| Autor: |
Webber RJ; Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA., Plotkin DA; Department of Geophysical Sciences, University of Chicago, Chicago, Illinois 60637, USA., O'Neill ME; Department of Earth System Science, Stanford University, Stanford, California 94305, USA., Abbot DS; Department of Geophysical Sciences, University of Chicago, Chicago, Illinois 60637, USA., Weare J; Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA. |
| Abstrakt: |
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. |
