This paper presents systematic lossless data compression studies conducted at Cooperative Institute of Meteorological
Satellite Studies (CIMSS), University of Wisconsin-Madison in support of the real-time rebroadcast of NOAA's future
GOES ultraspectral sounders. Ultraspectral sounders provide data with high spectral and spatial resolutions. Since an
ultraspectral sounder could be either a grating spectrometer or a Michelson interferometer, we have
investigated/developed various 2D and 3D lossless compression techniques for both grating and interferometer data.
The lossless compression results are obtained and compared from wavelet/multiwavelt transform-based (e.g.
JPEG2000, 3D SPIHT, MWT), prediction-based (e.g. JPEG-LS, CALIC), projection-based (e.g. Lossless PCA,
Optimized Orthogonal Matching Pursuit-based Linear Prediction, PLT), and clustering-based (e.g. PPVQ, FPVQ,
AVQLP) methods. Robust data preprocessing schemes (e.g. BAR, MST reordering) are also demonstrated to improve
compression gains of existing state-of-the-art compression methods such as JPEG2000, 3D SPIHT, JPEG-LS, and
CALIC for high-spectral-resolution data compression. Our studies show that high lossless compression gains are
achievable for both grating and interferometer data.