Spatial and temporal structure of zero-rainfall probabilities

Precipitation is a highly variable, non-continuous process in space and time. It is characterized by relatively long dry periods punctuated by shorter rain events with complex spatial and temporal structures. This constant alternating between dry and rainy periods, called rainfall intermittency, significantly affects the environment and the ecosystems. The major difficulty with rainfall intermittency is the fact that it varies significantly with respect to the considered scale, both in space and time. For example, short time periods are more likely to be (completely) dry than long ones, and small areas are more likely to be dry than large ones. The ability to quantify the probability of zero-rainfall at multiple timescales and spatial resolutions is crucial for many practical applications in hydrology, meteorology and remote sensing of precipitation. For example, it is important to downscale/upscale intermittent rainfall fields. In this presentation, the zero-rainfall probability is analyzed and quantified at multiple space and timescales using radar and disdrometer data. Parametric models are fitted and used to describe the statistical and structural characteristics of rainfall intermittency for spatial scales between 0 and 30 km and temporal resolutions between 0 and 2 hours