Stochastic perturbations in the semi-Lagrangian advection algorithm of the SL-AV global atmosphere model-Reference-Cited by-同舟云学术

Stochastic perturbations in the semi-Lagrangian advection algorithm of the SL-AV global atmosphere model

Published:2024-02-01 Issue:1 Volume:39 Page:1-11
ISSN:1569-3988
Container-title:Russian Journal of Numerical Analysis and Mathematical Modelling
language:en
Short-container-title:

Author:

Alipova Kseniya A.¹²,Mizyak Vasiliy G.¹,Tolstykh Mikhail A.¹²,Goyman Gordey S.²³

Affiliation:

1. Hydrometeorological Centre of Russia , Moscow , Russia

2. Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences , Moscow , Russia

3. Moscow institute of Physics and Technology (National Research University) , Dolgoprudnii , Russia

Abstract

Abstract An algorithm for stochastic perturbation of the semi-Lagrangian trajectories is implemented in the ensemble weather prediction system based on the global atmosphere model SL-AV20 with a horizontal resolution of approximately 20 km, 51 vertical levels, and Local Ensemble Transform Kalman Filter (LETKF). The combined use of methods for stochastic perturbation of trajectories and the parameters and tendencies of subgrid-scale processes parameterizations allows to generate ensembles with a larger spread compared to ensembles without stochastic perturbations of trajectories. An improvement in probabilistic estimates of the ensemble forecasts for various variables is shown. The comparison of two versions of ensemble prediction system is presented.

Publisher

Walter de Gruyter GmbH

Link

https://www.degruyter.com/document/doi/10.1515/rnam-2024-0001/pdf

Reference32 articles.

1. K. A. Alipova, G. S. Goyman, M. A. Tolstykh, V. G. Mizyak, and V. S. Rogutov, Stochastic perturbation of tendencies and parameters of parameterizations in the global ensemble prediction system based on the SL-AV model. Russ. J. Numer. Anal. Math. Modelling 37 (2022), No. 6, 331–347.

2. J. L. Anderson and S. L. Anderson, A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Monthly Weather Review 127 (1999), No. 12, 2741–2758.

3. J. Berner, G. J. Shutts, M. Leutbecher, and T. N. Palmer, A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system. J. Atmos. Sci. 66 (2009), 603–626.

4. N. E. Bowler, A. Arribas, and K. R. Mylne, The benefits of multianalysis and poor man’s ensembles. Mon. Wea. Rev. 136 (2008), 4113–4129.

5. R. Buizza, M. Miller, and T. Palmer, Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Q. J. R. Meteorol. Soc. 125 (1999) No. 560, 2887–2908.