1. 5 th ICMCSDong-Kyou Lee Seoul National University Dong-Kyou Lee, Hyun-Ha Lee, Jo-Han Lee, Joo-Wan Kim Radar Data Assimilation in the Simulation of Mesoscale Convective Systems

2. 5 th ICMCSDong-Kyou Lee CONTENTS 1. Introduction 2. Heavy Rainfall Case 3. Tunings of 3DVAR for Radar Data 4. Radar Data Assimilation and Model Experiments 5. Summary and Conclusions

3. 5 th ICMCSDong-Kyou Lee INTRODUCTION  Heavy rainfall is one of the major severe weathers over East Asia producing devastating flash flood, and consequently causing fatalities and property damage. Heavy rainfall is usually resulted from individual mesoscale storms or mesoscale convective systems (MCSs) embedded in synoptic-scale disturbances (Kim and Lee, 2006).  In order to understand the evolution and development mechanisms of mesoscale convective storms responsible for heavy rainfall and better predict heavy rainfall events, high-resolution observations and data assimilation techniques are important components.  Radar data assimilation is a key scientific issue in numerical weather prediction of convective systems for very short-range forecasting (Wilson et al., 1998). In recent years considerable progress has been made in the assimilation of radar observations into convective-scale numerical models for heavy rainfall prediction, and the assimilation of radar rainfall estimates.

4. 5 th ICMCSDong-Kyou Lee OBJECTIVES  The objective of this study is to investigate very short-range forecasting of the WRF model through the 3DVAR data assimilation of Dual-Doppler radar data (radial velocity and reflectivity) for a heavy rainfall case accompanying mesoscale convective systems (MCSs) over the Korean Peninsula.  The WRF 3DVAR system is modified by tuning scale lengths and observation error statistics. The increment analysis update (IAU) and the rapid update cycle (RUC) are performed using radar data in the modified 3DVAR system.  With the characteristics of radar data for the period of MCSs development, sensitivity tests of assimilation window and updated frequency are investigated.

5. 5 th ICMCSDong-Kyou Lee HEAVY RAINFALL CASE (24~25 July 2003) 12UTC 24 July 2003 00UTC 25 July 2003 850 hPa

6. 5 th ICMCSDong-Kyou Lee SATELLITE AND RADAR IMAGE (21UTC 24~ 03UTC 25 July)

7. 5 th ICMCSDong-Kyou Lee SATELLITE IMAGES (GMS 3D)

8. 5 th ICMCSDong-Kyou Lee 25/0030 UTC25/0000 UTC24/2330 UTC24/2300 UTC 25/2230 UTC25/2200 UTC24/2130 UTC24/2100 UTC 25/0230 UTC25/0200 UTC25/0130 UTC25/0100 UTC RADAR REFLECTIVITY

9. 5 th ICMCSDong-Kyou Lee Sang-Ju (62.0 mm/6 hr) Dae-Gu (53.2 mm/6 hr) Jeon-Ju (96.7 mm/6 hr) 6-h accumulated and hourly rainfall 2223 00 25 Jul 010203

10. 5 th ICMCSDong-Kyou Lee CAPE : 8 J/Kg LFC : 1012 m AGL CAPE : 1080 J/Kg LFC : 764 m AGL CAPE rapidly increased for 6 hours from 18 UTC 24 to 00 UTC 25 July. Mesoscale convective systems with 3 convective cells developed in this unstable environment. Gwangju 2418 UTCGwangju 2500 UTC Skew T-Log P DIAGRAM

11. 5 th ICMCSDong-Kyou Lee RADAR DATA WSR-88D(NEXRAD) Radar Beam S Band (wave length : 10cm) Detection range Reflectivity : 450 km Radial velocity : 240 km Format Raw data Level Ⅱ  Radar position  Radar Information

12. 5 th ICMCSDong-Kyou Lee 5.5 km 5.0 km 4.5 km 3.0 km 3.0 km : 6 points 4.5 km : 6 points 5.0 km : 14 points 5.5 km : 16 points TIME-LAGGED AUTOCORRELATION OF REFLECTIVITY AND RADIAL VELOCITY 42 sequential data points of reflectivity and radial velocity for 6 hours were obtained. 21 UTC 24 – 01 UTC 25 July 2003 (6min interval)

13. 5 th ICMCSDong-Kyou Lee Reflectivity Radial Velocity 5.5 km 5.0 km 4.5 km 3.0 km For the 42 data points RESULT OF AUTOCORRELATION

