Comparison of Typhoon Track Forecast using Dynamical Initialization Schemeinstalled

Transcription

1 IWTC-LP 9 Dec 2014, Jeju, Korea Comparison of Typhoon Track Forecast using Dynamical Initialization Schemeinstalled WRF Hyeonjin Shin, WooJeong Lee, KiRyong Kang, 1) Dong-Hyun Cha and Won-Tae Yun National Typhoon Center / KMA 1) Ulsan National Institute of Science and Technology

2 Outline Introduction Experiment design Results Summary

3 Background There are not enough observation data in the ocean. Numerical models tend to simulate TCs weakly. Numerical models use unique techniques to simulate more realistic TCs The National Typhoon Center has operated the WRF model for typhoon forecast (TWRF) Typhoon Sanba (1216) (Source : MODIS)

4 Track error of numerical models (2010~2013) JMA NCEP ECMWF KMA Model 48h DPE S D #Cases GDAPS RDAPS KEPS TWRF ECMWF GFS JGSM TEPS Mean So, we need to improve TWRF! Track Error (Direct Position Error) : great-circle distance between the forecast position and the best-track position of a TC center. (unit : km)

5 Objectives - To investigate the impact of Dynamical Initialization (DI) scheme in the numerical models - To examine the characteristics of track and intensity error Model Description Bogus Resolution Forecast hr TWRF Cases : Khanun (1207) 1200 UTC 16 July 2012 ~1200 UTC 18 July 2012 Tembin (1214) 1200 UTC 25 Aug ~0000 UTC 30 Aug Sanba (1216) 1200 UTC 12 Sep ~0000 UTC 17 Sep Frequency /day Typhoon Weather Research Forecast (KMA, 2008) GFDL 15 km nodi WRF without the use of the DI scheme No 6 km DIWRF DI scheme installed-wrf (KMA, 2013) DI 6 km ECMWF Integrated Forecast System No 50 km GFS Global Forecast System No 50 km 120 2

6 TWRF DIWRF WRF version ARW ARW 3.3 TC Initialization GFDL-type bogussing Dynamical Initialization Horizontal resolution 15 km ( ) 6 km Vertical ( ) layers(model top) 42 sigma layers (50hPa) (50hPa) 28 sigma layers Input data UM forecast data GFS forecast data (0.5 ) Time step for integration 60s 30s Cumulus Parameterization Kain-Fritsch Microphysics Radiation (long/short) Boundary Layer GFS SST data (1 ) WSM6 RRTM/ Dudhia YSU Land-Surface thermal diffusion Noah LSM

7 TC Initialization (KMA, 2008) Separate TC vortex component Remove weak TC vortex Generate bogus vortex Replace with enhanced vortex TWRF GFDL-type bogussing Empirical functions (wind) & FDDA (other variables) Simplicity of application Difficulty in generating the asymmetric structure of the TC vortex associated with the TC motion. DIWRF Dynamical Initialization Integration of dynamical model Requirement of additional model integration. Consistency in the dynamics and physics of the forecast model. Generation of the asymmetric structure of TC vortex. (Cha and Wang, 2013)

8 Results Trend and Error of track forecast Intensity Forecast Trend Relationship between - track error and maximum wind speed - track and intensity error

9 Definition of track errors 1. Direct Position Error (DPE) 2. Along Track Bias (+fast /-slow) 3. Cross Track Bias (+right /-left) 2 Direction of Observed Track 3 12 OBS 1 00 OBS +12hr Fcst

10 Track Forecast Error KHANUN (1207) TWRF nodi DIWRF ECMWF GFS left slow

11 TEMBIN (1214) TWRF ECMWF nodi GFS DIWRF slow

12 SANBA (1216) TWRF nodi DIWRF ECMWF GFS left fast slow

13 Intensity Forecast Trend KHANUN (1207) TEMBIN (1214) SANBA (1216)

14 DPE and Max. Wind Speed KHANUN (1207) 00 ~ 48H: Negative correlation 60 ~120H: Positive correlation TEMBIN (1214) SANBA (1216)

15 Track and Intensity Error DPE vs. Wmax error DPE vs. Pc error KHANUN (1207) TEMBIN (1214) SANBA (1216)

16 Summary DIWRF has smaller track error than TWRF over most of forecast time. While it can be affect by input data, WRF version, resolution and so on, it may explain that DI scheme contributed to improve forecast of track and intensity. TWRF overestimates intensity, but tends to estimate trend of central pressure change well. Models have slow bias tendency. There are some rooms for improvement in along track component. Models perform better for strong TCs at forecast time within 48h. This implies that there is a reliance of track forecast on the initial characteristics of a TC. I will perform the experiment with same input data and tracking algorithm using TWRF and DIWRF.

17 References Chen et al., 2013: THE PERFORMANCE OF GLOBAL MODELS IN TC TRACK FORECASTING OVER THE WESTERN NORTH PACIFIC FROM 2010 TO Tropical Cyclone Research and Review, 2, Dong-Hyun Cha and Yuqing Wang, 2013: A Dynamical Initialization Scheme for Real- Time Forecasts of Tropical Cyclones Using the WRF Model*. Mon. Wea. Rev., 141, Fogarty, C., and P. Bowyer, 2008: An analysis of along- and cross-track forecast errors and error biases for TCs in the Atlantic Basin. the 28th Conference on Hurricanes and Tropical Meteorology, Amer. Meteor. Soc. Kim et al., 2014: Evaluation of the Intensity Predictability of the Numerical Models for Typhoons in Atmos., 24, KMA, 2008: Construction of numerical model specialized typhoon and development of rainfall distribution algorithm (Ⅰ). KMA, 167 pp. KMA, 2013: Development of short- and medium range forecasting techniques of typhoon (Ⅰ). KMA, 607 pp.

18 Thank you

19 Tracking algorithm 및기타 if(max_rv850.ge.3.5e-5) icount=icount+1 if(max_ws850.ge.max_ws300) icount=icount+1 if(max_ws10.ge.12) icount=icount+1 if(t_dev.ge.0.5) icount=icount+1 t_dev=t300(ix,jx,n)+t500(ix,jx,n)+t700(ix,jx,n)-tmean300-tmean500-tmean700 #con1 : local min slp #con2 : rv850 > 4.9*10-5 #con3 : max ws850 > max ws300 #con4 : max ws10 > 17 m/s #con5 : warm core TWRF 전문에서 30KT 이상이어야수행됨., 기압이가장낮은곳위경도, 그기압, 바람이가장센곳. TWRF 개발당시초기입력장에따른보거싱효과를비교했을때, 0514 NABI, 0613 SHANSHAN 에서는 GFS 분석장을초기장으로한실험에서진로및강도예보예측성이좋았으나 0815 JANGMI 사례에서는 UM 을초기장으로한실험이특히진로예보에있어좋은성능을보였다.

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