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Bormin Huang, Alok Ahuja, and Hung-Lung Huang

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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Hung-Lung Huang and George R. Diak

Abstract

A new microwave algorithm, analogous to the infrared “radiance-ratioing” method (Eyre and Menzel 1989) is developed to retrieve the height and “effective” fraction (defined as the product of the emissivity times the actual physical fractional coverage) of nonprecipitating water clouds using various pairs of the 20 microwave channels planned for the Advanced Microwave Sounding Unit (AMSU), an instrument slated to fly on polar-orbiting satellites beginning in 1994. The results of a simulation study are presented to provide some insights into the potentials of this technique using different AMSU channel combinations. This study suggests that the use of the oxygen channels 3 and 5 and water vapor channels 19 and 20 will produce the most accurate retrievals of liquid water cloud parameters and the highest percentage of good-quality retrievals over a range of meteorological and cloud conditions. The use of channels 1, 2, 16, and 17, which all may have a strong surface component in their measured brightness temperature, does not give optimal results chiefly because the large uncertainties in the microwave surface temperature and emissivity obscure the brightness–temperature signatures of cloud liquid water. As with the infrared radiance ratioing method (and similar C02 slicing techniques), the best retrieval of cloud parameters is for high cloud, with poorer results for those at middle and low levels.

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Jason A. Otkin, Derek J. Posselt, Erik R. Olson, Hung-Lung Huang, James E. Davies, Jun Li, and Christopher S. Velden

Abstract

A novel application of numerical weather prediction (NWP) models within an end-to-end processing system used to demonstrate advanced hyperspectral satellite technologies and instrument concepts is presented. As part of this system, sophisticated NWP models are used to generate simulated atmospheric profile datasets with fine horizontal and vertical resolution. The simulated datasets, which are treated as the “truth” atmosphere, are subsequently passed through a sophisticated forward radiative transfer model to generate simulated top-of-atmosphere (TOA) radiances across a broad spectral region. Atmospheric motion vectors and temperature and water vapor retrievals generated from the TOA radiances are then compared with the original model-simulated atmosphere to demonstrate the potential utility of future hyperspectral wind and retrieval algorithms. Representative examples of TOA radiances, atmospheric motion vectors, and temperature and water vapor retrievals are shown to illustrate the use of the simulated datasets.

Case study results demonstrate that the numerical models are able to realistically simulate mesoscale cloud, temperature, and water vapor structures present in the real atmosphere. Because real hyperspectral radiance measurements with high spatial and temporal resolution are not available for large geographical domains, the simulated TOA radiance datasets are the only viable alternative that can be used to demonstrate the new hyperspectral technologies and capabilities. As such, sophisticated mesoscale models are critically important for the demonstration of the future end-to-end processing system.

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