Kevin Baggett

International MODIS and AIRS processing package: AIRS products and applications

The high-spectral-resolution AIRS (Atmospheric InfraRed Sounder) instrument onboard the NASA (National Aeronautics and Space Administration) Earth Observing System (EOS)-Aqua satellite represents the most advanced sounding system in space and provides unprecedented wealth of highly accurate radiance measurements. This paper describes a standalone and fast single field-of-view (FOV) algorithm to retrieve atmospheric sounding profiles (temperature, humidity, ozone) and surface parameters (surface skin temperature, surface emissivity) from AIRS Level 1B (L1B) clear only infrared radiance measurements. The retrieval algorithm is part of the International MODIS (Moderate Resolution Imaging Spectroradiometer)/AIRS Processing Package (IMAPP) software package, which provides international users with the capability of receiving and processing direct broadcast data in real-time. The IMAPP AIRS retrieval algorithm is based on principal component regression to obtain fast and accurate estimates of the atmospheric state at single FOV. This algorithm is designed specifically for real-time direct broadcast applications where sounding products can be processed efficiently at highest possible spatial resolution. Simulated radiance data is trained on a global set of profiles, representative of a wide variety of atmospheric scenes, which makes the algorithm globally applicable. The results presented and discussed in this paper demonstrate that the IMAPP AIRS retrieval product is rigorously evaluated by various product sources such as numerical weather prediction model analysis fields, retrieved parameters from the operational AIRS L2 product and data from other instruments.

Data compression studies for NOAA Hyperspectral Environmental Suite (HES) using 3D integer wavelet transforms with 3D set partitioning in hierarchical trees

The next-generation NOAA/NESDIS GOES-R hyperspectral sounder, now referred to as the HES (Hyperspectral Environmental Suite), will have hyperspectral resolution (over one thousand channels with spectral widths on the order of 0.5 wavenumber) and high spatial resolution (less than 10 km). Hyperspectral sounder data is a particular class of data requiring high accuracy for useful retrieval of atmospheric temperature and moisture profiles, surface characteristics, cloud properties, and trace gas information. Hence compression of these data sets is better to be lossless or near lossless. Given the large volume of three-dimensional hyperspectral sounder data that will be generated by the HES instrument, the use of robust data compression techniques will be beneficial to data transfer and archive. In this paper, we study lossless data compression for the HES using 3D integer wavelet transforms via the lifting schemes. The wavelet coefficients are processed with the 3D set partitioning in hierarchical trees (SPIHT) scheme followed by context-based arithmetic coding. SPIHT provides better coding efficiency than Shapiros original embedded zerotree wavelet (EZW) algorithm. We extend the 3D SPIHT scheme to take on any size of 3D satellite data, each of whose dimensions need not be divisible by 2N, where N is the levels of the wavelet decomposition being performed. The compression ratios of various kinds of wavelet transforms are presented along with a comparison with the JPEG2000 codec.

Lossless data compression studies for NOAA hyperspectral environmental suite using 3D integer wavelet transforms with 3D embedded zerotree coding

Hyperspectral sounder data is a particular class of data that requires high accuracy for useful retrieval of atmospheric temperature and moisture profiles, surface characteristics, cloud properties, and trace gas information. Therefore compression of these data sets is better to be lossless or near lossless. The next-generation NOAA/NESDIS GOES-R hyperspectral sounder, now referred to as the HES (Hyperspectral Environmental Suite), will have hyperspectral resolution (over one thousand channels with spectral widths on the order of 0.5 wavenumber) and high spatial resolution (less than 10 km). Given the large volume of three-dimensional hyperspectral sounder data that will be generated by the HES instrument, the use of robust data compression techniques will be beneficial to data transfer and archive. In this paper, we study lossless data compression for the HES using 3D integer wavelet transforms via the lifting schemes. The wavelet coefficients are then processed with the 3D embedded zerotree wavelet (EZW) algorithm followed by context-based arithmetic coding. We extend the 3D EZW scheme to take on any size of 3D satellite data, each of whose dimensions need not be divisible by 2N, where N is the levels of the wavelet decomposition being performed. The compression ratios of various kinds of wavelet transforms are presented along with a comparison with the JPEG2000 codec.

Introducing HYDRA: A Multispectral Data Analysis Toolkit

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 chann...

