Assimilation of X‐Band Phased‐Array Radar Data With EnKF for the Analysis and Warning Forecast of a Tornadic Storm-Reference-Cited by-同舟云学术

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

Published:2021-10 Issue:10 Volume:13 Page:
ISSN:1942-2466
Container-title:Journal of Advances in Modeling Earth Systems
language:en
Short-container-title:J Adv Model Earth Syst

Author:

Wang Chen¹,Zhao Kun¹^ORCID,Zhu Kefeng²^ORCID,Huang Hao¹^ORCID,Lu Yinghui³,Yang Zhengwei¹,Fu Peiling⁴,Zhang Yu⁴,Chen Binghong⁴,Hu Dongming⁵

Affiliation:

1. Key Laboratory of Mesoscale Severe Weather/MOE and School of Atmospheric Sciences Nanjing University Nanjing, China, and State Key Laboratory of Severe Weather and Joint Center for Atmospheric Radar Research of CMA/NJU Beijing China

2. CMA Key Laboratory of Transportation Meteorology Nanjing Joint Institute for Atmospheric Sciences Nanjing China

3. Department of Meteorology and Atmospheric Science and Center for Advanced Data Assimilation and Predictability Techniques The Pennsylvania State University University Park State College PA USA

4. Guangzhou Meteorological Observatory Guangzhou China

5. Guangdong Meteorological Observatory Guangzhou China

Publisher

American Geophysical Union (AGU)

Subject

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

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020MS002441

Reference42 articles.

1. A Three-Dimensional Variational Data Assimilation System for MM5: Implementation and Initial Results

2. Assimilation of 3D radar reflectivities with an ensemble Kalman filter on the convective scale

3. A Comparison between the 4DVAR and the Ensemble Kalman Filter Techniques for Radar Data Assimilation

4. Ensemble Kalman Filter Assimilation of Radar Observations of the 8 May 2003 Oklahoma City Supercell: Influences of Reflectivity Observations on Storm-Scale Analyses

5. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics

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