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Clear Air Turbulence Forecasting in a Changing World: New Results from Research and Operations

Clear Air Turbulence Forecasting in a Changing World: New Results from Research and Operations. (in three parts) John Knox , Alan Black, Erik Galicki , Jared Rackley , Maria Augutis , Corey Dunn, Jeremiah Grant, Patrick Malone, and Stephanie Phelps University of Georgia, Athens, GA

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Clear Air Turbulence Forecasting in a Changing World: New Results from Research and Operations

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  1. Clear Air Turbulence Forecasting in a Changing World: New Results from Research and Operations (in three parts) John Knox, Alan Black, Erik Galicki, Jared Rackley, Maria Augutis, Corey Dunn, Jeremiah Grant, Patrick Malone, and Stephanie Phelps University of Georgia, Athens, GA Emily Wilson Delta Air Lines, Atlanta, GA Paul Williams University of Reading (UK) with assistance from Gary Ellrod, Manoj Joshi, Steve Silberberg, Bob Sharman, and UCAR/COMET NCAR Workshop on Aviation Turbulence, 28 August 2013

  2. Part 1: Research to Operations with the Ellrod-Knox (2010) Index • Ellrod and Knapp (1992 Weather and Forecasting) “Turbulence Index”, TI(TI1 in EK92) combines vertical wind shear and deformation, based on frontogenetical concepts TI = VWS x DEF • New diagnostic: EKI (Ellrod and Knox 2010 WaF) combines TI with a “divergence trend” DVT to account for CAT in unbalanced or highly divergent situations (Knox 1997 MWR) EKI = TI + DVT • Divergence trend used instead of tendency because tendencies calculated from model time steps are ~ 2 orders of magnitude smaller than VWS and DEF DVT = C [(du/dx + dv/dy)h2 - (du/dx +dv/dy)h1] where C is an empirical constant (scaled divergence tendency). Tests at the Aviation Weather Center yielded good results for GFS for C = 100 and a time step of 3 hours. • Forecasts made for 200-250 hPa layer (FL 340-390); calculated at top of layer

  3. Part 1, cont.: Research methodology for TI to EKI comparison • Two winters analyzed: December-March 2009-10 and December-March 2010-11 using 1° x 1° GFS output • Larger database of PIREPs than Ellrod and Knox (2010): • 2009-10 winter: Over 3500 PIREPs, over 1200 moderate-or-greater (MOG) reports • 2010-11 winter: Over 4000 PIREPs, over 1100 MOG reports • TI and EKI forecasts calculated from 24-h GFS forecasts (23-km horizontal resolution) valid at 0Z and 18Z each day • PIREPs within +/- 1 h of forecast time included in analysis • PIREPs west of Denver, CO ignored (to eliminate most mountain wave turbulence) • PIREPs within 50 miles of radar reflectivities of 50 dBz or greater ignored (to eliminate turbulence due to deep convection; Ellrod and Knox 2010) • Performance evaluated using various index thresholds for both TI and EKI: 0, 4, 6, 8, 10, 12 and 16 (x 10-9 s-2)

  4. Part 1, cont.: Results comparing TI to EKI All MOG

  5. Part 1, cont.: Results comparing TI to EKI (0Z and 18Z MOG, 2010-11 winter) • PODy: EKI improves upon TI by 37-79% • TSS: EKI improves upon TI by 12-90% • CSI: EKI improves upon TI by 13-79% • AUC: TI: 0.6726 EKI: 0.7149

  6. Part 1, conclusion: Changing operational practices • Dr. Steve Silberberg, AWC, June 19, 2013: “…Ellrod-Knox is now the number 1 turbulence guidance used by AWC forecasters.” • Example from AWC: “12Z GFS run from today, Wed June 19, 2013.  It is a 6 h forecast valid at 18Z.  The forecaster on duty used this forecast to issue a SIGMET for severe turbulence south of Kamchatka in the region of very high ElrdKnox and it immediately verified.”

