Editorial Type:
Article
Article Type:
Research Article

Basic Diagnosis and Prediction of Persistent Contrail Occurrence Using High-Resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part II: Evaluation of Sample Models

David P. Duda National Institute of Aerospace, Hampton, Virginia

Search for other papers by David P. Duda in
Current site
Google Scholar
PubMed Close
and
Patrick Minnis Science Directorate, NASA Langley Research Center, Hampton, Virginia

Search for other papers by Patrick Minnis in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Sep 2009
DOI:
https://doi.org/10.1175/2009JAMC2057.1
Page(s):
1790–1802
Restricted access

Abstract

A probabilistic forecast to accurately predict contrail formation over the conterminous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and the Rapid Update Cycle (RUC) combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The most common predictors selected for the SURFACE models tend to be related to temperature, relative humidity, and wind direction when the models are generated using RUC or ARPS analyses. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The most common predictors for the OUTBREAK models tend to be wind direction, atmospheric lapse rate, temperature, relative humidity, and the product of temperature and humidity.

Corresponding author address: David P. Duda, NASA Langley Research Center, Mail Stop 420, Hampton, VA 23681-2199. Email: david.p.duda@nasa.gov

Abstract

A probabilistic forecast to accurately predict contrail formation over the conterminous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and the Rapid Update Cycle (RUC) combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The most common predictors selected for the SURFACE models tend to be related to temperature, relative humidity, and wind direction when the models are generated using RUC or ARPS analyses. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The most common predictors for the OUTBREAK models tend to be wind direction, atmospheric lapse rate, temperature, relative humidity, and the product of temperature and humidity.

Corresponding author address: David P. Duda, NASA Langley Research Center, Mail Stop 420, Hampton, VA 23681-2199. Email: david.p.duda@nasa.gov

Save
Cover Journal of Applied Meteorology and Climatology
Journal of Applied Meteorology and Climatology

  • Appleman, H., 1953: The formation of exhaust condensation trails by jet aircraft. Bull. Amer. Meteor. Soc., 34 , 14–20.

  • Benjamin, S. G., G. A. Grell, J. M. Brown, T. G. Smirnova, and R. Bleck, 2004a: Mesoscale weather prediction with the RUC hybrid isentropic-terrain-following coordinate model. Mon. Wea. Rev., 132 , 473–494.

    • Search Google Scholar
    • Export Citation

  • Benjamin, S. G., and Coauthors, 2004b: An hourly assimilation-forecast cycle: The RUC. Mon. Wea. Rev., 132 , 495–518.

  • Brooks, D. R., and F. M. Mims III, 2001: Development of an inexpensive handheld LED-based Sun photometer for the GLOBE program. J. Geophys. Res., 106 , (D5). 4733–4740.

    • Search Google Scholar
    • Export Citation

  • Carleton, A. M., D. J. Travis, K. Master, and S. Vezhapparambu, 2008: Composite atmospheric environments of jet contrail outbreaks for the United States. J. Appl. Meteor. Climatol., 47 , 641–667.

    • Search Google Scholar
    • Export Citation

  • DeGrand, J. Q., A. M. Carleton, D. J. Travis, and P. J. Lamb, 2000: A satellite-based climatic description of jet aircraft contrails and associations with atmospheric conditions, 1977–79. J. Appl. Meteor., 39 , 1434–1459.

    • Search Google Scholar
    • Export Citation

  • Duda, D. P., and P. Minnis, 2009: Basic diagnosis and prediction of persistent contrail occurrence using high-resolution numerical weather analyses/forecasts and logistic regression. Part I: Effects of random error. J. Appl. Meteor. Climatol., 48 , 1780–1789.

    • Search Google Scholar
    • Export Citation

  • Duda, D. P., P. Minnis, L. Nguyen, and R. Palikonda, 2004: A case study of the development of contrail clusters over the Great Lakes. J. Atmos. Sci., 61 , 1132–1146.

    • Search Google Scholar
    • Export Citation

  • Duda, D. P., R. Palikonda, and P. Minnis, 2009: Relating observations of contrail persistence to numerical weather analysis output. Atmos. Chem. Phys., 9 , 1357–1364.

