Detecting and locating outdoor border crossing events is valuable information in curbing drug trafficking, reducing poaching, and protecting high-asset equipment and goods. However, border sensing is notoriously challenging, prone to false alarms and missed detections, with serious consequences. Weather events, like rain and wind, make it even more challenging to maintain a low level of missed detections and false alarms. In this paper, we propose and test an automated system of wireless sensors which uses received signal strength (RSS) measurements to localize where a border crossing occurs. In addition, we develop new RSS-based statistical models and methods that can quickly be initialized and updated by using link RSS statistics to adapt to time-varying RSS changes due to weather events. These models are implemented in two new classifiers that localize border crossings with few missed detections and false alarms. We validate our proposed methods by implementing one of the classifiers in a three month long deployment of a solar-powered, real-time system that captures images of the border for ground truth validation. Furthermore, over 75 hours of RSS measurements are collected with an emphasis on collection during weather events, like rain and wind, during which we expect our classifiers to perform the worst. We demonstrate that the proposed classifiers outperform four other baseline classifiers in terms of false alarm probability by 1 to 4 orders of magnitude, and in terms of the misclassification probability by 1 to 2 orders of magnitude.