Evaluation of cloud-cleared radiances for numerical weather prediction and cloud-contaminated sounding applications

The direct assimilation of satellite-measured infrared radiances into numerical weather prediction and cloud sounding applications is currently prohibited when these measurements include cloud radiative effects. The difficulty arises from the microphysical complexity of clouds and their radiative responses that are only now being adequately modeled for current and next generation satellite sensors. The parameterization of cloud properties to deliver much needed im-provements in speed and accuracy of forward radiative transfer models is still under development. Indirect use of cloud-contaminated radiances by way of cloud-cleared radiances has thus become the initial focus of efforts to improve the spatial density of useful satellite radiance measurements. This is particularly important for satellite sensors with relatively wide fields of view as the probability of entirely cloud-free observations can be surprisingly low. Two classes of cloud-cleared radiance retrieval approaches developed so far comprise the synergistic use of 1) collo-cated infrared and microwave measurements, and 2) collocated infrared imaging and sounding measurements. For example, AIRS/AMSU and AIRS/MODIS cloud-cleared algorithms are being demonstrated by NASA Earth Observing System and are to be adopted by NPP/NPOESS that have similar measurements available from the instrument suites CrIS/ATMS and CrIS/VIIRS. In this paper, the characteristics of these cloud-cleared radiances and their potential for numerical weather prediction and cloudy sounding applications are evaluated. Preliminary results have shown that these two approaches, though quite different in character, are both effective and complementary. Where microwave measurements are unavailable, the synergistic imaging/sounding approach to cloud-clearing is the only reliable indirect use of cloud-contaminated infrared measurements, as is the case for geostationary platforms due to the antenna requirements for a meteorologically useful microwave radiometer at 35,000 km.

Global analysis and characterization of AIRS/MODIS cloud clearing

The Atmospheric Infrared Sounder (AIRS) and MODerate-Resolution Imaging Spectroradiometer (MODIS) on board the EOS Aqua spacecraft measure the upwelling infrared radiance used for numerous remote sensing and climate related applications. AIRS provides high spectral resolution infrared radiances while MODIS provides collocated high spatial resolution radiances at sixteen broad infrared bands. An optimal algorithm for cloud-clearing has been developed for AIRS cloudy soundings at the University of Wisconsin-Madison where the spatially and spectrally collocated AIRS and MODIS data has been used to verify this algorithm. A global analysis and characterization of the AIRS cloud-clearing using the bias and the standard deviation between AIRS cloud-cleared brightness temperature and the nearby clear brightness temperature are studied.

AIRS Single Field of View Cloud Detection and Cloud Property Retrieval

A great need exists amongst X-band direct broadcast regional users for near real-time, high spatial resolution cloud detection and cloud property retrieval to support regional interdisciplinary applications. As part of the International MODIS and AIRS Processing Package (IMAPP), the objective treatment of spatial and spectral information, including principal component and residual techniques, is provided by the AIRS single field of view clear and cloud detection and cloud property retrieval algorithm. This algorithm, known as Minimum Local Emissivity Variance (MLEV), is used to retrieve both cloud height and cloud spectral emissivity. The ECMWF model analysis is used to demonstrate that high quality clear radiances can improve the yield and quality of cloud spectral emissivity and height, quantities that are precursors to retrieving cloud micro-physical properties and cloudy sounding profiles. In this paper we describe in detail the procedure employed to achieve this goal. The use of cloud spectral emissivity and height in retrieving cloud micro-physical properties is discussed together with their utility in identifying cloud contaminated soundings in the IMAPP AIRS only single field of view retrieval.

Hyperspectral radiance simulator: cloudy radiance modeling and beyond

Current and future advanced atmospheric profile sounding and imaging instruments are evolving to enable global or hemispherical hyperspectral resolution measurements from space. The NASA/Navy/NOAA Geosynchronous Imaging FTS (GIFTS) for EO-3, NOAA Hyperspectral Environmental Sounder (HES) for GOES-R, and the currently operational Atmospheric Infrared Sounder (AIRS) on the NASAs Aqua Spacecraft will collect infrared high-spectral resolution/hyperspectral radiance spectra for remote sensing of the atmosphere, clouds, land, and ocean surfaces. These semi-continuous infrared high spectral- resolution/hyperspectral radiances will provide unprecedented information in the infrared region that is highly sensitive to absorption and emission of clouds. For sounding the atmospheric profiles one must perform cloud clearing or model the radiative effects of cloud explicitly if sounding is desired under cloud-contaminated conditions. We will describe the approach for modeling cloud attenuation in a fast-parameterized forward model that treats clouds as an additional absorber. Together with the usual clear forward model spectroscopic inputs, cloud altitude, effective particle size and shape and its ice or liquid water content are required input variables. Based on this efficient cloudy radiative transfer model, the simulation of the spatial and temporal coherent radiance images in three dimensions becomes possible. We will further explain how these 3-D GIFTS radiance cubes are used as test bed for a variety of trade studies.