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 degrees- 40 degrees 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.

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.

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.

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.

Abstract : Modern high power Doppler VHF radar wind profilers are a valuable source of upper tropospheric turbulence intensity information. These radars can provide more comprehensive characterization of turbulence statistics than any in-situ measurement database, or even models developed from such databases. Sample data sets from two high power radar profilers, located at WSMR, New Mexico, and KSC, Florida, were obtained. The physical processes involved in turbulence production, maintenance, and dissipation were reviewed along with the phenomenology of its detection using radar. Subsequently, methodologies were developed for the retrieval of turbulence intensity statistics, or the so-called turbulence exceedance probabilities (TEP), from such data. The WSMR data analysis shows that, in the 8 to 20 km altitude region, the median turbulence intensity was less than or equal 0.5 m/s. In more turbulent conditions, the 90% and 95% TEP, turbulence intensities were observed to be close to, or slightly in excess of 1.0 m/s, and 1.5 m/s, respectively. The KSC turbulence data analysis resulted in the median close to 1.0 m/s, the 90% TEP near 1.5 m/s, and the maximum 95% TEP of 1.7 m/s. Also considered are the capabilities of a recently developed Clear Air Turbulence (CAT) forecast product. The conclusion is that available TEP guidance could be validated and extended by analysis of data from high power radars.