Comparing Extreme Weather Events Generated by 36- and 12-km WRF Simulations

Extreme weather events—such as heat waves, drought, flooding, and tropical cyclones—can have catastrophic impacts on society. These extreme events can adversely affect the economy, infrastructure, ecosystems, agriculture, transportation, air quality, and human health. Governing organizations typically account for extreme events to some degree in their planning processes to minimize the impacts of extreme events.

There is a growing consensus in the literature that extreme events are likely to intensify through this century. Consequently, more emphasis has been placed on quantifying the changes to the frequency and intensity of these events. Several modeling techniques have been used to project potential changes to weather and climate across the next century, and there are several public data sets available that are currently used by federal, state, and local agencies to aid in the decision-making process. However, not all data sets can characterize the extremes associated with these events.

In this study, the Weather Research and Forecasting (WRF) model is configured as a regional climate model, and simulations are conducted on historical (verifiable) data sets to emulate the dynamical downscaling procedure that would be applied to refine global climate model projections. Here, we use 36-km and 12-km modeling domains, and we compare categories of extreme weather events that would influence governmental planning organizations. We evaluate these WRF simulations against observations to demonstrate the trade-offs between the computational expense of 12-km modeling domain versus developing a broader ensemble at 36-km on extreme event realization.