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The compression–error trade-off for large gridded data sets

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Academic year: 2017

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Table 1. Summary of the data sets used in this study. Abbreviations: ID – index, NWP – numerical weather prediction, CTM – chemistry- chemistry-transport model, Dims – dimensions of the variable, TS Dims – dimensions of the thin slice, # vars – number of v
Figure 2. The relationship between normalized errors and compres- compres-sion ratio for the lossy comprescompres-sion methods considered
Figure 3. Relative performance of LAY compared with NSD3 (left column) or NSD4 (right column), in terms of compression (top row) and errors (bottom row)

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