• Abernethy, J. A., 2008: A domain analysis approach to clear-air turbulence forecasting using high-density in-situ measurements. Ph.D. dissertation, University of Colorado Boulder, 152 pp.

  • Alaka, M. A., 1961: The occurrence of anomalous winds and their significance. Mon. Wea. Rev., 89, 482494, doi:10.1175/1520-0493(1961)089<0482:TOOAWA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Anderson, D. A., J. C. Tannehill, and R. H. Pletcher, 1984: Computational Fluid Mechanics and Heat Transfer. McGraw-Hill, 599 pp.

  • Bacmeister, J. T., P. A. Newman, B. L. Gary, and K. R. Chan, 1994: An algorithm for forecasting mountain wave–related turbulence in the stratosphere. Wea. Forecasting, 9, 241253, doi:10.1175/1520-0434(1994)009<0241:AAFFMW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bass, E. J., 1999: Towards a pilot-centered turbulence assessment and monitoring system. Proc. 18th Digital Avionics Systems Conf., St. Louis, MO, Institute of Electrical and Electronics Engineers, 6.D.3-1–6.D.3-8, doi:10.1109/DASC.1999.821980.

  • Benjamin, S. G., and Coauthors, 2016: A North American hourly assimilation and model forecast cycle: The Rapid Refresh. Mon. Wea. Rev., 144, 16691694, doi:10.1175/MWR-D-15-0242.1.

    • Search Google Scholar
    • Export Citation
  • Birner, T., 2006: Fine-scale structure of the extratropical tropopause region. J. Geophys. Res., 111, D04104, doi:10.1029/2005JD006301.

  • Brown, B. G., and G. S. Young, 2000: Verification of icing and turbulence forecasts: Why some verification statistics can’t be computed using PIREPs. Preprints, Ninth Conf. on Aviation, Range, and Aerospace Meteorology, Orlando, FL, Amer. Meteor. Soc., 393398.

  • Brown, R., 1973: New indices to locate clear-air turbulence. Meteor. Mag., 102, 347361.

  • Calabrese, P. A., 1966: Forecasting mountain waves. U.S. Weather Bureau Tech. Memo. FCST-6, 12 pp.

  • Cho, J. Y. N., R. E. Newell, B. E. Anderson, J. D. W. Barrick, and K. L. Thornhill, 2003: Characterizations of tropospheric turbulence and stability layers from aircraft observations. J. Geophys. Res., 108, 8784, doi:10.1029/2002JD002820.

    • Search Google Scholar
    • Export Citation
  • Colson, D., and H. A. Panofsky, 1965: An index of clear air turbulence. Quart. J. Roy. Meteor. Soc., 91, 507513, doi:10.1002/qj.49709139010.

    • Search Google Scholar
    • Export Citation
  • Cornman, L. B., 2016: Airborne in situ measurements of turbulence. Aviation Turbulence: Processes, Detection, Prediction, R. Sharman and T. Lane, Eds., Springer, 97–120, doi:10.1007/978-3-319-23630-8_5.

  • Cornman, L. B., C. S. Morse, and G. Cunning, 1995: Real-time estimation of atmospheric turbulence severity from in-situ aircraft measurements. J. Aircr., 32, 171177, doi:10.2514/3.46697.

    • Search Google Scholar
    • Export Citation
  • Doswell, C. A., III, R. Davies-Jones, and D. L. Keller, 1990: On summary measures of skill in rare event forecasting based on contingency tables. Wea. Forecasting, 5, 576585, doi:10.1175/1520-0434(1990)005<0576:OSMOSI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Doyle, J. D., and Coauthors, 2011: An intercomparison of T-REX mountain-wave simulations and implications for mesoscale predictability. Mon. Wea. Rev., 139, 28112831, doi:10.1175/MWR-D-10-05042.1.

    • Search Google Scholar
    • Export Citation
  • Dutton, M. J. O., 1980: Probability forecasts of clear-air turbulence based on numerical output. Meteor. Mag., 109, 293310.

  • Eckermann, S. D., J. Ma, and D. Broutman, 2004: The NRL Mountain Wave Forecast Model (MWFM). Preprints, Symp. on the 50th Anniversary of Operational Numerical Weather Prediction, College Park, MD, Amer. Meteor. Soc., P2.9. [Available online at http://www.dtic.mil/cgi-bin/GetTRDoc?Location=U2&doc=GetTRDoc.pdf&AD=ADA465017.]

