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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)