The impact of conventional space‐time aggregation on the dynamics of continuous‐time rainfall

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AbstractRainfall is a continuous‐time phenomenon typically characterized by precipitation states such as rain, showers, and dry whose dependence varies over a variety of space‐time scales. Here attention is focused on the effective identification of rain and shower precipitation states over a region where these states have been determined by a hidden semi‐Markov model of continuous‐time precipitation. The states identified provide an accurate description of precipitation dynamics and can be regarded as close proxies to synoptic weather types of the same name. The stochastic properties and structure of these states (rather than precipitation amounts) are explored and delineated. A primary objective of the paper is to better understand the impact of conventional space‐time aggregation on the dynamics of rainfall. What aggregation time scales result in more faithful descriptions of the space‐time dynamics of continuous‐time rainfall? While rain might be expected to be more spatially coherent than showers and involve longer time scales, dry periods involve much longer time and space scales again than either rain or showers. These issues are discussed and conclusions drawn which provide guidance and insights useful for the development of space‐time precipitation models and, more generally, the design of rainfall observation networks and data archives.