Bibliographic Details
| Title: |
Improving Tornado Intensity Prediction by Assimilating Radar-Retrieved Vortex Winds After Vortex Relocation |
| Authors: |
Qin Xu, Kang Nai, Li Wei, Nathan Snook, Yunheng Wang, Ming Xue |
| Source: |
Remote Sensing, Vol 16, Iss 24, p 4628 (2024) |
| Publisher Information: |
MDPI AG, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Science |
| Subject Terms: |
radar observation, vortex wind retrieval, numerical prediction of tornado, Science |
| More Details: |
A time–space shift method was recently developed for relocating the best ensemble member predicted tornado vortex to the radar-observed location, aiming to improve the model’s initial condition and subsequent prediction of tornadoes. To further improve tornado prediction, a variational method for analyzing vortex flows, referred to as VF-Var, is used in this paper to retrieve high-resolution vortex winds from the earliest radar volume scan of tornado and the retrieved vortex winds are then assimilated as “observations” after the vortex relocation. The previous three-step method is also adaptively modified to estimate the tornado vortex center location, denoted by xc ≡ (xc, yc) as a continuous function of height z and time t, from the earliest two consecutive radar volume scans of the tornado, so the estimated xc(z, t) can have the VF-Var required accuracy for retrieving high-resolution vortex winds and the retrieved vortex winds can be assimilated as “observations” with a minimized observation latency. This approach, combined with vortex relocation, is applied to the 20 May 2013 Oklahoma Newcastle–Moore tornado, and is shown to be very effective in further improving the tornado intensity prediction and the continuity of predicted tornado track. Although assimilating the retrieved high-resolution vortex winds after the vortex relocation does not greatly affect the overall trajectory of the predicted tornado track, it proves highly beneficial. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2072-4292 |
| Relation: |
https://www.mdpi.com/2072-4292/16/24/4628; https://doaj.org/toc/2072-4292 |
| DOI: |
10.3390/rs16244628 |
| Access URL: |
https://doaj.org/article/b2e646386f64451c83a9a05d019422fc |
| Accession Number: |
edsdoj.b2e646386f64451c83a9a05d019422fc |
| Database: |
Directory of Open Access Journals |
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