In the light of the three geoscientific satellite missions CHAMP, GRACE and GOCE the overall scientific aim is to achieve an automatism for the recovery of the Earth’s gravity field respectively the physical shape of the Earth, namely the geoid. Furthermore, an improved understanding of the spatial and temporal variations of the geoid is of great benefit for the study of the dynamics of the Earth’s lithosphere and upper mantle, global sea level variations, ocean circulation and ocean mass and heat transport, ice mass balance, the global water cycle and the interaction of these phenomena. This involves the determination of up to a hundred thousand unknown coefficients of the corresponding series expansion model from data sets which amount to several millions of observations provided by the satellites. The resulting system of equations which has to be solved for such an analysis cannot be evaluated without simplistic assumptions or within a satisfying time frame on personal computers due to hardware limitations. Consequently this challenging problem has to be tackled by means of high performance computing strategies. Only adoption of parallel programming standards such as MPI or OpenMP in conjunction with highly efficient numerical libraries allows for successfully accomplishing the demands of gravity field analysis. Indeed, the huge amount of data provided by satellite sensors, together with a high-resolution gravity field modeling, requires the determination of several ten thousands of unknown parameters and leads to the assignment that this problem is a true “challenge of calculus”.
[1]
Erik W. Grafarend,et al.
Analysis of the Earth’s Gravitational Field from Semi-Continuous Ephemeris of a Low Earth Orbiting GPS-Tracked Satellite of Type CHAMP, GRACE or GOCE
,
2002
.
[2]
Michael A. Saunders,et al.
Algorithm 583: LSQR: Sparse Linear Equations and Least Squares Problems
,
1982,
TOMS.
[3]
M. Hestenes,et al.
Methods of conjugate gradients for solving linear systems
,
1952
.
[4]
Steven J. Benbow,et al.
Solving Generalized Least-Squares Problems with LSQR
,
1999,
SIAM J. Matrix Anal. Appl..
[5]
Michael A. Saunders,et al.
LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
,
1982,
TOMS.
[6]
Oliver Baur,et al.
A parallel iterative algorithm for large-scale problems of type potential field recovery from satellite data
,
2004
.
[7]
E. Grafarend,et al.
Harmonic analysis of the Earth's gravitational field by means of semi-continuous ephemerides of a low Earth orbiting GPS-tracked satellite. Case study: CHAMP
,
2003
.
[8]
P. Hansen,et al.
Subspace Preconditioned LSQR for Discrete Ill-Posed Problems
,
2003
.