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Satellite vertical atmospheric sounding was initiated more than 50 years ago and has evolved to provide the most critical component of today's global observation system. However, the operational use of today's polar orbiting satellite hyperspectral infrared (IR) observations in numerical weather prediction (NWP) has been limited to a small fraction of the radiance information being provided. On the other hand, research systems are in operation that combines high vertical resolution polar hyperspectral radiance measurements with high spatial and time resolution geostationary multispectral radiance measurements that demonstrate the promise of future geo-hyperspectral sounding observations to significantly improve the forecast location and warning time for the development of localized tornadic storms. This article has a twofold objective: 1) to demonstrate that there is much more information available in current IR sounding data, than is being used to benefit the current NWP operation and 2) to illustrate the importance of the spectrometer technology (i.e., Fourier transform vs. dispersive grating) used for achieving the vertical profile resolution required to improve both extended range and localized severe weather forecasts. These objectives are achieved by performing both theoretical physics-based radiance information content (IC) studies and empirical analyses of current hyperspectral radiance measurements. The IC studies clearly demonstrate the unique importance of longwave IR (9−15 μm) radiance observations. The empirical studies demonstrate the importance of using Fourier transform spectrometers for providing the high spectral fidelity needed to resolve the small-scale vertical features in atmospheric temperature and moisture profiles, which impact weather forecast accuracy.

Simplex volume is the most commonly used parameter for nonnegative matrix factorization (NMF)-based endmember estimation methods, and one of the most popular methods is the NMF method with minimum volume constraint (MVC-NMF). However, when outliers exist in the image, MVC-NMF tends to extract them as endmembers. In most cases, those outlier endmembers could be either physically meaningless or not representative enough for prevalent land covers. So how to extract prevalent land covers instead of outliers as endmembers is a very challenging question. In this letter, we propose a new NMF method with the dual constraints of simplex volume and information content, named the “minimum volume and information constraint NMF” (MIVC-NMF). The method is based on the following facts: when a real endmember is replaced by an outlier, it will cause some pixels containing the replaced endmember not to locate within the endmember hyperplane, and the overall information content contained in the endmember hyperplane will be reduced. The experimental results based on the simulated and real data show that the proposed method outperforms several other commonly used endmember extraction approaches. [ABSTRACT FROM AUTHOR]