Long temporal autocorrelations in tropical precipitation data and spike train prototypes

Abstract

AbstractTemporal precipitation autocorrelations drop slower than exponentially at long lags, and there is a range from tens to thousands of minutes where it is relevant to ask if a scale‐free process might underlie the long autocorrelations. A simple stochastic model in which precipitation appears as variable‐length spikes provides a reasonable prototype for this behavior. In both observations and the model, separating the component of the autocorrelation within wet events from the interevent contribution suggests long autocorrelation behavior is primarily associated with the latter. When precipitation spikes are short compared to dry events, a true power law is obtained with analytical exponent −0.5 and precipitation autocorrelation is determined by dry‐spell model parameters. In more realistic cases, wet‐spell termination is also important. Although a variety of apparent power law exponents can be obtained for different parameters, the fundamental long‐lag process appears to be that of the interevent correlation.