Hierarchical Bayesian modeling of multisite daily rainfall occurrence: Rainy season onset, peak, and end

[1] A quantitative definition of and ability to predict the onset and duration of the dominant rainfall season in a region are important for agricultural and natural resources management. In this paper, a methodology based on an analysis of daily rainfall occurrence is proposed and applied to define the onset and end of the rainfall season in northeast Brazil. Multiple rainfall gauges are considered simultaneously, and a hierarchical Bayesian framework is used for parameter estimation. The proposed model can be used to identify the rainy season onset by finding the first day of year in which the estimated probability of rainfall is greater than a specified number, e.g., 0.5. The determination of the end of the rainy season follows a similar procedure. Thus, the rainfall season can be defined as a period during which rainfall is most likely to occur. Monotonic trends are identified for the maximum probability of daily rainfall across the region, suggesting evidence of climate change. However, only the southern stations in the region exhibit a trend in the corresponding date. An examination of the correlations of dates of maximum rainfall probability with leading climate indicators leads to a promising direction for 1–3 month ahead forecasts of onset, which will be very useful for effective planning. Particularly, El Niño events in December are found to be associated with delays in the oncoming rainy season onset over a large part of southern northeast Brazil. Negative anomalies in the tropical South Atlantic sea surface temperature between January and March can anticipate the rainy season onset in the northern region and along the eastern coast of northeast Brazil.