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Weather forecasts often affect daily lives of billions of people globally. Accurate forecasts can help combat and effectively mitigate damage caused by extreme weather. Alternatively, faulty forecasts can consequently lead to unnecessary financial investments and a waste of resources. Our work explores what is the extent of variability in errors of the National Weather Service predictions as observed in 113 cities in the United States between July 1, 2014 and September 1, 2017 and attempts to model the distribution of errors. Simultaneously, we deliver an interactive tool for future researchers to explore the actual and forecast weather data as well as expose hidden patterns in the data. [ABSTRACT FROM AUTHOR]

Accident taxonomy or classification can be used to direct the attention of policymakers to specific concerns in traffic safety, and can subsequently bring about effective regulatory change. Despite the widespread usage of accident taxonomy for general motor vehicle crashes, its use for analyzing bus crashes is limited. We apply a two-stage clustering-based approach based on self-organizing maps followed by neural gas clustering to construct a data-driven taxonomy of bus crashes. Using the 2005–2015 data from general estimates system, we identify four clusters and expose the qualitative traits that characterize four distinct types of bus crash. Our analysis suggests that cluster characteristics are largely stable over time. Consequently, we make targeted policy recommendations for each of the four subtypes of bus crash. [ABSTRACT FROM AUTHOR]