TY - JOUR
T1 - Assimilation of X-Band Phased-Array Radar Data With EnKF for the Analysis and Warning Forecast of a Tornadic Storm
AU - Wang, Chen
AU - Zhao, Kun
AU - Zhu, Kefeng
AU - Huang, Hao
AU - Lu, Yinghui
AU - Yang, Zhengwei
AU - Fu, Peiling
AU - Zhang, Yu
AU - Chen, Binghong
AU - Hu, Dongming
N1 - Funding Information:
This work was primarily supported by the National Natural Science Foundation of China (grants 42025501, 41875053, 61827901), the National Key Research and Development Program of China (grant number 2017YFC1501703), the 5th “333 High‐level Personnel Training Project” of Jiangsu Province (BRA2019037) and the Open Research Program of the State Key Laboratory of Severe Weather (2020LASW‐A01). We acknowledge Guangzhou Meteorological Bureau and Zhuhai Naruida Technology Co., Ltd. for collecting and archiving the radar data.
Funding Information:
This work was primarily supported by the National Natural Science Foundation of China (grants 42025501, 41875053, 61827901), the National Key Research and Development Program of China (grant number 2017YFC1501703), the 5th ?333 High-level Personnel Training Project? of Jiangsu Province (BRA2019037) and the Open Research Program of the State Key Laboratory of Severe Weather (2020LASW-A01). We acknowledge Guangzhou Meteorological Bureau and Zhuhai Naruida Technology Co., Ltd. for collecting and archiving the radar data.
Publisher Copyright:
© 2021 The Authors. Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union.
PY - 2021/10
Y1 - 2021/10
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85118254370&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85118254370&partnerID=8YFLogxK
U2 - 10.1029/2020MS002441
DO - 10.1029/2020MS002441
M3 - Article
AN - SCOPUS:85118254370
SN - 1942-2466
VL - 13
JO - Journal of Advances in Modeling Earth Systems
JF - Journal of Advances in Modeling Earth Systems
IS - 10
M1 - e2020MS002441
ER -