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http://dx.doi.org/10.14191/Atmos.2018.28.2.223

Development of the Global-Korean Aviation Turbulence Guidance (Global-KTG) System Using the Global Data Assimilation and Prediction System (GDAPS) of the Korea Meteorological Administration (KMA)  

Lee, Dan-Bi (Department of Atmospheric Sciences, Yonsei University)
Chun, Hye-Yeong (Department of Atmospheric Sciences, Yonsei University)
Publication Information
Atmosphere / v.28, no.2, 2018 , pp. 223-232 More about this Journal
Abstract
The Global-Korean aviation Turbulence Guidance (G-KTG) system is developed using the operational Global Data Assimilation and Prediction System of Korea Meteorological Administration with 17-km horizontal grid spacing. The G-KTG system provides an integrated solution of various clear-air turbulence (CAT) diagnostics and mountain-wave induced turbulence (MWT) diagnostics for low [below 10 kft (3.05 km)], middle [10 kft (3.05 km) - 20 kft (6.10 km)], and upper [20 kft (6.10 km) - 50 kft (15.24 km)] levels. Individual CAT and MWT diagnostics in the G-KTG are converted to a 1/3 power of energy dissipation rate (EDR). 12-h forecast of the G-KTG is evaluated using 6-month period (2016.06~2016.11) of in-situ EDR observation data. The forecast skill is calculated by area under curve (AUC) where the curve is drawn by pairs of probabilities of detection of "yes" for moderate-or-greater-level turbulence events and "no" for null-level turbulence events. The AUCs of G-KTG for the upper, middle, and lower levels are 0.79, 0.69, and 0.63, respectively. Comparison of the upper-level G-KTG with the regional-KTG in East Asia reveals that the forecast skill of the G-KTG (AUC = 0.77) is similar to that of the regional-KTG (AUC = 0.79) using the Regional Data Assimilation and Prediction System with 12-km horizontal grid spacing.
Keywords
Global-korean aviation turbulence guidance system; global data assimilation and prediction system; in-situ flight Eddy Dissipation Rate (EDR) data;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 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
2 Tung, K. K., and W. W. Orlando, 2003: The $k^{-3}$ and $k^{-5/3}$ energy spectrum of atmospheric turbulence: Quasigeostrophic two-level model simulation. J. Atmos. Sci., 60, 824-835.   DOI
3 Lane, T. P., R. D. Sharman, S. B. Trier, R. G. Fovell, and J. K. Williams, 2012: Recent advances in the understanding of near-cloud turbulence. Bull. Amer. Meteorol. Soc., 93, 499-515, doi:10.1175/BAMS-D-11-00062.1.   DOI
4 Lee, D.-B., and H.-Y. Chun, 2014: Development of the seasonal Korean aviation Turbulence Guidance (KTG) system using the regional Unified Model of the Korea Meteorological Administration (KMA). Atmosphere, 24, 235-243, doi:10.14191/Atmos.2014.24.2.235 (in Korean with English abstract).   DOI
5 Lee, D.-B., and H.-Y. Chun, 2015: Development of the Korean Peninsula-Korean aviation Turbulence Guidance (KPKTG) system using the Local Data Assimilation and Prediction System (LDAPS) of the Korea Meteorological Administration (KMA). Atmosphere, 25, 367-374, doi:10.14191/Atmos.2015.25.2.367 (in Korean with English abstract).   DOI
6 Mason, I., 1982: A model for assessment of weather forecasts. Aust. Meteor. Mag., 30, 291-303.
7 McCann, D. W., 2001: Gravity waves, unbalanced flow, and aircraft clear air turbulence. Natl. Wea. Dig., 25, 3-14.
8 Min, J.-S., H.-Y. Chun, and J.-H. Kim, 2011: An investigation of synoptic condition for clear-air turbulence (CAT) events occurred over South Korea. Atmosphere, 21, 69-83 (in Korean with English abstract).
9 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.].
