Chung-Chieh Wang and Chien-Chang Huang Department of Earth Sciences, National Taiwan Normal University,

Taipei, Taiwan

2015 CWB Conference on Weather Analysis and Forecasting 15-17 September 2015, CWB, Taipei, Taiwan

Understanding Extreme Typhoon Quantitative Precipitation Forecasts (QPFs)

in Taiwan by the 2.5-km CReSS Model: An Update

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Presentation outline

1. Brief Introduction of the CReSS Model

2. Weather Forecasts using the CReSS Model 2.1 Typhoon Forecasts at Cloud-Resolving Resolution 2.2 Quantitative Precipitation Forecasts (QPFs) and Evaluation

3. The Dependency Property and Performance 3.1 The More Rain, the Better the Model Performs (2010-2012) 3.2 Updated Results through the 2013 Season 3.3 Implications in the evaluation and use of model QPFs

4. Summary and Conclusion

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Cloud-Resolving Storm Simulator (CReSS): A cloud model developed by Prof. K. Tsuboki of the Hydrospheric Atmospheric Research Center (HyARC), Nagoya University, Japan

Nonhydrostatic and compressible equation set, terrain-following vertical coordinate based on height (with stretched level spacing), no nesting

1. Brief Introduction of the CReSS Model

water vapor

graupel rain water snow

cloud water cloud ice

Nucleation: Deposition/sorption, condensation-freezing, homogeneous process, contact, secondary

Phase change: Deposition, evaporation, sublimation, melting, freezing

Particle interaction: Falling, collection, conversion, aggregation, water shedding

Use only explicit cloud microphysics, no cumulus parameterization

Bulk cold rain scheme in CReSS

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Real-time (and all past) results available at http://cressfcst.es.ntnu.edu.tw

2.1 Typhoon Forecasts at Cloud-Resolving Resolution

Current forecasts (40L) every 6 h out to 78 h: 5 km (216 x 180) 2.5 km (600 x 480)

Routinely provided to TTFRI of Taiwan as the only cloud-resolving member (∆x = 2.5 km)

NTNU/Department of Earth Sciences – CReSS 2.5km Realtime Forecast

(Current 2.5-km domain: 1500 km x 1200 km)

domain in 2010-2011

Using NCEP GFS 1° x 1° analyses/forecasts as IC/BCs (0.5° x 0.5° since 2013)

2. Weather Forecasts using the CReSS Model

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Real-time forecast website at http://cressfcst.es.ntnu.edu.tw

5-, 3.5-, 4-, and 5-km forecasts through the years since 2006

2.5-km 3-day forecasts since 2010 (currently 1500 km x 1200 km)

2.5-km 8-day forecasts (currently 1860 km x 1360 km)

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Commonly-used verification methods for QPFs: Both subjective (visual) and objective verifications Widely-used skill scores: Threat score (TS), bias score (BS), probability of

detection (POD), and false-alarm rate (FAR) 24-h accumulative rainfall (day 1, 2, 3), from forecasts starting at 0000 or 1200 UTC Rainfall thresholds: 0.05 to 1000 mm Evaluated on rain-gauge sites (about 400

points) with equal weight

2.2 Quantitative Precipitation Forecasts and Evaluation

FCST

OBS

HIT

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For Fanapi (2010): Best track and observed daily rainfall over Taiwan

D 9/17

A 9/19

Fanapi (2010)

C 9/20

B 9/18

3.1 The More Rain, the Better the Model Performs

B

A

C D

CReSS initial time

3. The Dependency Property and Performance

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Real-time 2.5-km forecasts made at 9/17 00 UTC, for Fanapi (2010), from 9/18 06 UTC to 9/20 00 UTC (t = 30-72 h):

9/19 00-20 LST

1080 mm >800 mm

Model forecasts of MSLP, sfc winds, and 15-min rainfall (mm)

Radar reflectivity composite (dBZ)

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Real-time 2.5-km forecasts made at 9/17 00 UTC, for Fanapi (2010):

