The rain-rate analysis is a realization from a random process. Observed rain-rate realization change from year to year. A complete description of the rain-rate process must include information on year-to-year variability. The rain-rate realization itself would contain this information if the number of independent rain-rate samples in each year were known. This paper presents a new algorithm that provides both annual and monthly rain-rate distribution predictions. The parameters for the model are derived from long term climate data. The scheme not only provides a fast and efficient way to build a new neural network rainfall estimation model but also can provide a way to maintain an existing neural network rainfall estimation model [1].

Full text: IJEIR-382 Final.pdf