Comparative regression analysis coupled with rational verification for forecasting night cooling

Abstract

AbstractA set of linear regression equations has been developed for forecasting night‐time minimum temperatures. The predictors used in each regression are the dry‐bulb and wet‐bulb temperatures recorded at various times during the day. This set of regression equations can be used to begin estimating the expected minimum temperature early in the day and then update the estimate in the course of the day. It is shown that these regression equations are quite insensitive to small deviations in the input data and some special cases are discussed. The usefulness of simple or multiple regression is also examined. Under certain conditions, linear regression on hygrometrically derived humidity parameters have been transformed into non‐linear expressions of dry and wet‐bulb temperatures. It is shown by an example that these non‐linear relationships may fit the data equally as well as the linear relationships. Comparative verification of the scheme has shown that successive updating of the predicted minimum temperature has certain advantages. Questions associated with the representativeness of the data base are also discussed. The effect of the turbulent mixing by the wind on the night cooling is incorporated in the scheme as a further upgrading of the method.