  • Eckermann, S. D., D. Broutman, J. Ma, and J. Lindeman, 2006: Fourier-ray modeling of short-wavelength trapped lee waves observed in infrared satellite imagery near Jan Mayen. Mon. Wea. Rev., 134, 28302848, doi:10.1175/MWR3218.1.

    • Search Google Scholar
    • Export Citation
  • Ellrod, G. P., and D. I. Knapp, 1992: An objective clear-air turbulence forecasting technique: Verification and operational use. Wea. Forecasting, 7, 150165, doi:10.1175/1520-0434(1992)007<0150:AOCATF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ellrod, G. P., and J. A. Knox, 2010: Improvements to an operational clear-air turbulence diagnostic index by addition of a divergence trend term. Wea. Forecasting, 25, 789798, doi:10.1175/2009WAF2222290.1.

    • Search Google Scholar
    • Export Citation
  • Endlich, R. M., 1964: The mesoscale structure of some regions of clear-air turbulence. J. Appl. Meteor., 3, 261276, doi:10.1175/1520-0450(1964)003<0261:TMSOSR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Fahey, T. H., III, 1993: Northwest Airlines atmospheric hazards advisory and avoidance system. Preprints, Fifth Int. Conf. on Aviation Weather Systems, Vienna, VA, Amer. Meteor. Soc., 409413.

  • Fahey, T. H., III, M. Pfleiderer, and R. Sharman, 2002: Mountain wave activity and turbulence—Aviation forecasts and avoidance. Preprints, 10th Conf. on Aviation, Range, and Aerospace Meteorology, Portland, OR, Amer. Meteor. Soc., 303306.

  • Fahey, T. H., III, E. N. Wilson, R. O’Loughlin, M. Thomas, and S. Klipfel, 2016: A history of weather reporting from aircraft and turbulence forecasting for commercial aviation. Aviation Turbulence: Processes, Detection, Prediction, R. Sharman and T. Lane, Eds., Springer, 31–58, doi:10.1007/978-3-319-23630-8_2.

  • Frehlich, R., and R. Sharman, 2004a: Estimates of turbulence from numerical weather prediction model output with applications to turbulence diagnosis and data assimilation. Mon. Wea. Rev., 132, 23082324, doi:10.1175/1520-0493(2004)132<2308:EOTFNW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Frehlich, R., and R. Sharman, 2004b: Estimates of upper level turbulence based on second order structure functions derived from numerical weather prediction model output. Preprints, 11th Conf. on Aviation, Range, and Aerospace Meteorology, Hyannis, MA, Amer. Meteor. Soc., 4.13. [Available online at https://ams.confex.com/ams/pdfpapers/81831.pdf.]

  • Frehlich, R., and R. Sharman, 2010: Climatology of velocity and temperature turbulence statistics determined from rawinsonde and ACARS/AMDAR data. J. Appl. Meteor. Climatol., 49, 11491169, doi:10.1175/2010JAMC2196.1.

    • Search Google Scholar
    • Export Citation
  • Frehlich, R., Y. Meillier, M. L. Jensen, and B. Balsley, 2004: A statistical description of small-scale turbulence in the low-level nocturnal jet. J. Atmos. Sci., 61, 10791085, doi:10.1175/1520-0469(2004)061<1079:ASDOST>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Frehlich, R., R. Sharman, F. Vandenberghe, W. Yu, Y. Liu, J. Knievel, and G. Jumper, 2010: Estimates of Cn 2 from numerical weather prediction model output and comparison with thermosonde data. J. Appl. Meteor. Climatol., 49, 17421755, doi:10.1175/2010JAMC2350.1.

    • Search Google Scholar
    • Export Citation
  • Frisch, U., 1995: Turbulence: The Legacy of A. N. Kolmogorov. Cambridge University Press, 296 pp.

  • Fritts, D. C., and M. J. Alexander, 2003: Gravity wave dynamics and effects in the middle atmosphere. Rev. Geophys., 41, 1003, doi:10.1029/2001RG000106.

  • Gill, P. G., 2014: Objective verification of World Area Forecast Centre clear air turbulence forecasts. Meteor. Appl., 21, 311, doi:10.1002/met.1288.

    • Search Google Scholar
    • Export Citation
  • Gill, P. G., 2016: Aviation turbulence forecast verification. Aviation Turbulence: Processes, Detection, Prediction, R. Sharman and T. Lane, Eds., Springer, 261–283.

