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Hung-Lung Huang and Paolo Antonelli

Abstract

A simulation study is used to demonstrate the application of principal component analysis to both the compression of, and meteorological parameter retrieval from, high-resolution infrared spectra. The study discusses the fundamental aspects of spectral correlation, distributions, and noise; the correlation between principal components (PCs) and atmospheric-level temperature and water vapor; and how an optimal subset of PCs is selected so a good compression ratio and high retrieval accuracy are obtained.

Principal component analysis, principal component compression, and principal component regression under certain conditions are shown to provide 1) nearly full spectral information with little degradation, 2) noise reduction, 3) data compression with a compression ratio of approximately 15, and 4) tolerable loss of accuracy in temperature and water vapor retrieval. The techniques will therefore be valuable tools for data compression and the accurate retrieval of meteorological parameters from new-generation satellite instruments.

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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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Hung-Lung Huang, William L. Smith, and Harold M. Woolf

Abstract

A theoretical analysis is performed to evaluate the accuracy and vertical resolution of atmospheric profiles obtained with the HIRS/2, GOES I/M, and HIS instruments. In addition, a linear simultaneous retrieval algorithm is used with aircraft observations to validate the theoretical predictions. Both theoretical and observational results clearly indicate that the accuracy and vertical resolution of the retrieval profile would be improved by high spectral resolution and broad spectral coverage of infrared radiance measurements.

The HIS is found to possess the equivalent of 11 pieces of temperature-and 9 pieces of water vapor-independent precise measurements. The characteristics for temperature include a vertical resolution of 1–6 km with an accuracy of 1 K and for water vapor a vertical resolution of 0.5–3.0 km with an accuracy of 3 K in dewpoint temperature. The HIS is a factor of 2–3 times better in vertical resolution and a factor of 2 times better in accuracy than the GOES 1/M and HIRS/2 filter radiometers.

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Jason A. Otkin, Thomas J. Greenwald, Justin Sieglaff, and Hung-Lung Huang

Abstract

In this study, the accuracy of a simulated infrared brightness temperature dataset derived from a unique large-scale, high-resolution Weather Research and Forecasting (WRF) Model simulation is evaluated through a comparison with Spinning Enhanced Visible and Infrared Imager (SEVIRI) observations. Overall, the analysis revealed that the simulated brightness temperatures realistically depict many of the observed features, although several large discrepancies were also identified. The similar shapes of the simulated and observed probability distributions calculated for each infrared band indicate that the model simulation realistically depicted the cloud morphology and relative proportion of clear and cloudy pixels. A traditional error analysis showed that the largest model errors occurred over central Africa because of a general mismatch in the locations of deep tropical convection and intervening regions of clear skies and low-level cloud cover. A detailed inspection of instantaneous brightness temperature difference (BTD) imagery showed that the modeling system realistically depicted the radiative properties associated with various cloud types. For instance, thin cirrus clouds along the edges of deep tropical convection and within midlatitude cloud shields were characterized by much larger 10.8 − 12.0-μm BTD than optically thicker clouds. Simulated ice clouds were effectively discriminated from liquid clouds and clear pixels by the close relationship between positive 8.7 − 10.8-μm BTD and the coldest 10.8-μm brightness temperatures. Comparison of the simulated and observed BTD probability distributions revealed that the liquid and mixed-phase cloud-top properties were consistent with the observations, whereas the narrower BTD distributions for the colder 10.8-μm brightness temperatures indicated that the microphysics scheme was unable to simulate the full dynamic range of ice clouds.

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Suzanne W. Seemann, Eva E. Borbas, Robert O. Knuteson, Gordon R. Stephenson, and Hung-Lung Huang

Abstract

A global database of infrared (IR) land surface emissivity is introduced to support more accurate retrievals of atmospheric properties such as temperature and moisture profiles from multispectral satellite radiance measurements. Emissivity is derived using input from the Moderate Resolution Imaging Spectroradiometer (MODIS) operational land surface emissivity product (MOD11). The baseline fit method, based on a conceptual model developed from laboratory measurements of surface emissivity, is applied to fill in the spectral gaps between the six emissivity wavelengths available in MOD11. The six available MOD11 wavelengths span only three spectral regions (3.8–4, 8.6, and 11–12 μm), while the retrievals of atmospheric temperature and moisture from satellite IR sounder radiances require surface emissivity at higher spectral resolution. Emissivity in the database presented here is available globally at 10 wavelengths (3.6, 4.3, 5.0, 5.8, 7.6, 8.3, 9.3, 10.8, 12.1, and 14.3 μm) with 0.05° spatial resolution. The wavelengths in the database were chosen as hinge points to capture as much of the shape of the higher-resolution emissivity spectra as possible between 3.6 and 14.3 μm. The surface emissivity from this database is applied to the IR regression retrieval of atmospheric moisture profiles using radiances from MODIS, and improvement is shown over retrievals made with the typical assumption of constant emissivity.

