What question did this study set out to answer?

To improve minimum temperature prediction for fruit-growing areas in South Australia using regression analysis.

February 5, 2026

Further work on minimum temperature prediction in the fruit-growing areas of South Australia

Key Points

To improve minimum temperature prediction for fruit-growing areas in South Australia using regression analysis.
Analyzed synoptic data from Renmark and Nuriootpa
Utilized multiple linear regression techniques
Compared regression coefficients of radiational heat loss and wind effects
Assessed variance explained by regression models for both locations
Regression coefficients for radiational heat loss are consistent across years and locations.
Wind effect coefficients differ between Renmark and Nuriootpa.
70% variance in minimum temperature explained at Nuriootpa; 80% at Renmark.
Using mean cloud cover for Renmark increased variance explained to 86%.

Abstract

Synoptic data from Renmark and Nuriootpa have been analysed by using multiple linear regression, supplementing earlier work on frost prediction for Nuriootpa (Veitch 1959). It has been shown that the regression coefficients associated with radiational heat loss are homogeneous between years and between stations, but those associated with wind effects are distinct between the two stations. The constant terms of the regression equations are heterogeneous between stations and between years. Using 1800 C.S.T. data, the percentage of variance of minimum temperature explained by regressions is 70 at Nuriootpa and 80 at Renmark. If mean cloud cover overnight is used for Renmark regressions, the variance explained by regression rises to 86 per cent providing direct evidence that errors associated with variable cloud cover fundamentally limit further progress.

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Further work on minimum temperature prediction in the fruit-growing areas of South Australia

Key Points

Abstract

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