People all over the world use forecasts from various sources such as the National Weather Service, local TV/radio stations, or apps on smartphones for a variety of applications. Examples include preparing for routine activities, such as planning outdoor events, or preparing for rare, yet hazardous scenarios like a thunderstorm passage near a sporting event. The most common quantities of interest are maximum/minimum temperatures, wind speed, and rainfall (chance and amount). To approximate these quantities, weather information sources utilize forecasts made from numerical weather prediction models of which there are about ten used in the United States. Numerical models of the atmosphere consist of equations that describe the current state of the atmosphere and how it changes with time and location. These equations are solved on powerful computers as the number of calculations are immense. Using the information from these models, individual forecasts can be made. Atmospheric Science students at UW are practicing forecast techniques through participation in a national competition called WxChallenge—a contest where participants from various academic institutions predict these quantities for selected US cities. For help with forecasting, the UW team has developed a website that holds a suite of model information which easily analyzes and compares that data and assesses the skill of each individual model. This website, however, needs updates and improvements. Therefore, I am converting the existing system to an object-oriented format by creating Forecast objects for individual weather models. For example, I have written a Forecast object for the DarkSky weather model using Python code. This type of improvement will reduce redundant code and allow for future developments to be implemented with ease. I am confident that this updated system will help the UW team improve the accuracy of their forecasts.