Accuracy of RUC-1 and RUC-2 Wind and Aircraft Trajectory Forecasts by Comparison with ACARS Observations

Barry E. Schwartz NOAA/ERL/Forecast Systems Laboratory, Boulder, Colorado

Search for other papers by Barry E. Schwartz in
Current site
Google Scholar
PubMed
Close
,
Stanley G. Benjamin NOAA/ERL/Forecast Systems Laboratory, Boulder, Colorado

Search for other papers by Stanley G. Benjamin in
Current site
Google Scholar
PubMed
Close
,
Steven M. Green NASA Ames Research Center, Moffett Field, California

Search for other papers by Steven M. Green in
Current site
Google Scholar
PubMed
Close
, and
Matthew R. Jardin NASA Ames Research Center, Moffett Field, California

Search for other papers by Matthew R. Jardin in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

As part of an investigation into terminal airspace productivity sponsored by the NASA Ames Research Center, a study was performed at the Forecast Systems Laboratory to investigate sources of wind forecast error and to assess differences in wind forecast accuracy between the 60-km Rapid Update Cycle, version 1 (RUC-1), and the newer 40-km RUC-2. Improved knowledge of these errors is important for development of air traffic management automation tools under development at NASA Ames and elsewhere. This information is also useful for operational users of RUC forecast winds. To perform this study, commercial aircraft reports of wind reported through Aircraft Communications, Addressing, and Reporting System (ACARS) were collected in a region over the western and central United States for a 13-month period, along with RUC-1 and RUC-2 wind forecasts. Differences between forecasts and ACARS observations and estimates of ACARS wind observation error itself were both calculated.

It was found that rms vector differences between observations and forecasts from either version of the RUC increased as wind speed increased, and also as altitude increased and in winter months (both associated with higher wind speed). Wind errors increased when thunderstorms were nearby and were smaller in wintertime precipitation situations. The study also showed that considerable progress has been made in the accuracy of wind forecasts to be used for air traffic management by the introduction of the RUC-2 system, replacing the previous RUC-1 system. Improvement was made both in the intrinsic accuracy as well as in the time availability, both contributing to the overall improvement in the actual wind forecast available for air traffic management purposes. Using 3-h forecasts, RUC-2 demonstrated a reduction in mean daily rms vectors of approximately 10% over that for RUC-1 based on accuracy improvements alone. This error reduction increased to about 22% when time availability improvements were added. It was also found that the degree of improvement from the RUC-2 increased substantially for periods with a large number of significant wind errors. The percentage of individual vector errors greater than 10 m s−1 was reduced by RUC-2 from 8% (RUC-1) to 3% overall and from 17% to 7% during the worst month. Such peak error periods have a strong impact on air traffic management automation tools. Last, it was found that the estimated trajectory projection errors from the RUC-2 using 1–2-h forecasts averaged 9 s for ascent/descent flight segments of approximately 15 min, and about 10 s for en route segments of the same duration.

Corresponding author address: Barry E. Schwartz, NOAA/ERL/Forecast Systems Laboratory, 325 Broadway, Boulder, CO 80301.

Abstract

As part of an investigation into terminal airspace productivity sponsored by the NASA Ames Research Center, a study was performed at the Forecast Systems Laboratory to investigate sources of wind forecast error and to assess differences in wind forecast accuracy between the 60-km Rapid Update Cycle, version 1 (RUC-1), and the newer 40-km RUC-2. Improved knowledge of these errors is important for development of air traffic management automation tools under development at NASA Ames and elsewhere. This information is also useful for operational users of RUC forecast winds. To perform this study, commercial aircraft reports of wind reported through Aircraft Communications, Addressing, and Reporting System (ACARS) were collected in a region over the western and central United States for a 13-month period, along with RUC-1 and RUC-2 wind forecasts. Differences between forecasts and ACARS observations and estimates of ACARS wind observation error itself were both calculated.

