Book Description
Several stochastic processes have been explored to simulate the areal climatic characteristics of the weather. The success or failure of a model of areal cover, or partial cover, has been judged partly by how well the resulting horizonal field of correlation resembles the natural field. The evaluation of each model, however, is based mostly on its efficiency in approximating the probability distribution of partial or complete coverage, by a weather condition, of an area. Emphasis, in application, is placed on the probability of cloud cover, that should vary from clear or zero cover, to partly cloudy, to overcast or 100% coverage. In addition to the size of the area, the probability distribution is directly related to the horizontal persistence of the weather element, which is parameterized in each model. The parameter is called scale distance. When the model successfully fits the observed areal extent, as viewed by a ground observer, it is then useful in application to other areal dimensions, as might be viewed by a satellite. With each of the several tested models, the snapshot picture of a field can be changed stochastically in a Markov process, simulating thereby a time sequence of weather patterns. Further investigation is well justified.
