Probabilistic Quantitative Precipitation Forecasts for Tropical Cyclone Rainfall

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1 Probabilistic Quantitative Precipitation Forecasts for Tropical Cyclone Rainfall WOO WANG CHUN HONG KONG OBSERVATORY IWTCLP-III, JEJU 10, DEC 2014

2 Scales of Atmospheric Systems

3 Advection-Based Nowcasting Systems HKO SWIRLS JMA VSRF BoM SPROG / STEPS UKMO GANDOLF / NIMROD / STEPS NCAR AutoNowcaster McGill U MAPLE

4 Principles of Advection-Based Nowcasting

5 Steps in Nowcasting 1. Quantitative Precipitation Estimation 2. Storm Motion Field Estimation by ROVER 3. Extrapolation by Semi-Lagrangian Advection 4. Products for Rainfall up to 6 hours

6 HKO s SWIRLS Nowcasting System

7 SWIRLS - Domain Based on single radar Domain Size: Input: 512 x 512 km Output: 256 x 256 km Grid 480 x 480 (about 500 m)

8 Location-specific Rainfall Nowcast Rainfall Forecast in half hour interval in the next two hours Displayed as icons and as maps Notifications!

9 Quantitative Precipitation Estimate (QPE) in SWIRLS

10 QPE in SWIRLS least square matching (Zawadzki 1987) based on latest radar reflectivity and raingauge data linear regression to find a & b: dbz i b 10log Ri 10log a y m updated every 5 min x rainfall accumulations estimated by integrating the rainfall rates at different times c dbz dbg

11 Quantitative Precipitation Forecast (QPF) in SWIRLS ROVER - Real-time Optical-flow by Variational method for Echoes of Radar Radar images pre-processed: Based on the VarFlow algorithm developed by Bruhn et al. (2003 & 2005)

12 More on ROVER the radar echo tracking algorithm There are 6 tunable parameters in ROVER: 1. Gaussian convolution for field smoothing s (SIGME) 2. Gaussian convolution for local vector field smoothing (thru the field gradients) r (RHO) 3. regularization parameter, i.e. the weight of smoothness constraint on motion field a (ALPHA) 4. min_scale for setting the finest spatial scale L f (MIN_SCALE) 5. max_scale for setting the coarsest spatial scale L c (MAX_SCALE) 6. the time interval for tracking radar echoes T r (INTERVAL_FOR_VARFLOW)

13 TC Module in SWIRLS Original Advection Scheme:

14 TC Module in SWIRLS Enhancement Method: Separate the motion of TC before radar echo tracking

15 TC Module in SWIRLS Improvements:

16 Probabilistic Location-Specific Precipitation Nowcast

17 Why? 1. Better support for Rainfall Warning System 2. Facilitate cost-benefit analyses 3. More tailored to the needs of organizations under various operational constraints

18 Experimental SWIRLS Ensemble Rainfall Nowcast By tuning the 6 parameters, 36 sets of parameters have been experimented, i.e. ensemble of 36 members.

19 Probabilistic QPF (PQPF)

20 PQPF Product 1 Rainfall Intensity Contour Map For Specific Exceedance Probability:

21 A Rain Storm Case: Time: 2014/5/8 11:00PM

22 PQPF Product 2 Probability Contour Map For Specific Intensity Threshold

23 A Rain Storm Case: Time: 2014/5/8 11:00PM Threshold rainfall intensity = 0.5mm/hr

24 A Rain Storm Case: Time: 2014/5/8 11:00PM Threshold rainfall intensity = 5mm/hr

25 A Rain Storm Case: Time: 2014/5/8 11:00PM Threshold rainfall intensity = 30mm/hr

26 480 Verification and Analyses Verified against Radar QPE data: resolution 480X480 pixels Generated every 6 minutes 480 One datum for each grid Data from March, April and May Data size ~ (Five Billion)

27 Verification and Analyses

28 Verification and Analyses

29 Results plotted as Reliability Diagram

30 Relative Operating Characteristics (ROC) Together with the observed QPE data and the contingency table, count the number of hits, false alarms, misses or correct negatives Plot probability of detection (POD) vs probability of false detection (POFD) Contingency Table Yes Observed No Yes hits false alarms Total forecast yes POD = hits hits + misses [1] Forecast No misses correct negatives forecast no POFD = [1] false alarms correct negatives + false alarms Total observed yes observed No Total

31 POD Results shown in Relative Operating Characteristics 1 Relative Operating Characteristic [CELLRANGE] [CELLRANGE] [CELLRANGE] 0.9 [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] 0.8 [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] 0.7 [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] 0.6 [CELLRANGE] [CELLRANGE] [CELLRANGE] 0.5 [CELLRANGE] [CELLRANGE] [CELLRANGE] 0.4 [CELLRANGE] [CELLRANGE] [CELLRANGE] 0.5mm 5mm 30mm no skill 0.1 [CELLRANGE] 0 [CELLRANGE] POFD

32 Conclusions 1. This study introduces a method to generate probabilistic rainfall nowcast, by perturbing motion vectors of radar echoes. 2. Arrives at fairly reliable PQPF for light and moderate rainfall, though somewhat overestimated heavy rain 3. ROC curves indicate an capability to distinguish between rain and no-rain

33 Future Work Verification for Other Seasons Verification for Land-falling TCs

Challenges and Advances related to TC Rainfall Forecast. WOO Wang-chun Hong Kong Observatory IWTCLP-III, Jeju, Korea 10 Dec 2014

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