typhoon forecasting and qpf technique development in cwb kuo-chen lu central weather bureau

Typhoon Forecasting and QPF Technique Development in

CWB

Kuo-Chen LuCentral Weather Bureau

2

QPF in CWBTyphoon Nora (7315)

Strong gale and 50 ~ 100 mm in South Taiwan are expected

OutlineDefinition

QPF is the expected amount of melted precipitation accumulated over a specified time period over a specified area

DescriptionForecast Products and the performance

Advance approach by ensemble QPFEnsemble Typhoon QPF

Pattern Recognition by Convection Pattern

Conclusion

Grid QPF since 2006

Verification system operates in real time

Issued twice a day on 00z and 12z2.5 km resolution for next 24 hours.

0-12h 12-24h

Verification on 24-h QPF

T.S. improve gradually, Bias reduce significantly on 50 mm

Threat Score Bias

2006

2007

2008

2009

2010

2011

2012

2013

2014

0

0.1

0.2

0.3

0.4

0.5

0.6

2006200720082009201020112012201320140

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5mm

D1: 45-km 222 X 128

D2: 15-km 184 X 196

D3: 5-km 151 X 181

5km

Terrain Peak: 3,271m/3,952m

4 runs/day, 6-hourly updated

(Lee, Hong etc, MIC, CWB)

WRF Prediction System

OBS

Examples of the member QPFs with similar typhoon locationIf the designed is good enough, the answer of the forecast is just inside the stamps

7

80 Ensemble members a day for QPF

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

Advance strategy for Ensemble product

Selected consensus by Typhoon location for typhoon

QPF by convection pattern for

Nowcasting

9

• 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 forecast10

Member of QPF map selected by

Typhoon location surrounding the official typhoon forecast track

(Ensemble Typhoon Quantitative Precipitation Forecast, ETQPF)

Subjective Forecast: ETQPF on different Track

TY Fung-wong (2014)TY Matmo (2014)

Obs

ETQPF

Obs

ETQPF

ETQPF performance for the 2014 typhoons

Max:956 mm

Max:995 mmIf track is trustable.ETQPF is also trustable

Hong (2015)

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 circulationDepend on case, has the potential to capture the mesoscale precipitation process

WeaknessUncertainty to the track forecast

Official track forecast still better than the model forecastDifficult to configure a model that performs THE BEST all the time

Uncertainty from the initial condition and the model physical processLimitation 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

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

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

Radar CV05/15 1200Z

0-6 -3 63

05/15 00Z

4 lag runs

05/14 18Z

05/14 12Z

05/14 06Z

12 h126 9 1815

18 21 24 3027

24 27 30 3633

12 15 18 2421

Frmo ensemble member N (N=1, 2, …, 22)forecast at target time (say 0 hr)

Sampling, Recognizing and Ranking

TOP 1 ~ 20 by ranking the similarity of Radar CV

Similar Convection Pattern in EPS Case of Meiyu Front

3 hours QPF according to Rank 1 ~ 20

QPF from Similar Convection Pattern in EPS Case of Meiyu Front

3 hours QPF according to Rank 1 ~ 20

QPF from Similar Convection Pattern in EPS Case of Meiyu Front

ETS

3-hourly QPF Verification for consensus During a Mei-Yu Front period in May 2015

Selected consensus (top 20%)

simple consensus0

1.0

Th

reat

Sco

reFcst. hours

Selected Consensus

3 6

Simple Consensus

from 19th12UTC to 25th12UTC May 2015Threshold : above 10 mm/3-h

3 6 9 12

3 6 9 12

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ConclusionMulti-stage Quantify Precipitation Forecast

Weekly (Qualitative)GFS, Synaptic Analysis, statistics,

analogy, conceptual model1-3 daily QPF (Quantitative)

Regional (Ensemble) Forecast System, Statistics, advanced Ensemble Forecast0-6 hr (or 0-12 hr)

QPFLAPS/STMAS, ARPS, VDRAS,

Cloud model0-1 hr QPFRadar extrap., ANC,

SCAN0 hr

NowcastingRadar, gagues,

lighting

Storm scale data assimilation

Traditional 3d/4d data assimilatoin

Radar Extrapolation

Pattern Recognition

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