Enhancement of OMI aerosol optical depth data assimilation using artificial neural network

This study presents the development of an artificial neural network (ANN) model to quantitatively estimate the atmospheric aerosol load (in terms of aerosol optical depth, AOD), with an emphasis on dust, over the Mediterranean basin using images from Meteosat satellites as initial information. More specifically, a back-propagation ANN model scheme was developed to estimate visible (at 550 nm) aerosol optical depth (AOD550 nm) values at equal temporal (15 min) and spatial (4 km) resolutions with Meteosat imagery. Accuracy of the ANN model was thoroughly tested by comparing model estimations with ground-based AOD550 nm measurements from 14 AERONET (Aerosol Robotic NETwork) stations over the Mediterranean for 34 selected days in which significant dust loads were recorded over the Mediterranean basin. Using a testbed of 3076 pairs of modeled and measured AOD550 nm values, a Pearson correlation coefficient (rP) equal to 0.91 and a mean absolute error (MAE) of 0.031 were found, proving the satisfactory accuracy of the developed model for estimating AOD550 nm values.

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

Remote Sensing ◽

10.3390/rs10071022 ◽

2018 ◽

Vol 10 (7) ◽

pp. 1022 ◽

Cited By ~ 29

Author(s):

Wenmin Qin ◽

Lunche Wang ◽

Aiwen Lin ◽

Ming Zhang ◽

Muhammad Bilal

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Radiative Effect ◽

Artificial Neural ◽

Aerosol Radiative

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Extracting Taklimakan Dust Parameters from AIRS with Artificial Neural Network Method

Remote Sensing ◽

10.3390/rs11242931 ◽

2019 ◽

Vol 11 (24) ◽

pp. 2931

Author(s):

Zhigang Yao ◽

Jun Li ◽

Zengliang Zhao ◽

Lin Zhu ◽

Jin Qi ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Correlation Coefficient ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Orthogonal Polarization ◽

Taklimakan Desert ◽

Retrieval Model ◽

Retrieval Models ◽

Artificial Neural

Two back-propagation artificial neural network retrieval models have been developed for obtaining the dust aerosol optical depth (AOD) and dust-top height (DTH), respectively, from Atmospheric InfraRed Sounder (AIRS) brightness temperature (BT) measurements over Taklimakan Desert area. China Aerosol Remote Sensing Network (CARSNET) measurements at Tazhong station were used for dust AOD validation. Results show that the correlation coefficient of dust AODs between AIRS and CARSNET reaches 0.88 with a deviation of −0.21, which is the same correlation coefficient as the AIRS dust AOD and the Moderate-Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product. In the AIRS DTH retrieval model, there is an option to include the collocated MODIS deep blue (DB) AOD as additional input for daytime retrieval; the independent dust heights from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used for AIRS DTH validation, and results show that the DTHs derived from the combined AIRS BT measurements and MODIS DB AOD product have better accuracy than those from AIRS BT measurements alone. The correlation coefficient of DTHs between AIRS and independent CALIOP dust heights is 0.79 with a standard deviation of 0.41 km when MODIS DB AOD product is included in the retrieval model. A series of case studies from different seasons were examined to demonstrate the feasibility of retrieving dust parameters from AIRS and potential applications. The method and approaches can be applied to process measurements from advanced infrared (IR) sounder and high-resolution imager onboard the same platform.

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