Journal of Data Science

Satellite precipitation products have the potential to be employed for the purpose of better understanding extreme precipitation events in remote mountainous terrain, where weather stations and radar data tend to be sparse. For this reason, it is crucial to assess how closely satellite estimates agree with ground observations during extreme events, and how that agreement varies across such regions. We use asymptotic dependence from multivariate extreme value theory as the primary tool in this study. After presenting two measures of asymptotic dependence and their associated estimators, we illustrate these ideas using simulated data. We then model the level of asymptotic dependence between PERSIANN-CDR and SNOTEL station data over the US Northern Rocky Mountains. We consider both asymptotic dependence estimators, and based on hypothesis tests and visual diagnostics, both estimates of asymptotic dependence indicate positive spatial dependence. We also investigate whether geographical factors influence the levels of asymptotic dependence over this region. Using a spatial correlation analysis, we find that elevation is negatively correlated with both asymptotic dependence estimators and average summer temperature is positively correlated with both asymptotic dependence estimators. However, we did not find any geographical covariates to be statistically significant in the model.