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Process of User-oriented interactive flooding-leading rain forecast system Chen Jing 1 Zhongwei Yan 2 Jiarui Han 3 Jiao Meiyan 4 1. Numerical Weather Prediction Center, CMA 2. RCE-TEA, Institute of Atmospheric Physics, Beijing 3. Research Center for Strategic Development, CMA. - PowerPoint PPT Presentation
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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
Downscalingcomponent
Dynamicaldownscaling
Statistical downscaling
Downscalingcomponent
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
Initial user-end module
Elements Temporal scale
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
FLRT in Regression method
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
FLRT in Regression method
FLRT in Hydrological model method
1Jun.-31 Sep. 2008
FLRT v.s. TIGGE grand ensemble mean
FLRT in Regression method
FLRT in Hydrological model method
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
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