thorpex asia, kunming, 1 nov 2012

Process of User-oriented interactive

flooding-leading rain forecast system

Chen Jing1 Zhongwei Yan2 Jiarui Han3 Jiao Meiyan4

1. Numerical Weather Prediction Center, CMA

2. RCE-TEA, Institute of Atmospheric Physics, Beijing

3. Research Center for Strategic Development, CMA

THORPEX Asia, Kunming, 1 Nov 2012

limit of predictability

Why user-oriented?

The meteorological model, as a chaotic system, is of

limited predictability. General improvement of large-scale

forecast has, asymptotically, been limited.

However, for a given user, at a specified scale, there is

still great potential of improvement, especially in the

context of ensemble forecast.

User’s needs &decision-making

information

Conceptual User-oriented Interactive Forecast System

Meteorologicalforecast system

What to be user-oriented?

Key variable Initial condition with sensitive perturbations

Target/local observationsKey decisions

DownscalingExperience-calibrationUser-based assessment

Climate background

Downscalingcomponent

Dynamicaldownscaling

Statistical downscaling

...

User-end professional

models

Hydrologicalmodels

Electricmodels

Targetingobservations

Observation

Assimilation

Forecast

Physical prediction component

Verifi-cation

Decision-making

Inte

ract

ion

s b

etw

een

for

ecas

ts

and

use

rs' n

eed

s

Dynamic

interaction

User-oriented

Dow

nsc

alin

g to

use

r-en

d

User-oriented assessing module

Global ensembleforecasts

Interaction

s betw

een d

ecision ap

plication

san

d u

ser-end

inform

ationInitial user-end module

Elements Temporal scale

Riskthreshold

Spatialscale

Forecasting Targets








...

User-end professional

models

Hydrologicalmodels

Electricmodels

Targetingobservations

Observation

Assimilation

Forecast

Physical prediction component

Verifi-cation

Decision-making

Inte

ract

ion

s b

etw

een

for

ecas

ts

and

use

rs' n

eed

s

Dynamic

interaction

User-oriented

Dow

nsc

alin

g to

use

r-en

d

User-oriented assessing module

Global ensembleforecasts

Interaction

s betw

een d

ecision ap

plication

san

d u

ser-end

inform

ationInitial user-end module


Riskthreshold

Spatialscale

Forecasting Targets

Initial user-end module


Riskthreshold

Spatialscale

Forecasting Targets

Components in User-oriented Interactive Forecast System

How user-end information could provide a dynamic forecast target

for forecast system?

Focus on dynamic flood-leading rainfall thresholdin Wangjiaba sub-basin

^

130°E120°E110°E100°E90°E80°E70°E

50°N

40°N

30°N

20°N

10°N 4

^ Sheet1$ Events降水站点淮河流域

121E108E

38N

28N

120 stations

Observation:1 Jun.-31 Sep. 2003-2010

Target region ： Wangjiaba sub-basin

Wangjiaba Sluice

Precipitation station

Huaihe river basin

3×3 grid boxes

Target region ： Wangjiaba sub-basin

Huaihe river basin

Hydrological user’s experience:

Heavy rainfall over 50mm/day usually causes floods

in the next days;

However, less heavy rainfall may lead to floods if

there has been rainfall in preceding days

Hydrological user’s need: flood-leading rainfall forecast (considering 3 factors)

preceding rainfall : determines to some extent the current local soil

water content, among other hydrological conditions The effective preceding

rainfall is defined as: Pan = (Pan-1 +γPan-2) ×γ, where Pan is the effective

preceding rainfall for day n counting from the first day of the flood season, Pan-

1 is the same quantity for a day before, and γ= 0.85 is an empirical coefficient

based on users' experience in Linyi. The effective preceding rainfall is then

iteratively estimated as Pan = (Pan-1 + Pan-2×0.85)×0.85.

water levels: In general, the flood-leading risk increases as the water level

rises.

stream flow: In general, the flood-leading risk increases as the water flow

increases.

