Articles | Volume 27, issue 2
© Author(s) 2023. This work is distributed underthe Creative Commons Attribution 4.0 License.
Seasonal forecasting of snow resources at Alpine sites
- Final revised paper (published on 27 Jan 2023)
- Preprint (discussion started on 09 Mar 2022)
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor |
: Report abuse
RC1: 'Comment on hess-2022-32', Anonymous Referee #1, 10 Apr 2022
- AC1: 'Reply on RC1', Silvia Terzago, 14 Jun 2022
RC2: 'Comment on hess-2022-32', Anonymous Referee #2, 27 Apr 2022
- AC2: 'Reply on RC2', Silvia Terzago, 14 Jun 2022
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (16 Jul 2022) by Markus Weiler
AR by Silvia Terzago on behalf of the Authors (12 Aug 2022) Author's response Author's tracked changes Manuscript
ED: Referee Nomination & Report Request started (22 Aug 2022) by Markus Weiler
RR by Anonymous Referee #1 (19 Sep 2022)
RR by Anonymous Referee #2 (10 Oct 2022)
ED: Publish subject to minor revisions (review by editor) (18 Oct 2022) by Markus Weiler
AR by Silvia Terzago on behalf of the Authors (28 Oct 2022) Author's response Author's tracked changes Manuscript
ED: Publish as is (12 Dec 2022) by Markus Weiler
AR by Silvia Terzago on behalf of the Authors (13 Dec 2022) Manuscript
Terzago et al. present some encouraging results on seasonal forecasting of snow at levels that could be of commercial use. There are overlaps with the aims of the PROSNOW project (http://prosnow.org/); I haven’t seen a published demonstration of seasonal forecasting from that project, but Koberl et al. (2021) on the market for seasonal forecast services for ski resorts is relevant.
The conclusion that bias correction of precipitation forecasts has little influence on snow depth skill scores might be surprising, or it might be misleading as no observations are used in the bias correction of precipitation (which is not mentioned in the abstract).
The GEM15 forecasts used by Bellaire et al. (2011) were only out to 48 hours and were found to overestimate precipitation, so differences in conclusions from this study might be expected. Forster et al. (2018) is a much more direct precursor of this study.
“this study” would more accurately be described as “that study”, i.e. Forster et al. (2018), not Terzago et al. (2022).
As stakeholders were involved in designing the prototype system, it is curious that none are included in the author list or acknowledgements.
6 decimal places in latitude and longitude locate the stations to within 10 cm, which seems unnecessary.
“Total radiation” here is, I think, net radiation.
Table 2 contains only a small amount of information that could easily be incorporated in the text.
I find the bias correction hard to understand. What is the elevation of the ECMWFS5 temperature forecast in Figure 2a? Is it surprising that there is such a large cold bias compared with a station at 2410 m elevation? If the green line was produced by quantile mapping the raw data onto the ERA5 CDF, why is it further from ERA5 than the raw data? If the discontinuity in the green line is due to the monthly quantile mapping, why does it only appear in mid-February?
Use superscripts for W/m2
The x-axis is labelled in months, not days. “Downscaled data” means different things for temperature and precipitation that is not apparent from the figure or caption. Why does cumulated precipitation appear to decrease in mid-February?
( missing before Matheson
The blue and dark blue lines are hard to distinguish when printed.