23/04/2008 1

Comparison of satellite rainfall estimates with raingauge data for Africa

David GrimesTAMSATAcknowledgementsRoss MaidmentTeo Chee KiatGulilat Tefera Diro

TAMSAT = Tropical Applications of Meteorology using SATellite data

23/04/2008 2

Use of raingauge data to evaluate satellite-based rainfall estimates for Africa

David GrimesTAMSATAcknowledgementsRoss MaidmentTeo Chee KiatGulilat Tefera Diro

TAMSAT = Tropical Applications of Meteorology using SATellite data

23/04/2008 3

Use of raingauge data to evaluate satellite-based rainfall estimates for Africa

David GrimesTAMSATAcknowledgementsRoss MaidmentTeo Chee KiatGulilat Tefera Diro

TAMSAT = Tropical Applications of Meteorology using SATellite data

23/04/2008 4

Overview of talk

The importance of rainfall monitoring for AfricaGround based observationsAlternative methodsThe validation problemCase studiesEnsemble approachConclusion

23/04/2008 5

improved nutrition

timely flood warning

famine mitigation

improved crop yields

better risk management

better long term economic

planning

better crop selection

improved short range rainfall

forecast

improved seasonal forecast

start

improved knowledge of

current climate

improved climate forecast

better understanding of

rainfall mechanisms

end

dry spells

total

How can meteorology help Africa?

23/04/2008 6

improved nutrition

timely flood warning

famine mitigation

improved crop yields

better risk management

better long term economic

planning

better crop selection

improved short range rainfall

forecast

improved seasonal forecast

start

improved knowledge of

current climate

improved climate forecast

better understanding of

rainfall mechanisms

end

dry spells

total

better rainfall

measurements

How can meteorology help Africa?

23/04/2008 7

Current state of African rainfall measurement

Africa raingauge station density = 1:26,000 km2

8x lower than WMO recommended minimum

23/04/2008 8

African rainfall variability

Spatial variability of rainfall events, Ibecetene Niger

mean = 15.7mm mean = 49.8mm

23/04/2008 9

Alternatives to raingauge data

Radar

Satellite

NWP model

- virtually non-existent in Africa for technical + economic reasons- cheap, full area coverage but no ‘perfect’ algorithm- cheap, full area coverage but problems with representation of African rainfall

23/04/2008 10

Satellite rainfall estimatesExample: TAMSAT operational product

Seasonal totalOct 2007 - Mar 2008

Seasonal anomalyOct 2007 - Mar 2008

23/04/2008 11

Useful protocolConvert satellite and gauge data to same spatial scaleFocus on areas with ‘sufficient’ gaugesAssess uncertainty on raingauge areal estimates

How can we evaluate the rainfall estimates?

Problemslack of independent data for validationhigh spatial variability of rainfall

g

mean = 49.8mm

gg

mean = 49.8mm

23/04/2008 12

Comparison of satellite and gauge data – conversion to pixel scale by kriging

• Rainfall zA for block A is computed as a weighted sum of surrounding gauge observations using block kriging• Weights λi depend on distance from pixel ri and strength of correlation at that distance • Kriging provides an uncertainty estimate for each gauge-pixelvalue

z1

r1

r2

z3

zA

r3

z2

1

1

;

1

n

t i ii

n

ii

z zλ

λ

=

=

=

=

∑

∑A

23/04/2008 13

Comparison of pixel average v. raw gauge data for Zambia

Zambian rainfall daily data1994-2001

y = 0.53x + 3.3xR2 = 0.87

0

20

40

60

80

100

120

140

160

180

0 20 40 60 80 100 120 140 160 180

gge/mm

pixe

l/mm

23/04/2008 14

Daily calibrations using pixel and gauge data for Zambia, March (10 gauges)

Calibration Zambia MarchDaily mean 10 gauges (all data)

y = 0.6608x + 0.8827R2 = 0.4624

0

5

10

15

20

25

30

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

CCD/h

gge/

mm

Calibration Zambia MarchDaily mean 10 gauge pixels (all data)

y = 0.566x + 1.3958R2 = 0.5925

0

5

10

15

20

25

30

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

CCD/h

pixg

ge/m

m

kriged gauge-pixel v RFE

raw gauge v RFE

R2 = 0.59

R2 = 0.46

23/04/2008 15

Evaluation of TAMSAT approach over Sahel

Dekadal rain gauge data interpolated to 1.00 grid using o data from Agrhymet (AMMA project ~ 600 gauges)o data from GPCC (60 gauges)

