Browse > Article
http://dx.doi.org/10.14191/Atmos.2022.32.4.323

Retrieval and Quality Assessment of Atmospheric Winds from the Aircraft-Based Observation Near Incheon International Airport, Korea  

Kim, Jeongmin (School of Earth and Environmental Sciences, Seoul National University)
Kim, Jung-Hoon (School of Earth and Environmental Sciences, Seoul National University)
Publication Information
Atmosphere / v.32, no.4, 2022 , pp. 323-340 More about this Journal
Abstract
We analyzed the high-resolution wind data of Aircraft-Based Observation from the Mode-Selective Enhanced Surveillance (Mode-S EHS) data in Korea. For assessment of its quality, the Mode-S wind data was compared with the ECMWF ReAnalysis 5 (ERA5) reanalysis and Aircraft Meteorological Data Relay (AMDAR) data for more than 3-months from 7 May 2021 to 24 August 2021 near Incheon International Airport, Korea. Considering that the AMDAR reports are not provided by all commercial aircraft, total number of the Mode-S derived wind data with a second sampling rate was about twice larger than that of available AMDAR wind data. After the quality control procedures by removing erroneous samples, it was found that the root mean square errors (RMSEs) of the Mode-S retrieved winds are similar to that from the AMDAR winds. In particular, between 550 and 650 hPa levels, RMSE of the Mode-S (AMDAR) zonal wind against ERA5 data was about 2.3 m s-1 (1.9 m s-1), and those increased to 3.3 m s-1 (2.4 m s-1) in 200~500 hPa levels. A similar trend was found in the meridional wind, but a distinct positive mean bias of 2.16 m s-1 was observed between 875 and 1,000 hPa levels. Winds retrieved from the Mode-S also showed a good agreement directly with AMDAR data. As the Mode-S provides a large amount of data with a reliable quality, it can be useful for both data assimilation in the numerical weather prediction model and situational awareness of wind and turbulence for aviation safety in Korea.
Keywords
Aircraft-based observation; Wind measurement; Mode-Selective Enhanced Surveillance (Mode-S EHS); Aircraft Meteorological Data Relay (AMDAR); Quality assessment;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Huntley, M. S. Jr., J. W. Turner, C. S. Donovan, and E. Madigan, 1995: FAA Aircraft Certification Human Factors and Operations Checklist for Standalone GPS Receivers (TSO C129 Class A). Federal Aviation Administration, 230 pp.
2 Cardinali, C., L. Isaksen, and E. Andersson, 2003: Use and impact of automated aircraft data in a global 4DVAR data assimilation system. Mon. Wea. Rev., 131, 1865-1877.   DOI
3 Dee, D. P., and Coauthors, 2011: The ERA-interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc., 137, 553-597, doi:10.1002/qj.828.   DOI
4 de Haan, S., 2011: High-resolution wind and temperature observations from aircraft tracked by mode-S air traffic control radar. J. Geophys. Res. Atmos., 116, D10111, doi:10.1029/2010JD015264.   DOI
5 de Haan, S., M. de Haij, and J. Sondij, 2013: The Use of a Commercial ADS-B Receiver to Derive Upper Air Wind and Temperature Observations from Mode-S EHS Information in The Netherlands. Royal Netherlands Meteorological Institute (KNMI).
6 Drue, C., and G. Heinemann, 2001: Airborne investigation of arctic boundary-layer fronts over the marginal ice zone of the Davis Strait. Bound. Layer Meteorol., 101, 261-292.   DOI
7 Kim, S.-H., J. Kim, J.-H. Kim, and H.-Y. Chun, 2022: Characteristics of the derived energy dissipation rate using the 1 Hz commercial aircraft quick access recorder (QAR) data. Atmos. Meas. Tech., 15, 2277-2298, doi:10.5194/amt-15-2277-2022.   DOI
8 Kim, J.-H., and H.-Y. Chun, 2011: Statistics and possible sources of aviation turbulence over South Korea. J. Appl. Meteorol. Climatol., 50, 311-324, doi:10.1175/2010JAMC2492.1.   DOI
9 Drue, C., W. Frey, A. Hoff, and T. Hauf, 2008: Aircraft type-specific errors in AMDAR weather reports from commercial aircraft. Q. J. R. Meteorol. Soc., 134, 229-239.   DOI
10 Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Q. J. R. Meteorol. Soc., 146, 1999-2049, doi:10.1002/qj.3803.   DOI
11 Kim, J.-H., and H.-Y. Chun, 2012: A numerical simulation of convectively induced turbulence above deep convection. J. Appl. Meteorol. Climatol., 51, 1180-1200, doi:10.1175/JAMC-D-11-0140.1.x   DOI
12 Kim, S.-H., and H.-Y. Chun, 2016: Aviation turbulence encounters detected from aircraft observations: spatiotemporal characteristics and application to Korean aviation turbulence guidance. Meteorol. Appl., 23, 594-604, doi:10.1002/met.1581.   DOI
13 Sharman, R., C. Tebaldi, G. Wiener, and J. Wolff, 2006: An integrated approach to mid- and upper-level turbulence forecasting. Wea. Forecasting, 21, 268-287.   DOI
14 World Meteorological Organization [WMO], 2003: Aircraft Meteorological Data Relay (AMDAR) Reference Manual. WMO, 80 pp.