OBS Day 1 Day 2 Day 3

Day 1 Day 2 Day 3

A

Threshold (mm) Threshold (mm)

1110 mm

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For Meranti (2010) and Doksuri (2012): Best track, rainfall, and TS scores

Day 1 Day 2 Day 3

6/26 00 UTC

6/27 00 UTC

6/28 C

Doksuri (2012)

C

Threshold (mm) Threshold (mm)

9/7 00 UTC

9/8 00 UTC

Meranti (2010)

B

C D

9/9 B

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Identify 24-h segments (00-24 or 12-12Z) from all 15 TCs in 2010-2012 During warning periods (both sea and land warnings) issued by CWB Part of rainfall must link to TC (e.g., outer circulation, distant rainband) At least one segment from each case A total of 99 segments identified (50 00-24Z and 49 12-12Z ones)

Model QPFs often show stronger dependency on rainfall amount than range Classify target periods based on observed rainfall amount

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Classify the above 99 segments based on observed rainfall Groups A, B, C, and D from the most to the least overall amount At least 50 sites (~1/8) ≥ 100 mm (A), 50 mm (B), 25 mm (C), or not (D) Top 5 cases: Most-rainy segment from Fanapi, Megi, Nanmadol, Saola,

and Tembin, one from each typhoon, a subset of group A (hazardous)

2 killed, USD 150 M 38 killed, USD 43 M

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Results of classification: Sample size (number of segments): A-D: 26, 21, 26, and 26 Most rain (A) to least rain (D) received, high potential for hazards only

on specific days (i.e., the top 5 cases)

7 killed, USD 40 M

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Averaged TS among the 5 groups: Strong dependency on rainfall amount Th

reat

scor

e

0.67 0.67 0.58

0.51

0.32

0.62 0.52

0.38 0.30

0.21

0.75 0.73

0.57

0.42

0.17

0.65 0.53

0.30 0.22

0.11

0.64 0.57

0.37 0.33 0.22

0.49 0.38

0.22 0.14

0.07

The heavier the rain, the better model performs!

For top cases, scores of day-3 QPFs not much lower, with lead-

time of 2-2.5 days!

Day 3

Threshold (mm) Threshold (mm)

Day 2

Day 1

• 0.27

• 0.15

• • • •

•

0.82 0.73

0.48

0.81 0.86

• • • •

•

0.87

0.69

• 0.50

• 0.38

0.82 0.83 0.93

Most rain

Least rain Lower skill, but not hazardous (not important)

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Thre

at sc

ore

0.67 0.67

0.58 0.51

0.32

0.62 0.52

0.38 0.30

0.21

Lower skill, but not hazardous!

Obs

erve

d ba

se ra

te (%

)

Non-repre-sentative at mid-to-high thresholds,

Sample sizes often very small (O/N < 1-3%) above 100-mm threshold!

Threshold (mm)

Threshold (mm)

Morakot • • •

•

•

Day 1

x x

x x

Average x x x x

x x x x x

x x

and predictability is low anyway

• •

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3.2 Updated Results through the 2013 Season Add 6 typhoons and 54 segments from the 2013 season (total: 153) Classify into A, B, C, D groups using identical scheme

Add 3 top cases: Most-rainy segment from Soulik, Trami, and Kong-Rey

3 killed, USD 49 M 6 killed, USD 30 M Compute scores after summing entries from all segments into one 2 x 2

contingency table

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For Soulik (2013): Best track and observed daily rainfall over Taiwan

Soulik (2013)

N/A 7/10 12Z

D 7/11 12Z

A 7/12 12Z

C 7/13 12Z

A

C

D

CReSS initial time

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Real-time 2.5-km forecasts made at 7/10 12 UTC, for Soulik (2013), from 7/12 00 UTC to 7/13 18 UTC (t = 36-78 h):

Model forecasts of MSLP, sfc winds, and 1-h rainfall (mm)

Radar reflectivity composite (dBZ)

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N/A 7/10 12Z

D 7/11 12Z

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Real-time 2.5-km forecasts made at 7/10 12 UTC, for Soulik (2013):

