A stable and accurate scheme for nonlinear diffusion equations: Application to atmospheric boundary layer

Stability concerns are always a factor in the numerical solution of nonlinear diffusion equations, which are a class of equations widely applicable in different fields of science and engineering. In this study, a modified extended backward differentiation formulae (ME BDF) scheme is adapted for the solution of nonlinear diffusion equations, with a special focus on the atmospheric boundary layer diffusion process. The scheme is first implemented and examined for a widely used nonlinear ordinary differential equation, and then extended to a system of two nonlinear diffusion equations. A new temporal filter which leads to significant improvement of numerical results is proposed, and the impact of the filter on the stability and accuracy of the results is investigated. Noteworthy improvements are obtained as compared to other commonly used numerical schemes. Linear stability analysis of the proposed scheme is performed for both systems, and analytical stability limits are presented.

[1]  Joao Teixeira Stable Schemes for Partial Differential Equations , 1999 .

[2]  A. R. Gourlay,et al.  The Extrapolation of First Order Methods for Parabolic Partial Differential Equations, II , 1978 .

[3]  Jeff Cash,et al.  On the integration of stiff systems of O.D.E.s using extended backward differentiation formulae , 1980 .

[4]  Eugenia Kalnay,et al.  Time Schemes for Strongly Nonlinear Damping Equations , 1988 .

[5]  Liviu Gr. Ixaru Runge-Kutta method with equation dependent coefficients , 2012, Comput. Phys. Commun..

[6]  Maja Telišman Prtenjak,et al.  Stabilization of Nonlinear Vertical Diffusion Schemes in the Context of NWP Models , 2000 .

[7]  Jean-Noël Thépaut,et al.  Simplified and Regular Physical Parameterizations for Incremental Four-Dimensional Variational Assimilation , 1999 .

[8]  J. Verwer Explicit Runge-Kutta methods for parabolic partial differential equations , 1996 .

[9]  Andrew Staniforth,et al.  Aspects of the dynamical core of a nonhydrostatic, deep-atmosphere, unified weather and climate-prediction model , 2008, J. Comput. Phys..

[10]  E. Süli,et al.  Numerical Solution of Ordinary Differential Equations , 2021, Foundations of Space Dynamics.

[11]  Terry Davies,et al.  An improved implicit predictor–corrector scheme for boundary layer vertical diffusion , 2006 .

[12]  Elaine S. Oran,et al.  Numerical Simulation of Reactive Flow , 1987 .

[13]  J. Lambert Numerical Methods for Ordinary Differential Equations , 1991 .

[14]  A. P. Siebesma,et al.  A Combined Eddy-Diffusivity Mass-Flux Approach for the Convective Boundary Layer , 2007 .

[15]  Morten Hjorth Jensen,et al.  COMPUTATIONAL Physics , 2015 .

[16]  Nigel Wood,et al.  A monotonically‐damping second‐order‐accurate unconditionally‐stable numerical scheme for diffusion , 2007 .

[17]  Jeff Cash,et al.  The integration of stiff initial value problems in ODEs using modified extended backward differentiation formulae , 1983 .

[18]  N. Rashevsky,et al.  Mathematical biology , 1961, Connecticut medicine.

[19]  K. Burrage,et al.  Stability Criteria for Implicit Runge–Kutta Methods , 1979 .

[20]  Kevin Judd,et al.  Time Step Sensitivity of Nonlinear Atmospheric Models: Numerical Convergence, Truncation Error Growth, and Ensemble Design , 2007 .

[21]  Claude Girard,et al.  Stable Schemes for Nonlinear Vertical Diffusion in Atmospheric Circulation Models , 1990 .

[22]  J. R. Cash,et al.  Two New Finite Difference Schemes for Parabolic Equations , 1984 .