1 Remote Detection and Statistical Diagnosis of Convectively-Induced Turbulence John K. Williams, Gary E. Blackburn, Jason A. Craig, and Greg Meymaris NCAR Workshop on Aviation Turbulence Boulder, CO August 28, 2013 This research is, in part, in response to requirements and funding by the Federal Aviation Administration (FAA). The views expressed are those of the authors and do not necessarily represent the official policy or position of the FAA.

2 Motivation Reflectivity (dBZ) is NOT a reliable indicator of turbulence location –Airspace between or around high-echo regions may be turbulent! Area above or beside clouds is NOT necessarily safe –Out-of-cloud does not mean out-of-turbulence! Convectively-induced turbulence (CIT) can be small-scale and evolve quickly –Storm observations are key for accurate and timely diagnosis Environmental conditions impact turbulence location and severity –Storm observations are not enough by themselves Therefore: –Need to use remote sensing information (e.g., Doppler weather radar) to identify in-cloud turbulence –Need to fuse observations with environmental information (e.g., from NWP models) to diagnose in- and near-cloud turbulence –Need ways to get tactical CIT info to pilots and dispatchers quickly

3 Detect and censor contaminated data (sun spikes, artifacts) Assess spectrum width (SW) measurement quality via fuzzy logic, based on –Operational mode for that sweep –Signal-to-noise ratio (SNR) –Overlaid Power Ratio (PR) –Clutter and overlaid clutter contamination –Insect contamination “Scale” SW to EDR using range- dependent function Compute local confidence-weighted average EDR and confidence Use data from 133 NEXRADs to create CONUS 3-D mosaic –Real-time at NCAR since 2008 In-cloud turbulence detection: NEXRAD Turbulence Detection Algorithm (NTDA) NTDA (Radar by Radar) NTDA (Radar by Radar) NTDA (Radar by Radar) Level 2 NEXRAD data NTDA (Radar by Radar) NTDA Mosaic

4 Case 1: United Flight 1727 Severe Turbulence Encounter over N. Gulf of Mexico, 4 April 2012 A Boeing 737, UA Flight 1727 from Tampa to Houston ASDI data: altitude declined from FL 380 to FL 321 in one minute near 11:57 UTC Five passengers and two flight attendants were injured, according to United Airlines At least three people were transported to a hospital The pilots declared an emergency, and emergency personnel met the aircraft after landing The flight left Tampa International Airport at 6:30 a.m. EDT and arrived in Houston at 7:47 a.m. CDT. 4

5 Case 1: United Flight 1727 Severe Turbulence Encounter over N. Gulf of Mexico, 4 April FL 360 Reflectivity, 11:50 UTC FL 360 NTDA EDR, 11:50 UTC Reflectivity cross-section, 11:50 UTC NTDA EDR cross-section, 11:50 UTC

6 Case 1: United Flight 1727, cont. UCAR Confidential and Proprietary. © 2012, University Corporation for Atmospheric Research. All rights reserved. 6 FL 360 Reflectivity, 12:05 UTC FL 360 NTDA EDR, 12:05 UTC Reflectivity cross-section, 12:05 UTC NTDA EDR cross-section, 12:05 UTC

7 NTDA EDR at FL 120 at 22:05 UTC NTDA dBZ at FL 120 at 22:05 UTC Vertical x-section NTDA EDRVertical x-section NTDA dBZ Flight track of FFT283 Case 2: Frontier 283, 12 August 2013 ~22:06 UTC, FL130, moderate turbulence, 4 injuries

8 Case 2: Frontier 283, cont NTDA EDR at FL 150 at 22:05 UTC

9 NTDA Deployment in Taiwan NTDA modified to run on Gematronik radars NTDA + GTG contours, 9/14/12 10:00 Z, FL 210 dBZ with GTG contours overlaidNTDA EDR with GTG contours overlaid

10 NTDA-2.5 Dynamic SNR (DSNR) QC Compute signal-to-noise ratio fuzzy logic interest map “on the fly” based on radar operating parameters –E.g., volume coverage pattern, number of pulses, operating modes, and software build Method duplicates human reasoning process using quantitative data Many NEXRAD system changes may now be handled by conducting simulations and updating the performance database NEXRAD Simulation Database Operational mode (N, T s, etc.), range gate, performance requirements SNR-to-confidence interest map Performance data 10

