Uncertain data is found in many different contexts in life and often people need to make decisions based on this uncertain data. An example of such uncertain data is weather forecasts. People make decisions of varying importance, based on the weather forecast, almost everyday. In order to increase their confidence and make an informed decision, many people compare weather forecasts from multiple sources, which is a very time consuming and cumbersome task. This master thesis aims to find the best method to combine multiple weather forecast sources in a single application, so that the users would have the confidence of comparing multiple sources without the time consuming nature of doing it manually. We have identified three methods to aggregate weather data from multiple sources and compared them using different representations. Together with our aggregation methods we have also included a single source option in our research to compare aggregated weather forecasts with single source forecasts. We have conducted a survey to decide on which aggregation method to test further in our research and then implemented a mobile application to test our aggregation method in real world user study. Our results showed that independent of the representation method, users prefer aggregated weather forecasts over single source forecasts. Additionally we have found that users prefer aggregations that provide more information when making important decisions and prefer computationally aggregated methods for daily use.