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Rainfall estimation for food security in Africa, using the
Meteosat Second Generation (MSG) satellite.
Robin Chadwick
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Contents of presentation
• Motivation for satellite rainfall estimation in Africa• TAMSAT satellite rainfall estimation methodology• Met office NIMROD nowcasting precipitation estimation
product.• Extension of Met office rainfall estimates to Africa• AMMA Sahelian rain-gauge dataset• Comparison of Met office rainfall estimates against
TAMSAT estimates and AMMA gauge data• Current and future work
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Motivation for satellite rainfall estimation in Africa
• Accurate near-real time estimates of rainfall are vital for humanitarian applications such as famine prediction and prevention, and flood prediction.
• Very few precipitation radar networks. Rain-gauges sparce and badly maintained.
• Satellite based rainfall estimation algorithms offer one solution to this problem.
• Several algorithms exist, using IR data (from geostationary satellites) , passive microwave data (from polar orbiting satellites) or a combination.
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MSG wavelength channels
Visible
IR window channelsWater vapour
Near
IR IR
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The TAMSAT rainfall estimation method
• Utilises one infrared channel (10.8 microns) from the MSG
• Simple method based on the concept of Cold Cloud Duration
• Produces operational dekadal rainfall estimates for Africa
• Intercomparisons of various satellite rainfall products over Africa have found that the TAMSAT method is as accurate as more complex algorithms.
• Should be possible to improve on this because of the information on rainfall provided by other channels on the MSG.
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TAMSAT methodology
•CCD is the Cold Cloud Duration; the length of time each pixel is below the threshold temperature
•Rainfall, R = a + b(CCD)
•Threshold temperature and coefficients a, b calibrated for each region using historical rain-gauge data
tT
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TAMSAT rainfall estimate for 2007 October 1st dekad
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Met office NIMROD nowcasting precipitation estimation product
• Rain-rate estimates over Europe produced operationally every 15 minutes
• Radar Satellite Analysis
+ =
Francis et al ‘06
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Calibration of NIMROD using only 2 MSG channels
Radar rain-rateMeteosatIR channel
MeteosatVis channel
Nimrod satelliterain-rate
72.3376.8959.8634.364
19.3841.9732.8615.353
2.9013.6620.619.222
0.002.465.995.471
4321% Rain
Infrared (10.8 m) channel
Visible (0.8 m) channel
72.3376.8959.8634.364
19.3841.9732.8615.353
2.9013.6620.619.222
0.002.465.995.471
4321% Rain
Infrared (10.8 m) channel
Visible (0.8 m) channel
10266 /14193
3137 /4080
3877 /6477
706 / 20554
1342 /6925
1340 /3193
1842 /5605
610 /39753
76 /2620
408 /2987
980 /4756
621 /67372
0 /286
59 /2401
279 /4656
1028 /187971
4321Rain /
Total
Infrared (10.8 m) channel
Visible (0.8 m) channel
10266 /14193
3137 /4080
3877 /6477
706 / 20554
1342 /6925
1340 /3193
1842 /5605
610 /39753
76 /2620
408 /2987
980 /4756
621 /67372
0 /286
59 /2401
279 /4656
1028 /187971
4321Rain /
Total
Infrared (10.8 m) channel
Visible (0.8 m) channel
11104
01003
00002
00001
4321Rain /No rain
Infrared (10.8 m) channel
Visible (0.8 m) channel
11104
01003
00002
00001
4321Rain /No rain
Infrared (10.8 m) channel
Visible (0.8 m) channel
BrightDark
Cold
Warm
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Extension of NIMROD to multiple channels
SZA Primary correlation method
0o-75o 4-d (0.8/1.6/3.9 refl/10.8)
75o-80o 4-d (0.8/1.6/3.9 refl/10.8) => 3-d (0.8/1.6/10.8)
80o-85o 3-d (0.8/1.6/10.8)
85o-88o 3-d (0.8/1.6/10.8) => 3-d (3.9BT/10.8/12.0)
>88o 3-d (3.9BT/10.8/12.0)
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10th/11th October 2006
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Extension of Met office algorithm to Africa
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Current domain of Met office algorithm extension
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The AMMA Sahelian rain-gauge dataset
• O.5 degree resolution gridded rain-gauge dataset for May – September 2004 covering the Sahel.
• Met office estimates processed for this period & region using historical MSG data
• Estimates still use (historical) European radar data for calibration
• Comparison of Met office estimates against AMMA gauge data, for grid cells containing gauges only.
• Comparison of TAMSAT estimates against AMMA gauge data
• Comparison of Met office estimates against TAMSAT estimates
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Validation domain
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Comparison of Satellite rainfall estimates and gauge data over the Sahel for the July dekad 2 2004
Met office – Raingauge anomaly
TAMSAT – Raingauge anomaly
Met office – TAMSAT anomaly
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Met office and TAMSAT vs gauge dekadal estimates for May to September 2004
Met office vs Raingauge TAMSAT vs Raingauge
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Met office vs TAMSAT dekadal rainfall totals for all dekads May to September 2004
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Statistical summary
Bias RMSE
Met office 14.2 33.9 0.55
TAMSAT 4.3 18.1 0.72
2R
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Current and Possible Future work
• Use of historical local radar data (AMMA or TRMM) to calibrate the Met office algorithm
• Use of historical gauge data to constrain or calibrate an MSG based algorithm
• Neural network based algorithm