11 Example VCP 12 Case: KDVN, 27 May 2011 dBZ Original NTDA EDR DSNR NTDA EDR NTDA-2.5 uses DSNR to increase NTDA EDR coverage by as much as 30% while maintaining quality

12 Diagnosis of Convectively-Induced Turbulence Want to diagnose CIT both in and around clouds Include observational data for accuracy, resolution Operational data sources include –near-storm environment fields and clear-air turbulence diagnostics derived from NWP models (e.g., WRF-RAP) –lightning and satellite-derived features and turbulence signatures (e.g., overshooting tops) –3-D radar reflectivity and derived features Use AI techniques to select predictors, build and test empirical models –In situ automated EDR reports used as “truth” –Random forest, logistic regression, k-nearest neighbors methods tested

13 AUC Max TSS Max CSI Variable Selection Sampled independent training/testing data subsets from odd/even Julian days Performed variable selection (iterated 2 forward selection steps, 1 back), repeated for various AI methods Repeated for 8 training/testing samples; aggregated results Iteration Mean skill scores Variable selection cross-validation results 17 vars34 vars

14 Identifying Top Predictor Variables Top predictor sets are different: random forest results include fields not monotonically related to turbulence (e.g., pressure)

15 Evaluating performance Compared random forest with NWP model-based Graphical Turbulence Guidance version 3 and distances to “storms” (regions with echo tops > 10 km) Evaluation performed on 32 cross-evaluation subset pairs from odd and even Julian days

16 DCIT Case Study May 26, :45 UTC Composite reflectivity and SIGMETs Echo tops and ASDI DCIT probability, FL330DCIT probability, FL390 Note: DCIT predicts more turbulence at FL390 than FL330, particularly near Chicago.

17 Real-time DCIT Successive versions at NCAR/RAL since 2008 –15-min update –6 km horizontal, 1,000 ft vertical resolution –calibrated to EDR, downscaled to 13 km for GTG Nowcast –Newly redesigned to omit NTDA, cover 0-45,000 ft DCIT EDR, 0000 UTC May 14, 2009, FL370 FL380, with in situ EDR overlaid

18 Tactical CIT data needs to be transmitted to pilots and dispatchers with minimal latency to be most useful. 18 NTDA cockpit uplinks were demonstrated in Today’s EFBs and onboard internet should make dissemination of GTGN to pilots increasingly practical.

19 Summary and Conclusions Current information available to pilots and dispatchers (e.g., dBZ) is not adequate for accurately identifying convective turbulence hazards and can be misleading. Radar-based NTDA provides in-cloud turbulence detection –Useful for developing in-cloud DCIT, studying storm evolution AI fusion of observation and model data via DCIT provides skillful deterministic and probabilistic CIT assessments –Fusion methods can be applied using any available data –E.g., can be run globally with NWP model + satellite data Real-time tactical CIT information requires new dissemination methods to provide information to pilots and dispatchers in time to avoid or mitigate encounters EFBs could provide capability via on-board internet Automated alerting via EFBs or ACARS

20 Questions? CIDD research display: NTDA mosaic 6-hr loop on 11 August 2008, 36,000 ft overlaid are in situ EDR measurements from United Airlines B-757s

21 Extra slides follow

22 Reflectivity X-Z plot and U, W windsNTDA EDR X-Z plot Thunderstorm turbulence and updrafts Reflectivity Y-Z plot and V, W windsNTDA EDR Y-Z plot Example: 21 Feb 2005, Huntsville, AL dual-Doppler analysis

23 NTDA Fuzzy Logic Algorithm 23

24 Example: VCP 12 SNR Interest Maps

25 GTGN Evaluation Courtesy of Julia Pearson Verification via in situ EDR reports shows that adding NTDA considerably improved GTGN skill Prediction of Moderate or Greater Turbulence

26 Calibration and Evaluation Based on 32 cross-evaluations using even/odd and odd/even Julian day training/testing sets ROC curves for RF DCIT (blue), GTG (green), and storm distance (magenta) RF votes to probability calibration