Rainfall estimation for food security in Africa, using the

Meteosat Second Generation (MSG) satellite.

Robin Chadwick

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

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.

MSG wavelength channels

Visible

IR window channelsWater vapour

Near

IR IR

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.

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

TAMSAT rainfall estimate for 2007 October 1st dekad

Met office NIMROD nowcasting precipitation estimation product

• Rain-rate estimates over Europe produced operationally every 15 minutes

• Radar Satellite Analysis

+ =

Francis et al ‘06

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

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)

10th/11th October 2006

Extension of Met office algorithm to Africa

Current domain of Met office algorithm extension

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

Validation domain

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

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

Met office vs Raingauge TAMSAT vs Raingauge

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

Statistical summary

Bias RMSE

Met office 14.2 33.9 0.55

TAMSAT 4.3 18.1 0.72

2R

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