Exploring spatial dependency of meteorological attributes for multivariate analysis: A granger causality test approach

Analysis of meteorological attributes often involves the investigation of other secondary attributes which exhibit high spatial correlation with the primary attribute and among themselves. For multivariate spatial analysis, one of the major research challenges is to choose the influential secondary attributes with respect to the primary one and rank them accordingly as per their degree of influence. This research work focuses on electing the most suitable secondary attribute(s) which is/are correlated with the primary and prioritize them individually or as a group, for the corresponding spatial analysis. Time-series forecasting of the meteorological attributes has been chosen as the candidate analysis technique for the case study. A Granger causality test based approach is carried out to determine how a group of secondary time-series is statistically significant to forecast the primary. Among several meteorological attributes, the rainfall is chosen as the primary time-series, and the soil moisture, relative humidity, and land surface temperature are considered as the secondary time-series to forecast the rainfall.