Data compression techniques as a means of reducing the storage requirements for satellite data: a quantitative comparison

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>Quantifying volcanic ash emissions syneruptively is an important task for the global aviation community. However, due to the near real time nature of volcano monitoring, many parameters important for accurate ash mass estimates cannot be obtained easily. Even when using the best possible estimates of those parameters, uncertainties associated with the ash masses remain high, especially if the satellite data is only available in the traditional 10.8 and 12.0 μm bands. To counteract this limitation, we developed a quantitative comparison between the ash extents in satellite and model data. The focus is the manual cloud edge definition based on the available satellite reverse absorption (RA) data as well as other knowledge like pilot reports or ground-based observations followed by an application of the Volcanic Ash Retrieval on the defined subset with an RA threshold of 0 K. This manual aspect, although subjective to the experience of the observer, can show a significant improvement as it provides the ability to highlight ash that otherwise would be obscured by meteorological clouds or, by passing over different surfaces with unaccounted temperatures, might be lost entirely and thus remains undetectable for an automated satellite approach. We show comparisons to Volcanic Ash Transport and Dispersion models and outline a quantitative match as well as percentages of overestimates based on satellite or dispersion model data which can be converted into a level of reliability for near real time volcano monitoring. </span></p></div></div></div>

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Lossless Satellite Data Compression

Optical Satellite Data Compression and Implementation ◽

10.1117/3.1002297.ch2 ◽

2013 ◽

Keyword(s):

Data Compression ◽

Satellite Data

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Optimal Compression of High Spectral Resolution Satellite Data via Adaptive Vector Quantization with Linear Prediction

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2007jtecha917.1 ◽

2008 ◽

Vol 25 (6) ◽

pp. 1041-1047 ◽

Cited By ~ 2

Author(s):

Bormin Huang ◽

Alok Ahuja ◽

Hung-Lung Huang

Keyword(s):

Data Compression ◽

Vector Quantization ◽

Satellite Data ◽

Spectral Resolution ◽

Image Coding ◽

Linear Prediction ◽

Lossless Compression ◽

High Spectral Resolution ◽

Adaptive Vector Quantization ◽

Space Data

Abstract Contemporary and future high spectral resolution sounders represent a significant technical advancement for environmental and meteorological prediction and monitoring. Given their large volume of spectral observations, the use of robust data compression techniques will be beneficial to data transmission and storage. In this paper, a novel adaptive vector quantization (VQ)-based linear prediction (AVQLP) method for lossless compression of high spectral resolution sounder data is proposed. The AVQLP method optimally adjusts the quantization codebook sizes to yield the maximum compression on prediction residuals and side information. The method outperforms the state-of-the-art compression methods [Joint Photographic Experts Group (JPEG)-LS, JPEG2000 Parts 1 and 2, Consultative Committee for Space Data Systems (CCSDS) Image Data Compression (IDC) 5/3, Context-Based Adaptive Lossless Image Coding (CALIC), and 3D Set Partitioning in Hierarchical Trees (SPIHT)] and achieves a new high in lossless compression for the standard test set of 10 NASA Atmospheric Infrared Sounder (AIRS) granules. It also compares favorably in terms of computational efficiency and compression gain to recently reported adaptive clustering methods for lossless compression of high spectral resolution data. Given its superior compression performance, the AVQLP method is well suited to ground operation of high spectral resolution satellite data compression for rebroadcast and archiving purposes.

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