Cloud Detection and Classification Algorithms for Himawari-8 Imager Measurements Based on Deep Learning

Abstract

A deep-learning-based cloud detection and classification algorithm for advanced Himawari imager (AHI) measurements from the geostationary satellite Himawari-8 has been developed. It is found that a combination of observed radiances and simulated clear-sky radiances can substantially improve cloud phase discrimination, especially for optically thin clouds. Therefore, cloud detection, cloud phase classification, and multilayer cloud detection are obtained simultaneously from multispectral observed radiances and simulated clear-sky radiances using deep neural networks (DNNs). Two DNN models are established for all-day and daytime-only applications, respectively, using active Cloud Profiling Radar (CPR) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) merged cloud products from 2016 as reference labels. The independent dataset from 2017 is used to validate the DNN models. It is shown that both the DNN models outperform the official Moderate Resolution Imaging Spectroradiometer (MODIS) and AHI products in cloud detection and phase discrimination, and the enhancement is more significant over land than over water surface. For multilayer cloud detection, the probability of detecting multilayer clouds reaches ~60% for the all-day model and is increased to ~70% for the daytime model, which is substantially better than MODIS and AHI products. In practical cases, multilayer cloud detection by DNN models is more consistent with CPR/CALIOP than two official products. In addition, the DNN models have superior capability in detecting the optically thin cirrus, which is omitted by MODIS and AHI products. Specifically, the cases also demonstrate that the DNN models can provide effective mixed-phase cloud identification. This deep-learning-based algorithm has the potential for measurements from other similar instruments.