Retrieval of Horizontal Visibility Using MODIS Data: A Deep Learning Approach

Clicks: 228
ID: 111779
2019
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Abstract
Horizontal visibility (HVIS) is a primary index used for assessing air quality. Although satellite images provide information regarding atmospheric aerosols, atmospheric visibility is not directly measured. In this paper, a deep learning approach is proposed to retrieve HVIS using moderate-resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data, the European Centre for Medium-Range Weather Forecasts reanalysis dataset, and ground-based visibility observations. The deep neural network model comprises a multi-layer unsupervised restricted Boltzmann machine (RBM) and a layer for supervised learning. The dropout mechanism was used in the training process to overcome the errors caused by over-fitting. The results demonstrate that the correlation coefficient values between HVIS observations and retrievals during training, pre-validating, and evaluation were 0.74, 0.723, and 0.697, respectively. The retrieved HVIS in Eastern China exhibited a north-to-south increasing trend, increasing and decreasing in summer and winter, respectively. In conclusion, the proposed model presents an effective and more reliable method for HVIS retrieval. However, the small samples, low AOD, low albedo, high total column water, high longitude, and the low vertical wind component at 10 m likely cause HVIS bias.
Reference Key
hu2019atmosphereretrieval Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Bo Hu;Xingying Zhang;Rui Sun;Xianchun Zhu;Hu, Bo;Zhang, Xingying;Sun, Rui;Zhu, Xianchun;
Journal atmosphere
Year 2019
DOI
10.3390/atmos10120740
URL
https://doi.org/10.3390/atmos10120740##framed_div_cited_count
Keywords
Deep learning modis aod hvis rbm eastern china

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