Wind prediction to support reduced aircraft wake vortex separation standards

Transcription

1 Wind prediction to support reduced aircraft wake vortex separation standards Rodney Cole Weather Sensing Group WakeNet-2 Europe Dec 1, 2004 Wakenet-2 Europe-1

2 Outline Overview of closely spaced parallel runway departures Overview crosswind prediction A look at two approaches to predicting surface winds Next steps Summary WakeNet-2 Europe-2

3 Closely Spaced Parallel Runway Departures Wake transport is a function of wind and self induced transport left right wind < 2500 ft WakeNet-2 Europe-3

4 Closely Spaced Parallel Runway Departures Wake transport is a function of wind and self induced transport Actually takes crosswind in the wrong direction to push the wake across We ll work with thresholds from 0 kts to -10 kts left right wind < 2500 ft WakeNet-2 Europe-4

5 Outline Overview of closely spaced parallel runway departures Overview crosswind prediction A look at two approaches to predicting surface winds Next steps Summary WakeNet-2 Europe-5

6 Strawman Wind Prediction System Airport Anemometers surface wind prediction This is just notional Human forecaster Weather models Regional weather obs weather-type analysis profile prediction wind profile prediction FAA weather systems wind aloft prediction tactical safety net WakeNet-2 Europe-6

7 Outline Overview of closely spaced parallel runway departures Overview crosswind prediction A look at two approaches to predicting surface winds Next steps Summary WakeNet-2 Europe-7

8 Prediction of Surface Crosswind Need to predict when 2-minute mean crosswind will stay above threshold Time horizon much shorter for departures than arrivals 20 minutes for planning 5 minutes for safety Approach: Predict the range of future crosswinds. When the range of crosswinds are all above threshold, reduced spacing allowed. Crosswind, kts Center Field Winds Time, min Frankfurt has a statistical surface wind prediction system. How well does it work in our environment? WakeNet-2 Europe-8

9 Frankfurt Model Slice up data into wind classes Direction Speed Variability: high or low St Louis wind frequency Compute 95%-tile ranges for wind speed and direction for each bin From current wind direction, speed, and variability (high or low), look up predicted range of wind speed and direction Wind speed, kts Wind direction WakeNet-2 Europe-9

10 Lincoln Model Predict mean crosswind and variability (std dev) Predicted range of crosswind = mean + n*standard deviations Take advantage of trends Take advantage of current variability Use a mix of scales Time, min -2 Use linear regression to build models Crosswind, kts Crosswind, kts Time, min Crosswind, kts Time, min WakeNet-2 Europe-10

11 Frankfurt vs Lincoln Model - STL Models trained on one year of ASOS data Models tested on one year of ASOS data Models tuned to give same benefit Probability of needing to use safety net E E E E E E E E E E E+00 Frakfurt 12R/30L Frankfurt 12L/30R Lincoln 12R/30L Lincoln 12L/30R Crosswind threshold in knots WakeNet-2 Europe-11

12 STL Low Level Windshear Alert System anemometers on ~90 ft poles 10 second update, averaged to 2-min with a one minute update 2 12RA/30LD 12LA/30RD 8 1 ~1.8 nmi RA/12LD 30LA/12RD Used for triggering 30 departure safety net 5 Used for triggering 12 departure safety net WakeNet-2 Europe-12

13 Prediction of Surface Crosswind Need to predict when 2-minute mean crosswind will stay above threshold in region of interest Time horizon much shorter for departures than arrivals 20 minutes for planning 5 minutes for safety ASOS approach: Predict the range of future crosswinds. When the range of crosswinds are all above threshold, reduced spacing allowed. Crosswind, kts Center Field Winds Time, min LLWAS approach: Predict the minimum crosswind directly WakeNet-2 Europe-13

14 LLWAS Model Predict minimum crosswind and model prediction error (std dev) Predicted minimum crosswind = minimum + n*standard deviations Take advantage of trends Take advantage of current variability Use a mix of scales Time, min -2 Use linear regression to build models Crosswind, kts Crosswind, kts Time, min Crosswind, kts Time, min WakeNet-2 Europe-14

15 LLWAS Model ~ 1 year of data, 4/2003-4/2004 Even days used to train models, odd days used to test models Data divided into bins by headwind and crosswind Models built for each bin Predictors used: 2-min network headwind, crosswind, speed 20-min network headwind, crosswind, standard deviations, & min crosswind Trends in 5-min headwind, crosswind, standard deviations, speed, & min crosswind Fewer predictors than used with ASOS due to fewer training cases WakeNet-2 Europe-15

16 Trading Benefits vs Use of Safety Net hours Runway hours/year reduced separations High Benefit Medium Benefit Low Benefit Threshold, knots minutes Runway minutes/year safety net use High Benefit Medium Benefit Low Benefit Threshold, knots WakeNet-2 Europe-16

17 Use of Safety Net by Forecast Horizon minutes Threshold = -4 knots Runway minutes/year safety net use Forecast horizon, minutes WakeNet-2 Europe-17

18 Use of Safety Net by Forecast Horizon Minutes/year WakeNet-2 Europe minutes minutes minute Forecast error, knots

19 Use of safety net by hour & month Minutes/year Threshold = -4 knots Counts of minutes/year safety net use Month Minutes/year June Hour of day, UTC WakeNet-2 Europe-19

20 Outline Overview of closely spaced parallel runway departures Overview crosswind prediction A look at two approaches to predicting surface winds Next steps Summary WakeNet-2 Europe-20

21 Near Term Work WakeNet-2 Europe-21

22 Summary Statistical prediction of surface winds is very effective for the short duration predictions needed for a departure system Primary failure mode is convective weather Near term future work will focus on increasing reliability of surface wind predictions and extending to wind profiles WakeNet-2 Europe-22