Radar Observations of Rainfall Variability Using Non-Rayleigh Signal Fluctuations

Abstract

The spatial variability and temporal variability of precipitation are widely recognized. In particular, rainfall rates can fluctuate widely in regions where the raindrops are clustered and where mean conditions are changing (statistical heterogeneity). Indeed, at times, the ambiguity associated with an estimated average rainfall rate may become very large. Therefore, in quantitative measurements of precipitation, it would be useful to identify where this occurs. In this work a technique is proposed and applied to quantify the variability in rainfall rates introduced by statistical heterogeneity and raindrop clustering using deviations from Rayleigh statistics of intensity fluctuations. This technique separates the Rayleigh contributions to the observed relative dispersion from those arising from clustering and statistical heterogeneities. Applications to conventional meteorological radar measurements are illustrated using two scans. Often, but not always, the greatest ambiguities in estimates of the average rainfall rate occur just where the rainfall rates are the largest and presumably where accurate estimates are most important. This ambiguity is not statistical; rather, it indicates the presence of important sub-beam-scale fluctuations. As a consequence, no single average value can be applied uniformly to the entire domain. The examples provided here also demonstrate that the appropriate observations are feasible using current conventional meteorological radars with adequate processing capabilities. However, changes in radar technology that improve and increase pulse-to-pulse statistical independence will permit such observations to be gathered more routinely at finer spatial resolution and with enhanced precision.