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Ui -Yong Byun , Song-You Hong, Hyeyum Shin Deparment of Atmospheric Science, Yonsei Univ.

WRF-based short-range forecast system of the Korea Air Force : v erification of prediction skill in 2009 summer. Ui -Yong Byun , Song-You Hong, Hyeyum Shin Deparment of Atmospheric Science, Yonsei Univ. Ji -Woo Lee, Jae- Ik Song, Sook -Jung Ham, Jwa-Kyum Kim, Hyung -Woo Kim

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Ui -Yong Byun , Song-You Hong, Hyeyum Shin Deparment of Atmospheric Science, Yonsei Univ.

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  1. WRF-based short-range forecast system of the Korea Air Force : verification of prediction skill in 2009 summer Ui-Yong Byun, Song-You Hong, Hyeyum Shin Deparment of Atmospheric Science, YonseiUniv. Ji-Woo Lee, Jae-Ik Song, Sook-Jung Ham, Jwa-Kyum Kim, Hyung-Woo Kim 73rd Weather Group, Republic of Korea Air Forece

  2. Outline • Introduction • Configuration of KAF-WRF • Configuration of verification system • Results • Further study • Summary

  3. Introduction • WRF model is designed for both research and operational applications. • Research of extreme weather in Korea peninsula using WRF model • Lee et al, 2005 : Orographic effect for a heavy rainfall • Lim et al, 2007 : Heavy snowfall over the Ho-Nam province • WRF model operation in forecast institution of Korea • Jo et al, 2005 : KWRF construction and test run in KMA • The 73rd Weather Group (73WG) of Republic of Korea Air Force (ROKAF) operatesthe KAF-WRFmodel based on Weather Research and Forecasting (WRF) model since 2007. • In this study, KAF-WRF model results in 2009 summer are evaluated using quantitative verification system.

  4. Configuration of KAF-WRF • Operation model • Experimental model DM 1 DM 2 DM 3 DM 1 DM 2 DM 3 Model version KAF-WRF V09 (based on WRFv3.1) Model version KAF-WRF V07 (based on WRFv2.2) Vertical Layer 31 Layer Vertical Layer 31 Layer Resolution 18 km 6 km 2 km Resolution 18 km 6 km 2 km Grids 230 x 170 211 x 211 250 x 250 Grids 230 x 170 211 x 211 250 x 250 FCST 84hr 84hr 24hr FCST 84hr 84hr 24hr WSM3 WDM6 Microphysics Microphysics WSM6 Cumulus Kain-Fritcsh None Cumulus Kain-Fritcsh None PBL YSU PBL PBL YSU PBL Radiation RRTM-G(LW), Goddard SW(SW) Radiation RRTM(LW), Dudhia Scheme (SW) LSM Noah LSM LSM Noah LSM + Ocean Mixed Layer + MODIS Land use data

  5. Domain 1 • Domain 2 18 km 6 km 00 12 24 36 48 60 72 84 4times/day 84hour fcst. Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University

  6. Configuration of verification system (domain 1) 2-day 00 UTC : 1-day 12 UTC : 1-day 00 UTC : 00 12 24 36 48 Making difference data  Monthly mean data  Field figure & score – SLP., 500hPa GPH., Temp., wind

  7. Configuration of verification system (domain 2) Model output process AWS data process Extracting precipitation field from model output Extracting 1hr precipitation from AWS data Changing the precipitation data from field to point Making 6hr precipitation data Making skill score Using contingency table

  8. Result • Verification • Model : KAF-WRF V07, V09 • Period : 2009. JJA • Parameter • Domain 1 : SLP., 500hPa GPH, Temperature, Wind • Domain 2 : 6 hour accumulated precipitation • Statistics • Domain 1 : RMSE, Bias score • Domain 2 : Skill score

  9. Result - domain 1 2009. 07. 24hr fcst SLP 500hPa GPH. KAF-WRF V07 9.438 1.593 KAF-WRFV09 7.768 1.515

  10. Result - domain 1 2009. 07. 24hr fcst 500hPa Temp. 500hPa Wind KAF-WRF V07 U : 2.837 V : 2.821 0.725 KAF-WRFV09 U : 2.781 V : 2.744 0.718