  • Gill, P. G., and A. J. Stirling, 2013: Including convection in global turbulence forecasts. Meteor. Appl., 20, 107114, doi:10.1002/met.1315.

    • Search Google Scholar
    • Export Citation
  • Gill, P. G., and P. Buchanan, 2014: An ensemble based turbulence forecasting system. Meteor. Appl., 21, 1219, doi:10.1002/met.1373.

  • Guyon, I., and A. Elisseeff, 2003: An introduction of variable and feature selection. J. Mach. Learn. Res., 3, 11571182.

  • Haltiner, G. J., and R. T. Williams, 1983: Numerical Prediction and Dynamic Meteorology. 2nd ed. John Wiley and Sons, 477 pp.

  • Hopkins, R. H., 1977: Forecasting techniques of clear-air turbulence including that associated with mountain waves. WMO Tech. Note WMO/TN-155, 31 pp.

  • ICAO, 2001: Meteorological service for international air navigation. Annex 3 to the Convention on International Civil Aviation, 14th Ed., ICAO Rep., 128 pp.

  • Jolliffe, I. T., and D. B. Stephenson, 2003: Forecast Verification: A Practitioner’s Guide in Atmospheric Science. John Wiley and Sons, 239 pp.

  • Kaplan, M. L., and Coauthors, 2004: Characterizing the severe turbulence environments associated with commercial aviation accidents: A Real-Time Turbulence Model (RTTM) designed for the operational prediction of hazardous aviation turbulence environments. NASA Rep. NASA/CR-2004-213025, 54 pp. [Available online at https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20040110976.pdf.]

  • Kim, J.-H., and H.-Y. Chun, 2011: Statistics and possible sources of aviation turbulence over South Korea. J. Appl. Meteor. Climatol., 50, 311324, doi:10.1175/2010JAMC2492.1.

    • Search Google Scholar
    • Export Citation
  • Kim, J.-H., H.-Y. Chun, R. D. Sharman, and T. L. Keller, 2011: Evaluations of upper-level turbulence diagnostics performance using the Graphical Turbulence Guidance (GTG) system and pilot reports (PIREPs) over East Asia. J. Appl. Meteor. Climatol., 50, 19361951, doi:10.1175/JAMC-D-10-05017.1; Corrigendum, 50, 2193, doi:10.1175/JAMC-D-11-0188.1.

    • Search Google Scholar
    • Export Citation
  • Kim, J.-H., H.-Y. Chun, R. D. Sharman, and S. B. Trier, 2014: The role of vertical shear on aviation turbulence within cirrus bands of a simulated western Pacific cyclone. Mon. Wea. Rev., 142, 27942813, doi:10.1175/MWR-D-14-00008.1.

    • Search Google Scholar
    • Export Citation
  • Kim, J.-H., W. N. Chan, B. Sridhar, and R. D. Sharman, 2015: Combined winds and turbulence prediction system for automated air-traffic management applications. J. Appl. Meteor. Climatol., 54, 766784, doi:10.1175/JAMC-D-14-0216.1.

    • Search Google Scholar
    • Export Citation
  • Kim, Y.-J., and A. Arakawa, 1995: Improvement of orographic gravity wave parameterization using a mesoscale gravity wave model. J. Atmos. Sci., 52, 18751902, doi:10.1175/1520-0469(1995)052<1875:IOOGWP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kim, Y.-J., and J. D. Doyle, 2005: Extension of an orographic-drag parametrization scheme to incorporate orographic anisotropy and flow blocking. Quart. J. Roy. Meteor. Soc., 131, 18931921, doi:10.1256/qj.04.160.

    • Search Google Scholar
    • Export Citation
  • Kim, Y.-J., S. D. Eckermann, and H.-Y. Chun, 2003: An overview of the past, present and future of gravity-wave drag parametrization for numerical climate and weather prediction models. Atmos.–Ocean, 41, 6598, doi:10.3137/ao.410105.

    • Search Google Scholar
    • Export Citation
  • Knox, J. A., 1997: Possible mechanisms of clear-air turbulence in strongly anticyclonic flows. Mon. Wea. Rev., 125, 12511259, doi:10.1175/1520-0493(1997)125<1251:PMOCAT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Knox, J. A., D. W. McCann, and P. D. Williams, 2008: Application of the Lighthill–Ford theory of spontaneous imbalance to clear-air turbulence forecasting. J. Atmos. Sci., 65, 32923304, doi:10.1175/2008JAS2477.1.

