Articles | Volume 28, issue 4
https://doi.org/10.5194/npg-28-615-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-28-615-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Nov 2021
Research article |
| 01 Nov 2021
| 01 Nov 2021
Reduced non-Gaussianity by 30 s rapid update in convective-scale numerical weather prediction
RIKEN Center for Computational Science, Kobe, Japan
Center for Oceanic and Atmospheric Research (CIMA-UBA/CONICET); Atmospheric and Oceanographic Science Department, FCEyN, University of Buenos Aires; French-Argentinean Institute for the Study of Climate and its Impacts (UMI-IFAECI/CNRS-CONICET-UBA), Buenos Aires, Argentina
Guo-Yuan Lien
Research and Development Center, Central Weather Bureau, Taipei, Taiwan
Keiichi Kondo
Department of Observation and Data Assimilation Research, Meteorological Research Institute, Tsukuba, Japan
Shigenori Otsuka
RIKEN Center for Computational Science, Kobe, Japan
RIKEN Cluster for Pioneering Research, Kobe, Japan
RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program, Kobe, Japan
Takemasa Miyoshi
CORRESPONDING AUTHOR
RIKEN Center for Computational Science, Kobe, Japan
RIKEN Cluster for Pioneering Research, Kobe, Japan
RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program, Kobe, Japan
Atmospheric and Oceanic Science Department, University of Maryland, College Park, Maryland, USA
Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
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Short summary
Effective use of observations with numerical weather prediction models, also known as data assimilation, is a key part of weather forecasting systems. For precise prediction at the scales of thunderstorms, fast nonlinear processes pose a grand challenge because most data assimilation systems are based on linear processes and normal distribution errors. We investigate how, every 30 s, weather radar observations can help reduce the effect of nonlinear processes and nonnormal distributions.
Effective use of observations with numerical weather prediction models, also known as data...
