Development of a high spatiotemporal resolution cloud-type classification approach using Himawari-8 and CloudSat

Abstract

With the cloud scenario products from CloudSat, we developed a high spatiotemporal resolution cloud-type classification procedure for Himawari-8 multispectral datasets using maximum-likelihood estimation (MLE) and random forests (RF) classification. The training and classification procedures were processed independently, and both algorithms provided cloud-type results with a good performance. Validation indicated that the use of the visible (VIS) channel significantly improved the cloud-type identification capabilities, while the use of three or more channels simultaneously resulted in considerable improvements over the use of bispectral combinations. The comparison among different classifiers also revealed that RF was more sensitive than MLE to the quality and distribution of the training data. After retraining the RF using MLE-based clustered samples, we produced two more-reasonable and efficient classifiers that can be used during the day and night.