14. 5 th ICMCSDong-Kyou Lee Average of the 42 data points RESULT OF AUTOCORRELATION Ref Vr Minute Correlation Radial velocity (Vr) had a longer autocorrelation time scale than that of reflectivity. The autocorrelations dropped to zero before 30 minutes. Update frequency smaller than 60 minutes (2 x τ ) might be used for radar data assimilation, especially for RUC and IAU. τ

15. 5 th ICMCSDong-Kyou Lee 1. TUNING of 3DVAR

16. 5 th ICMCSDong-Kyou Lee DATA ASSIMILATION SYSTEM  The scale lengths of background error (currently about 110 km for wind and 40 km for mixing ratio) were estimated for high resolution radar observations.  The average minimization ratio (about 25 %) of the cost function allowed further minimization to be possible by adjusting the observation errors.  A radar data assimilation system for Increment Analysis Update (IAU) and Rapid Update Cycle (RUC) with 3DVAR was developed. Tuning of 3DVAR (Version 2.1) and a radar data assimilation system

17. 5 th ICMCSDong-Kyou Lee TUNING OF BACKGROUND SCALE-LENGTH The O-B (observation – background values) of radar data was calculated from two MCS cases and one frontal case. The O-B correlation decreased in short distance. It means that the radar observation can detect short-term scale phenomena. The locality of the radar reflectivity was higher than that of radical velocity O-B Statistics

18. 5 th ICMCSDong-Kyou Lee SCALE LENGTHS 9 km 4 km reflectivity radial velocity - The locality of the radar observation can be reflected by tuning the scale length of the background error against a recursive filter. - 9 km and 4 km of scale lengths are proper for radial velocity and reflectivity, respectively.

19. 5 th ICMCSDong-Kyou Lee  The expectation value of the minimized cost function is given by a half of the effective number of observations (Desroziers and Ivanov, 2001).  Further minimization of the cost function can be made by scaling a cost function term to satisfy the expectation value of the minimized cost function.  The adjustment of scaling parameters are done by iteration. ERROR TUNING IN 3DVAR

20. 5 th ICMCSDong-Kyou Lee  To adjust the real cost function to the expectation value, scaling parameters (or error factors) will be applied to each cost function term and to each observation type, and determined iteratively. where i means iteration number. The estimation of and (Desroziers and Ivanov, 2002). 3DVAR ERROR TUNING

21. 5 th ICMCSDong-Kyou Lee ObservationPS(0)S(1)S(2)S(3) Radial Velocity168201.000.720.690.68 Reflectivity269771.001.95 JbJb 1.001.041.111.13 The numbers of effective radar radial velocity and reflectivity observations are 16,820 and 26,977, respectively, and the tuning parameters of the observation error converge rapidly. 3DVAR ERROR FACTORS

22. 5 th ICMCSDong-Kyou Lee J/p for ideal: 0.5 J/p for un-tuned: 1.38 J/p for tuned: 0.53 IMPACT OF TUNING ON MINIMIZATION The decrease in the cost function of the un-tuned 3DVAR and the tuned 3DVAR is 25 % and 64 %, respectively. In particular, the ratio of the minimized cost function (J) and the number of used observation (p) is 1.38 and 0.53 for the un-tuned 3DVAR and the tuned 3DVAR, respectively.

23. 5 th ICMCSDong-Kyou Lee 2. MODEL EXPERIMENTS

24. 5 th ICMCSDong-Kyou Lee MODEL DESCRIPTION Physics processes Domain 1 (30 km) Domain 2 (10 km) Domain 3 (3.3 km) Horizontal Dimensions 191 X 171160 X 178241 X229 Time interval (Δt)90 sec30 sec10 sec Cumulus Parameterization Kain-Fritsch scheme none Explicit moistureLin et al. scheme PBLYSU scheme Radiation RRTM/ Dudhia scheme RRTM/ Dudhia scheme RRTM/ Dudhia scheme Surface-LandNoah LSM Initial and Boundary data NCEP / FNL analysis

25. 5 th ICMCSDong-Kyou Lee EXPERIMENTS  3-hr assimilation period with 1-hr update frequency for 6-hr forecast  Three initialization experiments are performed. - CON: IAU with un-tuned 3DVAR increments - RUC: RUC with tuned 3DVAR results - IAU: IAU with tuned 3DVAR increments

26. 5 th ICMCSDong-Kyou Lee INITIALIZATION EXPERIMENTS 24 JUL 18 UTC 19 UTC20 UTC21 UTC22 UTC23 UTC 25 JUL 00 UTC 01 UTC02 UTC03 UTC 3DVAR Analysis Grid nudging The 3 cycles are performed with 1-h intervals for a 3-h assimilation window RUC IAU CON 6 h forecast

27. 5 th ICMCSDong-Kyou Lee 6-h ACCUMULATED RAINFALL AMOUNT 96.9114.5 96.7 224.9 OBS CON RUCIAU

28. 5 th ICMCSDong-Kyou Lee TIME SERIES AT MAXIMUM RAINFALL POINT

29. 5 th ICMCSDong-Kyou Lee Radar Reflectivity (dbz)OBS CON RUC IAU

30. 5 th ICMCSDong-Kyou Lee RMSEs of radial velocity The tuning of 3DVAR results in improvement in the forecast of wind field. The improvement is effective up to 4-hour forecast.

