A Neural Network Method for Ozone Retrieval Using Himawari-8/AHI Geo-Satellite Observations

Ozone is a crucial atmospheric trace gas that protects life on Earth from harmful ultraviolet radiation. However, at low altitudes in the troposphere, ozone adversely impacts climate, human health, and ecosystems. Therefore, monitoring atmospheric ozone concentration and spatial distribution is essential. Ground-based monitoring sites are sparse and do not provide comprehensive coverage. Satellites have been used for global ozone monitoring for decades. Polar-orbiting satellites have the disadvantage of low temporal resolution, providing data from one overpass per day under cloud-free conditions. Better coverage is obtained by using geostationary data offering multiple observations daily. This study proposes a neural network for ozone retrieval using data from the Himawari-8 (H8) geostationary satellite (NNO3-G), which has a strong ozone absorption spectral band at $9.6~\mu $ m. ERA5 total column ozone (TCO) data is used to train a fully connected neural network (FCNN) model to retrieve TCO in cloud-free areas of H8 images. FCNN shows best accuracy compared with other machine learning models. The retrieved TCO product has a spatial resolution of $2\times 2$ km and a temporal resolution of 10 min. The NNO3-G-retrieved TCO data are well-correlated with Pandora ground-based measurements, with Pearson’s correlation coefficient R of 0.95, ${R} ^{2}$ of 0.89, and mean absolute error (MAE) of 8.88 DU. The retrieval accuracy is better in low-latitude regions than in high-latitude regions, with the best performance in summer. The major outcome of this study is the use of one geostationary satellite for retrieving TCO, offering both high time resolution and high precision.