Numerical Prediction of Tropical Cyclones (PAGASA Experience)

Transcription

1 Numerical Prediction of Tropical Cyclones (PAGASA Experience) BONIFACIO G. PAJUELAS Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) Science Garden, Agham Road, Diliman, Quezon City, 11 PHILIPPINES

2 Outline Introduction The Adapted Model Sensitivity Test TCs that Impacted Phil. in 213 Track Errors Summary/Challenges

3 Introduction 21, PAGASAs acquisition of ihpcs Why ihpcs? ihpcs HW : Linux cluster of 76 highend compute nodes, connected thru an infiniband for 24/7 fast interlink for data processing, integration, generation and distribution of products. ihpcs SW : MesoDSS & HDSS

4 ihpcs Data Flow

5 PAGASA ihpcs-wrf Regional Model NWP Model: WRF-ARW Lateral Boundary/Initial Condition: NCEP GFS Local data inclusion thru LAPS Parameterization Schemes Microphysics : Ferrier Convective : Kain-Fritsch PBL : YSU Shortwave radiation : Dudhia Longwave radiation : RRTM Surface layer : Monin-Obukhov Land surface : Noah

6 Grids and Domain Coarse: 12-km 115 o E-135 o E, 3 o N-25 o N Initialization: 3-hrly, forecast up to 84-hr hrly, forecast up to 24-hr Fine: 3-km 116 o E-127 o E, 5 o N-21 o N Initialization: 3-hrly, forecast up to 48-hr hrly, forecast up to 12-hr

7 Sensitivity Tests

8 photo - credit: AFP Central Command from their Facebook page: &type=1

9 Impacts of Varying WRF-ARW Physics Options on Forecasts of Typhoon Parma (Collaborative work with WDT, Inc.)

10 Summary of Sensitivity Test Differences in model track were more sensitive to changes in the convective scheme than to changes in the Microphysics or PBL schemes. Kain-Fritsch convective scheme produced a track that impacted the northern Philippines during its NW journey across the Philippine sea. All other convective schemes produced tracks well to the NE of the observed track. Differences in the max 1m wind speed were quite sensitive to both the microphysics and convective schemes and much less sensitive to the choice of PBL schemes. The simulation with the most number of vertical levels (36) produced the best typhoon track, but not obvious if this produced the best wind speed. The bogusing capability within the WRF system provides the ability for better initializing intense typhoons like Parma. Although this capability provided little long-term improvement in the simulation, other than perhaps a slightly improved track.

11 STS 131 Sonamu TS 132 Shanshan TS 133 Yagi TS 134 Leepi TS 135 Bebinca STS 136 Rumbia TY 137 Soulik TS 138 Cimaron STS 139 Jebi TS 131 Mangkhut TY 1311 Utor STS 1312 Trami STS 1313 Pewa TS 1314 Unala STS 1315 Kong-rey TS 1316 Yutu STS 1317 Toraji TY 1318 Man-yi TY 1319 Usagi STS 132 Pabuk TY 1321 Wutip TS 1322 Sepat TY 1323 Fitow TY 1324 Danas TY 1325 Nari TY 1326 Wipha TY 1327 Francisco TY 1328 Lekima TY 1329 Krosa TY 133 Haiyan TS 1331 Podul Time Series of TC Activity in 213 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Courtesy of RSMC, Tokyo

12 213 TCs that Impacted the Philippines

13 DPE (km) 8 Direct Positional Error (DPE) T+6 T+12 T+18 T+24 T+3 T+36 T+42 T+48 T+54 T+6 T+66 T+72 T+78 T+84 Forecast Lead Time (hr) N Haiyan Nari Utor Trami Krosa Shanshan Rumbia Mean T

14 CTE (km) 5 Cross Track Error (CTE) T+6 T+12 T+18 T+24 T+3 T+36 T+42 T+48 T+54 T+6 T+66 T+72 T+78 T Forecast Lead Time (hr) N Haiyan Nari Utor Trami Krosa Shanshan Rumbia Mean

15 ATE (km) 6 Along Track Error (ATE) 4 2 T+6 T+12 T+18 T+24 T+3 T+36 T+42 T+48 T+54 T+6 T+66 T+72 T+78 T Forecast Lead Time (hr) N Haiyan Nari Utor Trami Krosa Shanshan Rumbia Mean

16 Summary/Challenges PAGASA ihpcs adapted the WRF-ARW version modeling system to provide up to 3-day weather guidance for the Philippine Area of Responsibility (PAR). The model is configured using the best results of the sensitivity tests. TD track errors is pulling up the average of the 213 TCs. To improve track forecast and develop ensemble prediction system. Undertake researches in understanding unusual weather pattern and extreme rainfall events; Undertake other initiatives in the advancement of numerical modeling on weather. Maintain database of all NWP products generated including related information acquired from known Meteorological Centers.

17 Thank You!

18 DPE (km) TC Haiyan DPE Forecast Time (hr) N Mean back

19 CTE (km) TC Haiyan CTE Forecast Time (hr) N Mean

20 ATE (km) TC Haiyan ATE Forecast Time (hr) N Mean

21 DPE (km) 8 TC Nari DPE Forecast Time (hr) N Mean back

22 CTE (km) TC Nari CTE Forecast Time (hr) N Mean

23 ATE (km) 2-2 TC Nari ATE Forecast Leadtime (hr) N Mean

24 DPE (km) back TC Utor DPE Forecast Lead Time (hr) N Mean

25 CTE (km) 4 TC Utor CTE Forecast Lead Time (hr) N Mean

26 ATE (km) TC Utor ATE Forecast Lead Time (hr) N Mean

27 DPE (km) 5 TC Trami DPE Forecast Lead Time (hr) N Mean back

28 CTE (km) TC Trami CTE Forecast Lead Time (hr) N Mean

29 ATE (km) 6 TC Trami ATE Forecat Lead Time (hr) Mean N

30 DPE (km) 2 TC Krosa DPE Forecast Lead Time (hr) N Mean back

31 CTE (km) TC Krosa CTE Forecast Lead Time (hr) N Mean back

32 ATE (km) 15 TC Krosa ATE Forecast Lead Time (hr) N Mean

33 DPE (km) TC Shanshan DPE Forecast Lead Time (hr) N Mean back

34 CTE (km) TC Shanshan CTE Forecast Lead Time (hr) N Mean

35 ATE (km) 4 TC Shanshan ATE Forecast Lead Time (hr) N Mean

36 DPE (km) TC Rumbia DPE Forecast Lead Time (hr) N Mean

37 Shanshan (Crising) [ Reached TS intensity outside the PAR ] back

38 Rumbia (Gorio)