The coastal region of East Africa has its Ashort rains@ season in boreal autumn, and its Along rains@ period in boreal spring. The diagnostics of East African rainfall anomalies have been discussed in Hastenrath et al. (1993). In particular, the autumnal rains were shown to have a strong relationship with concurrent equatorial zonal winds. The predictand here is a 7-station average of the normalized departures from the 1931-60 mean for Sep-Oct-Nov-Dec (SOND) and for Oct-Nov (ON). The stations are Lamu, Malindi, Mombasa and Voi in Kenya; and Dar Es Salaam, Tanga and Bagamoyo in Tanzania. The predictors are simply the values of the predictand itself for the 7 years immediately preceding the forecast year, averaged over the respective target seasons (SOND or ON). That the most recent predictor data is nearly a year prior to the beginning of the target period provides the opportunity for multiseason lead times for these forecasts. Thus, once the data for December 1995 became available, this forecast for fall 1996 Ashort rains@ could be made.

The methodology used to relate the 7-station mean rainfall anomaly over the prior 7 years to that of the following year is a neural network, as described in Hastenrath et al. (1995) in the context of South African summer rainfall prediction. Neural nets have been found more successful in predicting East Africa=s Ashort rains@ than multiple regression, especially when the predictor is a series of values of the predictand itself for previous years. This success was found in forecasts for South Africa as well, although multiple regression also produced a reasonably skillful forecast (see the Greischar and Hastenrath presentation on p. 24 of the December 1994, and p. 27 of the December 1995, issues of this Bulletin).

The neural network models were developed over the training period of 1928-78, and tested on the independent verification period of 1979-94 for realistic skill estimation. Resulting skills are expressed in a correlation framework: For the SOND period 45% of the predictand variance is explained, and for ON 38% is explained.

The standardized rainfall anomalies over the last 7 years have been as follows:

Because the neural net models have an intermediate (hidden) layer of 3 nodes, the weighting formula applied to the past 7 years is not readily apparent from the system weights.

The neural-based forecast for boreal autumn 1996 is shown in the following table:

The 1995 predictions of -0.95 for SOND and -0.37 for ON performed less well than expected by the model statistics over the verification period. A diagnostic reason for this low performance cannot be identified from this purely statistical technique. For boreal autumn 1996, rainfall is expected to be close to the long-term mean.

Hastenrath, S., A. Nicklis and L. Greischar, 1993: Atmospheric-hydrospheric mechanisms of climate anomalies in the western equatorial Indian Ocean. J. Geophys. Res. (oceans), 98 (C11), 20219-20235.

Hastenrath, S., L. Greischar and J. Van Heerden, 1995: Prediction of the summer rainfall over South Africa. J. Climate, 8, 1511-1518.