AMMA 1.0 deg grid points

0

5

10

15

20

25

30

-20 -15 -10 -5 0 5 10 15 20 25 30 35 40

longitude/deg

latit

ude/

deg

23/04/2008 16

y = 0.3546x + 90.766R2 = 0.2536

0

100

200

300

400

500

600

0 100 200 300 400 500 600 700

Krig (mm)

GP

CC

(mm

)

.

y = 0.7669x + 47.1R2 = 0.8039

0

100

200

300

400

500

600

0 100 200 300 400 500 600 700

Krig (mm)

TAM

SA

T (m

m)

y = 0.6598x + 76.021R2 = 0.295

0

100

200

300

400

500

600

0 100 200 300 400 500 600 700

GPCC (mm)

TAM

SA

T (m

m)

TAM

SAT

RFE/

mm

TAM

SAT

RFE/

mm

GPCC

/mm

GPCC/mm

AMMA/mm

AMMA/mm

TAMSAT dekadal 10 estimates v gauge data (July 2004)

0

600

700

23/04/2008 17

Validation of ERA40/NCEP rainfall climatology for Ethiopia

I

IVaIII

II

IVb V

NCEP ERA40

Gauge

23/04/2008 18

Plot gauge-pixel values against rainfall estimates (rfe)Subtract proportion of scatter caused by uncertainty on gauge

pixel valuesUse statistical spread for each rfe to generate an ensemble of

rainfall fields taking into account expected spatial correlation

Ensemble approach to rainfall estimate uncertainty

rfe/mm

gge-

pixe

l/m

m

23/04/2008 19

1

2

4

3

5

7

6

8

Mean rainfall and 8 ensemble members, 7th August, 2001

Ensemble approach to rainfall estimate uncertainty

Mean rainfall

Frequency histogram for North Bank Division (400 simulations)

23/04/2008 20

Conclusions

There is no such thing as rainfall ground truthFor meaningful evaluation, rainfall estimates from different sources must be compared at the same spatial scaleUncertainty on validation data should be taken into accountWith care, even sparse data sets can be used to evaluate satellite or NWP estimates Use of an ensemble method facilitates area averaging and allows uncertainty to be propagated into downstream models (eg crop yield)

23/04/2008 21

Crop yield forecasting for EthiopiaCurrent project with Ethiopian National Met Agency (NMA) to predict maize yield in Oromiya provinceNMA has provided daily data from 200 gauges + crop yield data

23/04/2008 22

Validation – general points

Problem with using gauge data – mismatch of spatial scale

Usual approach: interpolate gauge data to give area averages at pixel (or coarser) scaleA geostatistical approach (kriging) is usually best becauseo uses information on spatial correlation of rainfallo gives error estimate for each interpolated value

Only use gridboxes with a minimum number of gauges (eg 1)

Target pixel+ gauge

Signal from this area

23/04/2008 23

Use Meteosat TIR imagery Identify cloud top temperature threshold Tt distinguishing between rain and no rainCalculate Cold Cloud Duration (CCD) for each pixel (length of time cloud top is colder than Tt )Estimate rain amount as rain = a0 + a1 CCDa0, a1, Tt are calibrated against local gauges using historic dataCalibration parameters vary in space and time

TAMSAT operational algorithm

23/04/2008 24

Thermal Infra-red (TIR) image from Meteosat 9

•Images every 15 minutes•3 km resolution

23/04/2008 25

Examples of additional data in African Met Services

Country GTS weather

GTS rain Other real-time weather

Other real time rain

Other stations

Algeria 54 54 43 3 370

Ethiopia 17 17 50 >50 >50

The Gambia

15 15 0 0 23

Libya 22 - - - 270

Nigeria 33 33 9 9 350

Zambia 19 25 25 6 25

(African Climate Report, DFID DEFRA, December 2004)

23/04/2008 26

Ethiopian gauge locations

34 36 38 40 42 44 46 48

4

6

8

10

12

14

aa

am

ataw

as

bd

bl

bg

cm

dd

dl

dm

gsgi

go

gd

gr

hm

ho

ji

jm

kd

ku

mj

mk

ml

mo

nj

ng

nk

rosk

sn

sd

wl

yc

zq

Longitude ( degree)

