Statistical modelling of spatial patterns of the urban heat island intensity in the urban environment of Augsburg, Germany

Spatial and temporal variability of meteorological variables across urban areas due to differences in land surface characteristics is a common phenomenon. Most pronounced is the effect of land cover on air temperature. In this study, parametric and non-parametric statistical approaches (stepwise multiple linear regression, random forests) were applied in order to model sub-daily and daily spatial patterns of the urban heat island intensity in the major city of Augsburg, Southern Germany, and its rural surroundings. A large number of model setups utilizing variables from different land surface data sets as predictors and taking into account different seasonal, daily and meteorological situations was examined. The results were compared concerning different measures of model performance (mean squared skill score, mean squared error, explained variance). For individual setups and situations considerable skill with a mean squared skill score of up to 0.85 was reached. The best performing models were obtained from multiple linear regression for situations with low wind speeds and cloud cover in the morning and evening. Selected models were utilized to derive continuous spatial distributions of the air temperature deviations from a rural reference station. The resulting maps can be useful for various applications, e.g. in the context of urban planning.