Analyzing user-end flood risk, to figure out dynamic forecasting target for

forecasting system

Identify riskwater level

Exclusion of low-risk cases

build a regression model

Using 3 factors

Dynamic flood-leading rainfall

threshold

Precipitation Probabilistic

forecast

Risk assessment

Main research flowReliable suggest

feedback to end-user

risk

Flood Discharge

(m3/s)

Water Level

(m)

Preceding Rain

(mm)

Average rainfall

(mm)

75 percentile 708.75 24.305 41.616 5.302925

85 percentile 1094.5 25.9845 56.43375 11.58525

90 percentile 1520 26.909 67.676 17.0295

92 percentile 1674.4 27.4432 73.459 21.7

95 percentile 2080 27.9875 85.34675 28.7455

97 percentile 2392.9 28.3416 94.03475 38.5765

99 percentile 2750 28.8643 124.2975 58.14125

Water level, preceding rain, flood discharge and average rainfall, under a certain risk condition (a certain percentile

in statistical sense)

Risk IdentificationThe statistic relationship of Flood risk and 3 influence factors

RiskTarge

t

is also the Flood Limiting Water Level for Wangjiaba sluice

Flood Limiting water level

As the possibility of flood risk increasing, all three influence factors increase correspondingly.

In 76 cases (water level exceeded Flood Limiting water level in 1 Jun.-31 Sep. 2003-2010), minimum of preceding rain is 49.1mm.

) 水位超过汛限共 76个案例，其中区域的前期影响雨量超过 60mm 的

72个案例，占 94%。

Preceding rain has a significant import impact on flood risk in Wangjiaba sub-basin

All cases that water level exceeded Flood Limiting water level in 1 Jun.-31 Sep. 2003-2010

27

27.5

28

28.5

29

29.5

30

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73

个例

水位（

m）

40

60

80

100

120

140

160

180

前期影响雨量（

mm）

49.1mm

Water LevelPreceding Rain

Identify Dynamic Forecasting Target （ Flood Leading Rain Threshold）

D1Preceding rain<49.1mm

D3Preceding rain≥49.1mm

Water level>=24.5

FLRT=50mm/dFLRT

(Flood leading rainthreshold)

???

Excluding low-risk cases for users, according to historical statistic results

we need to solve

D2Water level<24.5m

Water level gap in 24hrs hadn’t exceeded 3m, so 24.5m had no chance to increase to 27.5m (FLW) in target region

FLRT dynamic forecasting target —— based on regression model

Goal ： to quantify rainfall, which lead to water level increase to

Flood Limiting Water level (27.5m) from the nth day to the n+1th

day under a certain risk condition ( in a certain preceding rain,

discharge and rainfall), and to build a regression model based on

the historical cases (958 cases). And this rainfall is the dynamic

flood-leading forecast target.

Flood Limiting Water level

(27.5m)

FLRT——Dynamic forecasting targetHow much rainfall will

lead the water level up to Flood limiting water level on the n+1th day under current risk conditions

(preceding rain, discharge and rainfall)?

Water Level on the nth day

FLRT dynamic forecasting target —— based on regression model

Hence, the formula is 27.5—Wn=δ+αFLRTn+1+βQn+γPRn

In which ， 27.5 is the supposed water level on the n+1th day, Wn

is the known water level on the nth day;

•δ is a constant, equal to -0.01;

•FLRTn+1 is the FLRT on the n+1th day, its coefficient α=-0.387;

•Qn is flood discharge on the nth day, its coefficient β=1.486；

•PRn is the preceding rain on the nth day, PRn = （ PRn-1+PRn-

2×γ ） ×γ ， γ=0.85, its coefficient γ=0.713;

0

10

20

30

40

50

60

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 1011061111161212008年6月31日-9月30日

可能致洪降水阈值（

mm）

-2

0

2

4

6

8

10

与27

.5米的水位差（

m）

回归统计结果与27. 5的水位差

FLRT

Regression result

1 Jun.-31 Sep. 2003-2010

Regression resultWater level gap W

ater level gap to 27.5m

FL

RT

What TIGGE could provide?

