1. Time & Frequency Products R. Peřestý, J. Kraus, SWRM 4 th Data Quality Workshop 2-5 December 2014 GFZ Potsdam Recent results on ACC Data Processing 1 SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam

2. ACC design Capacitive three axial accelerometer with cubic proofmass. No active thermal control on instrument level. Heat exchange in space only through fixing points an by radiation. SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam2 /20

3. ACC performances SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam3 /20 Since the start of Swarm mission are observed:  Unexpected, very large temperature dependence  Discontinuity of ACC bias – fast signal disturbances

4. ACC performances – Temp. dependence The ACC output is heavily dependent on temperature changes. The sensitivity to temperature is approximately 10x - 100x higher in comparison to ground tests. The unexpected daily variation in temperature, which is relatively small to the orbital temperature variation (up to 10% on the surface of the ACC unit) propagates much more into the physical package of ACC. The resulting impact on measurement is comparable to influence of orbital temperature variation. This calls for much more precise temperature correction than planned. Therefore the new temperature correction approach is being developed. SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam4 /20

5. Temperature Correction Model SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam5 /20 The correction method is trying to depict the real physical behaviour responsible for temperature dependency. It uses temperature data from sensors and apply model of temperature transfer across the instrument to points where the offset change is generated. These “Sensitive Points” instantly generates the offset. The heat transfer is calculated by chain of discrete conductive elements an inserted heat capacities. The resulting temperature at sensitive point is transformed onto acceleration through linear or quadratic dependence and added to acceleration data. Nothing from acceleration is filtered and only effects which are present in temperature signal are subtracted.

6. Temperature Correction Model scheme SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam6 /20

7. Temperature Correction Model SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam7 /20 The most challenging task is finding the parameters of heat transfer model and level of influence on acceleration data. The present method focuses on components in spectral domain which are believed to be caused only by temperature changes. The parameters are derived by successive attempts and following direction towards better outputs of complete temperature correction.

8. Temperature Dependence Correction SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam8 /20 Green marks = Daily variations and harmonics Black marks = Orbitl variations and harmonics

9. Temperature Dependence Correction SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam9 /20 Latest updates Algorithm for model parameters obtaining is capable of tuning capacities (not only conductivities). Suppressed spectrum can be extended to frequency bands laying between harmonics of orbital frequency. Parallel computing helps to reduce calculation time which was increased by adding new dimensions of solution.

10. Temperature Dependence SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam10 /20 Experimental results The upgraded algorithm was tested on data from Swarm A and Swarm C in time interval DoY 216 to 270. Model architecture was standard two Sensitive Points with Two RC elements. Additional low frequency filtration by FFT was applied to suppress residual LF components. Swarm A signal was “amplified” by factor of 1.63 to minimize difference to SW C. before after

11. Temperature Correction - Optimistic result 1 SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam11 /20

12. Temperature Correction - Optimistic result 2 SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam12 /20

13. Temperature Correction - Pulses in signal SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam13 /20

14. Temperature Correction - Pesimistic SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam14 /20

15. Temperature Correction – Signal Spectrum SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam15 /20

16. Temperature Dependence Artificial disturbance SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam16 /20 Significant peak at frequency 21.69 f(orbit) corresponds to period of 260.0 seconds. Present only on SW-A

17. Temperature Dependence – Future steps SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam17 /20 The next possibilities to gain more information about temperature dependency are following: Use data from GPS transformed into accelerations. Use data from models to obtain preliminary verifications of correction possibilities. Introduce artificial periodic temperature signal laying in between orbital harmonics, which will provide information about instrument reaction at these frequency bands.

18. Fast disturbances - Steps in signal SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam18 /20 Spikes, steps and thrusts are all visible on the right two figures. The data set No.4 is presented. The blue curve is temperature corrected signal on Sat-C. Green bars indicate spikes and steps. Magenta bars indicate thruster firings.

19. Fast disturbances - Steps in signal SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam19 /20 The blue curve is temperature corrected signal on Sat-C. Green bars indicate spikes and steps. Magenta bars indicate thruster firings. One can see:  Steps triggered at similar orbit phase  Step triggered by thruster firing  Disturbances have no visible influence on temperature correction results

20. Fast disturbances - Steps in signal SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam20 /20 The blue curve is original signal on Sat-A. Green is correction signal. By temperature model. Red is temperature corrected signal. Magnitude of artificial steps: 0, 4.2x10 -8, 1.4x10 -7, 4.2x10 -7 m/s 2 Steps have influence to the slope of temperature corrected signal. Steps above 1.4x10 -7 m/s 2 cause visible signal „curling“.

21. Fast disturbances - Steps in signal SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam21 /20 The blue curve is original signal on Sat-A. Green is correction signal. By temperature model. Red is temperature corrected signal. Magnitude of artificial steps: 0, 4.2x10 -8, 1.4x10 -7, 4.2x10 -7 m/s 2 Steps have influence to the slope of temperature corrected signal. Steps above 1.4x10 -7 m/s 2 cause visible signal „curling“.

22. Fast disturbances - Steps in signal SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam22 /20 The blue curve is original signal on Sat-A. Green is correction signal. By temperature model. Red is temperature corrected signal. Magnitude of artificial step: 1.4x10 -7 m/s 2 Steps have influence to the slope of temperature corrected signal. Steps above 1.4x10 -7 m/s 2 cause visible signal „curling“.

23. Fast disturbances - Steps in signal SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam23 /20 The blue curve is original signal on Sat-A. Green is correction signal. By temperature model. Red is temperature corrected signal. Magnitude of artificial step: 4.2x10 -7 m/s 2 Steps have influence to the slope of temperature corrected signal. Steps above 1.4x10 -7 m/s 2 cause visible signal „curling“.

24. Fast disturbances – Effect Statistics SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam24 /20 Distribution by anomaly type: 83% Spikes 13% Steps and Other (combined) 2% Thruster firings Data period: January – August 2014 Classification of fast anomalies There are the following types of anomalies observed in Swarm ACC data:  Spikes (outliers with very fast exponential decay)  Steps (sudden changes of instrument offset )  Pulses and combined anomalies

25. Fast disturbances – Effect Statistics SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam25 /20 Distribution of anomaly types over Swarm satellites. Smaller number of anomalies on Swarm C, Swarm B has maximum of detected anomalies. Data period: January – August 2014

26. Recent results on ACC Data Processing SWARM 4-th Quality Workshop, 2-5 December 2014, GFZ Potsdam26 /20 Thank you for attention