Optimal Design of a Surface Precipitation Network in Canada

Abstract

The surface precipitation network in Canada suffers from large data gaps due to the challenge of covering a large country with a low population density. A proof-of-concept for an optimal network design is proposed to more efficiently estimate precipitation in Canada with the design goal of minimizing the interpolation uncertainty. The network design is based on a statistical model of precipitation that accounts for intermittency and non-Gaussianity of precipitation. Our results indicate that the greatest needs for new stations are in British Columbia, where coastal and mountain climate leads to more uncertainty in precipitation amounts, while the Prairie provinces (Alberta, Saskatchewan and Manitoba) could gain efficiencies by reducing their network size. Despite the current low density of stations in the territories north of Canada, these drier and colder regions only have a moderate need for more stations, mostly in the mountainous regions of Yukon. However, from a spatially varying wind undercatch measurement error model, it is shown that these northern regions are in the greatest needs for higher accuracy measurements.

A New GPM-DPR Algorithm to Estimate Snowfall in Mountain Regions

“…Gauge-based precipitation interpolation works efficiently in dense networks (Fallah et al, 2020;Tang et al, 2020), but their quality usually deteriorates in remote locations where networks are sparse. For instance, the network quality is still subpar in many regions of Canada (Mekis et al, 2018), especially in the mountains, the Arctic, and coastal regions (Brunet & Milbrandt, 2023). Atmospheric modelling through NWPs is rapidly evolving due to better physics representation and computational power and can now reliably estimate precipitation fields (Bauer et al, 2015).…”

“…For instance, Canadian Rockies IMERG daily precipitation gauge evaluation correlations are ~ 0.3 when compared to the neighbouring Canadian Prairies with correlations of ~ 0.6 (Asong et al, 2017). High mountain precipitation uncertainty in interpolation products can arise from wind undercatch (Biemans et al, 2009;Mekis et al, 2018;Smith, 2007), weighing gauge problems caused by evaporation losses and temperature and wind fluctuations (Pan et al, 2016), and sparse networks of gauges capable of measuring snowfall (Brunet & Milbrandt, 2023;Mekis et al, 2018). Mountain precipitation spatiotemporal variability has posed considerable challenges to the development of NWP models (Barros & Lettenmaier, 1994;Houze, 2012).…”