Submitted as: model description paper
|
14 Aug 2019
Submitted as: model description paper |

|
14 Aug 2019
Status: this preprint was under review for the journal GMD. A final paper is not foreseen.
Spatial and Temporal Evolution of a Lightning Diagnostic in HWRF (V3.7a)
Keren Rosado, Bin Liu, Vernon Morris, Vijay Tallapragada, and Lin Zhu
The operational Hurricane Weather Research and Forecast (HWRF) model has been used to investigate the role of lightning diagnostics in the life cycle of tropical cyclones. A lightning parameterization, the Lightning Potential Index (LPI), was implemented into HWRF with the motivation that an improvement in the forecast of lightning will lead to reductions in the HWRF model intensity forecast errors and bias. Three questions are addressed: (i) Can the HWRF model predict lightning temporal distributions with an acceptable degree of accuracy? (ii) How well does the HWRF model with lightning parameterization forecast lightning spatial distributions before, during, and after tropical cyclone intensification? (iii) What is the functional relationship between tropical cyclone wind speed and lightning frequency in the HWRF model forecast? A five-day simulation of Idealized tropical cyclones with and without eyewall replacement cycle, has been conducted, followed by two real cases e.g. hurricanes Earl and Igor to evaluate the evolution of the spatial distribution of lightning location. Results from this investigation led to the following observations: (1) the potential for lightning occurrence increases to its maximum peak prior to the maximum predicted wind intensity and (2) the numerical simulations predict a negative correlation between lightning occurrence and maximum winds during the storm’s peak intensity.
This preprint has been withdrawn.
Received: 16 May 2019 – Discussion started: 14 Aug 2019
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Keren Rosado, Bin Liu, Vernon Morris, Vijay Tallapragada, and Lin Zhu
Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
- Printer-friendly version
- Supplement
Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
- Printer-friendly version
- Supplement
Keren Rosado, Bin Liu, Vernon Morris, Vijay Tallapragada, and Lin Zhu
Keren Rosado, Bin Liu, Vernon Morris, Vijay Tallapragada, and Lin Zhu
Viewed
Total article views: 1,094 (including HTML, PDF, and XML)
HTML |
PDF |
XML |
Total |
BibTeX |
EndNote |
717 |
302 |
75 |
1,094 |
98 |
104 |
- HTML: 717
- PDF: 302
- XML: 75
- Total: 1,094
- BibTeX: 98
- EndNote: 104
Views and downloads (calculated since 14 Aug 2019)
Cumulative views and downloads
(calculated since 14 Aug 2019)
Viewed (geographical distribution)
Total article views: 960 (including HTML, PDF, and XML)
Thereof 957 with geography defined
and 3 with unknown origin.
Country |
# |
Views |
% |
United States of America | 1 | 416 | 43 |
Germany | 2 | 126 | 13 |
China | 3 | 74 | 7 |
Ireland | 4 | 40 | 4 |
Netherlands | 5 | 39 | 4 |
|
Total: |
0 |
HTML: |
0 |
PDF: |
0 |
XML: |
0 |
Latest update: 09 May 2025