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  • View in gallery

    Schematic of the scaled, nondimensionalized Fourier domain (unit square). The buffer region (dotted) is not meteorologically trustworthy. The domain of meteorological interest is thus smaller, non-dotted square (x, y) ∈ [D, 1 − D] ⊗ [D, 1 − D].

  • View in gallery

    Cross section along the line segment y = 1/2, x ∈ [1/2, 1] of the absolute value of the error in approximating the solution of the Poisson equation when forced by a typical inhomogeneous function, f = fcheckerboard (x, y) where the “checkerboard” function is defined later in the paper, by an unaccelerated bivariate Fourier series truncated at N = 160. [The analytical solution is (6) to (9) plus (14).]

  • View in gallery

    Maximum error over the unit square for the solution of uxx + uyy = fcheckerboard (top thin curve with black squares). As in top curve, but that errors at points within D = 1/5 of the boundary are excluded (bottom thick curve with black circles). The dashed lines are guidelines with slopes of 1/N2 and 1/N3, respectively.

  • View in gallery

    Absolute values of errors in the N = 40 Fourier approximation to the solution of the Poisson equation when forced by fcheckerboard(x, y). The domain of meteorological interest is shown schematically as the interior of the dashed square.

  • View in gallery

    (left) The periodic sine function σ(x) approximated by its sine series up to and including sin(19x). (right) As in (left), but for the piecewise linear sawtooth function with partial sums up to sin(20x).

  • View in gallery

    Absolute value of , plotted with logarithmic scales vs N.

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Applications of Bivariate Fourier Series for Solving the Poisson Equation in Limited-Area Modeling of the Atmosphere: Higher Accuracy with a Boundary Buffer Strip Discarded and an Improved Order-Raising Procedure

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  • 1 Atmospheric, Oceanic and Space Science, University of Michigan, Ann Arbor, Michigan, and Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • | 2 Graduate University of Chinese Academy of Sciences, and Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • | 3 Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
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Abstract

Bivariate Fourier series have many benefits in limited-area modeling (LAM), weather forecasting, and meteorological data analysis. However, atmospheric data are not spatially periodic on the LAM domain (“window”), which can be normalized to the unit square (x, y) ∈ [0, 1] ⊗ [0, 1] by rescaling the coordinates. Most Fourier LAM meteorology has employed rather low-order methods that have been quite successful in spite of Gibbs phenomenon at the boundaries of the artificial periodicity window. In this article, the authors explain why. Because data near the boundary between the high-resolution LAM window and the low-resolution global model are necessarily suspect, corrupted by the discontinuity in resolution, meteorologists routinely ignore LAM results in a buffer strip of nondimensional width D, and analyze only the Fourier sums in the smaller domain (x, y) ∈ [D, 1 − D] ⊗ [D, 1 − D]. It is shown that the error in a one-dimensional Fourier series with N terms or in a two-dimensional series with N2 terms, is smaller by a factor of N on a boundary-buffer-discarded domain than on the full unit square. A variety of procedures for raising the order of Fourier series convergence are described, and it is explained how the deletion of the boundary strip greatly simplifies and improves these enhancements. The prime exemplar is solving the Poisson equation with homogeneous boundary conditions by sine series, but the authors also discuss the Laplace equation with inhomogeneous boundary conditions.

Current affiliation: Department of Atmospheric, Oceanic and Space Science, University of Michigan, Ann Arbor, Michigan.

Corresponding author address: John P. Boyd, Department of Atmospheric, Oceanic and Space Science, University of Michigan, 2455 Hayward Ave., Ann Arbor, MI 48109. E-mail: jpboyd@umich.edu

Abstract

Bivariate Fourier series have many benefits in limited-area modeling (LAM), weather forecasting, and meteorological data analysis. However, atmospheric data are not spatially periodic on the LAM domain (“window”), which can be normalized to the unit square (x, y) ∈ [0, 1] ⊗ [0, 1] by rescaling the coordinates. Most Fourier LAM meteorology has employed rather low-order methods that have been quite successful in spite of Gibbs phenomenon at the boundaries of the artificial periodicity window. In this article, the authors explain why. Because data near the boundary between the high-resolution LAM window and the low-resolution global model are necessarily suspect, corrupted by the discontinuity in resolution, meteorologists routinely ignore LAM results in a buffer strip of nondimensional width D, and analyze only the Fourier sums in the smaller domain (x, y) ∈ [D, 1 − D] ⊗ [D, 1 − D]. It is shown that the error in a one-dimensional Fourier series with N terms or in a two-dimensional series with N2 terms, is smaller by a factor of N on a boundary-buffer-discarded domain than on the full unit square. A variety of procedures for raising the order of Fourier series convergence are described, and it is explained how the deletion of the boundary strip greatly simplifies and improves these enhancements. The prime exemplar is solving the Poisson equation with homogeneous boundary conditions by sine series, but the authors also discuss the Laplace equation with inhomogeneous boundary conditions.