  7. Part 2: Case Studies (Wilson M.S. thesis, UGA) • WRF-RR output (13 km, hourly) was analyzed for three outbreaks of CAT during 2010-11 (December, January, September). • GTG software used to create forecasts for six turbulence indices: TI; Ellrod-Knox (DTI); vertical wind shear; Richardson number; frontogenesis; and Lighthill-Ford* [*calculated from residual, scaled by Richardson number instead of including gravity wave physics as in Knox et al. (2008)] • Model output for the forecasts was normalized to the EDR scale for verification. • Model output was imported into ArcMap to create forecast maps • A total of 2,496 maps were created. • 16 forecast periods • 6 forecast indices at each forecast period • 26 flight levels in each forecast index (19,000 feet to 45,000 feet) • EDR reports (1-min. frequency; provided by Sharman) were imported into ArcMap and then used for verification. • Turbulence associated with convection excluded imperfectly as per Ellrod and Knox (2010)

  8. Case Study 1: 1800 UTC on 26 December 2010 to 1800 UTC on 28 December 2010

  9. Case Study 2: 1800 UTC on 6 January 2011 to 1800 UTC on 7 January 2011

  10. Case Study 3: 1200 UTC on 22 September 2011 to 1800 UTC on 22 September 2011

  11. Part 2, conclusion: Wilson Case Study Results Dec 2010 Jan 2011 Sept 2011 A L L M O G Ellrod-Knox the winner; Lighthill-Ford not generally successful in this implementation

  12. Part 3: Williams and JoshiCAT and Climate Change • CAT is linked to upper-level jet stream winds (Koch et al. 2005), which are robustly projected to be strengthened by anthropogenic climate change (Lorenz & DeWeaver 2007) • Four CAT diagnostics have increased by 40-90% over the period 1958-2001 in the North Atlantic, USA, and European sectors in ERA40 reanalysis data (Jaeger & Sprenger 2007) • However, “changes in the amount and type of assimilated data used for ERA40 were not taken into account and may have affected the absolute values of the calculated trends” • Moderate-or-greater upper-level turbulence has increased over the period 1994-2005 in USA pilot reports (Wolff & Sharman 2008) • However, “given that we only have 12 years worth of data, it is difficult to assign much significance to this trend… a more thorough analysis is required to verify its existence…”

  13. Williams & Joshi Methodology • We use the GFDL-CM2.1 model (Delworth et al. 2006) • this is the only CMIP3 model with a high top level and daily data • atmosphere resolution is 2.52.0, with 24 levels (5 above 200 hPa) • the upper-level winds in the northern extra-tropics agree well with reanalysis data (Reichler & Kim 2008) • the jet stream in the North Atlantic sector strengthens under global warming (Stouffer et al. 2006), consistent with other CMIP3 models • We take 20 years of daily-mean data from each of two simulations: pre-industrial control and doubled-CO2 • focus on winter, which is when Northern Hemispheric CAT is most intense (Jaeger & Sprenger 2007) • calculate a basket of 21 CAT diagnostics on the 200 hPa pressure level, which close to typical cruise altitudes • focus on the North Atlantic flight corridor, one of the world’s busiest, with 300 flights per day in each direction (Irvine et al. 2013)

  14. Median of TI1 in DJF 10-9 s-2 10-9 s-2 Williams & Joshi (2013)

  15. Histograms of TI1 in DJF 50-75N, 10-60W The median strength of CAT increases by 32.8% The probability of moderate-or-greater (MOG) CAT increases by 10.8% Williams & Joshi (2013)

  16. GTG GTG mostly in range 10-40% mostly in range 40-170% GTG GTG GTG GTG GTG

  17. Agreement on change in DJF LHRSFO Williams & Joshi (2013)

  18. Summary of Williams & Joshi • A basket of 21 CAT measures diagnosed from climate simulations is significantly modified if the CO2 is doubled • At cruise altitudes within 50‑75N and 10‑60W in winter, most measures show a 10‑40% increase in the median strength of CAT and a 40-170% increase in the frequency of occurrence of moderate-or-greater CAT • We conclude that climate change will lead to bumpier transatlantic flights by the middle of this century • Implications: • Flight paths may become more convoluted to avoid stronger, more frequent patches of turbulence, in which case journey times will lengthen and fuel consumption and emissions will increase • The large-scale atmospheric circulation could be impacted, because CAT contributes significantly to troposphere–stratosphere exchange

  19. Thank you!Questions?johnknox@uga.eduorPaul Williams at p.d.williams@reading.ac.uk

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