Contrail Recognition with Convolutional Neural Network and Contrail Parameterizations Evaluation

Abstract

There is growing attention that the contrail by aviation may affect the earth's energy balance and climate change. In this paper, we propose a novel approach, the convolutional neural network model termed ContrailMod, which can be used in contrail classification with Himawari-8 stationary satellite and outperforms the representative conventional algorithm contrail detection algorithm (CDA). We estimate the distribution of potential contrail formation using temperature and specific humidity from ECMWF reanalysis (ERA-Interim) in South China region. According to the convolutional neural network identification (CNNI) and artificial visual inspection (AVI), we adopt the contrail occurrence and persistence (COP) measured from Himawari-8 stationary satellite imagery to evaluate the potential contrail coverage (PCC) fractions of the ECMWF reanalysis data. There is a high correlation between contrail occurrence and persistence and potential contrail coverage. The correlation coefficient of convolutional neural network identification is close to artificial visual inspection, which illustrates that the parameterization is reliable by comparing the observation results and the actual reflection of contrail coverage in parameterization calculation of South China region.