609 Updated analysis of lossless compression techniques for the GOES-R Rebroadcasting (GRB) sub-system

Wednesday, 26 January 2011
Washington State Convention Center
Peter M Finocchio, Univ. of Miami/RSMAS, Miami, FL; and Y. He, D. B. Hogan, D. Hunt, and T. S. Zaccheo
Manuscript (252.0 kB)

This work describes an in-depth analysis of potential lossless data compression techniques for use in the design, development and deployment of the GOES-R ReBroadcasting (GRB) sub-system. GRB is the next generation transponder-based communication link integrated into the GOES-R system, and is designed to provide the NOAA Satellite Operations Facility (NSOF) and other real-time users with Level 1b and Level 2+ GOES data. The GRB data stream will include calibrated and geo-located Level 1b imagery from the Advance Baseline Imager (ABI), Level 1b Solar Ultraviolet Imager (SUVI) data and Level 2+ Geostationary Lightning Mapper (GLM) products. In order to ensure that the GRB bandwidth constraints are met at all times, lossless data compression must be employed. In this study we assess the potential use of szip and lossless jpeg2000 combined with other techniques such as K-L transformation of like spectral channels to meet these constraints. In addition, we assess the impact of simulated uncorrected channel errors on the resulting data reconstruction processes. We also explore the impacts of various data block configurations on the latency of the compression algorithms. The results from this study were developed using an integrated testbed environment that combines the use of modular software techniques with extensive proxy scenes derived from similar data. These data include a diverse set of temporally and seasonally varying information from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Lightning Imaging Sensor (LIS). This study indicates that well established lossless compression methods can be used to meet the GOES-R bandwidth constraints. Under nominal conditions, these constraints can be met with additional margin. However, rigorous design principles must be employed to meet the data demands of an extreme weather event.
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner