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Three-dimensional compression of mesoscale meteorological data based on JPEG 2000

Computer modeling programs such as the Battlescale Forecast Model are capable of generating three-dimensional (3-D) Meteorological (Met) data with sufficiently fine spatial resolution to warrant a study of data compression methods for efficient storage and/or transmission of this data. This paper illustrates the potential benefits of applying lossy/irreversible data compression techniques to such Met variables as air pressure. Because of the advanced state of development of digital image compression methods such as the JPEG 2000 algorithm which is already an international standard, the approach considered and illustrated in this paper uses the two-dimensional (2-D), single-component JPEG 2000 algorithm on horizontal 2-D slices of data. Much better results are obtained by first pre-processing the 3-D data in the vertical direction by applying a one-dimensional, energy compacting, reversible linear transformation. The best possible pre-processing which involves the Karhunen-Loeve Transform which is shown to increases compression ratios for the same signal-to-noise ration (SNR) by a factor of 10 over the 2-D (no pre-processing) approach. Alternatively, for the same bit rate, the SNR is improved by up to 40 dB.