Temporal dependence structure in weights in a multiplicative cascade model for precipitation

Abstract

We investigate the ability of the multiplicative random cascade model to accurately simulate temporal precipitation. Specifically, we explore the effect of the dependence structure in cascade weights due to clustering and within‐storm variability on the temporal correlation in simulated precipitation, and we compare the results with data at 69 stations with 10 min precipitation records in Switzerland. Correlation is quantified with the oscillation coefficient, which is a measure of patterns of fluctuations in data. Simulation results show that the assumption of temporal independence in cascade weights is generally not supported by observations of both rainfall and snowfall, which show generally higher correlation (lower fluctuations) at the hourly time resolution. Seasonal signatures are also apparent, with higher correlation in the cold season with dominant stratiform precipitation than in the warm season with convective precipitation. Measurement artifacts caused by the tipping bucket mechanism at high resolutions (10 min) are shown to play a significant role in the estimation of the correlation structure in cascade weights because of the quantization of precipitation intensity by the tip volume and sampling time resolution of the gauge. These effects are smoothed out at resolutions above 1 h when the oscillation coefficients become independent of resolution. Such measurement artifacts may have an important effect on the estimated scaling and correlation behavior in precipitation at high temporal resolutions.