Assimilation of X-Band Phased-Array Radar Data With EnKF for the Analysis and Warning Forecast of a Tornadic Storm

Chen Wang, Kun Zhao, Kefeng Zhu, Hao Huang, Yinghui Lu, Zhengwei Yang, Peiling Fu, Yu Zhang, Binghong Chen, Dongming Hu

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The impact of assimilating China's operational X-band Phased-Array radar's (X-PAR) data on the analysis and warning forecast of the vortex structure and intensity of the June 8, 2018 Foshan, Guangdong province, tornadic storm was investigated for the first time using an Ensemble Kalman Filter (EnKF) data assimilation system. Both radar radial velocity (Vr) and reflectivity (Z) from two S-band operational radars and one X-PAR were assimilated. Deterministic forecasts were launched every 6 min from 05:42 UTC (20 min before the tornado touched down) to 06:00 UTC from the EnKF mean analysis field. Five experiments were conducted to examine the added capability of Z assimilation of the EnKF system, and to investigate the impact of assimilating X-PAR data on the analysis and prediction of the tornadic storm. Compared to the experiment without Z assimilation, the assimilation of Z reduced the analysis error and greatly reduced the forecast error of Z. The assimilation of X-PAR data greatly improved the vortex structure of the tornadic storm at low levels, and improved the intensity of the rear inflow of the tornadic storm, especially with a higher assimilation frequency. Compared to the experiments without X-PAR data assimilation, assimilating X-PAR data improved the predictability of tornadic storm.

Original languageEnglish (US)
Article numbere2020MS002441
JournalJournal of Advances in Modeling Earth Systems
Volume13
Issue number10
DOIs
StatePublished - Oct 2021

All Science Journal Classification (ASJC) codes

  • Global and Planetary Change
  • Environmental Chemistry
  • General Earth and Planetary Sciences

Fingerprint

Dive into the research topics of 'Assimilation of X-Band Phased-Array Radar Data With EnKF for the Analysis and Warning Forecast of a Tornadic Storm'. Together they form a unique fingerprint.

Cite this