Articles | Volume 25, issue 2
https://doi.org/10.5194/hess-25-583-2021
https://doi.org/10.5194/hess-25-583-2021
Research article
 | 
10 Feb 2021
Research article |  | 10 Feb 2021

The use of personal weather station observations to improve precipitation estimation and interpolation

András Bárdossy, Jochen Seidel, and Abbas El Hachem

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
ED: Publish subject to revisions (further review by editor and referees) (15 Apr 2020) by Marie-Claire ten Veldhuis
AR by Jochen Seidel on behalf of the Authors (01 Jul 2020)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (03 Jul 2020) by Marie-Claire ten Veldhuis
RR by Nadav Peleg (07 Jul 2020)
RR by Marc Schleiss (03 Aug 2020)
ED: Publish subject to revisions (further review by editor and referees) (12 Aug 2020) by Marie-Claire ten Veldhuis
AR by Jochen Seidel on behalf of the Authors (28 Sep 2020)  Manuscript 
ED: Referee Nomination & Report Request started (27 Oct 2020) by Marie-Claire ten Veldhuis
RR by Marc Schleiss (24 Nov 2020)
ED: Publish subject to minor revisions (review by editor) (25 Nov 2020) by Marie-Claire ten Veldhuis
AR by Jochen Seidel on behalf of the Authors (04 Dec 2020)  Author's response   Manuscript 
ED: Publish as is (23 Dec 2020) by Marie-Claire ten Veldhuis
AR by Jochen Seidel on behalf of the Authors (28 Dec 2020)
Download
Short summary
In this study, the applicability of data from private weather stations (PWS) for precipitation interpolation was investigated. Due to unknown errors and biases in these observations, a two-step filter was developed that uses indicator correlations and event-based spatial precipitation patterns. The procedure was tested and cross validated for the state of Baden-Württemberg (Germany). The biggest improvement is achieved for the shortest time aggregations.