<!doctype html public "-//w3c//dtd html 4.0 transitional//en">EXPERIMENTAL FORECAST OF EAST AFRICAN RAINFALL FOR OCTOBER-DECEMBER 2003

contributed by Andrew Colman, Mike Davey

Seasonal Forecasting Group, Ocean Applications, Met Office, Bracknell, UK

INTRODUCTION

The Met 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 and appear in previous September issues of this bulletin .

The region covered by the East Africa prediction is between 5N and 15S and between 30E and the Indian Ocean coast. Forecasts for E Africa were produced using statistical methods and dynamically using the Met Office's GLObal SEAsonal (GLOSEA) forecast model.  This year, there is strong disagreement between the statistical and dynamical forecast and hence low forecast confidence so we have decided not to include forecasts for higher resolution (2.5o latitude x 3.75 o longitude rectangular) sub-regions as we have done in recent years.

The statistical forecast is made by using linear regression and discriminant analysis techniques, with three indices of global sea surface temperature (SST) anomaly patterns (Shown in Colman 2002 http://grads.iges.org/ellfb/Sep02/Colman/colman.htm ). The forecast model is derived from historical rainfall and SST information.

GLOSEA is a version of the HadCM3 coupled ocean-atmosphere general circulation model  modified for making seasonal predictions. The GLOSEA forecast was produced as a 40-member ensemble of predictions initialised with atmosphere and oceanic conditions observed in early August.  GLOSEA hindcasts produced as part of the DEMETER project (see www.ecmwf.int/research/demeter for more about DEMETER) were used to correct the GLOSEA forecast output for model bias and convert the forecast into probability format.

Forecasts are expressed as probabilities for 5 equi-probable categories (quints) for the whole region and as probabilities of 3 equi-probable categories (terciles) for the sub-regions.  The categories are equi-probable over the 1961-1990 climatology period.   

The category boundaries (as percentages of mean rainfall) are:
 

VeryDry/Dry

Dry/Average

Average/Wet

Wet /Very Wet

74%

86%

102%

124%

 

FORECAST SKILL

PERFORMANCE OF TRIAL FORECASTS

The statistical and dynamical forecasts were tested using trial forecasts over the period 1948 to 1997 and 1959 to 2000 respectively. 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.

Statistical forecast skill                  correlation=0.50
DEMETER GLOSEA skill            correlation=0.38

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 Nino and the 1998 La Nina events. The observed rainfalls for 2000 and 2001 were quite close to the dry-average and very dry-dry boundaries respectively so the forecasts for these seasons were as accurate as they could be.

Year

1994

1995

1996

1997

1998

1999

2000

2001

Forecast

Category

Very

Wet

Dry

Average

Wet

Dry or

Average

Dry

Dry or

Average

Dry or

Very Dry

Observed

Category

Wet

Dry

Very

Dry

Very

Wet

Very

Dry

Average

Dry

Very

Dry

 

Note: The categories used for the 1994-1998 forecasts are based on a 1951-1980 climatology. For the 1999 and later forecasts, 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 2003 SEASON

STATISTICAL FORECAST

Below average SST in the NW Pacific near Japan and in the tropical E Pacific are favouring below average rainfall in E Africa this year. The regression forecast is   % 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

Dry

Average

Wet 

Very Wet

0.30

0.36

0.22

0.04

0.09


 GLOSEA DYNAMICAL FORECAST

Based on the performance of DEMETER GLOSEA  hindcasts of rainfall from 1959 to 2000, the  dynamical forecast is presented as probabilities of 5 (1961-1990 based) observed rainfall categories which are:
 

Very Dry

Dry

Average

Wet 

Very Wet

0.15

0.08

0.08

0.13

0.56

 

OVERALL BEST ESTIMATE: This year, the dynamical forecast is favouring the VERY WET category whilst the empirical forecasts favour the DRY category, a clear disagreement.  Both forecasts give relatively low probabilities to the AVERAGE category so we have ruled out a compromise. Our best estimate forecast is for the DRY category which is favoured by the slightly more skilful statistical forecast. However, given the disagreement between forecasts, confidence in this best estimate is very low.

 References:

Mutai, C.C., Ward, M.N and Colman, A.W. Prediction of East Africa seasonal "short rainfall" rooted in evidence for widespread SST-forced variability during October-December. I.J.Climatol.18 975-997 (1998).

Colman, A.W.  Experimental Forecast of East African Rainfall for October-December 2002 Experimental Long Lead Bulletin Vol. 11 No 3 (2002) (http://grads.iges.org/ellfb/Sep02/Colman/colman.htm )

Acknowledgements:

 Thanks to  Sarah Ineson, Pete Mclean and Richard Graham for supplying dynamical forecast output.