Assimilation of Radar Reflectivity Using a Time‐Lagged Ensemble Based Ensemble Kalman Filter With the “Cloud‐Dependent” Background Error Covariances-Reference-Cited by-同舟云学术

Assimilation of Radar Reflectivity Using a Time‐Lagged Ensemble Based Ensemble Kalman Filter With the “Cloud‐Dependent” Background Error Covariances

Published:2022-05-19 Issue:10 Volume:127 Page:
ISSN:2169-897X
Container-title:Journal of Geophysical Research: Atmospheres
language:en
Short-container-title:JGR Atmospheres

Author:

Wang Haoliang¹²^ORCID,Liu Yubao¹²^ORCID,Duan Jing³^ORCID,Shi Yueqin³,Lou Xiaofeng³,Li Jiming³

Affiliation:

1. Key Laboratory for Aerosol‐Cloud‐Precipitation of China Meteorological Administration Nanjing University of Information Science and Technology Nanjing China

2. Precision Regional Earth Modeling and Information Center Nanjing University of Information Science and Technology Nanjing China

3. Key Laboratory for Cloud Physics of China Meteorological Administration (CMA) Beijing China

Publisher

American Geophysical Union (AGU)

Subject

Space and Planetary Science,Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021JD036207

Reference69 articles.

1. A Multicase Comparative Assessment of the Ensemble Kalman Filter for Assimilation of Radar Observations. Part I: Storm-Scale Analyses

2. A Multicase Comparative Assessment of the Ensemble Kalman Filter for Assimilation of Radar Observations. Part II: Short-Range Ensemble Forecasts

3. A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances

4. A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics

5. Microphysical Process Comparison of Three Microphysics Parameterization Schemes in the WRF Model for an Idealized Squall-Line Case Study

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