提升小波变换在气象格点数据无损压缩中的应用

doi:10.11867/j.issn.1001-8166.2012.04.0460

地球科学进展 ›› 2012, Vol. 27 ›› Issue (4): 460 -465. doi: 10.11867/j.issn.1001-8166.2012.04.0460

罗坚,姜勇强,戴彩悌

解放军理工大学气象学院，江苏南京211101

收稿日期:2011-08-22 修回日期:2012-03-15 出版日期:2012-04-10
通讯作者: 罗坚(1965-),男，湖南长沙人，副教授，主要从事气象业务自动化、热带气象学研究. E-mail:jetjsp@126.com
基金资助:
国家自然科学基金项目“热带气旋螺旋带波动性质的研究”(编号：40905021)资助.

An Application of LWT to Lossless Compression of Meteorological Grid Data

Luo Jian, Jiang Yongqiang, Dai Caiti

Institute of Meteorology, PLA University of Science and Technology, Nanjing211101, China

Received:2011-08-22 Revised:2012-03-15 Online:2012-04-10 Published:2012-04-10

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提升小波变换实现了整数到整数的变换，同时保证了小波变换的正交特性。在二维预测编码基础上引入提升小波变换，并结合“洗牌”技术和熵编码，设计了气象格点数据的无损压缩方案。以位势高度、经向风、纬向风、温度4个要素为例，对数据处理前后的熵值进行了对比分析，并设计了4种方案进行验证。结果表明：提升小波变换能有效去除数据间的相关性，消除冗余信息，降低数据集的熵值，与二维预测编码相结合能实现优势互补，提高压缩效率，并能保证在有效精度内数据完全无损，从而有效提高气象海量数据的存储和传输效率，为快速气象数据响应和预报服务提供数据支持。

关键词: 提升小波变换, 预测编码, 格点资料, 无损压缩

Lift-Wavelet Transformation (LWT) provided a method to transform integer into integer preserving the orthogonality. A lossless compression scheme for meteorological grid data is performed by use of LWT based on the two-dimensional prediction coding, shuffle method and entropy coding. The entropy of geopotential height, meridional wind, zonal wind and temperature by coding and decoding is compared with that of original fields. The comparisons of four compression experiments show that the LWT is available to eliminate the pertinency and redundancy of data, and then to decrease the entropy. LWT and two-dimensional prediction coding method can take the advantages of each other. The combination of these two methods could improve the compression efficiency, and the compressed data are completely lossless within available precision. The new method can promote the storage and exchange efficiency of mass meteorological data, supporting the rapid response of meteorological data and forecast services.

Key words: Lift-Wavelet Transformation (LWT), Prediction coding, Grid data, Lossless compression

中图分类号:

P456.1
TP302

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[1]	赵苏璇，罗坚，杨成荫. 基于BP神经网络的气象格点数据无损压缩方法[J]. 地球科学进展, 2008, 23(2): 206-213.
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