31. 5 th ICMCSDong-Kyou Lee 3. SENSITIVITY OF ASSIMILATION WINDOW AND UPDATE FREQUENCY

32. 5 th ICMCSDong-Kyou Lee EXPERIMENTS 1) UPDATE FREQUENCY 2) ASSIMILATION WINDOW

33. 5 th ICMCSDong-Kyou Lee SENSITIVITY EXPERIMENTS 24 JUL 18 UTC 19 UTC20 UTC21 UTC22 UTC23 UTC 25 JUL 00 UTC 01 UTC02 UTC03 UTC 1H_12MIN 1H_30MIN 3H_12MIN 3H_30MIN WINDOW_FREQUENCY 3H_60MIN

34. 5 th ICMCSDong-Kyou Lee RMSEs OF RADIAL VELOCITY Assimilation window was more effective than update frequency. 30-min update frequency had smaller RMSE during MCSs development. The 3-h assimilation window and 30-min update frequency was relatively better.

35. 5 th ICMCSDong-Kyou Lee 6-h ACCUMULATED RAINFALL AMOUNT 3H_12M3H_30M 1H_30M1H_12M 78.8143.2 117.6 79.9

36. 5 th ICMCSDong-Kyou Lee TIME SERIES AT MAXIMUM RAINFALL POINT

37. 5 th ICMCSDong-Kyou Lee SUMMARY AND CONCLUSION  In this study, radar data had much shorter scale-lengths in 3DVAR compared to the typical synoptic observations. The scale length of 9 km for radial velocity, and 4 km for reflectivity were used.  The error for radial velocity (2 m/s) was overestimated (70 % of the currently used error), and that for reflectivity (5 dBZ) was underestimated (190 % of the currently used error). Using the error factors led a rapid converge of radar data assimilation in one iteration.  In the radar data assimilation, the 3DVAR tuned by the error factors improved the maximum rainfall amount that was in better agreement with observation than that of the un-tuned 3DVAR. The RMS errors of radial velocity simulated by the tuned 3DVAR (RUC and IAU) were also smaller than that of the un-tuned 3DVAR.  The time-lagged autocorrelation of radar radial velocity and reflectivity during the period of the storm development was about 30 min and less than 30 min, respectively. In the IAU the 30-minute update cycles was better compared to other update cycles in this study.  In the 3DVAR assimilation using radar data, the assimilation window was more sensitive to the simulation of heavy rainfall than the update frequency.

38. 5 th ICMCSDong-Kyou Lee Thank You !

39. 5 th ICMCSDong-Kyou Lee

40. 5 th ICMCSDong-Kyou Lee SYNOPTIC CHART - SURFACE

41. 5 th ICMCSDong-Kyou Lee SYNOPTIC CHART - 500 hPa

42. 5 th ICMCSDong-Kyou Lee SYNOPTIC CHART - 300 hPa

43. 5 th ICMCSDong-Kyou Lee AWS WIND FIELD 0100 UTC0110 UTC0050 UTC 0030 UTC 0040 UTC0020 UTC Strong convergence formed by downdraft occurred with previous storm.

44. 5 th ICMCSDong-Kyou Lee 3.0 km : 38 points 3.5 km : 24 points 4.0 km : 47 points 4.5 km : 95 points 5.0 km : 133 points 5.5 km : 128 points 5.5 km 5.0 km 4.5 km 4.0 km 3.5 km 3.0 km 3 hours 465 Points RADAR DATA

45. 5 th ICMCSDong-Kyou Lee Ref Vr 465 data points More data points had a shorter time period, and did not much change the characteristics of the autocorrelation. AUTOCORRELATION

46. 5 th ICMCSDong-Kyou Lee GRID NUDGING AND IAU IN WRF  For the grid nudging, we have implemented time-variant nudging (=1) and target nudging (=3)  Surface (in planetary boundary layer) nudging (or IAU) is not yet implemented.  The nudging of U, V, and T is currently tested. Nudging IAU

47. 5 th ICMCSDong-Kyou Lee TIME-HEIGHT SECTION U W CON RUC IAU Area-mean u- and w- wind