The precipitation component of WGEN is a first-order Markov chain-gamma model. With the first-order Markov chain model, the probability of rain on a given day is conditioned on the wet or dry status of the previous day. For this application, a distribution with a minimum number of parameters was needed to minimize the problem of defining the parameters for a large number of locations. Richardson (1982a) has shown the two-parameter gamma distribution to be significantly better for describing daily precipitation amounts than the simple one-parameter exponential distribution. In WGEN, the precipitation parameters are constant for a given month but are varied from month to month.
Another model (link below) calculates the inputs required for the Markov Chain Precipitation Genenerator. Just load the time series data into that model and it will produce the PWW, PWD and Mean/SDev values. The functionality of this model is taken from the WGEN Weather Simulator.
Download the Model File:
For consistency, added the same time series of observed values used in WGEN. Added clarification that the mean and standard deviation values are calculated from only the wet days (dry days are ignored when calculating these).
Also added the Markov Rainfall Parameter Generator model to this article so you can generate the inputs using a time series of historic precipitation values.
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