Articles | Volume 15, issue 22
Geosci. Model Dev., 15, 8325–8348, 2022
https://doi.org/10.5194/gmd-15-8325-2022
Geosci. Model Dev., 15, 8325–8348, 2022
https://doi.org/10.5194/gmd-15-8325-2022
Development and technical paper
18 Nov 2022
Development and technical paper | 18 Nov 2022

A local particle filter and its Gaussian mixture extension implemented with minor modifications to the LETKF

Shunji Kotsuki et al.

Download

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-69', Anonymous Referee #1, 05 Jul 2022
    • AC1: 'Reply on RC1', Shunji Kotsuki, 07 Sep 2022
  • RC2: 'Comment on gmd-2022-69', Anonymous Referee #2, 13 Aug 2022
    • AC2: 'Reply on RC2', Shunji Kotsuki, 07 Sep 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Shunji Kotsuki on behalf of the Authors (07 Sep 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (08 Sep 2022) by Yuefei Zeng
RR by Anonymous Referee #1 (10 Sep 2022)
RR by S.G. Penny (20 Oct 2022)
ED: Publish as is (20 Oct 2022) by Yuefei Zeng
AR by Shunji Kotsuki on behalf of the Authors (26 Oct 2022)  Author's response    Manuscript
Download
Short summary
Data assimilation plays an important part in numerical weather prediction (NWP) in terms of combining forecasted states and observations. While data assimilation methods in NWP usually assume the Gaussian error distribution, some variables in the atmosphere, such as precipitation, are known to have non-Gaussian error statistics. This study extended a widely used ensemble data assimilation algorithm to enable the assimilation of more non-Gaussian observations.