Characterizing severe weather potential in synoptically weakly forced thunderstorm environments

Miller, Paul W.; Mote, Thomas L.

doi:https://doi.org/10.5194/nhess-18-1261-2018

Articles | Volume 18, issue 4

https://doi.org/10.5194/nhess-18-1261-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/nhess-18-1261-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 4

Research article

|

27 Apr 2018

Research article |

| 27 Apr 2018

Characterizing severe weather potential in synoptically weakly forced thunderstorm environments

Paul W. Miller and Thomas L. Mote

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Final revised paper (published on 27 Apr 2018)
Preprint (discussion started on 09 Oct 2017)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'An interesting paper worthy of publication', Tomas Pucik, 26 Oct 2017
- AC1: 'Reply to Referee #1', Paul Miller, 08 Jan 2018
RC2: 'Review', Anonymous Referee #2, 27 Oct 2017
- AC2: 'Response the Referee #2', Paul Miller, 08 Jan 2018

Peer-review completion

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

ED: Reconsider after major revisions (further review by editor and referees) (25 Jan 2018) by Uwe Ulbrich

AR by Paul Miller on behalf of the Authors (30 Jan 2018) Author's response Manuscript

ED: Referee Nomination & Report Request started (20 Feb 2018) by Uwe Ulbrich

RR by Anonymous Referee #2 (12 Mar 2018)

ED: Publish as is (02 Apr 2018) by Uwe Ulbrich

AR by Paul Miller on behalf of the Authors (05 Apr 2018)

Short summary

The likelihood of severe weather events in synoptically weakly forced thunderstorm (WFT) environments is best characterized by lapse-rate-based parameters. These measures, also among the most accurate model-derived variables, are posited to best capture the subtle convective environmental differences that favor WFT severity. Forecasters should consider weighting their WFT forecasts in favor of lapse-rate-based parameters over others that may be more sensitive to model biases.