predicted rainfall estimation in the huaihe river basin based on tigge
DESCRIPTION
Predicted Rainfall Estimation in the Huaihe River Basin Based on TIGGE. Fuyou Tian, Dan Qi, Jingyue Di, and Linna Zhao National Meteorological Center of China Meteorological Administration [email protected] 15 September 2009. Outline:. Data and Test Catchment - PowerPoint PPT PresentationTRANSCRIPT
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Predicted Rainfall Estimation
in the Huaihe River Basin
Based on TIGGE
Fuyou Tian, Dan Qi, Jingyue Di,
and Linna Zhao
National Meteorological Center of China Meteorological Administration
15 September 2009
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1. Data and Test Catchment• TIGGE 3 centers (CMA, ECMWF and NCEP) total precipitation data
• Huaihe River Basin, and its sub-catchment
• 19 Observations in Dapoling-Wangjiaba Reservoir
2. Method • Threat Score, Bias Score and Brier Score
• Percentile
3. Results • TS, B and BS
• Probabilistic forecast of Huaihe River Basin
• Percentile-based precipitation probabilistic forecast
4. Summary and future works
Outline:
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Meteorological Centers of TIGGE Data
CenterCountry/Domain
Model Ensemble Members
Spatial Resolution Forecast Length
National Center for Environmental
Predictions (NCEP)
China Meteorological Administration
(CMA)
European Center for Medium-Range
Weather Forecasts (ECMWF)
United States
China
Europe
T126
T213L31
T399L62T255L62
21
15
51
1°*1°
0.5625°*0.5625°
1°*1°
16
10
1-1011-15
•Total precipitation data from July 1 to August 6, 2008•Accumulated rainfall from 00:00 to 00:00(GMT) of the next day
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The Test Catchment and Observation Stations
•Distribution of 19 observation stations in the Dapoling-Wangjiaba sub-region
•Cumulative rainfall of every observation station from 00:00 to 00:00 of the next day
•Using the bilinear interpolation method to obtain the grid value
颖河 -阜阳
涡河 -蒙城以上
南四湖区 沂沭水系
蚌埠 -洪泽湖
王家坝 -蚌埠Target basin
大别山库区
淮河下游
Huai-bin
Xi-xianWang-jia-ba
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0. 0
10. 0
20. 0
30. 0
40. 0
50. 0
60. 0
70. 0
80. 0
90. 0
100. 0
01 Ju
ly02
July
03 Ju
ly4 J
uly5 J
uly6 J
uly7 J
uly8 J
uly9 J
uly10
July
11 Ju
ly12
July
13 Ju
ly14
July
15 Ju
ly16
July
17 Ju
ly18
July
19 Ju
ly20
July
21 Ju
ly22
July
23 Ju
ly24
July
25 Ju
ly26
July
27 Ju
ly28
July
29 Ju
ly30
July
31 Ju
ly01
Aug
ust
02 A
ugus
t3 A
ugus
t4 A
ugus
t5 A
ugus
t6 A
ugus
t
The Variation of Daily Areal Rainfall(mm)
Extreme event
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1. Data and Test Catchment• TIGGE 3 centers (CMA, ECMWF and NCEP) total precipitation data
• Huaihe River Basin, and its sub-catchment
• 19 Observations in Dapoling-Wangjiaba Reservoir
2. Method • Threat Score, Bias Score and Brier Score
• Percentile
3. Results • Probabilistic forecast of Huaihe River Basin
• Percentile-based precipitation probabilistic forecast
4. Summary and future works
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Criteria Adopted for Calculation of Rainfall Intensity
• To calculate the threat score, bias score and brier score, observations and forecasts of daily rainfall are divided into four classes, but very heavy rainfall are not included.
• The probabilistic and percentile rain not use this criteria, all rainfall intensities are take into consideration.
I II III VI<= 0.1 mm
No rain
0.1mm~10.0mm
Little
10.0mm~25.0mm
Moderate
25.0mm~50.0mm
Heavy
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Threat Score (TS), Bias (B) and Brier Score (BS)
• The Threat Score ( or CSI: Critical Success Index) and Bias (B) are given as (Wilks, D S, 1995)
TS = a / (a + b + c) B = (a +b)/ (a + c) where a, b and c represents hits, false alarms , and misses, respecti
vely. TS varies from 0.0 to 1.0, 1.0 indicates the perfect forecast. B=1.0 indicates that the event was forecast the same number of times it was observed.
• The Brier Score is defined as
BS = (fi - oi)2/N
in which N is the sample size, the observations oi are all binary, 1.0 if the event occurs and 0 if it doesn’t. The BS ranges from 0 for a perfect forecast to 1.0 for the worst possible forecast.
