the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 Sep 2023
Brief communication: SWM: Stochastic Weather Model for precipitation-related hazard assessments
Abstract. Long-term hazard and risk assessments are produced by combining many hazard-model simulations, each using slightly different set of inputs to cover the uncertainty space. While most input parameters for these models are relatively well-constrained, atmospheric parameters remain problematic unless working on very short-time scales (hours to days). Precipitation is a key trigger for many natural hazards including floods, landslides, and lahars. This work presents a stochastic catchment-scale weather model that takes openly available ERA5-land data, and produces long-term, spatially varying precipitation data that mimics the statistical dimensions of real-data. This allows precipitation to be robustly included in hazard-model simulations.
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Status: closed
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RC1: 'Comment on nhess-2023-160', Anonymous Referee #1, 06 Oct 2023
In this MS, the authors discussed the capabilities of stochastic weather models on predicting rainfall in the Rangitāiki-Tarawera catchment. They have demonstrated the potential of SWM based on the ERA5-land data. However, some issues need to be addressed:
The author is supposed to add the “ERA5-land data” in the title, on which this MS is based.
In Lines 41-42, I don’t get the point “converts values from accumulated to hourly rainfall”, free hourly precipitation data can be downloaded from the ERA5 website.
In Lines 68, 95 sets are obtained to provide ninety-fifth percentile bounds. According to the MS, the more sets the better results. The authors need to explain why they had to generate 95 sets?
Fig. 3, the authors are suggested to draw the ninety-fifth percentile bounds at (e) to (j).
Fig. 3, The ACF values approximate 0 over time, maybe adding a table could better illustrate the results.
Fig. 3, some small mistakes in the Y-AXIS of (f) and (h).
Citation: https://doi.org/10.5194/nhess-2023-160-RC1 - AC1: 'Reply on RC1', Melody Whitehead, 06 Dec 2023
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RC2: 'Comment on nhess-2023-160', Anonymous Referee #2, 02 Nov 2023
- AC2: 'Reply on RC2', Melody Whitehead, 06 Dec 2023
Status: closed
-
RC1: 'Comment on nhess-2023-160', Anonymous Referee #1, 06 Oct 2023
In this MS, the authors discussed the capabilities of stochastic weather models on predicting rainfall in the Rangitāiki-Tarawera catchment. They have demonstrated the potential of SWM based on the ERA5-land data. However, some issues need to be addressed:
The author is supposed to add the “ERA5-land data” in the title, on which this MS is based.
In Lines 41-42, I don’t get the point “converts values from accumulated to hourly rainfall”, free hourly precipitation data can be downloaded from the ERA5 website.
In Lines 68, 95 sets are obtained to provide ninety-fifth percentile bounds. According to the MS, the more sets the better results. The authors need to explain why they had to generate 95 sets?
Fig. 3, the authors are suggested to draw the ninety-fifth percentile bounds at (e) to (j).
Fig. 3, The ACF values approximate 0 over time, maybe adding a table could better illustrate the results.
Fig. 3, some small mistakes in the Y-AXIS of (f) and (h).
Citation: https://doi.org/10.5194/nhess-2023-160-RC1 - AC1: 'Reply on RC1', Melody Whitehead, 06 Dec 2023
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RC2: 'Comment on nhess-2023-160', Anonymous Referee #2, 02 Nov 2023
- AC2: 'Reply on RC2', Melody Whitehead, 06 Dec 2023
Model code and software
SWM code Melody G. Whitehead https://github.com/MelWhitehead/SWM
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