the aerosol radiative forcing effect (ARF). Great efforts have been made for the estimation of the aerosol optical depth (AOD), SSR and ARF using meteorological measurements and satellite observations. However, the accuracy, and spatial and temporal resolutions of these existing AOD, SSR and ARF models should be improved to meet the application requirements, due to the uncertainties and gaps of input parameters. In this study, an optimized back propagation (BP) artificial neural network (Genetic_BP) was developed for improving the estimation of the AOD values. The retrieved AOD values using the Genetic_BP model and meteorological measurements at China Meteorological Administration (CMA) stations were used to calculate SSR and bottom of the atmosphere (BOA) ARF (ARFB) using Yang’s Hybrid model (YHM). The result show that the Genetic_BP could be used for estimating AOD values with high accuracy (R = 0.866 for CASNET (China Aerosol Remote Sensing Network) stations and R = 0.865 for AERONET (Aerosol Robotic Network) stations). The estimated SSR also showed a good agreement with SSR measurements at 96 CMA radiation stations, with RMSE, MAE, R and R2 of 29.27%, 23.77%, 0.948, and 0.899, respectively. The estimated ARFB values are also highly correlated with the AERONET ARFB ones with RMSE, MAE, R and R2 of −35.47%, −25.33%, 0.843, and 0.711, respectively. Finally, the spatial and temporal variations of AOD, SSR, and ARFB values over Mainland China were investigated. Both AOD and SSR values are generally higher in summer than in other seasons. The ARFB are generally stronger in spring and summer than in other seasons. The ranges for the monthly mean AOD, SSR and ARFB values over Mainland China are 0.183–0.333, 10.218–24.196 MJ m−2day−1 and −2.986 to −1.244 MJ m−2day−1, respectively. The Qinghai-Tibetan Plateau has always been an area with the highest SSR, the lowest AOD and the weakest ARFB. In contrast, the Sichuan Basin has always been an ...
format Text
author Wenmin Qin
Lunche Wang
Aiwen Lin
Ming Zhang
Muhammad Bilal
author_facet Wenmin Qin
Lunche Wang
Aiwen Lin
Ming Zhang
Muhammad Bilal
author_sort Wenmin Qin
title Improving the Estimation of Daily Aerosol Optical Depth and Aerosol Radiative Effect Using an Optimized Artificial Neural Network
title_short Improving the Estimation of Daily Aerosol Optical Depth and Aerosol Radiative Effect Using an Optimized Artificial Neural Network
title_full Improving the Estimation of Daily Aerosol Optical Depth and Aerosol Radiative Effect Using an Optimized Artificial Neural Network
title_fullStr Improving the Estimation of Daily Aerosol Optical Depth and Aerosol Radiative Effect Using an Optimized Artificial Neural Network
title_full_unstemmed Improving the Estimation of Daily Aerosol Optical Depth and Aerosol Radiative Effect Using an Optimized Artificial Neural Network
title_sort improving the estimation of daily aerosol optical depth and aerosol radiative effect using an optimized artificial neural network
publisher Multidisciplinary Digital Publishing Institute
publishDate 2018
url https://doi.org/10.3390/rs10071022
op_coverage agris
long_lat ENVELOPE(15.532,15.532,66.822,66.822)
geographic Boa
geographic_facet Boa
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
op_source Remote Sensing; Volume 10; Issue 7; Pages: 1022
op_relation Atmospheric Remote Sensing
https://dx.doi.org/10.3390/rs10071022
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs10071022
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