TIGGECenter

(model name)Forecast

lengthmembers UTC

Period of forecasts used in the case

CMA (babj)

10 days 15 12 1Jun.-31 Sep. 2007-2010

ECMWF(ecmf)

15 days 51 12 1Jun.-31 Sep. 2007-2010

JMA(rjtd)

9 days 50 12 1Jun.-31 Sep. 2007-2010

NCEP

(kwbc)16 days 21 12 1Jun.-31 Sep.2007-2010

UKMO(egrr)

15 days 23 12 1Jun.-31 Sep.2007-2010

GrandEnsemble

- 160 12 1Jun.-31 Sep.2007-2010

114~121°E， 32~37°N, a 3°×3° grid-box Resolution 0.5°×0.5°

16

26

36

46

56

66

76

83% 86% 89% 92% 95% 98%

obgrandecmfbabj

0

5

10

15

20

25

30

53% 58% 63% 68% 73% 78% 83% 88%

obgrandecmfbabj

TIGGE Bias ---percentile distribution of the all TIGGE forecasts and

observations

0

50

100

150

200

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%百分位

(mm)

降雨量

ob

grand

ecmf

babj

If TIGGE forecast are accurate, the distribution of TIGGE forecasts and OB are almost the same. But there exists systematic forecast bias in all ensemble system, especially for more than 14.6mm. For this systematic bias, How to calibrate the bias ?

14.6mm

Distribution Calibration Method When samples size t was sufficiently large, precipitation observations on

user-end could form a distribution Ot, ,correspondingly precipitation

forecasts could also form a forecasts distribution Ft. . Because of

systematic forecast bias, on the same x percentile, forecast Pf was

different from observation Pob, that is Ft(x)≠ Ot(x).

If x<δ% ， Pf > Pob , if x>δ% ， Pf < Pob ； and if x=δ% ， Pf = Pob

theoretically, precipitation observations (Ot) and precipitation

forecasts (Ft ) were identically distributed, Ft = Ot（ Gneiting et al.,

2007） . That is, in the same x percentile, forecast Pf and

observation Pob should be the same.

Therefore, supposing (Ot) and precipitation forecasts (Ft ) were

identically distributed, let Ft(x)= Ot(x) in the same x percentile to

calibrate the forecast on user-end.

ETS verification results

Perfect score is 1; and 0 means no skill.

After calibration, forecasts improved.

BIAS Score

After calibration, all ensemble forecasts improved.

Perfect score is 1

Brier Score

0 is perfect score, and all ensemble

forecasts improved after calibration

User-oriented Interactive Forecasting System

Preliminary results

Dynamic Forecast Target——FLRT

FLRT in Regression method

The gap of water level

FLRT in Hydrological model method



the dynamic FLRT reflect a change of flood-risk on user-end, but it ignored the low-risk cases, which is the different from the hydrological model. And it not only shows users to prevent high-flood-risk cases, but

provides a forecast target for forecast system (TIGGE).

The gap of water level to 27.5m



1Jun.-31 Sep. 2008

FLRT v.s. TIGGE grand ensemble mean



TIGGE ensemble mean

Although, there are several heavy rainfall events in 1Jun.-31 Sep. 2008, not every heavy rain could lead to a flood-risk. TIGGE ensemble mean could catch some

heavy rainfall events but not flood-leading events.

Flood Leading rain risk probabilistic forecast

TIGGE

Grand

Ensemble

（ 162

member

s ）

-30

0

30

60

0 20 40 60 80 100 1200

20

40

60

80

100

120

140

Days since June 1, 2008

Percen

tag

e o

f R

isk

Po

ssib

ilit

y

Members > Threshold All TIGGE Members

Precip

ita

tio

n T

hresh

old

(m

m)

the predicted probability of occurrence of the FLR events in Wangjiaba sub-basin, based on TIGGE grand ensemble forecast and

the dynamic FLRT with the user-end information.

Conclusion

Forecasting flood-leading rainfall at a specific user-scale is feasible with TIGGE data, as long as the ensemble products are

well analyzed according to user-end information.

Thank you

thorpex asia, kunming, 1 nov 2012

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