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The sampling limitations of a single nadir-looking ocean altimeter have long been known, and multiple altimeter missions and bistatic altimeter configurations have been proposed as solutions. In this paper we suggest an alternate method that ovecomes the sampling issue through the combined use of nadir-looking altimetery and off-nadir radar interferometry.
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The sea state bias in altimeter measurements of sea surface height has been investigated by many authors, resulting in several proposed algorithms of the form SSB = epsilonH where H is the significant wave height and epsilon is a nondimensional function of wind and wave parameters. All known algorithms are presently rated. By analyzing the performance of different models we conclude that further progress can hardly be achieved by raising the degree of the polynomial for epsilon(U,H). Physically based approaches employing a small number of adjustable parameters and theoretically justified non-dimensional combinations of external wind and wave factors appear to be more promising.
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The impact of using altimeter range measurements as an observation type for navigation of spacecraft about small bodies is evaluated.
In this paper, we look at the characteristics of tropical storms and hurricanes, as observed by TOPEX/POSEIDON, during the 1993 and 1994 hurricane seasons in both Atlantic and Pacific.
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Range and velocity sensors based on lidar or radar with multiple beams are often used to measure the altitude and velocity, respectively, of a spacecraft above a targetbody. A difficulty that arises when navigating about small bodies such as asteroids or comets, is that the notion of altitude is largely obscured by the irregular shape of the target surface. This paper develops a method to incorporate the multibeam altimeter and Doppler velocimeter measurements into the on-board spacecraft state estimator by using information from a faceted shape model representation of the target body surface.
The Gravity Recovery and Climate Experiment (GRACE) mission has provided us with unforeseen information on terrestrial water-storage (TWS) variability, contributing to our understanding of global hydrological processes, including hydrological extreme events and anthropogenic impacts on water storage. Attempts to decompose GRACE-based TWS signals into its different water storage layers, i.e., surface water storage (SWS), soil moisture, groundwater and snow, have shown that SWS is a principal component, particularly in the tropics, where major rivers flow over arid regions at high latitudes. Here, we demonstrate that water levels, measured with radar altimeters at a limited number of locations, can be used to reconstruct gridded GRACE-based TWS signals in the Amazon basin, at spatial resolutions ranging from 0.5 to 3°, with mean absolute errors (MAE) as low as 2.5 cm and correlations as high as 0.98. We show that, at 3° spatial resolution, spatially-distributed TWS time series can be precisely reconstructed with as few as 41 water-level time series located within the basin. The proposed approach is competitive when compared to existing TWS estimates derived from physically based and computationally expensive methods. Also, a validation experiment indicates that TWS estimates can be extrapolated to periods beyond that of the model regression with low errors. The approach is robust, based on regression models and interpolation techniques, and offers a new possibility to reproduce spatially and temporally distributed TWS that could be used to fill inter-mission gaps and to extend GRACE-based TWS time series beyond its timespan.