Reduced non-Gaussianity by 30&thinsp;s rapid update in convective-scale numerical weather prediction

Ruiz, Juan; Lien, Guo-Yuan; Kondo, Keiichi; Otsuka, Shigenori; Miyoshi, Takemasa

doi:https://doi.org/10.5194/npg-28-615-2021

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.

https://doi.org/10.5194/npg-28-615-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 28, issue 4

Research article

|

01 Nov 2021

Research article |

| 01 Nov 2021

Reduced non-Gaussianity by 30 s rapid update in convective-scale numerical weather prediction

Juan Ruiz, Guo-Yuan Lien, Keiichi Kondo, Shigenori Otsuka, and Takemasa Miyoshi

Data sets

Phased Array Weather Radar Real-time observation data National Institute of Information and Communications Technology https://pawr.nict.go.jp/index_en.html

Model code and software

SCALE-LETKF version for npg-2021-15 manuscript (npg-2021-15) T. Honda, G.-Y. Lien, A. Amemiya, and H. Yashiro https://doi.org/10.5281/zenodo.5540380

SCALE (5.4.4) S. Nishizawa, H. Yashiro, T. Yamaura, S. A. Adachi, R. Yoshida, Y. Sato, K. Sueki, T. Matsushima, Y. Kawai, and H. Tomita https://doi.org/10.5281/zenodo.5146864

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.