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Percentile
• Firstly set the data in increasing order, a percentile is the value of a variable below which a certain percent of observations or values fall.
• Values of the target percentiles are estimated using the experience-based equations (Hyndman R J, et al 1996)
Qi(p) = (1 - r) X (j) + r X (j+1)
in which j = integer (p*n + (1+p)/3)
r = p*n + (1+p)/3) – j
where Qi(p) presents the returned ith percentile, n is the sample number, X the ordered data.
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1. Data and Test Catchment• TIGGE 3 centers (CMA, ECMWF and NCEP) total precipitation data
• Huaihe River Basin, and its sub-catchment
• 19 Observations in Dapoling-Wangjiaba Reservoir
2. Method • Threat Score, Bias Score and Brier Score
• Percentile
3. Results • TS, B and BS • Probabilistic forecast of Huaihe River Basin
• Percentile-based precipitation probabilistic forecast
4. Summary and future works
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0. 0
0. 5
1. 0
1. 5
2. 0
2. 5
3. 0
3. 5
4. 0
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
Bias Score
CMA EC NCEP GrandE
0. 0
0. 1
0. 2
0. 3
0. 4
0. 5
0. 6
0. 7
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
Th
reat
Sco
re
CMA EC NCEP GrandE
I II III IV
Results: TS and B of Ensemble Mean over the sub-region
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Results: TS and B of Control Forecast Member over the sub-catchment
0. 0
0. 5
1. 0
1. 5
2. 0
2. 5
3. 0
3. 5
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
Bia
s S
co
re
CMA EC NCEP GrandE
0. 0
0. 1
0. 2
0. 3
0. 4
0. 5
0. 6
0. 7
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
Th
reat
Sco
re
CMA EC NCEP GrandE
I II III IV
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0. 0
0. 1
0. 2
0. 3
0. 4
0. 5
0. 6
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
Brie
r Sc
ore
CMA EC NCEP GrandE
Results: Brier Score over Dapoling-Wangjiaba
I II III IV
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A B C D
Observation
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A B C D
Observation
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113.2 113.6 114.0 114.4 114.8 115.2 115.6
lon
31.4
31.6
31.8
32.0
32.2
32.4
32.6
32.8
33.0
33.2
33.4
33.6
lat
2008072308观 测
70.1
92.4
129.2
70.2
109.0
113.1
121.4
129.546.2
51.6
70.4
80.6
112.1
99.0
196.0122.3
56.2
83.5
90.2
11 3 .2 11 3 .6 11 4 .0 11 4 .4 11 4 .8 11 5 .2 11 5 .6
lon
31.4
31.6
31.8
32.0
32.2
32.4
32.6
32.8
33.0
33.2
33.4
33.6la
tC M A_FC ST_24H r_20080723
52.5
53.0
50.8
51.4
49.4
33.1
36.0
40.125.3
20.0
19.0
30.6
13.0
21.4
42.446.2
16.4
14.1
10.7 11111111111111111111111111111111111111111111111111
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111111111111111
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113.2 113.6 114.0 114.4 114.8 115.2 115.6
lon
31.4
31.6
31.8
32.0
32.2
32.4
32.6
32.8
33.0
33.2
33.4
33.6
lat
EC _FC ST_24H r_20080723
95.8
101.1
103.2
97.0
91.7
114.6
85.7
107.677.3
70.0
73.6
90.7
54.2
94.9
94.3107.3
63.7
55.1
44.5
11 3 .2 11 3 .6 11 4 .0 11 4 .4 11 4 .8 11 5 .2 11 5 .6
lon
31.4
31.6
31.8
32.0
32.2
32.4
32.6
32.8
33.0
33.2
33.4
33.6
lat
N C EP_FC ST_24H r_20080723
79.7
81.5
82.8
79.6
77.8
73.1
76.1
79.669.0
62.7
61.9
71.3
54.2
66.8
80.985.1
56.2
55.4
46.6
11 3 .2 11 3 .6 11 4 .0 11 4 .4 11 4 .8 11 5 .2 11 5 .6
lon
31.4
31.6
31.8
32.0
32.2
32.4
32.6
32.8
33.0
33.2
33.4
33.6
lat
G randE_FC ST_24Hr_20080723
93.3
94.2
93.3
91.0
85.8
95.5
80.9
90.872.3
68.6
71.0
81.0
52.1
89.4
86.698.1
57.7
53.9
44.6
The 95th percentile precipitation of the 19 observation stations in the Dapoling-Wangjiaba sub-region with a 1 day lead time of three
EPSs and their grand ensemble.
