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Changes on temperature patterns, on a local scale, are perceived byindividuals as the most direct indicators of global warming and climate change.As a specific example, for an Atlantic climate location, spring and fallseasons should present a mild transition between winter and summer, and summerand winter, respectively. By observing daily temperature curves along time,being each curve attached to a certain calendar day, a regression model forthese variables (temperature curve as covariate and calendar day as response)would be useful for modeling their relation for a certain period. In addition,temperature changes could be assessed by prediction and observation comparisonsin the long run. Such a model is presented and studied in this work,considering a nonparametric Nadaraya-Watson-type estimator for functionalcovariate and circular response. The asymptotic bias and variance of thisestimator, as well as its asymptotic distribution are derived. Its finitesample performance is evaluated in a simulation study and the proposal isapplied to investigate a real-data set concerning temperature curves.

A flexible functional-circular regression model for analyzing temperature curves