Application of Numerical Prognostic Heights to Surface Temperature Forecasts

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Abstract
Multiple regression equations for predicting 5-day mean surface temperature in the United States from prior anomalies of 700-mb height and surface temperature are derived by a screening procedure for fall, winter, and spring. The heights required for an objective forecast are approximated with the aid of daily numerical prognoses prepared at the 500-mb level by a barotropic model and at the 700-mb level by a baroclinic model; the required temperatures are estimated by combining observed daily values with short-range prognoses. The resulting objective temperature predictions are verified on independent data at a number of cities, and the sources of error are analyzed. Comparison is made with forecasts prepared by local temperature persistence, continuity of error, and conventional methods. It is concluded that the objective method can produce in a short time temperature predictions which have appreciable skill beyond persistence. Thus a valuable aid and base for further improvement has been made available to the forecaster. DOI: 10.1111/j.2153-3490.1960.tb01325.x