    • Search Google Scholar
    • Export Citation
  • Knox, J. A., A. W. Black, J. A. Rackley, E. N. Wilson, J. S. Grant, S. P. Phelps, D. S. Nevius, and C. B. Dunn, 2016: Automated turbulence forecasting strategies. Aviation Turbulence: Processes, Detection, Prediction, R. Sharman and T. Lane, Eds., Springer, 243–260, doi:10.1007/978-3-319-23630-8_12.

  • Krozel, J., V. Klimenko, and R. Sharman, 2011: Analysis of clear-air turbulence avoidance maneuvers. Air Traffic Control Quart., 19, 147168. [Available online at http://arc.aiaa.org/doi/pdf/10.2514/atcq.19.2.147.]

    • Search Google Scholar
    • Export Citation
  • Laikthman, D. L., and Y. Z. Al’ter Zalik, 1966: Use of aerological data for determination of aircraft buffeting in the free atmosphere. Izv. Akad. Nauk SSSR. Fiz. Atmos. Okeana, 2, 534536.

    • Search Google Scholar
    • Export Citation
  • Lane, T. P., J. D. Doyle, R. Plougonven, M. A. Shapiro, and R. D. Sharman, 2004: Observations and numerical simulations of inertia–gravity waves and shearing instabilities in the vicinity of a jet stream. J. Atmos. Sci., 61, 26922706, doi:10.1175/JAS3305.1.

    • Search Google Scholar
    • Export Citation
  • Lee, D. R., R. B. Stull, and W. S. Irvine, 1984: Clear air turbulence forecasting techniques. Air Force Global Weather Center Rep. AFGWC/TN-79-001, 76 pp. [Available online at www.dtic.mil/get-tr-doc/pdf?AD=ADA144854.]

  • Lindborg, E., 1999: Can the atmospheric kinetic energy spectrum be explained by two-dimensional turbulence? J. Fluid Mech., 388, 259288, doi:10.1017/S0022112099004851.

    • Search Google Scholar
    • Export Citation
  • MacCready, P. B., Jr., 1964: Standardization of gustiness values from aircraft. J. Appl. Meteor., 3, 439449, doi:10.1175/1520-0450(1964)003<0439:SOGVFA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Marroquin, A., 1998: An advanced algorithm to diagnose atmospheric turbulence using numerical model output. Preprints, 16th Conf. on Weather Analysis and Forecasting, Phoenix, AZ, Amer. Meteor. Soc., 7981.

  • Marzban, C., 2004: The ROC curve and the area under it as performance measures. Wea. Forecasting, 19, 11061114, doi:10.1175/825.1.

  • McCann, D. W., 2001: Gravity waves, unbalanced flow, and aircraft clear air turbulence. Natl. Wea. Dig., 25 (1–2), 314. [Available online at http://nwafiles.nwas.org/digest/papers/2001/Vol25No12/Pg3-McCann.pdf.]

    • Search Google Scholar
    • Export Citation
  • McCann, D. W., J. A. Knox, and P. D. Williams, 2012: An improvement in clear-air turbulence forecasting based on spontaneous imbalance theory: The ULTURB algorithm. Meteor. Appl., 19, 7178, doi:10.1002/met.260.

    • Search Google Scholar
    • Export Citation
  • Mogil, H. M., and R. L. Holle, 1972: Anomalous gradient winds: Existence and implications. Mon. Wea. Rev., 100, 709716, doi:10.1175/1520-0493(1972)100<0709:AGWEAI>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Muñoz-Esparza, D., J. A. Sauer, R. R. Linn, and B. Kosović, 2016: Limitations of one-dimensional mesoscale PBL parameterizations in reproducing mountain-wave flows. J. Atmos. Sci., 73, 26032614, doi:10.1175/JAS-D-15-0304.1.

    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., and H. Niino, 2009: Development of an improved turbulence closure model for the atmospheric boundary layer. J. Meteor. Soc. Japan, 87, 895912, doi:10.2151/jmsj.87.895.

    • Search Google Scholar
    • Export Citation
  • Nastrom, G. D., and K. S. Gage, 1985: A climatology of atmospheric wavenumber spectra of wind and temperature observed by commercial aircraft. J. Atmos. Sci., 42, 950960, doi:10.1175/1520-0469(1985)042<0950:ACOAWS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Nicholls, J. M., 1973: The airflow over mountains: Research 1958-1972. WMO Tech. Note WMO/TN-127, 73 pp.

