Articles | Volume 25, issue 7
Hydrol. Earth Syst. Sci., 25, 3819–3835, 2021
Hydrol. Earth Syst. Sci., 25, 3819–3835, 2021

Research article 02 Jul 2021

Research article | 02 Jul 2021

Conditional simulation of spatial rainfall fields using random mixing: a study that implements full control over the stochastic process

Jieru Yan et al.


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-56', Anonymous Referee #1, 03 Mar 2021
    • AC1: 'Reply on RC1', Jieru Yan, 05 Mar 2021
  • RC2: 'Comment on hess-2021-56', Remko Uijlenhoet, 20 Mar 2021
    • AC2: 'Reply on RC2 concerning the general remarks', Jieru Yan, 22 Mar 2021
      • RC3: 'Reply on AC2', Remko Uijlenhoet, 23 Mar 2021
        • AC4: 'Reply on RC3', Jieru Yan, 29 Mar 2021
    • AC3: 'Reply on RC2 concerning the specific remarks', Jieru Yan, 25 Mar 2021
  • RC4: 'Comment on hess-2021-56', Scott Sinclair, 20 Apr 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (23 Apr 2021) by Nadav Peleg
AR by Lorena Grabowski on behalf of the Authors (12 May 2021)  Author's response
ED: Referee Nomination & Report Request started (12 May 2021) by Nadav Peleg
RR by Remko Uijlenhoet (28 May 2021)
ED: Publish as is (30 May 2021) by Nadav Peleg
Short summary
Accurate spatial precipitation estimates are important in various fields. An approach to simulate spatial rainfall fields conditioned on radar and rain gauge data is proposed. Unlike the commonly used Kriging methods, which provide a Kriged mean field, the output of the proposed approach is an ensemble of estimates that represents the estimation uncertainty. The approach is robust to nonlinear error in radar estimates and is shown to have some advantages, especially when estimating the extremes.