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Swarm End-To-End Mission Performance Study Final Presentation

Swarm End-To-End Mission Performance Study Final Presentation. DSRI: Eigil Friis Christensen Flemming Hansen Alexei Kuvshinov Nils Olsen Per Lundahl Thomsen Susanne Vennerstrøm IPGP: Gauthier Hulot Mioara Mandea BGS: Vincent Lesur Susan Macmillan Alan Thomson. GFZ: Monika Korte

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Swarm End-To-End Mission Performance Study Final Presentation

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  1. Swarm End-To-End Mission Performance StudyFinal Presentation DSRI: Eigil Friis Christensen Flemming Hansen Alexei Kuvshinov Nils Olsen Per Lundahl Thomsen Susanne Vennerstrøm IPGP: Gauthier Hulot Mioara Mandea BGS: Vincent Lesur Susan Macmillan Alan Thomson GFZ: Monika Korte Hermann Lühr Stefan Maus Christoph Reigber Patricia Ritter Martin Rother GSFC: Michael Purucker Terence Sabaka IUEM: Pascal Tarits The Swarm E2E Consortium

  2. Outline of Presentations • Overview, Achieved Milestones (NIO) • Results:Comprehensive Inversion (TJS) • Results:Lithospheric Field Recovery Using Gradient Method (HL) • Results: Mapping of 3D Mantle Inhomogeneities (NIO) • Assessment (NIO) • General discussion

  3. Study Logic • Task 1: Industrial Module • to be used by industry for their system simulation • Output: software (Matlab) + documentation • Task 2+3: Swarm missionsimulation • Determination and evaluation of scientific benefit of different mission scenarios • Task 3: including s/c and payload errors (from models provided by industry)

  4. The Magnetic Field Contributions Described by the three Modules large scale

  5. Contents of Industrial Package

  6. Outline of Task 2+3Mission Performance Simulator

  7. Forward Scheme: Production of Synthetic Data • Design of constellations • Orbit Calculation • full mission: 4 years • mission start on January 1, 1997 (one solar cycle before anticipated launch) • Calculation of synthetic magnetic and electric field data • magnetic field generator • electric field generator • auxiliary data

  8. Orbit Design Constellation #1 • Two pairs of satellites450 and 550 km initial altitude86.0° and 85.4° inclination • lower satellites are close together separation a few hundred km • upper satellites are at antipodal position 180° separation • Different inclinations yields different drift rates0.44 min/day differential drift rate corresponding to 90° separation after 27 months

  9. Orbit Design Constellation #2 • Pool of 6 (7) satellites • Analysis of data from different combinations of up to 4 satellites • Final name convention • Swarm A (= 4) • Swarm B (= 5) • Swarm C (= 1) • Swarm D (= 2)

  10. Advantage of two satellites flying side-by-side Strong attenuation of large-scale magnetospheric terms Amplification of m»0 terms

  11. Magnetic Field Gradient at 400 km altitude Br Bq Bf Magnetic field East-West Gradient of Magnetic field

  12. Magnetic Field Generation

  13. Improved Forward Scheme for Constellation #2 • improved parameterization of magnetospheric sources: n=3, m=1 based on hour-by-hour analysis of world-wide distributed observatory data after removal of CM4 • induced contributions are considered using a 3D conductivity model (oceans, sediments + deep-located mantle inhomogeneities) • ”boosted” secular variation • Noise added (based on CHAMP experience and Swarm specifications)

  14. Power Spectral Density of Simulated Noise In January 2004 (production of data of constellation #2), the Phase A System Simulator models produces time series of magnetic field that are off by several nT. Therefore use of simple noise model, based on scaled CHAMPdata s = (0.1, 0.07, 0.07) nT in agreement with Swarm performance requirements simple noise model Phase A noise model

  15. Data Products • For each constellation: • 190 million satellite positions • 10,950 data files • 26.5 GB of data • Production of synthetic data for one constellation takes a couple of weeks

  16. In-flight Calibration and Alignmentof the Vector Fluxgate Magnetometer (VFM) Calibration: Determination of the instrument response (including time and temperature drifts) by comparison with the readings of the Absolute Scalar Magnetometer (ASM) • methods developed for present single satellite missions Ørsted and CHAMP • Successful application to simulated Swarm data • exact timing of the instruments is essential (Dt < 5 sec, cf. SRD) before in-flight calibration(using pre-flight values) after in-flight calibration

