Articles | Volume 27, issue 2
https://doi.org/10.5194/npg-27-209-2020
https://doi.org/10.5194/npg-27-209-2020
Research article
 | 
16 Apr 2020
Research article |  | 16 Apr 2020

Data-driven versus self-similar parameterizations for stochastic advection by Lie transport and location uncertainty

Valentin Resseguier, Wei Pan, and Baylor Fox-Kemper

Download

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Svenja Lange on behalf of the Authors (24 Jan 2020)  Author's response
ED: Referee Nomination & Report Request started (27 Jan 2020) by Wansuo Duan
RR by Anonymous Referee #1 (06 Feb 2020)
RR by Zheqi Shen (07 Feb 2020)
ED: Publish as is (09 Feb 2020) by Wansuo Duan
Download
Short summary
Geophysical flows span a broader range of temporal and spatial scales than can be resolved numerically. One way to alleviate the ensuing numerical errors is to combine simulations with measurements, taking account of the accuracies of these two sources of information. Here we quantify the distribution of numerical simulation errors without relying on high-resolution numerical simulations. Specifically, small-scale random vortices are added to simulations while conserving energy or circulation.