Published 2018 | Version v1
Software Open

Action minimization algorithm for TC rapid intensification in the WRF model

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Description

Direct computer simulation of intense tropical cyclones (TCs) in climate models is limited by computational expense. Intense TCs have small-scale structures and are relatively rare, making it difficult to produce large ensembles of storms at sufficiently high resolution. Further, models often fail to capture the process of rapid intensification, which is a distinguishing feature of the most intense TCs. The problem of rapid intensification is especially important in the context of global warming, which is often postulated to in crease the frequency of intense TCs. To better leverage computational resources for the study of rapid intensification, we present an action minimization code applied to the WRF and WRFPLUS models. The algorithm adds a series of perturbations to a model trajectory over time, biasing the model toward states with some characteristic of interest (in this case, an intense TC). Each perturbation is indistinguishable from noise and consists of an adjustment to each value in several two- or three-dimensional physical fields: zonal and meridional wind, temperature, surface pressure, and geopotential.

Abstract

The code presented here applies action minimization to the WRF and WRFPLUS models in order to enhance rapid intensification of TCs. It requires both models to be installed and requires initial/boundary conditions for the time horizon of integration, as well as namelists for the forward (WRF) and adjoint (WRFPLUS) models. The code produces output files with both the unperturbed trajectory and the action minimization output. In principle, the cost function can be modified in order to investigate processes leading to other final states (not just TCs).

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Additional details

Identifiers

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oai:knowledge.uchicago.edu:212

Funding

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We acknowledge support from the National Science Foundation under NSF award number 1623064. This work was supported by the Department of Energy Computational Science Graduate Fellowship Program of the Office of Science and National Nuclear Se- curity Administration in the Department of Energy under contract DE-FG02-97ER25308. RW and JW are supported by the Advanced Scientific Computing Research Program within the DOE Office of Science through award DE-SC0014205. This work was completed with resources provided by the University of Chicago Research Computing Center

UChicago Information

Division(s)
Physical Sciences Division
Department(s)
Geophysical Sciences