SWM: a Stochastic Weather Model for precipitation-related hazard assessments using ERA5-land data

Long-term hazard and risk assessments are produced by combining many hazard-model simulations, each based on a slightly different set of inputs to cover the uncertainty space. While most input parameters for these models are relatively well-constrained, atmospheric parameters remain problematic unless working on very short-time scales (hours to days). Precipitation is a key trigger for many natural hazards including floods, landslides, and lahars. This work presents a stochastic weather model that takes openly available ERA5-land data, and produces long-term (e.g., decadal), hourly, spatially varying precipitation data that mimics the statistical dimensions of real-data. Thus, allowing precipitation to be robustly included in hazard-model simulations.

The stochastic weather model (SWM) comprises three steps: Data conversion, block construction, and stochastic weather generation. Due to the relative simplicity of the model and exploiting some coding efficiencies in the R package dplyr, 10 years of hourly data can be generated across a 10 by 10 cell grid (~110 km by 110 km) on a standard desktop computer in < 5 seconds.