Abstract
Studying the well-known phenomenon “aurora” plays a pivotal role in investigating the solar–terrestrial coupling mechanism. A special auroral spectrograph in Antarctic Zhongshan Station constitutes a auroral observation joint system with satellite-borne sensors of the Defense Meteorological Satellite Program. Multipoint observation by this system provides more essential information for relevant studies than single observation by each instrument, but also results in a multifold increased volume of data that are difficult to be either stored or transmitted. To address this difficulty, we develop a clustering-based, generic lossless data compression framework that combines the usage of various ultimate compressors with a hierarchical clustering algorithm to exert the strength of all the compressors in data reduction. This framework achieves an always-best compression performance for different-sized datasets with a reasonable time consumption, which promises the design of pipelines using it for real-time data transmission.