Comparison of probabilistic post-processing approaches  for improving numerical weather prediction-based daily  and weekly reference evapotranspiration forecasts

Medina, Hanoi; Tian, Di

doi:https://doi.org/10.5194/hess-24-1011-2020

Articles | Volume 24, issue 2

https://doi.org/10.5194/hess-24-1011-2020

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

https://doi.org/10.5194/hess-24-1011-2020

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

Articles | Volume 24, issue 2

Research article

|

03 Mar 2020

Research article |

| 03 Mar 2020

Comparison of probabilistic post-processing approaches for improving numerical weather prediction-based daily and weekly reference evapotranspiration forecasts

Hanoi Medina and Di Tian

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Final revised paper (published on 03 Mar 2020)
Preprint (discussion started on 12 Aug 2019)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Referee comment to Comparison of probabilistic post-processing approaches for improving NWP-based daily and weekly reference evapotranspiration forecasts', Anonymous Referee #1, 24 Sep 2019
- AC1: 'Authors' responses', Di Tian, 21 Nov 2019
RC2: 'Review of "Comparison of probabilistic post-processing approaches for improving NWP-based daily and weekly reference evapotranspiration forecasts"', Anonymous Referee #2, 24 Sep 2019
- AC2: 'Authors' responses', Di Tian, 21 Nov 2019

Peer-review completion

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

ED: Publish subject to minor revisions (further review by editor) (02 Dec 2019) by Albrecht Weerts

AR by Di Tian on behalf of the Authors (02 Dec 2019)

ED: Publish subject to technical corrections (22 Jan 2020) by Albrecht Weerts

AR by Di Tian on behalf of the Authors (29 Jan 2020) Author's response Manuscript

Short summary

Reference evapotranspiration (ET₀) forecasts play an important role in agricultural, environmental, and water management. This study evaluated probabilistic post-processing approaches for improving daily and weekly ensemble ET₀ forecasting based on single or multiple numerical weather predictions. The three approaches used consistently improved the skill and reliability of the ET₀ forecasts compared with the conventional method, due to the adjustment in the spread of the ensemble forecast.