OBS

Day 1 Day 2 Day 3

FCST

Threshold (mm) Threshold (mm)

A 7/12 12Z

Max: 876 mm

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0.78 0.75 0.66

0.56

0.37

0.24

0.82 0.79

0.65 0.56

0.37

0.20

0.68 0.61

0.46 0.40

0.28

0.10

Day 1

Day 2 Day 3

21

0.73 0.62

0.38 0.32

0.22 0.12

A different classification scheme based on peak rainfall amount

0.83 0.77

0.65 0.55

0.37

0.21 •

• • •

•

0.82 0.73

0.48

0.81

• 0.15

0.27

• Morakot

0.82 0.77

0.62 0.54

0.35

0.21

• • • • 0.87 0.82 0.83

0.93

• 0.38 • 0.50

• 0.69

• Morakot

Day 1

Day 2 Day 3

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The Bias score: Only weak tendency of under-forecast at high thresholds toward day 3 Day 1

Day 2 Day 3

222

186

Total points at each threshold: 65631 Color number: observed pts (i.e., “O”)

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1

26

65631 31420 16012

7359 3448

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Without proper classification and evaluation of top events only, the ability of model QPFs in forecast of extreme events will never by understood

Superior performance by CReSS in predicting hazardous events in with strikingly high TS scores across the heavy-rainfall thresholds, even 2-2.5 days in advance with appreciable lead time for emergency action

The high scores are possible due precisely to the dependency property, since Taiwan can receive extreme amount of rainfall when hit by a TC

The ability of model QPFs in the forecasts of hazardous and extreme events is crucial for obvious reasons, but can be assessed only by: Scores for target periods with similar (extreme) magnitude through

proper classification, since the skill is different for smaller events The results of evaluation for any given case (usually significant) are

contaminated if the scores are averaged over multiple forecasts with equal weights, as those for small (more frequent) events are not important (non-hazardous), not statistically representative (from fewer points) and also lower (due to low predictability)

3.3 Implications in the Evaluation and Use of Model QPFs

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For forecasters, they should raise their confidence in model when it predicts a stronger event, not lower it, although real-time verification is always essential for model prediction to be used wisely and effectively

A B C D

Day 1 Day 2 Day 3

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The common belief that the models have little ability to predict extreme rainfall events is a misperception and the truth is exactly the opposite!

The (cloud-resolving) models are at their best in forecast large events due to the dependency property: The more rain, the higher the scores, and the better the model performs

However, such ability can only be understood by evaluation of target periods with extreme rainfall through proper classification

Operational 2.5-km CReSS shows very high skills in typhoon QPFs in 2010-2013, especially for the top cases (roughly top 5% with high hazard potentials) with appreciable lead time:

4. Summary and Conclusion

Event Threshold 50 mm 130 mm 200 mm 350 mm 500 mm

Morakot Day 1 (0-24 h) 0.81 0.82 0.73 0.48 0.27

Day 2 (24-48 h) 0.83 0.82 0.87 0.69 0.50

Top cases (2010-2013)

Day 1 (0-24 h) 0.75 0.66 0.56 0.37 0.24

Day 2 (24-48 h) 0.79 0.65 0.56 0.37 0.20

Day 3 (48-72 h) 0.61 0.46 0.40 0.28 0.10

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The above scores are strikingly high, precisely due to the dependency property, since Taiwan can receive extreme rainfall when hit by a typhoon The rainfall amount from Morakot (2009) approached the world record,

so the scores for it must be among the highest in the world (TS up to 0.38 at 1000 mm)

Heavy rainfall is phase locked by the topography of Taiwan and mainly controlled by large-scale factors (TC track, structure, monsoon, etc.), and they can be properly handled by the cloud-resolving models

Users need to trust the model more when extreme rainfall is predicted, not less, while real-time verification is essential in making judgments

Due to this key property of better model performance in larger events, our future in cloud-resolving model QPFs for extreme rainfall is bright!

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Thank you for listening! Questions?