所有大气模型是建立在数值计算来自(原始方程)(http://en.wikipedia.org/wiki/Primitive_equations)描述大气流动。Vilhelm Bjerknes的关系发现,从而成为数值天气预报的父亲。从概念上讲,这些方程可以被认为是描述一个包裹空气如何与周围环境的关系。例如,我们学习在年轻的时候,热空气会向上升。(静水)(http://en.wikipedia.org/wiki/Hydrostatic_pressure) Hydrostatic_pressure垂直动量方程解释了为什么_and_量化在什么条件下热空气会停止上升。(当它扩展和冷却的空气上升时,直到它到达流体静力学平衡)。另一个考虑其他类型的运动方程和传热。不幸的是,方程是非线性的,这意味着您不能简单地插入一些数字和得到有用的结果。相反,气候模型模拟,将大气分为三维网格和计算物质和能量流从一个立方体的空间到另一个在离散时间增量。实际大气流动是连续的,而不是离散,通过必要的模型近似。 Different models make different approximations appropriate to their specific purpose. Numerical models have been improving over time for several reasons: 1. More and better input data, 2. Tighter grids, and 3. Better approximations. Increasing computational power has allowed models to use smaller grid boxes. However, the number of computations increases exponentially with the number of boxes and the process suffers [diminishing returns](http://en.wikipedia.org/wiki/Finite_difference_method#Accuracy_and_order). On the input end of things, more and better sensors improve the accuracy of the initial conditions of the model. [Synoptic scale](http://en.wikipedia.org/wiki/Synoptic_scale_meteorology) and [mesoscale](http://en.wikipedia.org/wiki/Mesoscale_meteorology) models take input from [General Circulation Models](http://en.wikipedia.org/wiki/General_Circulation_Model), which helps set reasonable intial conditions. On the output end, [Model Output Statistics](http://en.wikipedia.org/wiki/Model_output_statistics) do a remarkable job of estimating local weather by comparing the current model state with historical data of times when the model showed similar results. Finally, [ensemble models](http://en.wikipedia.org/wiki/Ensemble_forecasting) take the output of several models as input and produce a range of possibly outcomes.