Causality and Feedback Analysis of Urban Heat Island and Precipitation

Urbanization has significant impacts on precipitation, which has been analyzed for over 50 years. The urbanization brings problems like urban heat island effect which further has a great impact on precipitation distribution and amount. However, the causality and feedback mechanism still remains unclear due to the complexity of land-atmosphere interactions. We hereby to apply temporal causality discovery models to the climate time series data to extract the potential causal and feedbacks among the urbanization and climate variables for Indianapolis, IN area. First, we are going to spatially quantify the urban heat island over the Indianapolis area. Second, we are going to explain how to apply the models to climate and urban for causality extraction. Third, lead-lag corrections and feedback parameters will be computed to determine and quantify the forcing of the feedback impacts between the two parameters. Third, data statistical significance tests, such as the P-value approach and Structural Equation Modeling (SEM), will be carried out to examine the hypothesis of the calculated causality.  Finally, we are going to interpret and decide whether causality due to direct connection, hidden common cause or both. The computed causal and feedback can be used to evaluate urban and precipitation interaction simulated by models with different scales of urbanization.