Turbulence Measurements. Turbulence Measurements In Low Signal-to-Noise. Larry Cornman National Center For Atmospheric Research

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1 Turbulence Measurements In Low Signal-to-Noise Larry Cornman National Center For Atmospheric Research Turbulence Measurements Turbulence is a stochastic process, and hence must be studied via the statistics of the process. Homogeneity, isotropy, eddy dissipation rate: these are all defined via the statistics.

2 Turbulence measurements in the nocturnal stable boundary layer CASES-99 kite profiles Courtesy Ben Balsley, Rod Frehlich & Mike Jensen Simulation Examples Wind field is generated so that it has correct spatial statistics This means that it will be aliased Am optional simulation incorporating an anti-aliasing filter is also used The spectral model is a von Karman one. Inputs: σ = u = ε = 2/3 ε estimates from max. likelihood method. 5.0 ms, L 500 m, 0.545m s 1 2/3 4/3 2

3 Simulation Model Spectra Original Sim. Von Karman Theoretical Sim. w/ anti-alias filter Averaged Spectra (red dots) Isotropy: 2/3 2/3 ε w ε Simulated data with u versus along a line. 2/3 2/3 Theory says, ε = ε u w 5 sec. windows 10 sec. windows 20 sec. windows 40 sec. windows

4 Homogeneity and sample size statistics: Histogram of 2/3 u ε over 10 and 40 second intervals. 10 Second Windows 40 Second Windows Kolomogorov energy spectrum: E() k = Aε k 2/3 5/3 Hence, the slope in log-log should be -5/3 on average! 10 Second Windows 40 Second Windows

5 Issues Regarding Low SNR Measurements Low SNR means different things for different devices Radar: low reflectivity (hydrometeors) Lidar: low backscatter (aerosol density) Wind profilers: low index of refraction Anemometers: low wind speed In each case, it is important to recognize when the device is giving meaningful information and when it isn t. Simulation with added Gaussian noise Averaged Spectra EDR estimation algorithms (e.g., ML) can be modified to accommodate additive noise but this only works to a point

6 Radar Simulation 3-D von Karman field. No anti-aliasing filter. Simulation performed at differing reflectivity levels => differing SNR EDR estimation via 2 nd moments from averaged Doppler spectra. Confidence values from SNR and other indicators. Radar Simulation for von Karman turbulence field, 30 dbz 2 nd Moments Confidence index Reflectivity 2/3 ε

7 Radar Simulation for von Karman turbulence field, 10 dbz 2nd Moments Reflectivity Confidence index ε 2/3 Radar Simulation for von Karman turbulence field, 0 dbz 2nd Moments Reflectivity Confidence index ε 2/3

8 Other Research Areas of Interest Ground-based and airborne radar turbulence detection In situ Turbulence Measurement Program Fine-scale numerical turbulence modeling Lidar simulations of wake vortices Remote Sensing of Turbulence Developing methods to infer turbulence levels from radar, lidar, GPS, other measurements Concentration has been on developing reliable turbulence detection algorithms for use on WSR-88D and TDWR radars Goal: Detect operationally significant convective turbulence events in lowreflectivity regions

9 Case study of extreme turbulence encounter in low reflectivity Regional jet encountered extreme turbulence in descent to Washington National Airport. Wing down bending limit exceeded by 10%. No structural damage and no injuries. Pilots: Airborne radar on, nothing painted (< 20 dbz) -0.5 to +0.5 g -1.6 to +3.4 g in 2 seconds Case study of extreme turbulence encounter in low reflectivity (cont.) KAKQ reflectivity (dbz) at 2.4 o NCAR 2nd moment EDR algorithm

10 Airborne turbulence detection using forward-looking radar NASA sponsored Turbulence Prediction and Warning System (TPAWS) Tactical avoidance of in-cloud turbulence using existing on-board radar Preliminary testing encouraging PODY ~ 80%, FAR ~ 10% Gives 1-2 min warning NASA Flight Test Case Clear detection 80 seconds (18 km) before encounter. Reflectivities less than 20 dbz at initial detection. Persistent detection.

11 Event (30 April 2002, 19:11:10 19:16:14) RMS winds RMS g-load 19:12:02 19:13:44 Event :42-1:31 Hazard detected 1:19, 18 km to encounter -1:19

12 Event (reflectivities at 19:12:25) -1:19 Event :07-0:55 Persistent detection -0:43

13 NASA Flight Test Case Event detection at reflectivities below 15 dbz. Event RMS winds RMS g-load 19:03:43 19:05:01

14 Event (reflectivities at 19:04:07) -0:54 In situ turbulence measurement program Goal: To augment/replace subjective PIREPs with objective and precise turbulence measurements Features: Automatically computes turbulence intensities (median and peak) Aircraft independent (eddy dissipation rate, edr) Automatically downloads data periodically during flight using ACARS network Adopted as ICAO Standard GTG AC 1 w edr or g 1 edr edr edr ACARS dissemination AC 2 edr g 2 edr Airline dispatch

15 Uses for the in situ turbulence data: Augmenting existing Pirep data. Provides near-time state of the atmosphere for pilots, dispatchers, airline and aviation Met. Services. Useful in providing quantitative database for the verification of turbulence forecasting algorithm. Useful as an input into turbulence diagnostic algorithms Useful in providing a climatology of turbulence. Potentially useful as a direct input into NWP models. 2 Hours of PIREP s, EDR and MDCRS reports

16 In-situ (cont.) UAL EDR-PIREP comparisons 1653 cases examined so far 15 questionable 13 explained by human analysis 2 still unknown An automated QC algorithm is being developed Implementation status Currently receiving data from 101 UAL s Adding 96 UAL 757s in next 4 months Example of data quality problem Example of pirepedr agreement In-situ (cont.) Verification that EDR data can be used to calculate aircraft loads Data from 2002 NASA 757 flight tests All the time the aircraft was above 10 kft = 40 hours of data, including maneuvers. Median/peak EDRs calculated over one min. intervals as in operational system These values are scaled into rms loads Compared to measured rms loads from 757

17 Examples of Maneuver Loads Comparison of one-minute median and peak RMS Loads from EDR algorithm and 757 measurements Measured rms loads median r=0.94 m= one-minute samples EDR-predicted rms loads peak r=0.95 m=1.03

18 Example of turbulence intermittency A posteriori subgrid small-scale merger Difficult to get cloud model at high enough resolution while still covering a large cloud But at the unresolved smaller scales, the isotropic von Karman representation is good So add the two, modulating the von Karman intensities by the large scale resolved motion Cloud model grid + Von Karman turbulence grid

19 Merger example Wind field with and without subgrid Structure functions 100 m model grid w/o subgrid turbulence 100 m model grid w/ subgrid turbulence x 100 = meters x 100 = meters Coupled wake vortex - lidar simulations Assume scanning lidar perpendicular to flight path Use vortex pair of known characteristics Determine lidar accuracy of measurement of vortex parameters

20 wake vortex simulations unstratified stratified