It was found that rms vector differences between observations and forecasts from either version of the RUC increased as wind speed increased, and also as altitude increased and in winter months (both associated with higher wind speed). Wind errors increased when thunderstorms were nearby and were smaller in wintertime precipitation situations. The study also showed that considerable progress has been made in the accuracy of wind forecasts to be used for air traffic management by the introduction of the RUC-2 system, replacing the previous RUC-1 system. Improvement was made both in the intrinsic accuracy as well as in the time availability, both contributing to the overall improvement in the actual wind forecast available for air traffic management purposes. Using 3-h forecasts, RUC-2 demonstrated a reduction in mean daily rms vectors of approximately 10% over that for RUC-1 based on accuracy improvements alone. This error reduction increased to about 22% when time availability improvements were added. It was also found that the degree of improvement from the RUC-2 increased substantially for periods with a large number of significant wind errors. The percentage of individual vector errors greater than 10 m s−1 was reduced by RUC-2 from 8% (RUC-1) to 3% overall and from 17% to 7% during the worst month. Such peak error periods have a strong impact on air traffic management automation tools. Last, it was found that the estimated trajectory projection errors from the RUC-2 using 1–2-h forecasts averaged 9 s for ascent/descent flight segments of approximately 15 min, and about 10 s for en route segments of the same duration.

Corresponding author address: Barry E. Schwartz, NOAA/ERL/Forecast Systems Laboratory, 325 Broadway, Boulder, CO 80301.

Save
  • Benjamin, S. G., K. A. Brewster, R. L. Brummer, B. F. Jewett, T. W. Schlatter, T. L. Smith, and P. A. Stamus, 1991: An isentropic three-hourly data assimilation system using ACARS aircraft observations. Mon. Wea. Rev.,119, 888–906.

    • Crossref
    • Export Citation
  • ——, K. J. Brundage, P. A. Miller, T. L. Smith, G. A. Grell, D. Kim, J. M Brown, and T. W. Schlatter, 1994: The Rapid Update Cycle at NMC. Preprints, 10th Conf. on Numerical Weather Prediction, Portland, OR, Amer. Meteor. Soc., 566–568.

  • ——, and Coauthors, 1997: Improvements in aviation forecasts from the 40-km RUC. Preprints, Seventh Conf. on Aviation, Range, and Aerospace Meteorology, Long Beach, CA, Amer. Meteor. Soc., 411–416.

  • ——, J. M. Brown, K. J. Brundage, B. E. Schwartz, T. G. Smirnova, and T. L. Smith, 1998: The operational RUC-2. Preprints, 16th Conf. on Weather Analysis and Forecasting, Phoenix, AZ, Amer. Meteor. Soc., 249–252.

  • ——, ——, K. J. Brundage, D. Kim, B. E. Schwartz, T. G. Smirnova, and T. L. Smith, 1999a: Aviation forecasts from the RUC2. Preprints, Eighth Conf. on Aviation, Range, and Aerospace Meteorology, Dallas, TX, Amer. Meteor. Soc., 486–490.

  • ——, B. E. Schwartz, and R. E. Cole, 1999b: Accuracy of ACARS wind and temperature observations determined by collocation. Wea. Forecasting,14, 1032–1038.

    • Crossref
    • Export Citation
  • Brown, J. M., T. G. Smirnova, and S. G. Benjamin, 1998: Introduction of MM5 level 4 microphysics into the RUC-2. Preprints, 12th Conf. on Numerical. Weather Prediction, Phoenix, AZ, Amer. Meteor. Soc., 113–115.

  • Cole, R. E., C. Richard, S. Kim, and D. Bailey, 1998: An assessment of wind field accuracy for ATM DST using the 60-km RUC augmented with real-time aircraft reports via the ITWS terminal wind algorithm. Project Rep. NASA/A-1, MIT Lincoln Laboratory, 63 pp. [Available from National Technical Information Service, 5285 Port Royal Rd., Springfield, VA 22161.].

