Characterising and visualizing spatio-temporal patterns in hourly precipitation records

Abstract

Knowledge about spatio-temporal patterns in meteorological data is essential for data assimilation and development of stochastic climate models. Here, we develop new techniques to summarize and visualize spatial patterns of coincidence in weather events such as more or less heavy precipitation at a network of meteorological stations. The cosine similarity measure, which has a simple probabilistic interpretation for vectors of binary data, is generalized to characterize spatial dependencies of events that may reach different stations with a variable time lag. More specifically, we reduce such patterns into three parameters (dominant time lag, maximum cross-similarity, and window-maximum similarity) that can easily be computed for each pair of stations in a network. Furthermore, we visualize such three-parameter summaries by using colour-coded maps of dependencies to a given reference station and distance-decay plots for the entire network. Applications to hourly precipitation data from a network of 93 stations in Sweden illustrate how this method can be used to explore spatial patterns in the temporal synchrony of precipitation events.

Climatology and Trends in Hourly Precipitation for the Southeast United States

“…This analysis expands upon previous research on trends in precipitation (e.g., Burauskaite-Harju et al 2012;Powell and Keim 2015;Fu et al 2016) by examining hourly precipitation data at 50 first-order weather stations across the SeUS. Previous studies (Groisman and Easterling 1994;Karl et al 1993;Skeeter et al 2019) focused on accumulations when determining change, but variations in accumulation could be the result of more hours with precipitation, more intense events, or a unique combination of changes in frequency, duration, and intensity.…”

Influence of El Niño and La Niña phenomena on seasonality of the relative frequency of rainfall in southern Amazonas mesoregion

“…Relative Frequency (RF) of rainfall studies provide important information and provide a better understanding of rainfall variability and distribution (Burauskaite-Harju et al, 2012). RF allows to determine the standard period of rainfall throughout the day for a given region, which is not found in analyzes of daily, monthly or annual average totals (Brown et al, 2018).…”

Hourly Precipitation Patterns in a Brazilian Tropical City

“…Studies of hourly Ppt pattern can provide important information to researchers, such as possible trends throughout the day, which are not observed in daily, monthly, or annual scale measurements (BROWN et al, 2018). The use of these information provides a better local view of the complex variability of Ppt (BURAUSKAITE-HARJU et al, 2012), and to measure the impacts on the soil, agricultural planning, runoff, degradation and water availability (WESTRA et al, 2013;JOSHI et al, 2019).…”