Time‐variable gravity field from satellite constellations using the energy integral

SUMMARY The magnetic field mission Swarm, expected to be launched in 2012, comprises a constellation of three satellites. As all of them are equipped with GPS receivers and accelerometers, they can be used for gravity field recovery. We study the capability of a Swarm-like constellation for (time-variable) gravity field recovery and compare it with a gravity field tandem mission of GRACE-type. Due to the lower accuracy of the GPS measurements compared with GRACE low–low satellite-to-satellite tracking (SST), the accuracy of a Swarm derived gravity field cannot compete with the state-of-the-art GRACE models. However, unlike the GRACE mission, Swarm allows for the derivation of GPS-baselines between the satellites in directions other than purely along-track. This makes Swarm an interesting case for studying the error structure of gravity field models derived from various constellation geometries. Therefore, one focus of this study is the general analysis of different baseline constellations independent of the observation accuracy, that is, we do not restrict ourselves to just the actual Swarm case of pure GPS-baselines. To make the results comparable to GRACE, we explicitly study the error behaviour of the different Swarm baseline geometries assuming GRACE-type K-band ranging (KBR) links. This gives an indication of candidate constellations for future missions; at least in regards to the general error structure. To study the potential of Swarm for recovering time-variable components of the gravity field, we have set up a 2-yr simulation to recover annual and semi-annual components of continental hydrology. We show that Swarm has the potential to recover the annual signal up to spherical harmonic degree 6. This is of interest should there be a gap between the end of the GRACE mission and the launch of a follow-on mission. All our simulations make use of the energy integral method. This method is usually (implicitly) formulated for static potential fields. Therefore, it is necessary for our study to investigate the properties of this method when applied to the analysis of time-variable fields.

[1]  F. Sansò,et al.  First GOCE gravity field models derived by three different approaches , 2011 .

[2]  G. Hulot,et al.  Swarm: A constellation to study the Earth’s magnetic field , 2006 .

[3]  S. Swenson,et al.  Post‐processing removal of correlated errors in GRACE data , 2006 .

[4]  Philip Moore,et al.  Annual and semiannual variations of the Earth's gravitational field from satellite laser ranging and CHAMP , 2005 .

[5]  Oliver Montenbruck,et al.  Precise GRACE baseline determination using GPS , 2005 .

[6]  G. Hulot,et al.  Swarm- The Earth's Magnetic Field and Environment Explorers , 2004 .

[7]  P. Steigenberger,et al.  A CHAMP‐only gravity field model from kinematic orbits using the energy integral , 2003 .

[8]  Christopher Jekeli,et al.  Static and temporal gravity field recovery using grace potential difference observables , 2003 .

[9]  Nico Sneeuw,et al.  Energy integral method for gravity field determination from satellite orbit coordinates , 2003 .

[10]  C. Provost,et al.  FES99: A Global Tide Finite Element Solution Assimilating Tide Gauge and Altimetric Information , 2002 .

[11]  Christopher Jekeli,et al.  Efficient gravity field recovery using in situ disturbing potential observables from CHAMP , 2002 .

[12]  C. Reigber,et al.  CHAMP mission status , 2002 .

[13]  P. Milly,et al.  Global Modeling of Land Water and Energy Balances. Part I: The Land Dynamics (LaD) Model , 2002 .

[14]  M. Wolff,et al.  Direct measurements of the Earth's gravitational potential using a satellite pair , 1969 .

[15]  M. Hotine,et al.  First integrals of the equations of satellite motion , 1969 .

[16]  J. O'keefe,et al.  An application of Jacobi's integral to the motion of an earth satellite , 1957 .

[17]  B. Elsaka Simulated satellite formation flights for detecting the temporal variations of the Earth's gravity field , 2010 .

[18]  L. Prange Global Gravity Field Determination Using the GPS Measurements Made Onboard the Low Earth Orbiting Satellite CHAMP , 2010 .

[19]  M. Sharifi,et al.  Future Mission Design Options for Spatio-Temporal Geopotential Recovery , 2010 .

[20]  P. Ditmar,et al.  On a Feasibility of Modeling Temporal Gravity Field Variations from Orbits of Non-dedicated Satellites , 2009 .

[21]  Stavros Kotsiaros,et al.  Development of algorithms and tools for data analysis, data visualization, and data validation for the Swarm satellite mission. , 2009 .

[22]  N. Sneeuw,et al.  One year of time-variable CHAMP-only gravity field models using kinematic orbits , 2005 .

[23]  M. Rothacher,et al.  Kinematic Precise Orbit Determination for Gravity Field Determination , 2005 .

[24]  Shin-Chan Han,et al.  Efficient Determination of Global Gravity Field from Satellite-to-satellite Tracking Mission , 2004 .

[25]  C. Reigber,et al.  First insight into temporal gravity variablility from CHAMP , 2003 .

[26]  Schweizerische Geodätische Kommission Geodätisch-Geophysikalische Arbeiten in der Schweiz , 1982 .