  • Palmer, T. N., G. J. Shutts, and R. Swinbank, 1986: Alleviation of a systematic westerly bias in general circulation and numerical weather prediction models through an orographic gravity wave drag parametrization. Quart. J. Roy. Meteor. Soc., 112, 10011039, doi:10.1002/qj.49711247406.

    • Search Google Scholar
    • Export Citation
  • Pan, L. L., W. J. Randel, B. L. Gary, M. J. Mahoney, and E. J. Hintsa, 2004: Definitions and sharpness of the extratropical tropopause: A trace gas perspective. J. Geophys. Res., 109, D23103, doi:10.1029/2004JD004982.

    • Search Google Scholar
    • Export Citation
  • Pearson, J. M., and R. D. Sharman, 2017: Prediction of energy dissipation rates for aviation turbulence. Part II: Nowcasting convective and nonconvective turbulence. J. Appl. Meteor. Climatol., 56, 339351, doi:10.1175/JAMC-D-16-0312.1.

  • Pepe, M. S., and M. L. Thompson, 2000: Combining diagnostic test results to increase accuracy. Biostatistics, 1, 123140, doi:10.1093/biostatistics/1.2.123.

    • Search Google Scholar
    • Export Citation
  • Press, W. H., B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, 1986: Numerical Recipes: The Art of Scientific Computing. Cambridge University Press, 848 pp.

  • Reap, R. M., 1996: Probability forecasts of clear-air-turbulence for the contiguous US. National Weather Service Office of Meteorology Tech. Procedures Bull. 430, 15 pp. [Available online at http://www.nws.noaa.gov/mdl/pubs/Documents/TechProcBulls/TPB_430.pdf.]

  • Roach, W. T., 1970: On the influence of synoptic development on the production of high level turbulence. Quart. J. Roy. Meteor. Soc., 96, 413429, doi:10.1002/qj.49709640906.

    • Search Google Scholar
    • Export Citation
  • Schumann, U., 2012: A contrail cirrus prediction model. Geosci. Model Dev., 5, 543580, doi:10.5194/gmd-5-543-2012.

  • Sharman, R., and T. Lane, Eds., 2016: Aviation Turbulence: Processes, Detection, Prediction. Springer, 523 pp., doi:10.1007/978-3-319-23630-8.

  • Sharman, R., C. Tebaldi, G. Wiener, and J. Wolff, 2006: An integrated approach to mid- and upper-level turbulence forecasting. Wea. Forecasting, 21, 268287, doi:10.1175/WAF924.1.

    • Search Google Scholar
    • Export Citation
  • Sharman, R., S. B. Trier, T. P. Lane, and J. D. Doyle, 2012: Sources and dynamics of turbulence in the upper troposphere and lower stratosphere: A review. Geophys. Res. Lett., 39, L12803, doi:10.1029/2012GL051996.

    • Search Google Scholar
    • Export Citation
  • Sharman, R., L. B. Cornman, G. Meymaris, J. Pearson, and T. Farrar, 2014: Description and derived climatologies of automated in situ eddy-dissipation-rate reports of atmospheric turbulence. J. Appl. Meteor. Climatol., 53, 14161432, doi:10.1175/JAMC-D-13-0329.1.

    • Search Google Scholar
    • Export Citation
  • Shutts, G., 1997: Operational lee wave forecasting. Meteor. Appl., 4, 2335, doi:10.1017/S1350482797000340.

  • Stone, P. H., 1966: On non-geostrophic baroclinic stability. J. Atmos. Sci., 23, 390400, doi:10.1175/1520-0469(1966)023<0390:ONGBS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Trier, S. B., and R. Sharman, 2016: Mechanisms influencing cirrus banding and aviation turbulence near a convectively enhanced upper-level jet stream. Mon. Wea. Rev., 144, 30033027, doi:10.1175/MWR-D-16-0094.1.

    • Search Google Scholar
    • Export Citation
  • Trier, S. B., R. Sharman, and T. P. Lane, 2012: Influences of moist convection on a cold-season outbreak of clear-air turbulence (CAT). Mon. Wea. Rev., 140, 2477–2496. doi:10.1175/MWR-D-11-00353.1.

  • Turner, J., 1999: Development of a mountain wave turbulence prediction scheme for civil aviation. Met Office Tech. Rep. 265, 34 pp.

  • Vosper, S., 2003: Development and testing of a high resolution mountain-wave forecasting system. Meteor. Appl., 10, 7586, doi:10.1017/S1350482703005085.