  17. In-flight Calibration and Alignmentof the Vector Fluxgate Magnetometer (VFM) Alignment: Determination of the rotation between the VFM and the star imager (ASC) • Single satellite methods work well provided that • the ”true” magnetic field is sufficiently well known • the difference DB has some special properties (distribution of DB in VFM frame) • Probably significantly relaxed conditions if constellation aspect (multi-satellite method) is considered • The mechanical stability of the VFM/ASC assembly (optical bench) is very essential! • Development of multi-satellite methods for in-flight alignment is needed

  18. Various Approaches for Field Recovery • Comprehensive Inversion (cf. presentation by T. J. Sabaka) • Core Field and Secular Variation - Method 1 • Core Field and Secular Variation - Method 2 • Lithospheric Field Recovery - Method 1 (cf. presentation by H. Lühr) • Lithospheric Field Recovery - Method 2 • 3-D Mantle conductivity - Method 1 (cf. presentation by N. Olsen) • 3-D Mantle conductivity - Method 2

  19. Test Plan • Closed-loop-simulation: Test of the forward and inversion approaches using noise-free data • using data that only contain source fields for which we invert for • Focus on field contributions that are main Swarm objectives • Core field and secular variation • Lithospheric field • Test quantities: Difference between recovered and original model • Power spectrum of the model SH coefficients • Degree correlation rn of coefficients • Sensitivity matrix • Global Maps (e.g., of Br) of the model difference • Results: • successful recovery of the original model using clean, noise-free and noisy data • Definition: noisy data data containing S/C and payload noisenoise-free data data without S/C and payload noiseclean data data that only contain source contribution that is inverted for

  20. Example of Closed-Loop Analysis • Recovery of lithospheric field ... • ... and of high-degree secular variation • using data from 4 Swarm satellites and 88 observatories • degree correlation rn > 0.9 • Achieved by Comprehensive Inversion; details will be given by T.J. Sabaka original model recovered model difference

  21. Results: Mission Performance • Recovery of all relevant source contributions by Comprehensive Inversion (T. J. Sabaka) • Recovery of the lithospheric field (H. Lühr) • 3-D Conductivity of the Mantle (N. Olsen)

  22. Comparison of Filter Method and CI • CI superior at n<70, especially for terms m close to 0 • Filter method is superior for n > 70

  23. Mapping of 3D Mantle Conductivity Forward conductivity model contains • near-surface conductors (oceans, sediments) • local (small-scale) inhomogeneities(plumes, subduction zones) • regional inhomogeneities(e.g., covering one plate) Attempt to map 3D mantle conductivity structure

  24. Transfer Function: C-response • C from local Bz and BH , derived using a SHA • Frequency dependence of C(w) (or of other transfer functions) provides information on conductivity-depth structure s(z) Electromagnetic Induction:Attenuation of B with depth z:

  25. Mapping of 3D Mantle Structure Real and imaginary part of the local C-response for a period of 7 days, reconstructed from time-series of spherical harmonic coefficients up to degree N. N = 5 N = 9 N = 45 (all terms)

  26. Assessment: Core Field and Secular Variation • Without Swarm: only ground station data • With Swarm: local time coverage and improved quality

  27. Assessment: Lithospheric Field • A: 4-5 times more accurate than CHAMP • Lower pair A+B (gradient) for detail • Higher C separates external sources • Combination A+B+C: optimal recovery up to n=130

  28. Assessment: Lithospheric Field (cont.)Br at ground degree n up to 60 degree n up to 130 nT

  29. Swarm A+B+C Swarm A km True Model Mission Performance: 3-D Mantle Conductivity • Detection of inhomogenities of mantle conductivity is possible with Swarm constellation #2 • Data from one satellite is sufficient to resolve inhomogeneities Period 7 days

  30. Conclusions and Recommendations • Full mission simulation performed for two constellations • Production of synthetic data of all relevant contributions to Earth’ smagnetic field • Recovery of the various field contributions using different approachComprehensive Inversion was chosen as the primary approach • Evaluation of Swarm constellations ... and of the methods for field analysis • Modified 3-satellite constellation (one pair of lower satellites, one higher satellite) fulfills the primary Swarm science objectives

  31. Recommendations for Future Studies • Develop methods for pre-flight determination and test of VFM / ASC rotation • Develop multi-satellite tools for in-flight alignment • More sophisticated methods for utilizing the magnetic field gradient in geomagnetic field modelling • Develop methods for imaging (mapping) of 3-D mantle inhomogeneities

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