There has been a surge of work on models for coupling surface‐water with groundwater flows which is at its core the Stokes–Darcy problem, as well as methods for uncoupling the problem into subdomain, subphysics solves. The resulting (Stokes–Darcy) fluid velocity is important because the flow transports contaminants. The numerical analysis and algorithm development for the evolutionary transport problem has, however, focused on a quasi‐static Stokes–Darcy model and a single domain (fully coupled) formulation of the transport equation. This report presents a numerical analysis of a partitioned method for contaminant transport for the fully evolutionary system. The algorithm studied is unconditionally stable with one subdomain solve per step. Numerical experiments are given using the proposed algorithm that investigates the effects of the penalty parameters on the convergence of the approximations.
Tornadoes pose a forecast challenge to National Weather Service forecasters because of their quick development and potential for life-threatening damage. The use of machine learning in severe weather forecasting has recently garnered interest, with current efforts mainly utilizing ground weather radar observations. In this study, we investigate machine learning techniques to discriminate between nontornadic and tornadic storms solely relying on the Rapid Update Cycle (RUC) sounding data that represent the pre-storm atmospheric conditions. This approach aims to provide for early warnings of tornadic storms, before they form and are detectable by weather radar observations. Feature analysis of a Random Forest machine learning model uncovers that the pressure variable has little impact on the classification process, which is consistent with known key physical attributes of tornado formation, demonstrating the ability of machine learning techniques to provide insight solely based on the data.
There has been a surge of work on models for coupling surface-water with groundwater flows which is at its core the Stokes-Darcy problem. The resulting (Stokes-Darcy) fluid velocity is important because the flow transports contaminants. The analysis of models including the transport of contaminants has, however, focused on a quasi-static Stokes-Darcy model. Herein we consider the fully evolutionary system including contaminant transport and analyze its quasi-static limits.
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