Preprints
https://doi.org/10.5194/npg-2021-15
https://doi.org/10.5194/npg-2021-15

  22 Mar 2021

22 Mar 2021

Review status: a revised version of this preprint was accepted for the journal NPG and is expected to appear here in due course.

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

Juan Ruiz1,4, Guo-Yuan Lien2, Keiichi Kondo3, Shigenori Otsuka4,5,6, and Takemasa Miyoshi4,5,6,7,8 Juan Ruiz et al.
  • 1Centro de Investigaciones del Mar y la Atmósfera (CIMA-UBA/CONICET); Departamento de Ciencias de la Atmósfera y de los Océanos, FCEN, Universidad de Buenos Aires; Unidad Mixta Internacional-Instituto Franco-Argentino para el Estudio del Clima y sus Impactos (UMI-IFAECI/CNRS-CONICET-UBA), Buenos Aires, Argentina
  • 2Central Weather Bureau, Taipei, Taiwan
  • 3Meteorological Research Institute, Tsukuba, Japan
  • 4RIKEN Center for Computational Science, Kobe, Japan
  • 5RIKEN Cluster for Pioneering Research, Kobe, Japan
  • 6RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program, Kobe, Japan
  • 7University of Maryland, College Park, Maryland, USA
  • 8Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

Abstract. Non-Gaussian forecast error is a challenge for ensemble-based data assimilation (DA), particularly for more nonlinear convective dynamics. In this study, we investigate the degree of non-Gaussianity of forecast error distributions at 1-km resolution using a 1000-member ensemble Kalman filter, and how it is affected by the DA update frequency and observation number. Regional numerical weather prediction experiments are performed with the SCALE (Scalable Computing for Advanced Library and Environment) model and the LETKF (Local Ensemble Transform Kalman Filter) assimilating every-30-second phased array radar observations. The results show that non-Gaussianity develops rapidly within convective clouds and is sensitive to the DA frequency and the number of assimilated observations. The non-Gaussianity is reduced by up to 40 % when the assimilation window is shortened from 5 minutes to 30 seconds, particularly for vertical velocity and radar reflectivity.

Juan Ruiz et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on npg-2021-15', Anonymous Referee #1, 27 Mar 2021
    • AC1: 'Reply on RC1', Juan Ruiz, 28 Jun 2021
  • RC2: 'Comment on npg-2021-15', Anonymous Referee #2, 19 Apr 2021
    • AC2: 'Reply on RC2', Juan Ruiz, 28 Jun 2021
  • RC3: 'Comment on npg-2021-15', Anonymous Referee #3, 20 Apr 2021
    • AC3: 'Reply on RC3', Juan Ruiz, 28 Jun 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on npg-2021-15', Anonymous Referee #1, 27 Mar 2021
    • AC1: 'Reply on RC1', Juan Ruiz, 28 Jun 2021
  • RC2: 'Comment on npg-2021-15', Anonymous Referee #2, 19 Apr 2021
    • AC2: 'Reply on RC2', Juan Ruiz, 28 Jun 2021
  • RC3: 'Comment on npg-2021-15', Anonymous Referee #3, 20 Apr 2021
    • AC3: 'Reply on RC3', Juan Ruiz, 28 Jun 2021

Juan Ruiz et al.

Juan Ruiz et al.

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Short summary
Effective use of observations with numerical weather prediction models, a.k.a. data assimilation, is a key part of weather forecasting systems. For precise prediction at the scales of thunderstorms, fast nonlinear processes bring a grand challenge because most data assimilation systems are based on linear processes and normal-distribution errors. We investigate how every 30-seconds weather radar observations can help reduce the effect of nonlinear processes and non-normal distributions.