In this paper, samples of AIRS data in the 1215 to 1615 cm-1 spectral region are analyzed to better understand the effects of water vapor in the mid to upper tropospheric region. Two days representing mid-latitude (20°-40° N) summer (warm and moist) and winter (cold and dry) maritime conditions are selected with cloud-free and 100% cloudy FOVs. The data, both in trend and differences, are well explained by the respective changes in atmospheric temperature and water vapor. These data are then compared with model simulation using MODTRAN. The results also compare favorably. Model simulation further illustrates the value of high spectral resolution for monitoring change in water vapor particularly in the upper troposphere. With the future GOES-R and NPOESS hyperspectral sensors expected to provide much improved atmospheric profile information, better monitoring of atmospheric water vapor will lead to improvements both in weather and climate applications.
This paper examines the use of bi-static lidar to remotely detect the release of aerosolized biological agent. The detection scheme exploits bio-aerosol induced changes in the Stokes parameters of scattered radiation in comparison to scattered radiation from ambient background aerosols alone. A polarization distance metric is introduced to discriminate between changes caused by the two types of aerosols. Scattering code computations are the information source. Three application scenarios are considered: outdoor arena, indoor auditorium, and building heating-ventilation-air-conditioning (HVAC) system. Numerical simulations are employed to determine sensitivity of detection to laser wavelength and to particle physical properties. Results of the study are described and details are given for the specific example of a 1.50 μm lidar system operating outdoors over a 1000-m range.
As we enter a new era of using satellite hyperspectral sensors for weather and other environmental applications, this paper discusses the applicability of using IR hyperspectral data for climate change monitoring; in particular, for quantifying the greenhouse effects. While broadband 1st order statistics quantify radiative forcings, the IR hyperspectral data provides a means of monitoring feedback processes. Radiative transfer modeling of the greenhouse effect is illustrated with examples: varying surface temperature, atmospheric temperature and water vapor. Three spectral greenhouse metrics are discussed: the difference between the surface emission and the outgoing longwave radiation (G), the surface-temperature normalized greenhouse effect (g) and vertical profile of cooling rate (C). Effects of changes in water vapor, clouds, carbon dioxide and methane are modeled and their potential observables identified.
VNIR-SWIR data from DOE MTI satellite are used to demonstrate the retrieval of aerosol and cloud properties. MTI data offer high spatial resolution and high SNR data. Furthermore, collection from both nadir and off-nadir views offer a unique opportunity to assess atmospheric path length effects both through clear and cloud conditions. Data sets were acquired to investigate cloud and aerosol properties: 29 July and 22 August 2000 over the coastal region of Massachusetts near Plymouth. Two topics are investigated: (1) retrieval of aerosol optical properties, and (2) characterization of water and ice clouds at nadir and off-nadir views. Data collection on 22 August 2000 represents a relatively clear atmospheric condition in the vicinity of Pilgrim Power Plant, Plymouth. Data over both vegetated land and ocean are analyzed. Two algorithms for aerosol retrieval over land are compared: the conventional dense-dark vegetation (DDV) algorithm and a generalized VIS-SWIR reflectance correlation and scatter-plot analysis (VSP) algorithm. Optical depths at multiple wavelengths and aerosol type were derived and compared with ground based AERONET data. It is demonstrated that the VSP algorithm captures the spectral variability in aerosol extinction, and thus performs better. Data collection from 29 July 2000 over the same area was investigated for cloud characteristics at different viewing geometries. Top-of-the-Atmosphere (TOA) reflectance statistics is computed for a common cloudy region. It is observed that in cloud free regions, nadir TOA reflectance is lower than that from off-nadir observations. This is due to the increased atmospheric scattering effect from the longer paths. On the other hand, TOA reflectance over cloud area depends on the scattering phase function and the look angle. Here we use simple expressions to illustrate that the effects for water and ice particles can be quite different resulting in very different viewing geometry effects between cumulus and cirrus clouds.
