Abstract
This work presents a stochastic daily precipitation generator that incorporates a climate index to reflect the associated, seasonally varying, influence on simulated precipitation statistics. The weather generator is based on a first-order, two-state Markov chain to simulate the occurrence of daily precipitation and a gamma distribution to compute the nonzero daily precipitation amounts. Therefore, it has four parameters that are, in turn, allowed to vary daily following an autoregressive linear model in Gaussian space that simulates the parameters’ deviations from their climatological seasonal cycle. This model is forced by the independently predicted evolution of a climate index and captures how the model parameters and, therefore, precipitation are gradually shifted by the associated climate signal. In this case, the Niño-3.4 index is used to account for the influence of the El Niño–Southern Oscillation (ENSO) phenomenon on precipitation in Uruguay. However, the methodology is general and could be readily transferable to indices of other climate modes or downscaling algorithms for seasonal climate prediction. The results show that the proposed methodology successfully captures the ENSO signal on precipitation, including its seasonality. In doing so, it greatly reduces the underestimation of the seasonal and interannual precipitation variability, a well-known limitation of standard weather generators termed the “overdispersion” phenomenon. This work opens interesting opportunities for the application of seasonal climate forecasts in several process-based models (e.g., crop, hydrological, electric power system, water resources), which may be used to inform the decision-making and planning processes to manage climate-related risks.
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