1. GEO Turbulence Detection: Tropopause Folds and Clear Air Turbulence Tony Wimmers Cooperative Institute for Meteorological Satellite Studies (CIMSS), UW-Madison MURI Workshop Wednesday, June 8, 2005

2. 14 12 10 8 6 4 150 200 300 400 500 600 700 (~100 km) subtropical air mass polar air mass stratosphere Pressure (hPa) Height (km) tropopause front From Shapiro, M. A. (1980): Turbulent mixing within tropopause folds as a mechanism for the exchange of chemical constituents between the stratosphere and the troposphere, J. Atmos. Sci., 37, 994-1004. CAT and tropopause folds Upper-air front

3. Total column ozone and WV indicate the tropopause “break” troposphere stratosphere Total column ozone tropopause break high ozone low ozone AWV-derived moisture water vapor very low water vapor (region of GOES WV response)

4. AWVWV GOES specific humidity product (WV channel) (specific humidity product)

7. Operation of the Model Cloud-masked Building a statistical model longitude latitude decreasing specific humidity

8. Operation of the Model Smoothed (  = 0.30  ) Building a statistical model longitude latitude decreasing specific humidity

9. Operation of the Model Gradient magnitude Building a statistical model longitude latitude

10. Operation of the Model Laplacian zero-crossing Building a statistical model longitude latitude

11. Operation of the Model Extend out 234 km toward the warm air mass Building a statistical model longitude latitude decreasing specific humidity

12. longitude latitude Building a statistical model Vertical component of the fold subtropical air mass polar air mass stratosphere tropopause Upper-air front  surface +15K -5K

13. Web product: Real-time pirep validation  Pirep data is provided courtesy of NCAR Aviation Digital Data Service (ADDS)

14. Web product: Real-time TAMDAR validation  TAMDAR (Tropospheric Airborne Meteorological Data Report) is part of the Great Lakes Field Experiment  Unfortunately, it is mostly lower and midtroposphere

15.  Manual  Subjective  A bit of politics involved  In-situ  Well-developed standards of reporting among pilots  Record goes back for years Validation: Pilot report (pirep) observations of turbulence

16.  April 8-30, 2005 1500-2300 UTC (peak time)  Eastern U.S. (away from mountain wave turbulence)  Above 15,000 feet (mid- and upper troposphere)  Areas of strong convection are filtered out (no C.A.T.)  If the pirep is in a modeled fold and reports turbulence, then this is a correctly classified “Yes” report. If the pirep is outside a modeled fold and reports no turbulence, this is a correctly classified “No” report.  2,293 pirep observations, 62% of ALL observations are turbulent. Validation: Details

17.  Find the model’s “Probability of Detection” for turbulence  Next, search for any further constraints on the model that improve the Probability of Detection Validation: Method

18. … accuracy is not clearly a function of direction 120°

19. … slight increase in accuracy with proximity to tropopause break

20. … lower accuracy for the weaker image gradients Eliminate gradients < 5

21. Eliminate folds shorter than 0.2 gcd (22 km) “long”

22. …Large tropopause folds are very robust

23. 100% accuracy at 0.2 gcd (22 km) from the image gradient

24. Number of Yes reports Proportion of Yes reports correctly classified Proportion of No reports mis- classified* 1. Initial model2960.770.63 2. Revised model: Longer folds 2400.780.63 3. Revised model (#2): Longer folds, higher gradients 1380.820.63 * Does not purport to classify all negative reports Statistics for tropopause fold turbulence prediction (N=2293, “background” rate of success=0.64)

25.  The tropopause folding model shows significant skill at predicting upper-tropospheric turbulence  The model increases in accuracy significantly as it is made more selective (Prob of Detection = 82%)  The most productive area of prediction (near the tropopause break) is the area that would benefit the most from hyperspectral sounding Preliminary conclusions: Trop folding + CAT subtropical air mass polar air mass stratosphere Upper-air front

26. http://cimss.ssec.wisc.edu/asap/exper/tfoldsVer2/pirepSep.html http://cimss.ssec.wisc.edu/asap/exper/tfoldsVer2/tamdarDisplay.html Web pages:

28.  Method of synoptic-scale clear-air turbulence (CAT) prediction with the GOES water vapor channel  Implementation of a predictive model to a real-time web product  Validation with pilot reports Outline

29. Comparison to NCAR Turbulence prediction model

30. Building a statistical model Estimating dimensions of a fold Tropopause fold size and water vapor gradient are uncorrelated (mean)

31. Hypothesis: Is flux and size of a TF proportional to the AWV gradient magnitude above a threshold? Building a statistical model

32. GOES imagery – AWV product (surface) (upper troposphere ~8 km high) Introduction

33. Elements of Strat-Trop Exchange (STE) streamers Cut-off Low (upper-tropospheric air mass boundary) Introduction 14 12 10 8 6 4 150 200 300 400 500 600 700 (~100 km) subtropical air mass polar air mass stratosphere Pressure (hPa) Height (km) tropopause Polar front