Blending Analysis with Spatial Filter in TWRF: Application to Typhoon Prediction over the Western North Pacific Ocean

Transcription

1 Blending Analysis with Spatial Filter in TWRF: Application to Typhoon Prediction over the Western North Pacific Ocean Der-Song Chen 1 Ling-Feng Hsiao 2 Ying-Hwa Kuo 3 Xiang-Yu Huang 3 Chun-Teng Cheng 1 Chin-Cheng Tsai 2 Chin-Tzu Fong 1 Jing-Shan Hong 1 Tien-Chiang Yeh 1 1 Central Weather Bureau, Taipei, Taiwan 2 Taiwan Typhoon and Flood Research Institute, Taipei, Taiwan 3 National Center for Atmospheric Research, Boulder, Colorado Workshop on Numerical Prediction of Tropical Cyclones May 2014, CWB, Taiwan

2 TWRF Model configurations A version of WRF has been implemented at the CWB for TC forecast from 2010 (Hsiao et al., 2010) 4 times per day (00, 06, 12, 18 UTC) 45/15/5-km resolution, 45 vertical levels Partial cycle, BC from NCEP GFS, CV5 BES with 3 outerloop Model physics Microphysics: Goddard MPS Cumulus: Kain-Fritch scheme with new trigger function PBL: YSU scheme Surface layer: Monin-Obukhov scheme Land process: NOAH Long wave and short wave radiation: RRTMG Gravity drag parameterization

3 Domain of CWB TWRF (Typhoon WRF) Here you are

4 Motivation to apply blending scheme in TWRF In a regional cycling data assimilation and forecasting system, forecasts suffer a general deficiency in effective representation of large scale features. We propose to blend analysis field from a coarse resolution global model(ncepgfs) and fine resolution regional model(twrf) to improve the quality of the TWRF analysis field. The primary goal of the blending method is to improve the description of large scale features in the TWRF analysis field. The blending is achieved through using of an incremental spatial filtering scheme.

5 Blending method First guess blending of a host forecast and a nest forecast with a spatial filter is expressed as (Yang 2005), A low-pass Raymond 6th order tangent implicit filter is used (Raymond and Garder 1991).

6 The spatial filter Amplitude response function: e is the filter parameter; dx is grid spacing L x is Cut-off Length Scale (CLS)

7 TWRF Different cut-off lengths GFS Different cut-off lengths Amplitude response for a cut-off length of 300, 600, 1200, 1800 and 2400 km, respectively.

8 apply diagnose procedure (Hsiao et al., 2010) The main reason is as following: 1. The hydrostatic dry surface pressure(mu) and geopotential height are also predicted variables in the WRF model 2. but cannot be directly included in the above blending procedure because they should be diagnosed from theta, water vapor mixing ratio, and ps (Skamarock et al. 2008). 3. Otherwise, an unbalanced WRF initial state will be created, which violates the diagnostic relationship. h d d

9 Blending scheme CLS = 1200 km Blended fields (13) We have tested different choice of cut-off length, including 300, 600, 1200, 1800 and 2400 km, and found that the 1200 km cutoff length gave the best performance - U, V, T, QVAPOR, PH, P, MU, - U10, V10, T2, Q2, PSFC, TH2 TWRF bld TWRF ana NCEP GFS ana sf TWRF ana sf

10 Example: 00UTC 21 Aug 2012 (Typhoon TEMBIN & BOLAVEN) GFS GFS (>1200km) GFS (smaller scale) TWRF TWRF (>1200km) TWRF (smaller scale)

11 Example: 00UTC 21 Aug 2012 (Typhoon TEMBIN & BOLAVEN) GFS (>1200km) TWRF (difference) large scale features Blending small scale features New initial field h d d apply diagnose procedure (Hsiao et al. 2010)

12 Experimental design Initial conditions Data Assimilation Analysis Blending Relocation PAR Partial cycling 3DVAR CV5 OL3 BLD Partial cycling 3DVAR CV5 OL3 GFS Interpolation from NCEP GFS(.5 x.5 )

13 Statistics of 279 cases 19 typhoons from July to October in 2013 over the western North Pacific Ocean. Typhoon Date Intensity Movement Case No. SOULIK ~ super typhoon Westward 20 CIMARON ~ tropical storm Northward 3 JEBI ~ tropical storm Westward 10 MANGKHUT ~ tropical storm Westward 2 UTOR ~ super typhoon Westward 19 TRAMI ~ tropical storm Recurved 14 KONG-REY ~ tropical storm Recurved 14 TORAJI ~ tropical storm Northward 5 MAN-YI ~ tropical storm Recurved 10 USAGI ~ super typhoon Westward 19 PABUK ~ typhoon Recurved 18 WUTIP ~ typhoon Westward 8 SEPAT ~ tropical storm Recurved 7 FITOW ~ typhoon Recurved 23 DANAS ~ super typhoon Westward 15 NARI ~ typhoon Westward 20 WIPHA ~ typhoon Recurved 18 FRANCISCO ~ super typhoon Recurved 35 LEKIMA ~ super typhoon Recurved 19

14 Experimental cases Typhoon tracks based on CWB best track of 19 typhoons from July to October in 2013 over the western North Pacific Ocean. The lines indicate the track for each typhoon during its life cycle. Colors of different segments of the track designate the storms at different stages.

