JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1220 10.6339/26-JDS1220 Data Science in Action Investigating Spatial Dependence in the Degree of Asymptotic Dependence between a Satellite Precipitation Product and Station Data in the Northern US Rocky Mountains RussellBrook T.brookr@clemson.edu1∗ Momo NizeghaKelie Marline1 MalshikaDulshan1 HuangWhitney K.1 School of Mathematical and Statistical Sciences, Clemson University, Clemson, South Carolina, USA Corresponding author. Email: brookr@clemson.edu. 2026123202600117 Supplementary Material

The authors have compiled a GitHub repository that contains files and data related to this manuscript, that is publicly available and located at https://github.com/brooktrussell/InvestInvestigatingSpatialDependence. More specifically, this repository contains the following materials:

Analysis.R — the R file that contains the code used in this analysis,

Objects.RData — an R workspace file that contains the data used in this analysis,

PRISMelevation — an R workspace file that contains the PRISM data used in this analysis, and

SimStudyCode.R — the R file that contains the code used in the simulation study.

15820251122026 2026 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2026 Open access article under the CC BY license.

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.

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