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

tianfy@cma.gov.cn

15 September 2009

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:

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

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

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

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

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

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.

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.

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

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

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

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

A B C D

Observation

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

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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

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

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

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

Comparison of box and whisker plots for 22 July 2008 at Huaibin station. Black circles are observations (56.2mm).

Comparison of box and whisker plots for 16 July 2008 at Huaibin station. Black circles are observations (25.8mm).

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

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

• 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

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?

Thanks for your attention!

Contact: tianfy@cma.gov.cn

• 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

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 .