15 PAR partial cycling GFS GFS interploation BLD blending scheme Verification against EC (RMSE) Pressure (hpa) Pressure (hpa) U (a) U(m/s) T Pressure (hpa) Pressure (hpa) H (b) H(m 2 s -2 ) Q PAR GFS BLD RMSE Over 279 cases 19 TCs (45km) BLD is generally significantly smaller than PAR for all variables (c) 1000 (d) T(K) Q(g/kg) Vertical profile of the analysis RMSE of (a) U, (b) H, (c) T, and (d) Q for three experiments. Error bars denote the 5% and 95% percentiles determined from bootstrap resampling.

16 Temperature differences at 700 hpa 279 cases 19 TCs PAR BLD GFS clearly systematic warm bias over the western North Pacific & Indian Ocean. With the regional analysis (PAR) on scale greater than 1200 km replaced by that of the NCEP GFS analysis, the large-scale errors are largely removed in the blended analysis (BLD). The differences between the composite analyses of PAR, BLD, and GFS experiments and ECMWF from 19 typhoons for temperature at 700 hpa.

17 Experimental results km grid 279 cases 19 TCs the BLD improvement just only giving over slightly partial better cycling on typhoon performance track than forecast GFS using and without the blended analysis statistically significant statistically significant 45km grid Mean typhoon track errors for PAR (black bars), BLD (light gray bars), GFS (dark gray bars) Error bars denote the 95% confidence interval of the mean difference between PAR and BLD (PAR-BLD; white bars).

18 BLD15 contains valuable mesoscale information and is dynamically consistent with the topography of the 15-km grid. the TWRF performance initialized of blended with the NCEP analysis GFS with has 15-km very giving similar superior performance performance on both than 15-km 45-km and & 45-km also GFS15 grids The comparison of mean track errors between 45- and 15-km resolutions in the BLD and GFS experiments for a total of 143 cases within the 15km domain. Error bars denote the 95% confidence interval of the mean difference for 15km resolution between GFS and BLD (GFS-BLD; white bars).

19 Case Study -- Typhoon SOULIK Typhoon Soulik is the only typhoon that made landfall on Taiwan, which produced heavy rainfall and caused significant agricultural, industry damage and loss of lives.

20 Similar track forecast on typhoon SOULIK ( ) How about the typhoon structure and precipitation forecast?

21 SLP at UTC (Typhoon SOULIK BY 5km) BLD GFS Tightly packed isobars and strong winds No packed isobars and weak wind The sea level pressure analysis from (a) BLD and (b) GFS experiment

22 Zonal cross section of anomaly temperature cutting through typhoon center from BLD and GFS experiments for model initial condition Vertical Profile at UTC (Typhoon SOULIK BY 5km grid) BLD GFS A warm core structure is clearly visible with the maximum temperature anomaly exceeding 8oC located at about 350 hpa weaker warm core with much reduced temperature gradients

23 5km-grid Precipitation 2013/07/12 06UTC, 24h forecast OBS max. 24-h accumulated rainfall 949 mm in Hsinchu County BLD GFS The superior rainfall forecast by BLD can be attributed to the improved analysis of the typhoon structure, circulation, track and intensity.

24 Zonal cross of two-dimensional wind vector (u,w) (m s -1, Pa s -1 ) and vertical velocity (shaded for every 1 Pa s -1 ) at 17-h forecast Vertical Profile at UTC BLD GFS The stronger westerly wind component in BLD forces a deep layer of upslope motion on windward side of the CMR, thus producing larger rainfall amount than that of GFS

25 Track mean forecast errors (km) Forecast errors and model improvement WRF( ) hr 48hr 72hr Operation TC bogus TC Relocation Partial cycling Outer loop New trigger KF TWRF( ) New TC initialization Blending GFS & TWRF

26 Track mean forecast errors (km) TC track forecast errors (inhomogeneous) of TWRF1.3, CWB, EC, NCEPGFS, JMAGFS, UKGFS for 2013 over N-W Pacific Ocean EC 303 NCEP UK JMA CWB TWRF TWRF1.3 compatible with EC, CWB Forecast Range(hours)

27 Track forecast Errors HWRF v.s. TWRF 26 typhoons 418 cases in 2013 TWRF_45km 2013HWRF_27km 2013HWRFstatic_45km Computational resource required HWRF : TWRF 2: 1 0(418) 12(375) 24(331) 36(290) 48(248) 60(210) 72(174) Verified against CWB tracks

