• Ballish, B. A., , and Kumar V. K. , 2008: Systematic differences in aircraft and radiosonde temperatures: Implications for NWP and climate studies. Bull. Amer. Meteor. Soc., 89, 16891708, doi:10.1175/2008BAMS2332.1.

    • Search Google Scholar
    • Export Citation
  • Cardinali, C., , Isaksen L. , , and Andersson E. , 2003: Use and impact of automated aircraft data in a global 4DVAR data assimilation system. Mon. Wea. Rev., 131, 18651877, doi:10.1175/2569.1.

    • Search Google Scholar
    • Export Citation
  • Cleveland, W., , Grosse E. , , and Shyu W. , 1992: Local regression models. Statistical Models in S, J. M. Chambers, and T. J. Hastie, Eds., Wadsworth & Brooks/Cole, 608 pp.

  • Corner, B. R., , Palmer R. D. , , and Larsen M. F. , 1999: A new radiosonde system for profiling the lower troposphere. J. Atmos. Oceanic Technol., 16, 828, doi:10.1175/1520-0426(1999)016<0828:ANRSFP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • de Haan, S., 2011: High-resolution wind and temperature observations from aircraft tracked by Mode-S air traffic control radar. J. Geophys. Res.,116, D10111, doi:10.1029/2010JD015264.

  • de Haan, S., , and Stoffelen A. , 2012: Assimilation of high-resolution Mode-S wind and temperature observations in a regional NWP model for nowcasting applications. Wea. Forecasting, 27, 918937, doi:10.1175/WAF-D-11-00088.1.

    • Search Google Scholar
    • Export Citation
  • de Haan, S., , Bailey L. J. , , and Können J. E. , 2013: Quality assessment of automatic dependent surveillance contract (ADS-C) wind and temperature observation from commercial aircraft. Atmos. Meas. Tech., 6, 199206, doi:10.5194/amt-6-199-2013.

    • Search Google Scholar
    • Export Citation
  • de Leege, A. M. P., , Van Paassen M. M. , , and Mulder M. , 2013: Using automatic dependent surveillance-broadcast for meteorological monitoring. J. Aircr., 50, 249261, doi:10.2514/1.C031901.

    • Search Google Scholar
    • Export Citation
  • Drüe, C., , Frey W. , , Hoff A. , , and Hauf T. , 2008: Aircraft type-specific errors in AMDAR weather reports from commercial aircraft. Quart. J. Roy. Meteor. Soc., 134, 229239, doi:10.1002/qj.205.

    • Search Google Scholar
    • Export Citation
  • Fisher, A. B., 2014: The case for geometric altimetry. Honourable Company of Air Pilots Discussion Paper, 4 pp. [Available online at http://www.airpilots.org/file/1661/the-case-for-geo-height.pdf.]

  • ICAO, 1993: Manual of the ICAO standard atmosphere: Extended to 80 kilometres (262 500 feet). 3rd ed. International Civil Aviation Organization Doc. 7488-CD, CD-ROM.

  • ICAO, 2012: Technical provisions for Mode S services and extended squitter. 2nd ed. International Civil Aviation Organization Doc. 9871, 326 pp.

  • ICAO, 2013: Global Navigation Satellite System (GNSS) manual. 2nd ed. International Civil Aviation Organization Doc. 9849, AN/457, 90 pp.

  • McIntosh, D., , and Thom A. S. , 1983: Essentials of Meteorology. Wykeham Science Series, Vol. 3, Taylor & Francis, 240 pp.

  • Painting, J., 2003: Aircraft Meteorological Data Relay (AMDAR) reference manual. World Meteorological Organization Doc. WMO-958, 80 pp.

  • Strajnar, B., 2012: Validation of Mode-S Meteorological Routine Air Report aircraft observations. J. Geophys. Res., 117, D23110, doi:10.1029/2012JD018315.

    • Search Google Scholar
    • Export Citation
  • Taylor, J. R., 1997: An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements. 2nd ed. University Science Books, 327 pp.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 41 41 33
PDF Downloads 27 27 22

Introducing an Approach for Extracting Temperature from Aircraft GNSS and Pressure Altitude Reports in ADS-B Messages

View More View Less
  • 1 Met Office, Exeter, United Kingdom
© Get Permissions
Restricted access

Abstract

Recently work has been conducted in using routine air traffic management (ATM) data from aircraft to derive meteorological observations (de Haan; de Haan and Stoffelen). The paper at hand introduces and provides an initial analysis for a method of finding layer temperatures from aircraft broadcast messages. The method is analyzed using error analysis and is shown capable of producing mean layer temperatures with below ±1-K error with a layer thickness of 2000 m. Observed aircraft data have been compared to the expected errors from the analysis and have shown to be consistent to within 0.01 K. An initial comparison using four Aircraft Meteorological Data Relay (AMDAR) flights is also provided. The new layer temperature, existing Mode-S enhanced surveillance (EHS)-derived temperature, and an average Mode-S EHS-derived temperature are all compared to the AMDAR temperatures. The averaged Mode-S EHS-derived layer temperature is shown to have the lowest spread (mean standard deviation K), followed by the layer temperature introduced by this paper (mean standard deviation K), and then the unaveraged Mode-S EHS-derived temperature (mean standard deviation K). The layer temperature method has the advantage that no requested data are required from the aircraft, as all of the required parameters are part of the routine broadcast messages, making the method ideal in areas with a limited air traffic management infrastructure where the existing methods would not work.

Corresponding author address: Ed Stone, Met Office, FitzRoy Road, Exeter, Devon EX1 3PB, United Kingdom. E-mail: ed.stone@metoffice.gov.uk

Abstract

Recently work has been conducted in using routine air traffic management (ATM) data from aircraft to derive meteorological observations (de Haan; de Haan and Stoffelen). The paper at hand introduces and provides an initial analysis for a method of finding layer temperatures from aircraft broadcast messages. The method is analyzed using error analysis and is shown capable of producing mean layer temperatures with below ±1-K error with a layer thickness of 2000 m. Observed aircraft data have been compared to the expected errors from the analysis and have shown to be consistent to within 0.01 K. An initial comparison using four Aircraft Meteorological Data Relay (AMDAR) flights is also provided. The new layer temperature, existing Mode-S enhanced surveillance (EHS)-derived temperature, and an average Mode-S EHS-derived temperature are all compared to the AMDAR temperatures. The averaged Mode-S EHS-derived layer temperature is shown to have the lowest spread (mean standard deviation K), followed by the layer temperature introduced by this paper (mean standard deviation K), and then the unaveraged Mode-S EHS-derived temperature (mean standard deviation K). The layer temperature method has the advantage that no requested data are required from the aircraft, as all of the required parameters are part of the routine broadcast messages, making the method ideal in areas with a limited air traffic management infrastructure where the existing methods would not work.

Corresponding author address: Ed Stone, Met Office, FitzRoy Road, Exeter, Devon EX1 3PB, United Kingdom. E-mail: ed.stone@metoffice.gov.uk
Save