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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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Paolo Antonelli, Tiziana Cherubini, Steven Businger, Siebren de Haan, Paolo Scaccia, and Jean-Luc Moncet

Abstract

Satellite retrievals strive to exploit the information contained in thousands of channels provided by hyperspectral sensors and show promise in providing a gain in computational efficiency over current radiance assimilation methods by transferring computationally expensive radiative transfer calculations to retrieval providers. This paper describes the implementation of a new approach based on the transformation proposed in 2008 by Migliorini et al., which reduces the impact of the a priori information in the retrievals and generates transformed retrievals (TRs) whose assimilation does not require knowledge of the hyperspectral instruments characteristics. Significantly, the results confirm both the viability of Migliorini’s approach and the possibility of assimilating data from different hyperspectral satellite sensors regardless of the instrument characteristics. The Weather Research and Forecasting (WRF) Model’s Data Assimilation (WRFDA) 3-h cycling system was tested over the central North Pacific Ocean, and the results show that the assimilation of TRs has a greater impact in the characterization of the water vapor distribution than on the temperature field. These results are consistent with the knowledge that temperature field is well constrained by the initial and boundary conditions of the Global Forecast System (GFS), whereas the water vapor distribution is less well constrained in the GFS. While some preliminary results on the comparison between the assimilation with and without TRs in the forecasting system are presented in this paper, additional work remains to explore the impact of the new assimilation approach on forecasts and will be provided in a follow-up publication.

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Tom Rink, W. Paul Menzel, Paolo Antonelli, Tom Whittaker, Kevin Baggett, Liam Gumley, and Allen Huang

A freeware-based multispectral data analysis tool kit for satellite data has been developed to assist research and development of remote-sensing applications as well as education and training of remote-sensing scientists; it is called HYDRA—HYper-spectral data viewer for Development of Research Applications. HYDRA provides a fast and flexible interface that allows users to explore and visualize relationships between radiances (or reflectances and brightness temperatures) and wavelength (or wavenumber) using spectra diagrams, cross sections, scatter plots, multichannel combinations, and color enhancements on a pixel-by-pixel basis with full access to the underlying metadata of location and time.

HYDRA enables interrogation of multispectral (and hyperspectral) fields of data so that a) pixel location and spectral measurement values can be easily displayed; b) spectral channels can be combined in linear functions and the resulting images displayed; c) false color images can be constructed from multiple channel combinations; d) scatter plots of spectral channel combinations can be viewed; e) pixels in images can be found in scatter plots, and vice versa; f) transects of measurements can be displayed; and g) soundings of temperature and moisture as well as spectra from selected pixels can be compared.

The World Meteorological Organization has added HYDRA to its Virtual Laboratory for Satellite Training and Data Utilization to enable research with satellite data and to enhance training capabilities. The Virtual Laboratory is designed to provide the instructors and students with a set of easy-to-use tools for creating and conducting training sessions. HYDRA is now part of this international tool kit.

This paper describes some of the procedures for displaying multispectral data using HYDRA and presents some examples with Moderate Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infrared Sounder (AIRS) 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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Paolo Antonelli, Henry E. Revercomb, Graziano Giuliani, Tiziana Cherubini, Steven Businger, Ryan Lyman, Stephen Tjemkes, Rolf Stuhlmann, and Jean-Luc Moncet

Abstract

The Space Science Engineering Center, in collaboration with the Mauna Kea Weather Center at the University of Hawai’i at Mānoa, has developed a regional retrieval processor for high-spectral-resolution infrared data. The core of the processor makes use of an inversion system, referred to as Mirto, which combines, in a Bayesian way, the a priori knowledge of the atmospheric state, based on available numerical weather prediction forecasts, with the physical information embedded in satellite observations. Forecast temperature and water vapor mixing ratio fields over the central North Pacific Ocean are adjusted to produce synthetic radiances closer and closer to the Suomi NPP Cross-track Infrared Sounder (CrIS) observations taken in clear-sky conditions. The paucity of synoptic observations over this area and the highly homogeneous background represented by the ocean provide a good framework for the implementation of this hyperspectral data inversion system. Nearly real-time (less than 60 min from overpass time) Internet publication of retrieved atmospheric profiles is made possible by the availability of a direct broadcast system that provides data from the Suomi NPP platform (CrIS and VIIRS). The main goal for the implemented system is to provide the forecasting community with products suitable for nowcasting applications and for optimal data assimilation. The implemented processor has been running routinely since August 2013. Validation based on the comparisons of retrievals with rawinsonde data from Hilo, Hawaii, and Lihue, Hawaii, and GPS-derived total precipitable water from four stations, performed over a time period of more than 1 year, shows a statistically significant improvement on the background atmospheric state used as a priori information.

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Robert E. Holz, Steve Ackerman, Paolo Antonelli, Fred Nagle, Robert O. Knuteson, Matthew McGill, Dennis L. Hlavka, and William D. Hart

Abstract

An improvement to high-spectral-resolution infrared cloud-top altitude retrievals is compared to existing retrieval methods and cloud lidar measurements. The new method, CO2 sorting, determines optimal channel pairs to which the CO2 slicing retrieval will be applied. The new retrieval is applied to aircraft Scanning High-Resolution Interferometer Sounder (S-HIS) measurements. The results are compared to existing passive retrieval methods and coincident Cloud Physics Lidar (CPL) measurements. It is demonstrated that when CO2 sorting is used to select channel pairs for CO2 slicing there is an improvement in the retrieved cloud heights when compared to the CPL for the optically thin clouds (total optical depths less than 1.0). For geometrically thick but tenuous clouds, the infrared retrieved cloud tops underestimated the cloud height, when compared to those of the CPL, by greater than 2.5 km. For these cases the cloud heights retrieved by the S-HIS correlated closely with the level at which the CPL-integrated cloud optical depth was approximately 1.0.

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