  • ——, S. Green, and M. Jardin, 2000: Improving RUC-1 wind estimates by incorporating near-real-time aircraft reports. Wea. Forecasting, in press.

  • DiMego, G. J., 1988: The National Meteorological Center Regional Analysis System. Mon. Wea. Rev.,116, 977–1000.

    • Crossref
    • Export Citation
  • Green, S., and R. Vivona, 1996: Field evaluation of Descent Advisor trajectory prediction accuracy. AIAA Guidance Navigation and Control Conf., AIAA Publ. 96-3764, 13 pp. [Available from American Institute of Aeronautics and Astronautics, 1801 Alexander Bell Dr., Suite 500, Reston, VA 22091.].

    • Crossref
    • Export Citation
  • Jardin, M. R., and H. Erzberger, 1996: Atmospheric data acquisition and interpolation for enhanced trajectory-prediction accuracy in the Center-TRACON Automation System. AIAA Aerospace Sciences Conf., AIAA Publ. 96-0271, 7 pp. [Available from American Institute of Aeronautics and Astronautics, 1801 Alexander Bell Dr., Suite 500, Reston, VA 22091.].

    • Crossref
    • Export Citation
  • ——, and S. M. Green, 1998: Atmospheric data error analysis for the 1994 CTAS Descent Advisor preliminary test. NASA Tech. Memo. TM-1998-112228, 24 pp. [Available from NASA Center for Aerospace Information, 7121 Standard Drive, Hanover, MD 21076.].

  • Kanamitsu, M., and Coauthors, 1991: Recent changes implemented into the global forecast system at NMC. Wea. Forecasting,6, 425–435.

    • Crossref
    • Export Citation
  • Moninger, W. R., and P. A. Miller, 1994: ACARS quality control, monitoring, and correction. Preprints, 10th Conf. on Numerical Weather Prediction, Portland, OR, Amer. Meteor. Soc., 4–6.

  • Paielli, R. A., and H. Erzberger, 1996: Conflict probability estimation for free flight. NASA Tech. Memo. TM-110411, 15 pp. [Available from NASA Center for Aerospace Information, 7121 Standard Drive, Hanover, MD 21076.].

    • Crossref
    • Export Citation
  • Reisner, J., R. M. Rasmussen, and R. T. Bruintjes, 1998: Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model. Quart. J. Roy. Meteor. Soc.,124, 1071–1107.

    • Crossref
    • Export Citation
  • Rogers, E., T. M. Black, D. G. Deaven, G. J. DiMego, Q. Zhao, M. Baldwin, N. W. Junker, and Y. Lin, 1996: Changes to the operational “early” eta analysis/forecast system at the National Centers for Environmental Prediction. Wea. Forecasting,11, 391–413.

    • Crossref
    • Export Citation
  • Saucier, W. J., 1955: Principles of Meteorological Analysis. Dover Publications, 438 pp.

  • Schwartz, B. E., and S. G. Benjamin, 1995: A comparison of temperature and wind measurements from ACARS-equipped aircraft and rawinsondes. Wea. Forecasting,10, 528–544.

  • Smirnova, T. G., J. M. Brown, and S. G. Benjamin, 1997: Performance of different soil model configurations in simulating ground surface temperature and surface fluxes. Mon. Wea. Rev.,125, 1870–1884.

    • Crossref
    • Export Citation
  • ——, ——, and ——, 1999: Parameterization of frozen soil physics in MAPS. J. Geophys. Res., in press.

  • Williams, D. H., and S. M. Green, 1998: Flight evaluation of the center/TRACON automation system trajectory prediction process. NASA Tech. Paper TP-1998-208439, 88 pp. [Available from NASA Center for Aerospace Information, 7121 Standard Drive, Hanover, MD 21076.].

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 597 225 25
PDF Downloads 198 60 1