Evaluating Weather Forecasting App Accuracy in Columbus, Ohio

Weather apps are powerful tools that are accessible to a majority of the population. Because of their widespread use, it’s imperative that they provide the most accurate forecasts possible. Forecasts created in weather apps begin with numerical weather prediction (NWP) models followed by their own algorithms, climatic data, and forecaster experience to make forecasts unique to each app. There are multiple numerical weather prediction models available, some main ones being the North American Mesoscale (NAM), Global Forecasting System (GFS), and the European Center for Medium-Range Weather Forecast (ECMWF). All NWP models utilize complex sets of equations and computer algorithms in order to simulate the state of the atmosphere at some point in the future. Because of the complexity of the atmosphere, each of these models will not be a perfect representation of every location; this is why meteorologists use these models as a starting point to combine with their analysis of the atmosphere. It is this combination of model complexity coupled with forecaster discretion that can create room for error in any forecast and different forecasts among weather apps for the same location. This investigation will perform a case study within Columbus, Ohio by deploying a weather station equipped with a thermometer, tipping bucket rain gauge, and cup anemometer. This study will gather temperature, precipitation, and wind data at 00Z, 06Z, 12Z, and 18Z each day and compare the data at the specified times to the forecasts of 3 popular weather apps: Apple Weather, The Weather Channel, and Accuweather. Through this process, the study will determine which app is the most accurate to the true, measured observations made by the weather station. This will provide insight to better enhance the algorithms used by weather apps and provide clarity on which app is currently most accurate in the Columbus region.