1. Typhoon Forecasting and QPF Technique Development in CWB Kuo-Chen Lu Central Weather Bureau

2. QPF in CWB Typhoon Nora (7315) Strong gale and 50 ~ 100 mm in South Taiwan are expected 2

3. Outline Definition QPF is the expected amount of melted precipitation accumulated over a specified time period over a specified area Description Forecast Products and the performance Advance approach by ensemble QPF Ensemble Typhoon QPF Pattern Recognition by Convection Pattern Conclusion

4. Grid QPF since 2006 Verification system operates in real time Issued twice a day on 00z and 12z 2.5 km resolution for next 24 hours. 0-12h 12-24h

5. Verification on 24-h QPF T.S. improve gradually, Bias reduce significantly on 50 mm Threat Score Bias

6.  D1: 45-km222 X 128  D2: 15-km184 X 196  D3: 5-km151 X 181 5km  Terrain Peak: 3,271m/3,952m  4 runs/day, 6-hourly updated (Lee, Hong etc, MIC, CWB) WRF Prediction System

7. OBS Examples of the member QPFs with similar typhoon location If the designed is good enough, the answer of the forecast is just inside the stamps 7 80 Ensemble members a day for QPF 80 Ensemble members a day for QPF

8. Question: Can we provide better QPF by applying the same process as typhoon track forecasting ? Multi-model consensus: Simple consensus, Selected consensus, Weighted consensus,,,,,, 8

9. Advance strategy for Ensemble product Selected consensus  by Typhoon location for typhoon QPF  by convection pattern for Nowcasting 9

10. Select the model QPF cases from ensemble members according to the prior estimate of the typhoon position, then produce the composite rain map and probability products based on the selected samples. Maximally use the ensemble QPF based on the optimal track forecast Select the model QPF cases from ensemble members according to the prior estimate of the typhoon position, then produce the composite rain map and probability products based on the selected samples. Maximally use the ensemble QPF based on the optimal track forecast 10 Member of QPF map selected by Typhoon location surrounding the official typhoon forecast track (Ensemble Typhoon Quantitative Precipitation Forecast, ETQPF)

11. Subjective Forecast: ETQPF on different Track

12. TY Fung-wong (2014) TY Matmo (2014) Obs ETQPF Obs ETQPF ETQPF performance for the 2014 typhoons Max:956 mm Max:995 mm If track is trustable. ETQPF is also trustable Hong (2015)

13. Brief summary of ETQPF Strength If track forecast is OK, the model QPF is worth to be a guidance Is able to represent the interaction between the environment and typhoon circulation Depend on case, has the potential to capture the mesoscale precipitation process Weakness Uncertainty to the track forecast Official track forecast still better than the model forecast Difficult to configure a model that performs THE BEST all the time Uncertainty from the initial condition and the model physical process Limitation to the model resolution … How to maximally take advantage of the strength and well handle the uncertainty due to the weakness from the model QPF? Hong et. al. 2015, Weather and forecasting

14. Piecewise recognition for selecting the similarity of convection pattern from EPS Pattern Recognition for convection (Chen, Huang, Lu etc, MFC, 2014) Radar Mosaic CV Model simulate CV

15. Big data/Grand Ensemble Set 3-hourly forecast in ±6hr-Window (5 frames) 22 WEPS members, 4 Lag Runs 22x4x5 = 440 samples Pattern Recognition: Mining useful information Piecewise, weighted, normalized moment invariant Correlation ? Big data/Grand Ensemble Set 3-hourly forecast in ±6hr-Window (5 frames) 22 WEPS members, 4 Lag Runs 22x4x5 = 440 samples Pattern Recognition: Mining useful information Piecewise, weighted, normalized moment invariant Correlation ? ‒6 h‒6 h +6 h Radar CV 05/15 1200Z 0-6-363 05/15 00Z 4 lag runs 05/14 18Z 05/14 12Z 05/14 06Z 12 h 126918151821243027 2427303633 1215182421 Frmo ensemble member N (N=1, 2, …, 22) forecast at target time (say 0 hr) Sampling, Recognizing and Ranking

16. TOP 1 ~ 20 by ranking the similarity of Radar CV Similar Convection Pattern in EPS Case of Meiyu Front

17. 3 hours QPF according to Rank 1 ~ 20 QPF from Similar Convection Pattern in EPS Case of Meiyu Front

18. 3 hours QPF according to Rank 1 ~ 20 QPF from Similar Convection Pattern in EPS Case of Meiyu Front ETS

19. 3-hourly QPF Verification for consensus During a Mei-Yu Front period in May 2015 Selected consensus (top 20%) simple consensus 0 1.0 Threat Score Fcst. hours Selected Consensus 3 6 Simple Consensus from 19 th 12UTC to 25 th 12UTC May 2015 Threshold : above 10 mm/3-h 3 6 9 12 3 6 9

20. Conclusion Multi-stage Quantify Precipitation Forecast 20 Weekly (Qualitative) GFS, Synaptic Analysis, statistics, analogy, conceptual model Weekly (Qualitative) GFS, Synaptic Analysis, statistics, analogy, conceptual model 1-3 daily QPF (Quantitative) Regional (Ensemble) Forecast System, Statistics, advanced Ensemble Forecast 1-3 daily QPF (Quantitative) Regional (Ensemble) Forecast System, Statistics, advanced Ensemble Forecast 0-6 hr (or 0-12 hr) QPF LAPS/STMAS, ARPS, VDRAS, Cloud model 0-6 hr (or 0-12 hr) QPF LAPS/STMAS, ARPS, VDRAS, Cloud model 0-1 hr QPF Radar extrap., ANC, SCAN 0-1 hr QPF Radar extrap., ANC, SCAN 0 hr Nowcasting Radar, gagues, lighting 0 hr Nowcasting Radar, gagues, lighting Storm scale data assimilation Traditional 3d/4d data assimilatoin Radar Extrapolation Pattern Recognition

21. THANK YOU FOR YOUR ATTENTION 21