10 Sharman, R., and T. Lane, Eds., 2016: Aviation Turbulence: Processes, Detection, Prediction. Springer, 523 pp.
11 Sharman, R., and J. Pearson, 2017: Prediction of energy dissipation rates for aviation turbulence. Part I: Forecasting nonconvective turbulence. J. Appl. Meteor. Climatol., 56, 317-337, doi:10.1175/JAMC-D-16-0205.1.   DOI
12 Sharman, R., and S. B. Trier, 2018: Influences of gravity waves on Convectively Induced Turbulence (CIT): A review. Pure Appl. Geophys., doi:10.1007/s00024-018-1849-2.   DOI
13 Sharman, R., C. Tebaldi, G. Wienner, and J. K Wolff, 2006: An integrated approach to mid- and upper-level turbulence forecasting. Wea. Forecasting, 21, 268-287.   DOI
14 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.   DOI
15 Sharman, R., L. B. Cornman, G. Meymaris, and J. Pearson, 2014: Description and derived climatologies of automated in situ eddy-dissipation-rate reports of atmospheric turbulence. J. Appl. Meteor. Climatol., 53, 1416-1432, doi:10.1175/JAMC-D-13-0329.1.   DOI
16 Strauss, L., S. Serafin, S. Haimov, and V. Grubisic, 2015: Turbulence in breaking mountain waves and atmospheric rotors estimated from airborne in situ and Doppler radar measurements. Q. J. R. Meteorol. Soc., 141, 3207-3225, doi:10.1002/qj.2604.   DOI
17 Dutton, J., and H. A. Panofsky, 1970: Clear air turbulence: A mystery may be unfolding. Science, 167, 937-944.   DOI
18 Ellrod, G. P., and D. I. Knapp, 1992: An objective clear-air turbulence forecasting technique: Verification and operational use. Wea. Forecasting, 7, 150-165.   DOI
19 Frehlich, R., and R. D. Sharman, 2004a: Estimates of turbulence from numerical weather prediction model output with applications to turbulence diagnosis and data assimilation. Mon. Wea. Rev., 132, 2308-2324.   DOI
20 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, 789-798, doi:10.1175/2009WAF2222290.1.   DOI
21 Frehlich, R., and R. D. 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.].
22 Frehlich, R., R. Sharman, F. Vandenberghe, W. Yu, Y. Liu, J. Knievel, and G. Jumper, 2010: Estimates of $C_n^2$ from numerical weather prediction model output and comparison with thermosonde data. J. Appl. Meteor. Climatol., 49, 1742-1755, doi:10.1175/2010JAMC2350.1.   DOI
23 Gill, P. G., and P. Buchanan, 2014: An ensemble based turbulence forecasting system. Meteor. Appl., 21, 12-19, doi:10.1002/met.1373.   DOI
24 Kaplan, M. L., and Coauthors, 2006: 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. Meteor. Atmos. Phys., 94, 235-270.   DOI
25 Kim, J.-H., and H.-Y. Chun, 2010: A numerical study of clear-air turbulence (CAT) encounters over South Korea on 2 April 2007. J. Appl. Meteor. Climatol., 49, 2381-2403, doi:10.1175/2010JAMC2449.1.   DOI
26 Kim, J.-H., H.-Y. Chun, W. Jang, and R. D. Sharman, 2009: A study of forecast system for clear-air turbulence in Korea, Part II: Graphical Turbulence Guidance (GTG) system. Atmosphere, 19, 269-287 (in Korean with English abstract).
27 Kim, J.-H., and H.-Y. Chun, 2011: Development of the Korean mid- and upper-level aviation Turbulence Guidance (KTG) system using the regional Unified Model. Atmosphere, 21, 497-506 (in Korean with English abstract).
28 Kim, J.-H., and H.-Y. Chun, 2012a: A numerical simulation of convectively induced turbulence above deep convection. J. Appl. Meteor. Climatol., 51, 1180-1200, doi:10.1175/JAMC-D-11-0140.1.   DOI
29 Kim, J.-H., and H.-Y. Chun, 2012b: Development of the Korean aviation Turbulence Guidance (KTG) system using the operational Unified Model (UM) of the Korea Meteorological Administration (KMA) and pilot reports (PIREPs). J. Kor. Soc. Aviat. Aeron., 20, 76-83, doi:10.12985/ksaa.2012.20.4.076.
30 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, 1936-1951, doi:10.1175/JAMC-D-10-05017.1.   DOI
31 Knox, J. A., 1997: Possible mechanisms of clear-air turbulence in strongly anticyclonic flows. Mon. Wea. Rev., 125, 1251-1259.   DOI
32 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, 3292-3304.   DOI
33 Koch, S. E., and Coauthors, 2005: Turbulence and gravity waves within an upper-level front. J. Atmos. Sci., 62, 3885-3908.   DOI