This study proposes an integrated framework for identifying weather-disruption indicators by news intelligence in maritime transportation systems and ascertaining their impact on a port's operation. Port performance can be affected by many external and operational factors, including labor disputes, geopolitical tensions, infrastructure constraints, economic disruptions, and extreme weather conditions. This paper focuses on weather-related disruptions and examines how such events can be detected in unstructured news sources and their impact on port reliability performance. The framework combines keyword-based retrieval, large language model (LLM) classification, structured event extraction, and temporal reconstruction to convert online news into interpretable disruption indicators capturing event occurrence, type, impact, and timing. The impact of these indicators is then determined on a port's monthly reliability measures derived from vessel stay-duration records. The findings provide evidence that news-derived weather indicators can support the contextual interpretation of port reliability changes.