    • Search Google Scholar
    • Export Citation
  • Williams, J. K., 2014: Using random forests to diagnose aviation turbulence. Mach. Learn., 95, 5170, doi:10.1007/s10994-013-5346-7.

  • Williams, J. K., and G. Meymaris, 2016: Remote turbulence detection using ground-based Doppler weather radar. Aviation Turbulence: Processes, Detection, Prediction, R. Sharman and T. Lane, Eds., Springer, 149–177, doi:10.1007/978-3-319-23630-8_7.

  • Wurtele, M. G., R. Sharman, and A. Datta, 1996: Atmospheric lee waves. Annu. Rev. Fluid Mech., 28, 429476, doi:10.1146/annurev.fl.28.010196.002241.

    • Search Google Scholar
    • Export Citation
  • Zovko-Rajak, D., and T. P. Lane, 2014: The generation of near-cloud turbulence in idealized simulations. J. Atmos. Sci., 71, 24302451, doi:10.1175/JAS-D-13-0346.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1613 838 59
PDF Downloads 1544 647 57

Prediction of Energy Dissipation Rates for Aviation Turbulence. Part I: Forecasting Nonconvective Turbulence

R. D. SharmanNational Center for Atmospheric Research, Boulder, Colorado

Search for other papers by R. D. Sharman in
Current site
Google Scholar
PubMed
Close
and
J. M. PearsonNational Center for Atmospheric Research, Boulder, Colorado

Search for other papers by J. M. Pearson in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Current automated aviation turbulence forecast algorithms diagnose turbulence from numerical weather prediction (NWP) model output by identifying large values in computed horizontal or vertical spatial gradients of various atmospheric state variables (velocity; temperature) and thresholding these gradients empirically to indicate expected areas of “light,” “moderate,” and “severe” levels of aviation turbulence. This approach is obviously aircraft dependent and cannot accommodate the many different aircraft types that may be in the airspace. Therefore, it is proposed to provide forecasts of an atmospheric turbulence metric: the energy dissipation rate to the one-third power (EDR). A strategy is developed to statistically map automated turbulence forecast diagnostics or groups of diagnostics to EDR. The method assumes a lognormal distribution of EDR and uses climatological peak EDR data from in situ equipped aircraft in conjunction with the distribution of computed diagnostic values. These remapped values can then be combined to provide an ensemble mean EDR that is the final forecast. New mountain-wave-turbulence algorithms are presented, and the lognormal mapping is applied to them as well. The EDR forecasts are compared with aircraft in situ EDR observations and verbal pilot reports (converted to EDR) to obtain statistical performance metrics of the individual diagnostics and the ensemble mean. It is shown by one common performance metric, the area under the relative operating characteristics curve, that the ensemble mean provides better performance than forecasts from individual model diagnostics at all altitudes (low, mid-, and upper levels) and for two input NWP models.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author e-mail: Dr. Robert D. Sharman, sharman@ucar.edu

Abstract

Current automated aviation turbulence forecast algorithms diagnose turbulence from numerical weather prediction (NWP) model output by identifying large values in computed horizontal or vertical spatial gradients of various atmospheric state variables (velocity; temperature) and thresholding these gradients empirically to indicate expected areas of “light,” “moderate,” and “severe” levels of aviation turbulence. This approach is obviously aircraft dependent and cannot accommodate the many different aircraft types that may be in the airspace. Therefore, it is proposed to provide forecasts of an atmospheric turbulence metric: the energy dissipation rate to the one-third power (EDR). A strategy is developed to statistically map automated turbulence forecast diagnostics or groups of diagnostics to EDR. The method assumes a lognormal distribution of EDR and uses climatological peak EDR data from in situ equipped aircraft in conjunction with the distribution of computed diagnostic values. These remapped values can then be combined to provide an ensemble mean EDR that is the final forecast. New mountain-wave-turbulence algorithms are presented, and the lognormal mapping is applied to them as well. The EDR forecasts are compared with aircraft in situ EDR observations and verbal pilot reports (converted to EDR) to obtain statistical performance metrics of the individual diagnostics and the ensemble mean. It is shown by one common performance metric, the area under the relative operating characteristics curve, that the ensemble mean provides better performance than forecasts from individual model diagnostics at all altitudes (low, mid-, and upper levels) and for two input NWP models.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author e-mail: Dr. Robert D. Sharman, sharman@ucar.edu
Save