A conventional approach to HSI processing and exploitation has been to first perform atmospheric compensation so that surface features can be properly characterized. In this paper, the application of visible and IR spectral information to atmospheric characterization is discussed and illustrated with hyperspectral data in the VNIR, SWIR and MWIR data. AVIRIS and ARES data are utilized. The Airborne Visible-InfraRed Imaging Spectrometer (AVIRIS) sensor contains 224 bands, each with a spectral bandwidth of approximately 10 nm, allowing it to cover the entire range between 4 and 2.5 mm. For a NASA ER-2 flight altitude of 20 km, each pixel is 20 m in size, yielding a ground swath width of approximately 10 km. The Airborne Remote Earth Sensing (ARES) sensor was flown on a NASA WB-57 aircraft operated from approximately 15 km altitude. Spectral radiance data from 2.0 to 6.0 micrometers in 75 contiguous bands were collected. Pixel resolution is approximately 17 by 4.5 m2 with a swath width of 800 m. Examples of data applications include atmospheric water vapor retrieval, aerosol characterization, delineation of natural and manmade clouds/plumes, and cloud depiction. It is illustrated that though each application may only require a few spectral bands, the ultimate strength of HSI exploitation lies in the simultaneous and adaptive retrievals of atmospheric and surface features. Inter-relationships among different bands are also demonstrated and these are the physical basis for the optimal exploitation of spectral information.
For hyperspectral data analysis, the general objective for atmospheric compensation algorithms is to remove solar illumination and atmospheric effects from the measured spectral data so that surface reflectance can be retrieved. This then allows for comparison with library data for target identification. Recent advances in spectral sensing capability have led to the development of a number of atmospheric compensation algorithms for hyperspectral data analysis. In this paper, three topics will be discussed: (1) algorithm evaluation of two physics-based approaches: ATREM and the AFRL model, (2) sensitivity analysis of the effects of various input parameters to surface reflectance retrieval, and (3) algorithm enhancements of how water vapor and aerosol retrievals can be better conducted than current algorithms. Examples using existing hyperspectral data, including those from HYDICE, AVIRIS will be discussed. Results will also be compared with truth information derived from ground and satellite based meteorological data.
Two approaches, one for discriminating features in a set of AVIRIS scenes dominated by areas of smoke, plumes, clouds and burning grassland as well as scarred (burned) areas and another for identifying those features are presented here. A semiautomated feature extraction approach using principal components analysis was used to separate the scenes into feature classes. Typically, only 3 component images were used to classify the image. A physics-based approach which utilized the spectral diversity of the features in the image was used to identify the nature of the classes produced in the component analysis. The results from this study show how the two approaches can be used in unison to fully characterize a smoke or cloud-filled scene.
The Cloud Depiction and Forecasting System II (CDFS II) is a major new initiative that will transition the Air Force Global Weather Central (AFGWC) to a new satellite data processing system and include extensive changes in cloud analysis/forecasting at AFGWC. The present cloud analysis model, the RTNEPH, combines reduced resolution DMSP OLS or NOAA AVHRR data with conventional observations. The RTNEPH domain and analysis frequency are limited by its dependence on polar-orbiting satellites. In the CDFS II era (1998+), AFGWC cloud forecast models will benefit directly from improved automated nephanalysis capabilities from multiplatform sensor data. Support of Environmental Requirements for Cloud Analysis and Archive (SERCAA) project is a research and development program sponsored by the Strategic Environmental Research and Development Program that will provide both the next generation nephanalysis model for CDFS II and a new global cloud algorithm for use in determining the radiative and hydrological effects of clouds on climate and global change. SERCAA cloud analysis products will be available to a wide community of users both within and outside of the Department of Defense. The SERCAA project consists of two phases, the first has provided algorithms for retrieval of cloud spatial parameters for the CDFS II initiative. The second phase is concentrating on development of radiative and microphysical cloud parameter algorithms and on the archive structure.