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The behavior of the atmosphere is governed by physical laws which can be expressed as mathematical equations. These equations represent how atmospheric quantities such as temperature, wind speed and direction, humidity, etc. If we can solve these equations, we will have a forecast. We can do this by sub-dividing the atmosphere into a 3-D grid of points and solving these equations at each point. These models have three main sources of error:. We have an imperfect description of what the atmosphere is doing right now, due to lack of data particularly over the oceans. When the model starts, is has an incorrect picture of the initial state of the atmosphere, so will always generate a forecast that is imperfect. Models are run on 3-D grids that cover the entire globe. Each grid point represents of piece of atmosphere perhaps 40 km on a side. Thus, processes smaller than that such as thunderstorms are not handled well, and must be "parameterized". This means we make up parameters fudge factors that do a good job giving the right forecast most of the time. Obviously, the fudge factors aren't going to work for all situations. Our basic understanding of the physics governing the atmosphere is imperfect, so the equations we're using aren't quite right. The best hurricane forecasting models we have are "global" models that solve the mathematical equations governing the behavior of the atmosphere at every point on the globe. Models that solve these equations are called "dynamical" models. These models take several hours to run on the world's most advanced supercomputers. There are also dynamical models that cover just a portion of the globe. These are less useful, unless the hurricane happens to start out inside the domain the model covers and stay there. Hurricanes moving from outside the model domain...

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