Screening multiple linear regression (SMLR) is used to predict seasonal temperature and precipitation amounts for locations over the mainland United States (Unger, 1996a). Predictor data consist of northern hemisphere 700-mb heights, near global SSTs and station values of mean temperature and total precipitation amount from the 3-mo period prior to the initial time of September 1, 1997. Regression relationships were derived from data for the 1955-96 period. Forecasts were produced from single station equations for 59 stations approximately evenly distributed throughout the U.S.

All predictors and predictands were expressed as standardized anomalies relative to the developmental data. Twenty-five candidate predictors, selected from gridpoint values in regions of known importance for climate prediction, were offered for screening in the regression development. A forward selection screening procedure was used for equation development.

A bi-directional retroactive real time validation technique was used to estimate forecast skill (Unger 1996b). The predictor screening and equation length determination was also done in retroactive real time mode, so that no knowledge of the "future" was used in any aspect of equation formulation. Equations varied from one to five terms. The verification is based on the temporal correlation coefficient between forecast and observation on the 42 independent cases at each of the 59 stations.

The final forecasts are post-processed to obtain an estimate of the likelihood of the above, normal, or below class being observed, as defined by the terciles of the distribution for each forecast element and location. A forecast is assigned a class on the basis of the forecast distribution and skill. An estimate of the increased likelihood of a given class is made to place the forecast in a format similar to the operational long lead forecasts issued by the CPC (O'Lenic, 1994). Each forecast is assigned a probability on the basis of forecast performance in the 1955-1996 period (Unger, 1997). Details of the method used to assign probabilities to these forecasts can be found in the June 1997 issue of this bulletin.

The forecasts for OND 1997 are shown in Figs. 1 and 3 with the corresponding skill estimates for each station shown in Figs. 2 and 4. Shading indicates areas of sufficient skill to assign a tercile category to the forecast. Contours within the shaded areas on the forecast maps indicate 5 and 10 percent probability anomaly estimates for the category. The numbers plotted in Figs. 1 and 3 indicate station values of the original regression forecasts, damped according to the forecast-observation correlation on independent data to minimize the squared error.

Regression forecasts for OND 1997 (Fig. 1) show above normal temperatures over much of the far western U.S. and in southern Florida. A large area of below normal temperatures is predicted for the central and southeast portions of the country.

Precipitation forecasts for OND 1997 (Fig. 3) show below median amounts for portions of the Pacific Northwest and Montana. Above median conditions are predicted for the southwest US and southern Florida, in western South Dakota and a small area in the northeastern U.S.

REFERENCES

O'Lenic, E., 1994: A new paradigm for production and dissemination of the NWS's long lead-time seasonal climate outlooks. Proceedings of the Nineteenth Annual Climate Diagnostics Workshop. College Park, Maryland, November 14-18, 1994, 408-411.

Unger, D. A., 1996a: Long lead climate prediction using screening multiple linear regression. Proceedings of the Twentieth Annual Climate Diagnostics Workshop. Seattle, Washington, October 23-27, 1995, 425-428.

Unger, D. A., 1996b: Skill assessment strategies for screening regression predictions based on a small sample size. Preprints, Thirteenth Conference on Probability and Statistics in the Atmospheric Sciences. San Francisco, CA., February 21-23, 1996, 260-267.

Unger, D. A., 1997: Conversion of Long Lead Climate Predictions from Continuous to Probabilistic Form. Proceedings of the Twenty-first Annual Climate Diagnostics and Prediction Workshop. Huntsville, Alabama, October 28-November 1, 1996, 44-47.