Mine-Weather is an analytical database and visualization system. With it, companies probe the accuracy of the weather forecasts used to plan their activities and preparedness. Typically, companies use this data incase of storm conditions.
Our tool captures and stores both weather forecast data and observational data for all stations, within a given geographical area.
We store data at hourly intervals. Additionally, we storedata for several years. Consequently, this facilitates the analysis of different types of weather events over a long period of time. As we configure a new system for a specific client, we upload existing historical data as well.
Weather forecast data plays a central role in the preparedness and capacity for various companies and governmental entities. This includes industries such as: electric and gas utilities, transportation companies and transit government entities. These industries have an inherent interest in understanding the relationship between infrastructure and their service capabilities across different weather conditions.
Mine-Weather provides the framework for companies to perform their own independent analysis on the accuracy of weather forecasts they rely. The evaluation is tailored for specific types of weather events that are most important to the client. Mine-Weather analyzes the critical issues, not the overall weather forecast accuracy over a season/year. Clients see how good their weather forecast data for the event types most important to a specific company.
We have developed various advanced scoring algorithms based on statistical methods to be incorporated into the Mine-Weather platform. This is the first among the three papers that we consider to start a discussion on the advanced methods, by setting up a conceptual framework that focuses all our thinking on weather forecast verification.
Use the power of analytics to tame the most uncontrollable business variable of all: Weather. Mine-Weather is a game-changing application evaluating the confidence of your weather forecast. It is backed by the power of data science and analytics.