    • Search Google Scholar
    • Export Citation

  • Gandin, L. S., and A. H. Murphy, 1992: Equitable skill scores for categorical forecasts. Mon. Wea. Rev., 120 , 361–370.

  • Garber, D. P., P. Minnis, and P. K. Costulis, 2005: A commercial flight track database for upper tropospheric aircraft emission studies over the USA and southern Canada. Meteor. Z., 14 , 445–452.

    • Search Google Scholar
    • Export Citation

  • Hosmer, D. W., and S. Lemeshow, 1989: Applied Logistic Regression. John Wiley & Sons, 307 pp.

  • Jackson, A., B. Newton, D. Hahn, and A. Bussey, 2001: Statistical contrail forecasting. J. Appl. Meteor., 40 , 269–279.

  • Keith, R., 2003: Optimization of value of aerodrome forecasts. Wea. Forecasting, 18 , 808–824.

  • Lee, T. F., 1989: Jet contrail identification using the AVHRR infrared split window. J. Appl. Meteor., 28 , 993–995.

  • Lin, Y-L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22 , 1065–1092.

    • Search Google Scholar
    • Export Citation

  • Mannstein, H., R. Meyer, and P. Wendling, 1999: Operational detection of contrails from NOAA-AVHRR data. Int. J. Remote Sens., 20 , 1641–1660.

    • Search Google Scholar
    • Export Citation

  • Marquart, S., M. Ponater, F. Mager, and R. Sausen, 2003: Future development of contrail cover, optical depth, and radiative forcing: Impacts of increasing air traffic and climate change. J. Climate, 16 , 2890–2904.

    • Search Google Scholar
    • Export Citation

  • Minnis, P., J. K. Ayers, M. L. Nordeen, and S. P. Weaver, 2003: Contrail frequency over the United States from surface observations. J. Climate, 16 , 3447–3462.

    • Search Google Scholar
    • Export Citation

  • Murphy, A. H., and M. Ehrendorfer, 1987: On the relationship between the accuracy and value of forecasts in the cost-loss ratio situation. Wea. Forecasting, 2 , 243–251.

    • Search Google Scholar
    • Export Citation

  • O’Shea, R. P., 1991: Thumb’s rule tested: Visual angle of thumb’s width is about 2 deg. Perception, 20 (3) 415–418. doi:10.1068/p200415.

    • Search Google Scholar
    • Export Citation

  • Ponater, M., S. Marquart, and R. Sausen, 2002: Contrails in a comprehensive global climate model: Parameterization and radiative forcing results. J. Geophys. Res., 107 , (D13). 4164. doi:10.1029/2001JD000429.

    • Search Google Scholar
    • Export Citation

  • Tao, W-K., J. Simpson, and M. McCumber, 1989: An ice-water saturation adjustment. Mon. Wea. Rev., 117 , 231–235.

  • Tompkins, A. M., K. Gierens, and G. Rädel, 2007: Ice supersaturation in the ECMWF Integrated Forecast System. Quart. J. Roy. Meteor. Soc., 133 , 53–63.

    • Search Google Scholar
    • Export Citation

  • Travis, D. J., A. M. Carleton, and S. A. Changnon, 1997: An empirical model to predict widespread occurrences of contrails. J. Appl. Meteor., 36 , 1211–1220.

    • Search Google Scholar
    • Export Citation

  • Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences. Academic Press, 467 pp.

  • Winker, D. M., W. H. Hunt, and C. A. Hostetler, 2004: Status and performance of the CALIOP lidar. Laser Radar Techniques for Atmospheric Sensing, U. N. Singh, Ed., International Society for Optical Engineering (SPIE Proceedings, Vol. 5575), 8–15.

    • Search Google Scholar
    • Export Citation

  • Xue, M., D-H. Wang, J-D. Gao, K. Brewster, and K. K. Droegemeier, 2003: The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation. Meteor. Atmos. Phys., 82 , 139–170.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 184 97 8
PDF Downloads 151 69 7