Practical rare event sampling for extreme mesoscale weather
For climate scientists and meteorologists needing efficient simulation of extreme weather events, this work presents a practical algorithm that improves sampling efficiency, though it is an incremental advance over existing rare event methods.
The paper introduces Quantile Diffusion Monte Carlo (Quantile DMC), a new rare event sampling algorithm for extreme mesoscale weather, and demonstrates its ability to sample extreme realizations of historical hurricanes with lower variance than existing methods.
Extreme mesoscale weather, including tropical cyclones, squall lines, and floods, can be enormously damaging and yet challenging to simulate; hence, there is a pressing need for more efficient simulation strategies. Here we present a new rare event sampling algorithm called Quantile Diffusion Monte Carlo (Quantile DMC). Quantile DMC is a simple-to-use algorithm that can sample extreme tail behavior for a wide class of processes. We demonstrate the advantages of Quantile DMC compared to other sampling methods and discuss practical aspects of implementing Quantile DMC. To test the feasibility of Quantile DMC for extreme mesoscale weather, we sample extremely intense realizations of two historical tropical cyclones, 2010 Hurricane Earl and 2015 Hurricane Joaquin. Our results demonstrate Quantile DMC's potential to provide low-variance extreme weather statistics while highlighting the work that is necessary for Quantile DMC to attain greater efficiency in future applications.