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Prediction of Summer Rainfall over Eastern South Africa
Using Empirical Methods
contributed by Larry Greischar1, Stefan Hastenrath1 and Johan van Heerden2
1Department of Atmospheric and Oceanic Sciences, University of Wisconsin, Madison, Wisconsin
2University of Pretoria, Pretoria, South Africa
Several empirical methods have been developed at the University of Wisconsin to forecast
Dec-Jan-Feb rainfall in the eastern part of South Africa (Hastenrath et al. 1995). The predicted
area is an index of precipi-tation in the Transvaal. The location of this Highveld region (called
TVR) is shown in Fig. 1. This climatic-ally sensitive region is representative of a larger area. The
predictors, based on empirical-diagnostic analyses, include (1) the Jul-Aug-Sep values of Tahiti
minus Darwin sea level pressure difference (a Southern Oscillation Index, or SOI), (2) the
preceding Jan-Feb-Mar 50 mb zonal wind (U50) over Singapore (an indication of the phase of the
quasi-biennial oscillation [QBO]), (3) an index of the Oct-Nov surface westerlies along the Indian
Ocean Equator (called UEQ), and (4) an index of November sea surface temperature (SST) in the
Southwestern Indian Ocean (called UKT).
Forecast skill experiments using stepwise multiple regression, linear discriminant analysis, and
neural networking were carried out using the above predictors to predict TVR summer rainfall.
The training period is 1954-78, and the forecast test period 1979-90. Regres-sion models using
U50, UEQ and UKT as predictors account for more than 30 percent of the variance in the
forecast test period. The linear discriminant analysis method does not perform well here. The most
skillful result was found using a neural networking model using U50 and SOI information through
the end of Septem-ber, in which 62 percent of the 1979-90 TVR variance was explained. Using
only the SOI and U50 as predic-tors and using the slightly longer 1979-93 test period, 26% of the
variance is explained for stepwise multiple regression and 50% for neural networking. It was
decided that these more economical models would be used. The regression equation is
TVR = 19.3(SOI) -1.36(U50) + 264.
Note that TVR rainfall is positively correlated with SOI and negatively with U50. In other words,
rainfall is enhanced by cold ENSO conditions two seasons earlier and by an easterly QBO phase
roughly one year earlier.
The 1997 values of the Jul-Aug-Sep SOI is -0.67 mb, compared with mean and standard deviation
values of and 1.74 and 1.50 mb, respectively. This will tend to suppress rainfall. The U50 value
for last Jan-Feb-Mar is -19.3 m/s, compared with mean and standard deviation values of -1.49 and
13.01 m/s, respectively. This will enhance the rainfall forecast. The resulting forecasts for TVR
rainfall for Dec-Jan-Feb 1996-97 follow.
Predicted TVR
Forecast Method Rainfall Anomaly
Stepwise Multiple -28 mm (-.36 SD)
Regression
Neural Networking -46 mm (-.60 SD)
These forecasts for slightly below-average TVR rainfall are expressed relative to a 1954-78 mean
of 305 mm and a standard deviation of 77 mm. The forecasts are comparable to what occurred in
the summers of 1959-60, 1964-65, 1965-66, 1985-86, and 1986-87.
The performances of the previous three predic-tions were excellent in 1994, poor in 1995 and
fairly satisfactory in 1996: In 1994 the forecasts were for -71 mm for regression and -.46 for
neural net; the observation was -88 mm. In 1994 the forecasts were for -4 mm for regression and
-27 mm for neural net; the observation was +191 mm. In 1996 the forecasts were for 0 mm for
regression and -6 mm for neural net; the observation was -54 mm.
Hastenrath, S., L. Greischar and J. van Heerden, 1995: Prediction of the summer rainfall over
South Africa. J. Climate, 8, 1511-1518.
Figure 1. Location of the rainfall predictand region (TVR) used at the University of Wisconsin.