1. Rainfall estimation for food security in Africa, using the Meteosat Second Generation (MSG) satellite. Robin Chadwick

2. 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

3. 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.

4. MSG wavelength channels
Visible
Near IR IR
Water vapour
IR window channels

5. 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.

6. 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

7. TAMSAT rainfall estimate for 2007 October 1st dekad

8. Met office NIMROD nowcasting precipitation estimation product
Rain-rate estimates over Europe produced operationally every 15 minutes
Radar Satellite Analysis + =
Francis et al ‘06

9. Calibration of NIMROD using only 2 MSG channels
Meteosat
IR channel
Radar rain-rate
Bright
Dark
Cold
Warm
Meteosat
Vis channel
Nimrod satellite
rain-rate

10. Extension of NIMROD to multiple channels
SZA Primary correlation method
0o-75o 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)

11. 10th/11th October 2006

12. Extension of Met office algorithm to Africa

13. Current domain of Met office algorithm extension

14. 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

15. Validation domain

16. 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

17. Met office and TAMSAT vs gauge dekadal estimates for May to September 2004
TAMSAT vs Raingauge
Met office vs Raingauge

18. Met office vs TAMSAT dekadal rainfall totals for all dekads May to September 2004

19. Statistical summary Bias RMSE Met office 14.2 33.9 0.55 TAMSAT 4.3
18.1
0.72

20. 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