28 TWRF: Future and Opportunities 1987 CDC Cyber 205 Power=1/3704 GFS: 275KM RFS: 90KM 1994 CRAY YMP-8i Power=1/303 GFS : 165KM TFS: 90/30KM 2000 FUJITSU VPP5000 Power=1/ GFS: 110KM TFS : 60/20KM IBM P5-575 Power =1 GFS : 55KM NFS : 45/15/5KM FUJITSU FX10/PFX10 Power=92 GFS : 40KM TWRF: 45/15/5KM EPS : 40 menmbers/day Global : <20KM RFS : WRF 1~2KM (finest) TFS : TWRF 1~2KM (finest) EPS : >100 members/days CWB WRF/TWRF has made progress over the years. New supercomputer offers opportunity for NWP development.

29 Future Plan of CWBWRF/TWRF Development of advanced data assimilation system --- hybrid (Enkf/3DVar), 4DVAR, hybrid (Enkf/4DVar) Improvement of the model resolution and physics Focusing on application: --- TC rainfall & wind forecast over Taiwan area

30 Improvement of TWRF Model Model resolution Model Physics On going (almost ready):land-soil DA On going (ready soon): 15/3 km nested Goal: a finer domain (<3km) around Taiwan, and coupled with convective scale DA Goal:examine combination of schemes and select one for operational use

31 Summary-1 1. To improve the initial conditions for TWRF, we adopt the blending scheme(bld) to merge the NCEP GFS(CLS >1200km) with the TWRF(CLS<1200km). 2. The BLD is shown to be superior to either the NCEP GFS or the original TWRF. Verified against EC, the TWRF shown systematic errors over the ocean. This systematic error is largely removed in BLD. 3. For the 45-km grid, for the 19 typhoons shows that the TWRF model initialized with the BLD gives a much improved typhoon track forecast about 80 km at 72 h forecast averaged over 279 cases. 4. For 15-km grid, the BLD possess superior mesoscale structure than that of the NCEP GFS. TWRF initialized with the BLD gives more accurate track forecast than that initialized with the NCEP GFS. The difference, which is also statistically significant, is about 20 km at 72-h averaged over 143 cases.

32 Summary-2 5. For Soulik, showed that the cycling analysis using TWRF forecast as the first guess has its advantages in capturing the mesoscale vortex structure. The mesoscale structure retained in the BLD and led to a more accurate rainfall forecast. 6. Our study shows that the BLD indeed has the best of both worlds. On one hand, it takes advantages of the large-scale fields from the NCEP GFS. This significantly reduces the systematic errors over the ocean and greatly improves the track forecast. On the other hand, it takes advantages of the mesoscale fields provided by the TWRF PAR. This improves the track forecast on the 15-km grid, as well as producing a much more accurate rainfall forecast on the 5-km grid over Taiwan. 7. With encouraging results, the BLD is recommended for operational TWRF at the CWB.

33 TC initialization of Typhoon WRF (TWRF) Vortex relocation + Vortex bogus synthetic observations (depend on TC size and intensity) 1. Winds : Ranking vortex + latest 6 hr movement of the cyclone 2. H field : Gradient wind balance 3. T field : Hydrostatic balance with H 4. Wind & Mass : Nonlinear normal mode Hsiao et al (Mon. Wea. Rev.)

34 Blending GFS analysis with TWRF analysis BLD ana TWRF ana TWRF ana RF GFS ana RF Small scale from TWRF Large scale from GFS TWRF Different cut-off lengths GFS CLS=1200 km low-pass Raymond 6 th order tangent implicit filter (Raymond and Garder 1991)

35 19 typhoons from July to October in 2013 over the western North Pacific

36 Taiwan topography and 512 rainfall stations

37 /48/72hr error 106/110/119 km Significant topography effect on typhoon movement and structure TY Saola (2012) track forecast

38 Partial Cycling I.C :6h-wrfout from UTC B.C:GFS 0.5deg ( UTC) I.C :GFS 0.5deg ( UTC) B.C:GFS 0.5deg ( UTC) I.C : 6h-wrfout from UTC B.C:GFS 0.5deg ( UTC) Cold start Update cycle Update cycle Forecast 6 hours Forecast 6 hours Forecast 96 hours Initial time hr Initial time hr Initial time hr

39 With the similar results on track and synoptic forecast. Why need BLD? BLD vs. GFS Case study (SOULIK )

40 On going : 3DVAR Hybrid DA Initial perturbation Multiple physics BC perturbation Ensemble prediction Recentered- EnKF Ensemble data assimilation system plays a key role to support the ensemble and deterministic prediction. Flow dependent BE Hybrid DA (Ens./3DVar) Deterministic prediction

41 WRF 4DVAR with Grid Intervals in 5km