Performance of TANC (Taiwan Auto- Nowcaster) for 2014 Warm-Season Afternoon Thunderstorm

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1 Performance of TANC (Taiwan Auto- Nowcaster) for 2014 Warm-Season Afternoon Thunderstorm Wei-Peng Huang, Hui-Ling Chang, Yu-Shuang Tang, Chia-Jung Wu, Chia-Rong Chen Meteorological Satellite Center, Central Weather Bureau, CHINESE TAIPEI

2 Overview Nowcast technique Nowcasting Forecast Country of origin Technique system period (min) MAPLE Canada United States radar echo extrapolation 60 CARDS Canada radar echo extrapolation 60 STEPS Australia United Kingdom radar echo extrapolation 60 TIFS Australia Ensemble human 60 BJANC China Fuzzy logic 60 Wilson et al. 2010

3 CWB nowcast technique Radar echo extrapolation Good skill for the cases with high persistence

4 Radar echo extrapolation Limitation The extrapolation method can t capture the storm initiation, growth, and dissipation.

5 Challenge for nowcast in Taiwan Warm-season afternoon thunderstorm

New nowcast technique TANC (Taiwan AutoNowCaster) CWB introduced the AutoNowCaster developed by NCAR in 2010.

6 New nowcast technique TANC (Taiwan AutoNowCaster) CWB introduced the AutoNowCaster developed by NCAR in TANC is localized for Taiwan to predict the initiation of the summer thunderstorm (reflectivity > 35 dbz). Based on the fuzzy logic concept and considering the factors from the radar, surface observation, and numerical model prediction to make the nowcast for 0-1 hour. TANC product

7 TANC (Taiwan AutoNowCaster) Fuzzy logic concept Fuzzy logic is a form of many-valued logic that deals with approximate, rather than fixed and exact reasoning. Compared to traditional binary logic (where variables may take on true or false values), fuzzy logic variables may have a truth value that ranges in degree between 0 and 1. Fuzzy logic has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false. From Wikipedia

8 TANC system methodology Flow chart Define predictors Data analysis Create the corresponding membership functions and weights Produce the 1 hour likelihood prediction Wind convergence Vertical velocity Instability index RH

9 TANC system methodology How to generate the nowcast product? 1. Convert the predictors to likelihood fields using the membership functions membership fn. 2. Weight the importance of each field 3. Sum * W Nowcast likelihood product * W1 + *W2 +

10 TANC Predictors for afternoon thunderstorm in operational TANC Radar climofreq Climatology Wt=0.6 Wt=0.7 Radar climotrend Wt=0.3 From Climate statistics using Radar data Large scale Wt=0.5 Stability Wt=0.1 CAPE Wt=0.4 CIN Wt=0.6 From WRF Entrainment Rhavg Storm initiation 60 min nowcast Mesoscale Wt=0.05 Wt=0.3 convergence Wt=1 Wt=1 SurfDiv Wt=1 From surface obs. CI proximity storminint Storm scale Wt=0.45 Wt=0.3 Cumulus Wt=0.7 Wt=1 RadarCu Wt=1 From real-time radar data

11 TANC Membership functions corresponding to the preditors 1. Large scale :

12 TANC Membership functions for afternoon thunderstorm 2. Mesoscale : 3. Storm scale :

13 TANC Afternoon thunderstorm case: Aug. 27, 2014 The ridge of subtropical high covers Taiwan with weak synoptic forcing. The convection initiation is captured well spatially and temporally, but a little bit over-forecast for southern Taiwan. Likelihood of storm initiation (colored area) from TANC (Dark blue line: Observed convection for verification)

14 TANC Afternoon thunderstorm case: Aug. 29, 2014 The ridge of subtropical high covers Taiwan with weak synoptic forcing. The convections occurs both in southern and northern Taiwan are captured well. Likelihood of storm initiation (colored area) from TANC (Dark blue line: Observed convection for verification)

15 Total: h nowcasts TANC performance statistics 2014 afternoon thunderstorm cases Case date: No. of validation times Start-end 30 Jun (34) UTC 01 Jul (24) UTC 27 Aug (33) UTC 29 Aug (30) UTC UTC 09 Sep (20) UTC

16 TANC performance statistics TS, Bias, POD, FAR and and SR TS=CSI=h/(h+f+m) varies from 0 1(best) Bias=(h+f)/(h+m) 1~best POD=h/(h+m) varies from 0 1(best) FAR=f/(h+f) varies from 0(best) 1 Success ratio (SR)=1-FAR varies from 0 1(best)

17 TANC performance for 2014 Sensitivity of scores to likelihood thresholds - Score median with 95% confidence interval The range of confidence interval is not large, so the verification results are robust.

18 TANC performance statistics TANC vs. STMAS-WRF(GFS) TANC STMAS-WRF STMASWRF: CV threshold = 35 dbz TANC: Prob. threshold = 0.6

19 Summary The performance of the daily real-time prediction in 2014 warm season shows the TANC has the basic ability to capture when and where the afternoon thunderstorm will initiate. According to the statistics of the 2014 cases, TANC has the better forecast skill compared to the hot-start model STMAS-WRF. The statistics show that the area covered by likelihood 0.6 is suggested to depict where the storm will occur. Proper interpretation of such nowcasts both spatially and temporally is needed to use TANC output as the guidance for nowcast operation.

20 Future work The dynamic forecast predictors(such as vertical velocity, wind convergence, moisture convergence) from high-resolution analysis will be investigated to improve the 0-1 hour nowcast. The TANC forecast predictors for the other weather regimes with strong synoptic forcing will be developed since 2016.

Auto-Nowcast System Tom Saxen (July) Huaqing Cai (Aug) National Center for Atmospheric Research

Auto-Nowcast System Tom Saxen (July) Huaqing Cai (Aug) National Center for Atmospheric Research Summer 2006 ATEC Forecaster Conference Photo courtesy of Greg Thompson Overview: Introductory comments on