Stochastic Downscaling of Hourly Precipitation Series From Climate Change Projections. Issue 10 (17th October 2022) - Yu, Ziwen; Montalto, Franco; Jacobson, Stefan; Lall, Upmanu; Bader, Daniel; Horton, Radley
Abstract: Stochastic precipitation generators (SPGs) are often used to produce synthetic precipitation series for water resource management. Typically, an SPG assumes a stationary climate. We present an hourly precipitation generation algorithm for nonstationary conditions informed by the global climate model (GCM) forecasted average monthly temperature (AMT). The physical basis for precipitation formation is considered explicitly in the design of the algorithm using hourly pressure change events (PCE) to define the relationship between hourly precipitation and AMT. The algorithm consists of a multivariable Markov Chain and a moving window driven by time, temperature, and pressure change. We demonstrate the methodology by generating a 100‐year, continuous, synthetic hourly precipitation time series using GCM AMT projections for the Northeast United States. When compared with historical observations, the synthetic results suggest that future precipitation in this region will be more variable, with more frequent mild events and fewer but intensified extremes, especially in warm seasons. The synthetic time series suggests that there will be less precipitation in the summers, while winters will be wetter, consistent with other research on climate change projections for the Northeast United States. This SPG provides physically plausible weather ensembles for water resource studies involving climate change. Key Points: This article introduces a nonparametric stochastic process in generating synthetic precipitations for nonstationary climate The basis of this algorithm is highly related to the physical mechanism of precipitation formation defined by pressure change events