2008072300 observation
A B C
DObservation
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11 3 .2 11 3 .6 11 4 .0 11 4 .4 11 4 .8 11 5 .2 11 5 .6
lon
31.4
31.6
31.8
32.0
32.2
32.4
32.6
32.8
33.0
33.2
33.4
33.6la
tC M A_FC ST_48H r_20080723
53.5
53.6
47.4
49.2
45.2
45.7
33.6
40.626.9
24.6
25.2
34.3
18.2
31.8
38.442.3
19.1
18.5
15.1
11 3 .2 11 3 .6 11 4 .0 11 4 .4 11 4 .8 11 5 .2 11 5 .6
lon
31.4
31.6
31.8
32.0
32.2
32.4
32.6
32.8
33.0
33.2
33.4
33.6
lat
EC _FC ST_48H r_20080723
155.3
152.8
144.9
148.2
126.8
104.3
110.8
125.814.2
65.0
67.6
101.6
47.8
86.5
129.5139.8
55.4
47.1
39.1
11 3 .2 11 3 .6 11 4 .0 11 4 .4 11 4 .8 11 5 .2 11 5 .6
lon
31.4
31.6
31.8
32.0
32.2
32.4
32.6
32.8
33.0
33.2
33.4
33.6
lat
N C EP_FC ST_48H r_20080723
48.3
49.0
42.6
42.9
40.2
53.0
39.8
42.840.7
42.2
42.9
41.4
43.2
44.0
40.843.2
38.6
42.3
40.5
11 3 .2 11 3 .6 11 4 .0 11 4 .4 11 4 .8 11 5 .2 11 5 .6
lon
31.4
31.6
31.8
32.0
32.2
32.4
32.6
32.8
33.0
33.2
33.4
33.6
lat
G randE_FC ST_48H r_20080723
147.9
146.0
127.8
138.1
118.3
86.9
99.6
115.973.4
59.3
59.5
85.7
46.7
72.9
117.3134.4
45.6
45.6
38.8
The 95th percentile precipitation of the 19 observation stations in the Dapoling-Wangjiaba sub-region with a 2 day lead time of three EP
S and their grand ensemble.
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113.2 113.6 114.0 114.4 114.8 115.2 115.6
lon
31.4
31.6
31.8
32.0
32.2
32.4
32.6
32.8
33.0
33.2
33.4
33.6
lat
2008072308观 测
70.1
92.4
129.2
70.2
109.0
113.1
121.4
129.546.2
51.6
70.4
80.6
112.1
99.0
196.0122.3
56.2
83.5
90.2
2008072300 observation
A B C
DObservation
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A comparison of probabilistic forecast of daily areal rainfall of the three EPSs and their grand ensemble with a 1 day lead time. The 5th, 25th,50th, 75th, 95th and 99th percentile o
f daily rainfall are shown, black circles are observations.
A B
C D
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Site Scale probability forecast
Probabilistic forecast of Daily precipitation of Huaibin Station (115.41 32.45) with a 1 day lead time
A B
C D
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Comparison of box and whisker plots for 22 July 2008 at Huaibin station. Black circles are observations (56.2mm).
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Comparison of box and whisker plots for 16 July 2008 at Huaibin station. Black circles are observations (25.8mm).
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1. Data and Test Catchment• TIGGE 3 centers (CMA, ECMWF and NCEP) total precipitation data
• Huaihe River Basin, and its sub-catchment
• 19 Observations in Dapoling-Wangjiaba Reservoir
2. Method • Threat Score, Bias Score and Brier Score
• Percentile
3. Results • Probabilistic forecast of Huaihe River Basin
• Percentile-based precipitation probabilistic forecast
4. Summary and future works
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Summary
• TS and B indicate that every EPS has its advantage, CMA is good at forecast little rain, EC is good at moderate rain
• BS indicates that grand ensemble take all the probabilities into consideration, and improves the performance
• Probability of daily rainfall exceeding 25mm/24hrs and 50mm/24hrs show that grand ensemble depicts the spatial distribution well
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• Variation of daily areal rainfall and site scale forecast indicate that grand ensemble has special advantage
• For forecasters who know little about the performance of every EPS, grand ensemble would be a good choice
• For hydrological users who pay special attention to key observation stations, grand ensemble based probabilistic forecast would be a good tool
Summary
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WRF 3D-Var (15km×15km)
ProbabilisticProbabilistic flood flood
forecastsforecasts
Future works
VIC model
precipitation
temperature
How to effectively use probabilistic forecast as input?
How to show the probabilistic flood forecast?
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• A low pressure locates at the SW of Huaihe River Basin,
wind shear shows a cyclonic vorticity clearly.
Synoptic analyses of 00:00 22 July 2008
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Synoptic analyses of 12:00 22 July 2008
• The low pressure and the cyclonic vorticity move slowly to the south-east, and produced very heavy localized rain .