Evaluation of two modified Kalman gain algorithms for radar data assimilation in the WRF model-Reference-Cited by-同舟云学术

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Evaluation of two modified Kalman gain algorithms for radar data assimilation in the WRF model

Published:2015-05-22 Issue:1 Volume:67 Page:25950
ISSN:1600-0870
Container-title:Tellus A: Dynamic Meteorology and Oceanography
language:en
Short-container-title:Tellus A: Dynamic Meteorology and Oceanography

Author:

Yang Chun¹,Min Jinzhong¹,Tang Youmin²³

Affiliation:

1. Nanjing University of Information Science & Technology Nanjing 210044, China

2. University of Northern British Columbia, Prince George, Canada V2N 4Z9

3. State Key Lab of Satellite Ocean Environment Dynamics Second Institute of Oceanography, Hangzhou, China

Publisher

Informa UK Limited

Subject

Atmospheric Science,Oceanography

Link

https://www.tandfonline.com/doi/pdf/10.3402/tellusa.v67.25950

Reference39 articles.

1. Sigma-Point Kalman Filter Data Assimilation Methods for Strongly Nonlinear Systems

2. An Ensemble Adjustment Kalman Filter for Data Assimilation

3. A Local Least Squares Framework for Ensemble Filtering

4. Adaptive Sampling with the Ensemble Transform Kalman Filter. Part I: Theoretical Aspects

5. Application of ATOVS radiance-bias correction to typhoon track prediction with Ensemble Kalman Filter data assimilation

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1. A local data assimilation method (Local DA v1.0) and its application in a simulated typhoon case;Geoscientific Model Development;2022-12-12

2. Impact of the hybrid gain ensemble data assimilation on meso-scale numerical weather prediction over east China;Atmospheric Research;2018-07

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