2017Monthly Weather ReviewRequires access

Using Polynomial Regression in Designing the Time Filters for the Leapfrog Time-Stepping Scheme

Daniel Yazgi, Ali R. Mohebalhojeh, Sarmad Ghader

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Abstract

Abstract A general framework is presented based on the least squares polynomial regression to design time filters for the leapfrog time-stepping scheme with required amplitude and phase properties. The well-known Robert–Asselin filter and its modification, the Robert–Asselin–Williams filter, are obtained using the zeroth-degree and first-degree polynomial regression, respectively. It is shown that using the second-degree polynomial regression, one can achieve seventh-order amplitude accuracy with only four time levels. In addition, the designed filter exhibits promising results when used with a semi-implicit time-stepping scheme.

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What this paper is about

Abstract A general framework is presented based on the least squares polynomial regression to design time filters for the leapfrog time-stepping scheme with required amplitude and phase properties. The well-known Robert–Asselin filter and its modification, the Robert–Asselin–Williams filter, are obtained using the zeroth-degree and first-degree polynomial regression, respectively. It is shown that using the second-degree polynomial regression, one can achieve seventh-order amplitude accuracy with only four time levels. In addition, the designed filter exhibits promising results when used with a semi-implicit time-stepping scheme.

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Available abstract

Abstract A general framework is presented based on the least squares polynomial regression to design time filters for the leapfrog time-stepping scheme with required amplitude and phase properties. The well-known Robert–Asselin filter and its modification, the Robert–Asselin–Williams filter, are obtained using the zeroth-degree and first-degree polynomial regression, respectively. It is shown that using the second-degree polynomial regression, one can achieve seventh-order amplitude accuracy with only four time levels. In addition, the designed filter exhibits promising results when used with a semi-implicit time-stepping scheme.

Key concepts: Polynomial regression, Polynomial, Degree (music), Regression, Filter (signal processing), Amplitude, Scheme (mathematics), Degree of a polynomial

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