# Practical rare event sampling for extreme mesoscale weather

## TL;DR

This paper introduces Quantile Diffusion Monte Carlo, a new sampling algorithm designed to efficiently simulate extreme mesoscale weather events, such as hurricanes, by accurately capturing rare tail behaviors.

## Contribution

The paper presents Quantile DMC, a novel, easy-to-implement rare event sampling method that improves the efficiency of simulating extreme weather phenomena.

## Key findings

- Quantile DMC effectively samples extreme tail events in weather simulations.
- It demonstrates lower variance in extreme weather statistics compared to existing methods.
- Application to historical hurricanes shows promising results for future extreme weather modeling.

## Abstract

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.