<p> The ionospheric total electron content (TEC) is the integrated electron density across a unit area. TEC is an important property of the ionosphere. Accurate estimation of TEC and TEC spatial distributions are needed for many space-based applications such as precise positioning, navigation, and timing. The Global Positioning System (GPS) provides one of the most versatile methods for measuring the ionosphere TEC, as it has global coverage, high temporal resolution, and relatively high spatial resolution. The objective of this thesis is to develop an algorithm for accurate estimation of the TEC using dual-frequency GPS receiver measurements and simultaneously estimate the receiver hardware bias in order to mitigate its effect on the TEC. This method assumes the TEC in the portion of sky visible to the receiver can be represented as a two dimensional sheet with an absolute value and spacial gradients with respect to latitude and longitude. A code-phase multipath noise estimation algorithm is integrated with the TEC estimation process to mitigate environmental multipath contamination of the measurements. The integrated algorithm produces an approximate map of local TEC using a single dual-frequency receiver while minimizing both multipath induced errors and the receiver hardware bias. The goal of this method is to provide an accurate map of the ionosphere TEC, in the region local to the receiver, without the need for a network of receivers and in the absence of knowledge of the receiver hardware induced bias. This thesis describes the algorithm, its implementation, and attempts to validate the method through comparison with incoherent scatter radar (ISR) data from low, mid, and high latitude locations.</p>

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The role of clouds in reflecting solar radiation to space and thereby reducing surface heating is of critical importance to climate. Combustion processes that produce greenhouse gases also increase cloud condensation nuclei (CCN) concentrations which in turn increase cloud drop concentrations and thereby cloud albedo. A calculation of cloud susceptibility, defined in this work as the increase in albedo resulting from the addition of one cloud drop per cubic centimeter (as cloud liquid water content remains constant), is made through satellite remote sensing of cloud drop radius and optical thickness. The remote technique uses spectral channels of the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the NOAA polar orbiting satellites. Radiative transfer calculations of reflectance and effective surface and cloud emissivities are made for applicable sun and satellite viewing angles, including azimuth, at various radii and optical thicknesses for each AVHRR channel. Emission in channel 3 (at 3.75 μm) is removed to give the reflected solar component. These calculations are used to infer the radius and optical thickness giving the best match to the satellite measurements. The effect of the atmosphere on the signal received by the satellite is included in the analysis. Marine stratus clouds are a focus of this work. As well as being important modifiers to climate, they are cleaner than continental clouds and so likely to be of higher susceptibility. Analysis of several stratus scenes, including some containing ship tracks, supports this expectation.

"Last biannual progress report and final report describing work under NASA contract no. NAS5-21812."

Digital analysis of thematic mapper images and digital elevation model (DEM) of the Puerto Libertad area, emphasized a NE-E structural pattern and a less evident N-S structural trend. These structures may correspond to the distensive mechanisms of the opening of the Gulf of California. Also, semicircular structures in the area were recognized. These are associated to plutonic bodies or local volcanic centers. Exploration targets are most likely to occur in the last type of structures. Analysis included multispectral ratios of bands 5/7 and 5/1 to enhance lithologic discrimination and alteration zones especially hydroxyl-bearing and ferric iron alterations. Principal component analysis to enhance spectral data was also performed. Images revealed new lithologic subdivisions in units previously mapped. Analysis of DEM data and gradient operators from the first principal component image were analyzed to extract textural information.