An improved post-processing technique for automatic precipitation gauge time series

Ross, Amber; Smith, Craig D.; Barr, Alan

doi:https://doi.org/10.5194/amt-13-2979-2020

Articles | Volume 13, issue 6

https://doi.org/10.5194/amt-13-2979-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-13-2979-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 6

Research article

|

05 Jun 2020

Research article |

| 05 Jun 2020

An improved post-processing technique for automatic precipitation gauge time series

Amber Ross, Craig D. Smith, and Alan Barr

Download

Final revised paper (published on 05 Jun 2020)
Preprint (discussion started on 20 Dec 2019)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

- Printer-friendly version

- Supplement

RC1: 'Interactive comment on “An improved post-processing technique for automatic precipitation gauge time series”', Anonymous Referee #1, 14 Jan 2020
- AC1: 'Authors response to referee #1', Craig Smith, 21 Apr 2020
RC2: 'Review of «An improved post-processing technique for automatic precipitation gauge time series» by Amber Ross, Craig D. Smith, and Alan Barr, Manuscript Number: amt-2019-423.', Anonymous Referee #2, 17 Jan 2020
- AC2: 'Authors response to referee #2', Craig Smith, 21 Apr 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Craig Smith on behalf of the Authors (21 Apr 2020) Author's response Manuscript

ED: Publish as is (30 Apr 2020) by Daqing Yang

Short summary

The raw data derived from most automated accumulating precipitation gauges often suffer from non-precipitation-related fluctuations in the measurement of the gauge bucket weights from which the precipitation amount is determined. This noise can be caused by electrical interference, mechanical noise, and evaporation. This paper presents an automated filtering technique that builds on the principle of iteratively balancing noise to produce a clean precipitation time series.