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Jun Li, Walter W. Wolf, W. Paul Menzel, Wenjian Zhang, Hung-Lung Huang, and Thomas H. Achtor

Abstract

The International Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) Processing Package (IAPP) has been developed to retrieve the atmospheric temperature profile, moisture profile, atmospheric total ozone, and other parameters in both clear and cloudy atmospheres from the ATOVS measurements. The algorithm that retrieves these parameters contains four steps: 1) cloud detection and removal, 2) bias adjustment for ATOVS measurements, 3) regression retrieval processes, and 4) a nonlinear iterative physical retrieval. Nine (3 × 3) adjacent High-Resolution Infrared Sounder (HIRS)/3 spot observations, together with Advanced Microwave Sounding Unit-A observations remapped to the HIRS/3 resolution, are used to retrieve the temperature profile, moisture profile, surface skin temperature, total atmospheric ozone and microwave surface emissivity, and so on. ATOVS profile retrieval results are evaluated by root-mean-square differences with respect to radiosonde observation profiles. The accuracy of the retrieval is about 2.0 K for the temperature at 1-km vertical resolution and 3.0–6.0 K for the dewpoint temperature at 2-km vertical resolution in this study. The IAPP is now available to users worldwide for processing the real-time ATOVS data.

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Hung-Lung Huang, William L. Smith, Jun Li, Paolo Antonelli, Xiangqian Wu, Robert O. Knuteson, Bormin Huang, and Brian J. Osborne

Abstract

This paper describes the theory and application of the minimum local emissivity variance (MLEV) technique for simultaneous retrieval of cloud pressure level and effective spectral emissivity from high-spectral-resolution radiances, for the case of single-layer clouds. This technique, which has become feasible only with the recent development of high-spectral-resolution satellite and airborne instruments, is shown to provide reliable cloud spectral emissivity and pressure level under a wide range of atmospheric conditions. The MLEV algorithm uses a physical approach in which the local variances of spectral cloud emissivity are calculated for a number of assumed or first-guess cloud pressure levels. The optimal solution for the single-layer cloud emissivity spectrum is that having the “minimum local emissivity variance” among the retrieved emissivity spectra associated with different first-guess cloud pressure levels. This is due to the fact that the absorption, reflection, and scattering processes of clouds exhibit relatively limited localized spectral emissivity structure in the infrared 10–15-μm longwave region. In this simulation study it is shown that the MLEV cloud pressure root-mean-square errors for a single level with effective cloud emissivity greater than 0.1 are ∼30, ∼10, and ∼50 hPa, for high (200– 300 hPa), middle (500 hPa), and low (850 hPa) clouds, respectively. The associated cloud emissivity root-mean-square errors in the 900 cm−1 spectral channel are less than 0.05, 0.04, and 0.25 for high, middle, and low clouds, respectively.

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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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Daniel K. Zhou, William L. Smith Sr., Xu Liu, Allen M. Larar, Stephen A. Mango, and Hung-Lung Huang

Abstract

A physical inversion scheme has been developed dealing with cloudy as well as cloud-free radiance observed with ultraspectral infrared sounders to simultaneously retrieve surface, atmospheric thermodynamic, and cloud microphysical parameters. A fast radiative transfer model, which applies to the clouded atmosphere, is used for atmospheric profile and cloud parameter retrieval. A one-dimensional (1D) variational multivariable inversion solution is used to improve an iterative background state defined by an eigenvector-regression retrieval. The solution is iterated in order to account for nonlinearity in the 1D variational solution. It is shown that relatively accurate temperature and moisture retrievals can be achieved below optically thin clouds. For optically thick clouds, accurate temperature and moisture profiles down to cloud-top level are obtained. For both optically thin and thick cloud situations, the cloud-top height can be retrieved with relatively high accuracy (i.e., error <1 km). National Polar-orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Testbed Interferometer (NAST-I) retrievals from the The Observing-System Research and Predictability Experiment (THORPEX) Atlantic Regional Campaign are compared with coincident observations obtained from dropsondes and the nadir-pointing cloud physics lidar (CPL). This work was motivated by the need to obtain solutions for atmospheric soundings from infrared radiances observed for every individual field of view, regardless of cloud cover, from future ultraspectral geostationary satellite sounding instruments, such as the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS). However, this retrieval approach can also be applied to the ultraspectral sounding instruments to fly on polar satellites, such as the Infrared Atmospheric Sounding Interferometer (IASI) on the European MetOp satellite, the Cross-track Infrared Sounder (CrIS) on the NPOESS Preparatory Project, and the follow-on NPOESS series of satellites.

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