  11. Bias : Sea level pressure • RMSE : Sea level pressure

  12. Bias : 500hPa Geopotential Height • Bias : 500hPa Temperature • RMSE : 500hPa Geopotential Height • RMSE : 500hPa Temperature

  13. Bias : 500hPa u-wind • Bias : 500hPa v-wind • RMSE : 500hPa u-wind • RMSE : 500hPa v-wind

  14. Result - domain 2 • Contingency table • POD = H / (M + H) ; Probability of Detection • FAR = F / (H + F) ; False Alarm Ratio • Bias = (H + F) / (H + M) ; Bias Score • ETS = (H – E) / (H + M + F – E) ( E = (H + F) x (H + M) / (H + M + F + C) ) ; Equitable Threat Score

  15. Result - domain 2 • Precipitation analysis 2009. 07. 1-month precipitation AWS KAF-WRF V09

  16. ’09. June 12hr fcst precipitation (6 hour accumulated) • POD • FAR • Bias • ETS  Found a problem with weak precipitation

  17. Int : 2008.02.23 00 UTC, 36hr fcst, 6hr precip. OBS : TMPA & FNL WSM6 exp. WDM6 exp. A B A : B : Hong et al., 2010 Nc Nr

  18. Further Study (1) • Purpose of further study • Finding the cause of low accuracy of KAF-WRF V09 on weak precipitation. • Improvement of accuracy of weak precipitation • Possibility 1 : Microphysics • Microphysics is changed from V07 to V09

  19. Int : 2010.08.24 00 UTC, Valid : 15 UTC, 3hr precip. KAF-WRF V07 WSM6-WSM6 OPR EXP KAF-WRF V09 WSM3-WDM6 KAF-WRF V09 WSM6-WDM6 KAF-WRF V09 WDM6-WDM6

  20. Further Study (2) • Possibility 2: Error of YSU PBL • Some error of YSU PBL was corrected in updated WRF model (ver. 3.2.1). • Minor bug fixes for PBL Prandtl number calculation in stable and unstable condition. • WRF model that based on WRF ver. 3.2.1 and that has same physics setting with ‘KAF-WRF V09’ , is defined ‘KAF-WRF V10’. • Select case - 2009. 07. 09. precipitation • Initial time : 2009. 07. 08. 12UTC : 2009. 07. 09. 00UTC : 2009. 07. 09. 12 UTC • Compare the verification score ; KAF-WRF V07, V09, V10

  21. 12hr fcst precipitation (6 hour accumulated) • POD • FAR • Bias • ETS

  22. Summary • Quantitative verification system is constructed • RMSE and Bias score of SLP, 500hPa Geopotential Height, temperature and wind of the KAF-WRF V09 shows better performance than V07. • Verification result of precipitation shows different patterns depending on precipitation intensity • Score of V07 is better than V09 in weak precipitation intensity (less than 3 mm/6hour) • Score of V09 is better than V07 in heavy precipitation intensity (more than 10 mm/6hour) • Accuracy oflight-precipitation prediction is possible to increase adapting microphysics change and PBL debug. • ROKAF has plan that is changed EXP model instead of OPR model

  23. Thank you

  24. Minor bug fixes for PBL Prandtl number calculation in stable and unstable condition. • PR = 1 + (PR_0 – 1) x exp(PR_fac) PR_0 = (ph_h/ph_m+ prfac) prfac = conpr / ph_m / (1 + 4 x karman * wstar3 / ust3)  prfac= conpr / ph_m / (1 + 4 x karman * wstar3 / ust3)^h1 (h1 = 0.33333335) • PR = momentum diffusivity(Km) / heat, moisture diffusivity(Kh) (0.25 <= PR <= 4.0) • It means ‘prfac’ of new ver. has larger values in same condition.Also, ‘PR_0’ and ‘PR’ has larger values. • In boundary layer, Km  Kh ; Kh↓ • In free atmosphere and stable condition, Kh Km ; Km ↑

  25. 2010.08.24 00 UTC, 48 hour precip. KAF-WRF V07WSM6 KAF-WRF V09WSM3 KAF-WRF V09WSM6 KAF-WRF V09WDM6

  26. Int : 2009.07.09 00 UTC, Valid : 12 UTC, 6hr precip. KAF-WRF V07 KAF-WRF V09 KAF-WRF test

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