Information-theoretic analysis of temporal dependence in discrete stochastic processes: Application to precipitation predictability
This work addresses the need for efficient stochastic rainfall models to enhance weather forecasting, though it is incremental as it builds on existing information-theoretic approaches.
The authors tackled the problem of quantifying temporal dependence in precipitation to improve weather predictability, introducing an information-theoretic method that outperforms AIC and BIC in tests and reveals daily rainfall is well described by low-order Markov chains with regional and seasonal variations.
Understanding the temporal dependence of precipitation is key to improving weather predictability and developing efficient stochastic rainfall models. We introduce an information-theoretic approach to quantify memory effects in discrete stochastic processes and apply it to daily precipitation records across the contiguous United States. The method is based on the predictability gain, a quantity derived from block entropy that measures the additional information provided by higher-order temporal dependencies. This statistic, combined with a bootstrap-based hypothesis testing and Fisher's method, enables a robust memory estimator from finite data. Tests with generated sequences show that this estimator outperforms other model-selection criteria such as AIC and BIC. Applied to precipitation data, the analysis reveals that daily rainfall occurrence is well described by low-order Markov chains, exhibiting regional and seasonal variations, with stronger correlations in winter along the West Coast and in summer in the Southeast, consistent with known climatological patterns. Overall, our findings establish a framework for building parsimonious stochastic descriptions, useful when addressing spatial heterogeneity in the memory structure of precipitation dynamics, and support further advances in real-time, data-driven forecasting schemes.