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 73 rd Weather Group, Republic of Korea Air Forece

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University Introduction Configuration of KAF-WRF Configuration of verification system Results Further study Summary

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University 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 73 rd Weather Group (73WG) of Republic of Korea Air Force (ROKAF) operates the KAF-WRF model based on Weather Research and Forecasting (WRF) model since In this study, KAF-WRF model results in 2009 summer are evaluated using quantitative verification system.

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University 250 x x x 170Grids 24hr84hr KAF-WRF V07 (based on WRFv2.2) Model version 31 LayerVertical Layer None Noah LSMLSM RRTM(LW), Dudhia Scheme (SW) Radiation YSU PBLPBL Kain-FritcshCumulus WSM6Microphysics 84hrFCST 2 km6 km18 kmResolution DM 3DM 2DM x x x 170Grids 24hr84hr KAF-WRF V09 (based on WRFv3.1) Model version 31 LayerVertical Layer None Noah LSMLSM RRTM-G(LW), Goddard SW(SW) Radiation YSU PBLPBL Kain-FritcshCumulus WDM6 Microphysics 84hrFCST 2 km6 km18 kmResolution DM 3DM 2DM 1 WSM3  Operation model  Experimental model + Ocean Mixed Layer + MODIS Land use data

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University  Domain 1  Domain 2 18 km 6 km times/day 84 hour fcst. Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University

day 00 UTC : 1-day 12 UTC : Making difference data  Monthly mean data  Field figure & score – SLP., 500hPa GPH., Temp., wind 2-day 00 UTC :

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University Extracting precipitation field from model output Changing the precipitation data from field to point Extracting 1hr precipitation from AWS data Making 6hr precipitation data Making skill score Using contingency table Model output processAWS data process

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University Verification  Model : KAF-WRF V07, V09  Period : 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

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University hr fcst hPa GPH.SLP KAF-WRF V07 KAF-WRF V

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University hr fcst KAF-WRF V07 KAF-WRF V U : V : U : V : hPa Temp.500hPa Wind

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University Bias : Sea level pressure RMSE : Sea level pressure

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University Bias : 500hPa Geopotential Height RMSE : 500hPa Geopotential Height Bias : 500hPa Temperature RMSE : 500hPa Temperature

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University Bias : 500hPa u-wind RMSE : 500hPa u-wind Bias : 500hPa v-wind RMSE : 500hPa v-wind

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University 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 Forecast YesNo Observation YesHMObservation yes NoFCObservation no Forecast yesForecast no

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University Precipitation analysis AWSKAF-WRF V month precipitation

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University ’09. June 12hr fcst precipitation (6 hour accumulated) POD Bias FAR ETS  Found a problem with weak precipitation

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University OBS : TMPA & FNL WSM6 exp.WDM6 exp. Hong et al., 2010 Int : UTC, 36hr fcst, 6hr precip. Nc Nr A : B : B A

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University 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 Domain 1Domain 2 KAF-WRF V07 (OPR)WSM6 KAF-WRF V09 (EXP)WSM3WDM6

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University Int : UTC, Valid : 15 UTC, 3hr precip. KAF-WRF V07 WSM6-WSM6 KAF-WRF V09 WSM3-WDM6 KAF-WRF V09 WDM6-WDM6 KAF-WRF V09 WSM6-WDM6 OPREXP

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University Possibility 2: Error of YSU PBL Some error of YSU PBL was corrected in updated WRF model (ver ).  Minor bug fixes for PBL Prandtl number calculation in stable and unstable condition. WRF model that based on WRF ver and that has same physics setting with ‘KAF-WRF V09’, is defined ‘KAF-WRF V10’. Select case precipitation  Initial time : UTC : UTC : UTC Compare the verification score ; KAF-WRF V07, V09, V10

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University 12hr fcst precipitation (6 hour accumulated) POD Bias FAR ETS

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University 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 of light-precipitation prediction is possible to increase adapting microphysics change and PBL debug. ROKAF has plan that is changed EXP model instead of OPR model

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University 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 = )  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 ↑

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University KAF-WRF V07 WSM6KAF-WRF V09 WSM3 KAF-WRF V09 WDM6KAF-WRF V09 WSM UTC, 48 hour precip.

Numerical Modeling Laboratory, Department of Atmospheric Sciences, Yonsei University KAF-WRF V07KAF-WRF V09KAF-WRF test Int : UTC, Valid : 12 UTC, 6hr precip.