Improving the Observation Operator for the Phased Array Weather Radar in the SCALE-LETKF System
Author:
Affiliation:
1. RIKEN Center for Computational Science (R-CCS)
2. RIKEN Cluster for Pioneering Research
Publisher
Meteorological Society of Japan
Subject
Atmospheric Science
Link
https://www.jstage.jst.go.jp/article/sola/16/0/16_2020-002/_pdf
Reference18 articles.
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2. Dee, D. P., and A. M. Da Silva, 1998: Data assimilation in the presence of forecast bias. Quart. J. Roy. Meteor. Soc., 124, 269-295, doi:10.1256/smsqj.54511.
3. Gustafsson, N., and co-authors, 2018: Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres. Quart. J. Roy. Meteor. Soc., 144, 1218-1256, doi:10.1002/qj.3179.
4. Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D: Nonlinear Phenomena, 230, 112-126, doi:10.1016/J.PHYSD.2006.11.008.
5. Jung, Y., M. Xue, and G. Zhang, 2010: Simultaneous estimation of microphysical parameters and the atmospheric state using simulated polarimetric radar data and an ensemble Kalman filter in the presence of an observation operator error. Mon. Wea. Rev., 138, 539-562, doi:10.1175/2009MWR2748.1.
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