15 Kim, S.-H., H.-Y. Chun, J.-H. Kim, R. D. Sharman, and M. Strahan, 2020: Retrieval of eddy dissipation rate from derived equivalent vertical gust included in aircraft meteorological data relay (AMDAR). Atmos. Meas. Tech., 13, 1373-1385, doi:10.5194/amt-13-1373-2020.   DOI
16 Ballish, B. A., and V. K. Kumar, 2008: Systematic differences in aircraft and radiosonde temperatures: im-plications for NWP and climate studies. Bull. Am. Meteorol. Soc., 89, 1689-1708.   DOI
17 de Haan, S., and Stoffelen, A. High resolution temperature and wind observations from commercial aircraft. In: Proceedings of the 8th International Symposium on Tropospheric Profiling; 19-23 October 2009; Delft, Netherlands.
18 Lee, D. K., H. R. Kim, and S. Y. Hong, 1998: Heavy rainfall over Korea during 1980~1990. Korean J. Atmos. Sci., 1, 32-50 (in Korean with English abstract).
19 Moninger, W. R., R. D. Mamrosh, and P. M. Pauley, 2003: Automated meteorological reports from commercial aircraft. Bull. Am. Meteorol. Soc., 84, 203-216.   DOI
20 Petersen, R. A., 2016: On the impact and benefits of AMDAR observations in operational forecasting-part I: a review of the impact of automated aircraft wind and temperature reports. Bull. Am. Meteorol. Soc., 97, 585-602, doi:10.1175/BAMS-D-14-00055.1.   DOI
21 Kim, J.-H., J.-R. Park, S.-H. Kim, J. Kim, E. Lee, S.-W. Baek, and G. Lee, 2021: A detection of convectively induced turbulence using in situ aircraft and radar spectral width data. Remote Sens., 13, 726, doi:10.3390/rs13040726.   DOI
22 de Leege, A. M. P., M. M. van Paassen, and M. Mulder, 2013: Using automatic dependent surveillance-broadcast for meteorological monitoring. J. Aircr., 50, 249-261, doi:10.2514/1.C031901.   DOI
23 Drue, C., T. Hauf, and A. Hoff, 2010: Comparison of boundary-layer profiles and layer detection by AMDAR and WTR/RASS at Frankfurt airport. Bound. Layer Meteorol., 135, 407-432, doi:10.1007/s10546-010-9485-0.   DOI
24 Kemp, D. E., 1968: Federal aviation administration air safety program. J. Air Law Commer., 34, 363.
25 Park, S. U., C. H. Joung, S. S. Kim, D. K. Lee, S. C. Yoon, Y. K. Jeong, and S. G. Hong, 1986: Synoptic-scale features of the heavy rainfall occurred over Korea during 1~3 September 1984. Asia-Pac. J. Atmos. Sci., 22, 42-81.
26 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. Meteorol. Climatol., 53, 1416-1432, doi:10.1175/JAMC-D-13-0329.1.   DOI
27 Stone, E. K., and M. Kitchen, 2015: Introducing an approach for extracting temperature from aircraft GNSS and pressure altitude reports in ADS-B messages. J. Atmos. Ocean. Technol., 32, 736-743, doi:10.1175/JTECH-D-14-00192.1   DOI
28 Trier, S. B., R. D. 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.   DOI
29 Stickland, J. J., 1998: An Assessment of Two Algorithms for Automatic Measurement and Reporting of Turbulence from Commercial Public Transport Aircraft. Bureau of Meteorology, 42 pp.
30 Stoffelen, A., 1998: Toward the true near-surface wind speed: error modeling and calibration using triple collocation. J. Geophys. Res. Oceans, 103, 7755-7766.   DOI
31 World Meteorological Organization [WMO], 2017: Guide to aircraft-based observations. WMO, 132 pp. [Available online at https://library.wmo.int/doc_num.php?explnum_id=4120] (Accessed 7 Jun 2022).
32 WMO AMDAR Panel, 2007: The international AMDAR program. World Meteorol. Organ. Inf. Fly., 12, 141 pp.
33 Trier, S. B., and R. D. Sharman, 2018: Trapped gravity waves and their association with turbulence in a large thunderstorm anvil during PECAN. Mon. Wea. Rev., 146, 3031-3052, doi:10.1175/MWR-D-18-0152.1.   DOI