In the approach used at the University of Wisconsin to forecast March-to-June precipitation in the Nordeste, several predictors are used with stepwise multiple linear regression and linear discriminant analysis. The predictand includes 27 selected stations in the Nordeste (Hastenrath and Greischar 1993), shown in Fig. 1. The forecasts shown here are made at one month lead--i.e., using data no later than January 1998. The potential predictors of March-June Nordeste rainfall (not all of which are necessarily used in a given prediction model) are listed below along with their correlations with the predictand over the training period, 1921-57. Correlation coefficients are in hundredths, with one and two asterisks indicating significance at the 5% and 1% levels, respectively.

1. October-January precipitation at the 27 predictand stations: +55**

(2) An index of January meridional surface wind component over the tropical Atlantic, 30^oN-30^oS: -35*

(3) An index of January SST in the equatorial Pacific: -11

(4) An index of January SST in the tropical Atlantic, 30^oN-30^oS: -57

(5) An index of Nov-Dec-Jan SST in the tropical Atlantic, 30^oN-30^oS: -70**

As shown above and described in Hastenrath and Greischar (1993), March-to-June rainfall in the Nordeste is correlated positively with the first and negatively correlated with all the other predictors. This pre-season, predictor values have been: (1) positive pre-season rain, (2) positive (i.e. southerly wind anomaly), (3) positive Pacific SST, (4) negative tropical Atlantic SST index (i.e. cold/warm SST anomalies to the North/South of the Atlantic equator), and (5) negative tropical Atlantic SST index for Nov-Dec-Jan (as in (4)). The meridional gradient of Atlantic SST and the pre-season Nordeste rainfall indicate a wetter than normal March-June 1998, while meridional surface wind component and Pacific SST point to dry conditions.

Table 1 shows skill evaluations for eight prediction models, each using a different combination of the predictors listed above. The eighth model uses a neural network rather than stepwise multiple linear regression.

Table 1. Skill of eight prediction models for Mar-Apr-May-Jun Nordeste rainfall (expressed as a percentage of predictand variance explained), followed by the forecast standardized Nordeste rainfall anomaly for Mar-Apr-May-Jun 1998. The model type is SMR (stepwise multiple regression) or NN (neural network), and predictors numbers (1)-(5) are as shown above.

The models indicate above average Nordeste precipitation. The 1912-56 historical mean and standard deviation are 500 and 200 mm, respectively.

Using the same sets of predictors, Nordeste rainfall was also predicted using linear discriminant analysis, in which five equiprobable categories of rainfall amount are defined and associated with predictor values using Bayes' theorem. In an individual forecast each of the categories is assigned a probability, given the pre-season values of the predictors. Table 11-2 in the March 1995 issue of this Bulletin shows, for predictor models 5 and 7, the five-by-five verification matrices obtained over the 1958-89 period using the earlier years to develop the models. The hit rates are 0.34 and 0.38, corresponding to Heidke skills of 0.18 and 0.22 and expected correlation skills of about 0.55 and 0.65. The quintile probability forecasts for Mar-Apr-May-Jun 1998 using each of these predictor models are:

Both models 5&7 show a maximum likelihood of a wetter than normal 1998.

In summary, the pre-season Nordeste rainfall and the meridional SST gradient in the Atlantic sector point to abundant March-June 1998 precipitation, while the equatorial Pacific SST and the field of the meridional wind component in the Atlantic sector favor drier conditions. Quantitatively, based on the various SMR and LDA models, we predict above average precipitation for the 1998 rainy season (MJ index +0.4 to +0.8, comparabale to the years 1957, 60, 71, 88, 94).

Hastenrath, S. and L. Greischar, 1993: Further work on the prediction of northeast Brazil rainfall anomalies. J. Climate, 6, 743-758.

Fig. 1. Locations of the 27 selected stations in the Nordeste, used as the predictand by Greischar and Hastenrath.