Answer:
The two models assimilate data differently using different equations.
Step-by-step explanation:
The weather models are based on mathematical equations in which the input variables are weather observations such as temperature, pressure, wind, moisture, etc that are obtained from the weather satellites and ground sensors. Using a process known as data assimilation, these weather observations are used to initialize numerical forecast models by combining the observations with prior forecasts.
The two global weather models, the European (European Center for Medium-Range Weather Forecast) and the American (National Weather Service’s Global Forecast System) models provide predictions all over the world. The mesoscale models or fine scale models can predict really small weather observations on more specific regions.
The atmosphere is divided into a three-dimensional grid in each model. At each grid point, the assimilated data is given and the mathematical equations are applied which are then stepped forward in time. The different weather models assimilate data differently using different equations and the outputs obtained over many time steps give the future weather.