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GRACE Gradiometeres with unprecedented accuracy is the primary science objective of the GRACE mission. The line of sight (LOS) acceleration difference between the satellite pair is the most frequently utilized form of the observable. It is the simplest form of the observable which can be easily employed. Nevertheles

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沒花的是打擾

Numerical Velocity Determination and Calibration Methods for , Using the Energy Balance Approach been successfully implemented by several groups around the world. This paper addresses two important aspects of the data processing. First, high-quality gravity recovery requires numerical differentiation of kinematic positions. Two methods are investigated using simulated and real dynamic data. It

轉(zhuǎn)折點(diǎn)

Upward Continuation of Ground Data for GOCE Calibration/Validation Purposesly, except for the polar gaps. An accuracy of a few mE (1 mE = 10. E?tv?s, 1 E = 10.s.) is required to derive, in combination with satellite-to-satellite tracking (SST) measurements, a global geopotential model up to about spherical harmonic degree 200 with an accuracy of 1 … 2 cm in terms of geoid

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Effect of geopotential model errors on the projection of GOCE gradiometer observablesng strategies to project the GOCE gravity gradients to a mean reference sphere. In the present simulation study the radial distance of the projection is in the order of 10 km, and can be done using the Taylor expansion of the gravity gradients..In this paper we present an error analysis of such a pr

輕快帶來危險(xiǎn)

Comparison of some robust parameter estimation techniques for outlier analysis applied to simulated fect of outliers. This paper presents computationally-feasible algorithms for Huber’s M-estimator (a classic robust estimator) as well as for the class of R-estimators which have not traditionally been considered for geodetic applications. It is shown that the computational time required for the pro

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Comparison of outlier detection algorithms for GOCE gravity gradientsigh accuracy and high resolution model of the gravitational field if systematic errors and/or outliers have been removed from the data. It is necessary to detect outliers in the data pre-processing because undetected outliers may lead to erroneous results when the data are further processed, for exa

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Melatonin