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TAMDAR Winds

TAMDAR Winds . Some results from a study of the ATReC/AIRS-II Campaign Data Robert Neece, NASA Langley Research Center. The Study. Objectives: Verify the quality of the data Identify sources of error Primarily looked at 2 days: 11/26/03 and 12/5/03 Challenges

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TAMDAR Winds

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  1. TAMDAR Winds Some results from a study of the ATReC/AIRS-II Campaign Data Robert Neece, NASA Langley Research Center

  2. The Study • Objectives: • Verify the quality of the data • Identify sources of error • Primarily looked at 2 days: 11/26/03 and 12/5/03 • Challenges • Many potential sources of error • Difficult to sort out effects • Problems with sampling rate and data dropouts • No real truth data • No accepted measure for comparison

  3. Conclusions • TAMDAR winds are very good • Primary sources of error are identifiable and can be addressed to improve accuracy

  4. Flight 11/26/03 • Chosen because of large errors over significant periods • Identified flight characteristics that might affect error • Divided flight into segments with isolated effects (e.g. climbing, cruising, turning, etc.) • Key findings • A vector correlation function was needed • Data latency is an important factor • Aircraft turning is associated with largest error

  5. Flight 11/26/2003 Wind Velocity Comparison

  6. Vector Correlation Coefficient • Discovered papers concerning a method of correlation for vector data like winds (e.g. Crosby, Breaker & Gemmil) • Eventually understood it and wrote a Matlab function to implement it • This function is a primary measurement of agreement • The correlation scale is 0 to 2

  7. Data Dropouts and Sampling • Data dropouts up to 360 s • Usually the interval between TAMDAR data points was 3, 6, 9, or 12 seconds • Option 1 – find matching data points in the Citation (1-second) data • Sparse sampling • Irregular rate • Option 2 – utilize the TAMDAR debug data with a 3-second sampling interval • Must calculate winds • Loses some TAMDAR products

  8. Computing the Wind • Wind vector = Vw = -(Vg – Va) • Vg is derived from GPS ground track data • Vw is derived onboard from aircraft heading and airspeed • When Vg and Va are large with respect to Vw, error becomes worse

  9. TAMDAR Data Quality • Segment Cruise1, 11/26/2003 • 42 minutes • Vector correlation with Citation = 1.92 • Tamdar: • 55.3 m/s @ 249°, mean • 2.3 m/s and 2.5°, std. dev. • Citation • 55.6 m/s @ 247°, mean • 2.1 m/s and 2.4°, std. dev.

  10. TAMDAR Data Quality • Segment Cruise2, 11/26/2003 • 19 minutes • Vector correlation = 1.63 • Tamdar: • 56.8 m/s @ 256°, mean • 2.3 m/s and 2.0°, std. dev. • Citation • 56.6 m/s @ 257°, mean • 2.3 m/s and 1.9°, std. dev.

  11. Data Latency and Filtering • Comparison of the Citation and TAMDAR data suggested different degrees of filtering and/or sensor response characteristics • Experimented with some filtering • Found this to be a minor effect • Clear evidence of significant latency differences • TAMDAR data lags Citation data by about 12s • This is an important factor when comparing Citation and TAMDAR data

  12. Latency Examples 12/05/2003 Segment ClimbHa

  13. Errors While Turning • Large errors occur when turning, even for brief heading corrections • Errors in corkscrew turns suggested a rotating vector error • Theory: a time difference in the latency of track versus heading data causes the error

  14. N Vg Va Va Vg E Vg Va Va Vg Wind Error Vector Vw = 0 Aircraft turns at constant speed. Va = Vg If Vg is delayed, an error vector appears as a rotating wind vector.

  15. Full Flight: no corrections

  16. Estimating Tau, the Time Delay • First estimated tau graphically in segment DM2, tau = 2.4 sec • Based on a short segment of seemingly noise-free data • Time-shifted heading data using tau and recalculated winds in DM2 • Error was successfully reduced

  17. Investigating Tau • Theorized that tau should be constant for a flight • Derived a formula for tau • Sign of tau is indeterminant • Calculated tau versus time in DM1 • tau = 1.5 sec (ave.) • Wrote Matlab functions to calculate tau and to apply corrections to wind calculations • Experimented with compensation using tau

  18. Segment DM1: testing tau

  19. Segment DM1: testing tau

  20. Segment DM2: testing tau

  21. Segment DM2: tau = -2.5 s

  22. Full Flight: tau = -1.5s

  23. Full Flight: tau = -2.5s

  24. Look for the Change in Tau

  25. Tau, Some Conclusions • There is a time delay between inertial data and GPS data • The time delay is the major source of error during turns • Error increases with turn rate • The time delay is not fixed during a flight • Time misalignment should be kept to less than 0.5 second

  26. Offset Errors Flight 11/26/2003 Segment MA

  27. Conclusions • TAMDAR wind data is very good • It can be significantly improved by addressing two sources of error • Time alignment of data streams • Inertial data should be delayed to match GPS data • Accuracy on the order of 0.5 second or better is desired • Offset errors appear to be due to a specific source and can potentially be mitigated

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