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 –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

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

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

Flight 11/26/2003 Wind Velocity Comparison

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

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

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

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

TAMDAR Data Quality Segment Cruise2, 11/26/2003 –19 minutes –Vector correlation = 1.63 –Tamdar: °, mean 2.3 m/s and 2.0°, std. dev. –Citation °, mean 2.3 m/s and 1.9°, std. dev.

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

Latency Examples 12/05/2003 Segment ClimbHa

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

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. N E Va Vg Va Vg Va Vg Va Vg

Full Flight: no corrections

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

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

Segment DM1: testing tau

Segment DM2: testing tau

Segment DM2: tau = -2.5 s

Full Flight: tau = -1.5s

Full Flight: tau = -2.5s

Look for the Change in Tau

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

Offset Errors Flight 11/26/2003 Segment MA

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