Experimental Forecast of East African Rainfall for

October-December 1999

contributed by Andrew Colman

UK Meteorological Office

The UK Meteorological Office is conducting research into the effects of sea surface temperatures and other climatic variables on tropical rainfall. As part of this research, experimental seasonal rainfall forecasts have been made for the Sahel and adjacent regions in tropical NW Africa since 1986, and for the Nordeste region of Brazil since 1987. Using similar statistical methods, forecasts for tropical East Africa October-December rainfall (the 'short rains') have been issued since 1994.

The region covered by the East Africa prediction is between 5N and 15S and between 30E and the Indian Ocean coast. These forecasts for East Africa were produced using statistical methods and by using the Met. Office's Atmospheric General Circulation Model (AGCM).

The statistical forecast is made by using linear regression and discriminant analysis techniques, with three indices of global sea surface temperature (SST) patterns, an index of local Indian Ocean SST and an index of tropical west Pacific SST as predictors. The forecast model is derived from historical rainfall and SST information.

The AGCM forecast was extracted from a nine member ensemble of AGCM predictions using sea temperatures and atmospheric conditions observed just prior to when the forecast was run (around August 26th).

The historical rainfall record is divided into 5 equi-probable categories. Based on 1961-1990 rainfall, the category boundaries (as percentages of mean rainfall) are:

Very Dry/Dry	74%
Dry/Average	86%
Average/Wet	102%
Wet/Very Wet	124%

Performance of Trial Forecasts for 50 Past Years

The statistical and dynamical forecasts were tested using trial forecasts over the period 1948 to 1997. The assessment measure used is correlation.

Statistical linear regression forecasts were assessed using a method where a trial forecast is made for each year using a regression equation calculated using data for the remaining years. This assessment provides a good measure of forecast skill from minimal data.

To provide an indication of AGCM skill, the performance of a long term AGCM run forced with observed SST in simulating rainfall is measured.

Statistical forecast correlation	0.50
AGCM simulation correlation	0.65

These correlations are statistically significant at the 5% level.

Performance of Real Time Empirical Forecasts

Forecasts have been made for this region since 1994. The forecasts for 1994 and 1995 were strongly influenced by above and below average SST in the NW Pacific respectively and the forecasts for 1997 and 1998 where influenced by the 1997 El Niño and the 1998 La Niña events. These past forecasts are shown in Table 1.

Note: The categories used for the 1994-1998 forecasts are based on a 1951-1980 climatology. For the 1999 forecast, categories based on the 1961-1990 climatology are used as 1961-1990 is the accepted WMO standard climatology period and is used by most forecasters. The 1961-1990 rainfall average is 104% of the 1951-1980 average.

Forecasts for the 1999 season

Statistical Forecast

A La Niña event is persisting in the Pacific which favors below average rainfall in East Africa. The signal from SST elsewhere is weak.

The regression forecast is 89% of the 1961-1990 average and is in the AVERAGE category.

The discriminant analysis technique gives the following probabilities for the 5 (1961-1990 based) categories:

Very Dry	0.33
Dry	0.30
Average	0.16
Wet	0.10
Very Wet	0.11

AGCM Forecast

The ensemble mean prediction provides a best estimate rainfall forecast which is:

82% of the 1961-1990 model climatology.

Based on the performance of AGCM simulations of rainfall from 1961 to 1990, the probability of the 5 (1961-1990 based) observed rainfall categories are:

Very Dry	0.33
Dry	0.23
Average	0.23
Wet	0.14
Very Wet	0.07

Overall Best Estimate: Based all the forecasts, our overall best estimate is for the DRY category with low confidence because of the weak signal from SST.

Caution: Please note that this forecast model is still under development: these forecasts are experimental and should be used with caution.

Acknowledgements:

Thanks to David Rowell for providing output from the HADAM3 model. Thanks to Robin Clark and Richard Graham for supplying dynamical forecast output.

Table 1

Year	1994	1995	1996	1997	1998
Forecast Category	Very Wet	Dry	Average	Wet	Dry-Average
Observed Category	Wet	Dry	Very Dry	Very Wet	Very Dry