Latit

ude

( deg

ree)

37 gauges (synoptic and reliable secondary)

23/04/2008 27

Validation Study 2

Comparison of algorithms over Ethiopia June - Sept 2000 to 2004 by Tufa Dinku at IRIDekadal totals evaluated for 10, 0.50, 0.250 gridboxesValidation data comprises 120 gauges interpolated geostatistically to gridbox averagesMinimum gauge density as in table

Box size/0 Min gauges

1.0 3

0.5 1

0.25 1

23/04/2008 28

Validation Study 2 - map

23/04/2008 29

Validation Study 2 - details

Comparison between TAMSAT, CPC, CMORPH, GPCP, TRMM-3B42

GPCP merges PMW, TIR and gauge dataTRMM-3B42 merges PMW, TIR and gauge data calibrated with TRMM radarCMORPH uses available PMW images and interpolates between them using Meteosat TIR

23/04/2008 30

Validation Study 2 - results

10 resolution, dekadal total, 2000-2004

N=1020 CPC-RFE

GPCP 3B42 TAM

CC 0.66 0.71 0.72 0.79

Efficiency -0.45 0.07 0.34 0.59

Bias 0.55 0.72 0.87 0.93

ME -30 -19 -9 -5

RMSE 58 46 39 31

CPC-RFE GPCP

TRMM-3B42 TAMSAT

23/04/2008 31

GPCC grid squares containing gauges, August 2004

No of gauges = 57

23/04/2008 32

AMMA grid squares containing gauges, August 2004

No of gauges = 800Interpolation by kriging

23/04/2008 33

Validation Study 1Geographical variations

June

July

Aug

Sept≤ 1σK

≤ 2σK > 2σK

Normalised monthly anomaly(normalised with respect to the kriging std error)Monthly anomaly

<1s.e. 1 to 2s.e. >2s.e.

23/04/2008 34

Current state of African rainfall measurement

Spatial distribution of monthly in situ precipitation stations with at least 10 years of data in GPCC Data base

23/04/2008 35

TAMSAT dekadal 10 estimates v gauge data (2004)

July

Aug

Sep

0

600/mm

600/mm

GPCC (all squares) GPCC (only squares with gauges)

AMMA

TAM

SAT

RFE/

mm

23/04/2008 36

y = 0.7669x + 47.1R2 = 0.8039

0

100

200

300

400

500

600

0 100 200 300 400 500 600 700

Krig (mm)

TAM

SA

T (m

m)

y = 1.0105x + 18.802R2 = 0.7516

0

100

200

300

400

500

600

0 100 200 300 400 500 600 700

Krig (mm)

TAM

SA

T (m

m)

.

y = 0.9788x + 13.316R2 = 0.7817

0

100

200

300

400

500

600

0 100 200 300 400 500 600 700

Krig (mm)

TAM

SA

T (m

m)

.

y = 0.3546x + 90.766R2 = 0.2536

0

100

200

300

400

500

600

0 100 200 300 400 500 600 700

Krig (mm)

GP

CC

(mm

)

.

y = 0.6287x + 64.958R2 = 0.6931

0

100

200

300

400

500

600

0 100 200 300 400 500 600 700

Krig (mm)

GP

CC

(mm

)

.

y = 0.7934x + 9.1214R2 = 0.766

0

100

200

300

400

500

600

0 100 200 300 400 500 600 700

Krig (mm)

GP

CC

(mm

) .

y = 0.6598x + 76.021R2 = 0.295

0

100

200

300

400

500

600

0 100 200 300 400 500 600 700

GPCC (mm)

TAM

SA

T (m

m)

y = 1.0976x + 6.0906R2 = 0.5058

0

100

200

300

400

500

600

0 100 200 300 400 500 600 700

GPCC (mm)

TAM

SA

T (m

m)

y = 0.9418x + 27.36R2 = 0.5947

0

100

200

300

400

500

600

0 100 200 300 400 500 600 700

GPCC (mm)

TAM

SA

T (m

m)

July

Aug

Sep

TAM

SAT

RFE/

mm

TAM

SAT

RFE/

mm

GPCC

/mm

GPCC/mm AMMA/mm AMMA/mm

TAMSAT dekadal 10 estimates v gauge data (July 2004)