**Sometimes, the Gaussian distribution curve is not a good description of the data because these are not symmetrically distributed with respect to their values. In such cases, non-symmetric distribution functions may be used to describe the data, such as the Gamma distribution:
**

**.
**

**The estimated ``shape parameter'' **
** (Note: **
**; p. 85 in Wilks (1995) [] may give the impression that the estimator uses the mean of the square) and the ``scale parameter'' **
**, so that **
**. Another method to estimate the gamma-parameters is the maximum-likelihood fitting described by Wilks (1995) [] on p. 89, but this method doesn't allow negative and zero values (can be avoided by using only non-zero values).
**

** denotes the gamma-function defined as:
**

**The gamma function has a useful property which is that:
**

**If the **
** is known for any value **
**, then it is easy to calculate the corresponding value for and number with similar decimal points.
**

**Fig. 3.4 shows the distribution function for daily precipitation in Oslo between 1883 and 1964.
**

**An alternative to the Gamma distribution is the Weibull function. Stephenson et al (1999) [] gives a discussion on the distribution of the Indian rainfall data.
**