Current affiliation: Department of Atmospheric, Oceanic and Space Science, University of Michigan, Ann Arbor, Michigan.

Corresponding author address: John P. Boyd, Department of Atmospheric, Oceanic and Space Science, University of Michigan, 2455 Hayward Ave., Ann Arbor, MI 48109. E-mail: jpboyd@umich.edu

1. Introduction

Global weather forecasting and climate models still have grids too coarse to resolve thunderstorms. A common remedy is to embed a high-resolution window—a limited-area model (LAM)—within a global model. A common LAM task is to solve the Poisson equation to calculate the streamfunction from the vorticity and the velocity potential from the divergence.

The Poisson equation arises frequently in applications in many fields, not merely meteorology:
e1
where ∂Γ denotes the boundary of the unit square. Because the equation is linear, the general solution can always be split into the superposition of the solution to (i) an inhomogeneous differential equation with homogeneous boundary conditions (i.e., Poisson equation) plus (ii) a solution to a homogeneous PDE with inhomogeneous boundary conditions (i.e., Laplace equation):
e2
e3
e4
From a Fourier perspective, the Poisson subproblem is more challenging then the Laplace subproblem, so we shall mostly concentrate upon the former before returning to discuss the Laplace equation near the end. Such a decomposition into subproblems is the heart of the harmonic-sine method of Chen and Kuo (1992a,b) and its successors (Chen et al. 1997).
To solve the Poisson equation with homogeneous boundary conditions, the first step is to expand f(x, y) in a bivariate sine series:
e5
where the coefficients can be calculated by the usual Fourier integrals:
e6
Assuming a similar expansion for u(x, y) with coefficients umn and matching terms gives
e7
where
e8
The simplicity of bivariate Fourier series has inspired a host of Fourier LAMs and meteorological data analysis schemes including Bubnová et al. (1995), Cocke (1998), Cocke and LaRow (2000), Denis et al. (2002), Errico (1985), Haugen and Machenhauer (1993), Hong et al. (1999), Hong and Leetmaa (1999), Juang (1992), Juang and Kanamitsu (1994), Juang et al. (1997), Juang and Hong (2001), Juang et al. (2003), Kuo and Williams (1992, 1998), Park et al. (2011), Radnoti (1995), Sasaki et al. (1995), Segami et al. (1989), Tatsumi (1986), Boyd (2005), Termonia et al. (2012), and Degrauwe et al. (2012).
Unfortunately, the series for f(x, y) has generally only a first-order rate of convergence while the coefficients umn have only a third-order rate of convergence:
e9
Worse still, the partial sums converge nonuniformly in space to a function that is discontinuous. If the series is truncated to include all coefficients umn with
e10
for some integer N, then Sköllermo (1975) has proved that maximum pointwise error is O(1/N2) as N → ∞.

In spite of this rather modest predicted rate of convergence, Fourier LAM models and data analysis have been successful, spawning the large number of applications already cited. As noted in the abstract, part of the reason for the success is that accuracy in the meteorologically interesting region is an order of magnitude higher than predicted for the maximum error over the entire Fourier domain (i.e., the square).

The reasons that meteorologists discard a strip of width D around the boundary of the unit square as shown schematically in Fig. 1 are fully described in Boyd (2005) and the references he cites. In this article, we shall concentrate on the mathematics. However, when a storm straddles the boundary between the high-resolution LAM domain (here nondimensionalized to the unit square), and the coarser grid of the global model that fills the rest of the sphere, the dynamics of the storm will be unreliable. Only data that is a finite distance away from the discontinuity in resolution can be trusted. Furthermore, there are several issues about inflow and outflow boundary conditions and blending the limited-area and global approximations smoothly through “Davies relaxation” and so on. It is then silly to worry about numerical accuracy in such regions where the meteorology is already corrupted.

Fig. 1.
Fig. 1.

Schematic of the scaled, nondimensionalized Fourier domain (unit square). The buffer region (dotted) is not meteorologically trustworthy. The domain of meteorological interest is thus smaller, non-dotted square (x, y) ∈ [D, 1 − D] ⊗ [D, 1 − D].

Citation: Monthly Weather Review 141, 11; 10.1175/MWR-D-13-00074.1

The reason that the exclusion of this boundary strip is important is illustrated in Fig. 2, which is a cross section of the error from the center of the domain to the boundary for a typical truncated, unaccelerated Poisson Fourier solution. The error is large near the boundary but diminishes very rapidly away from the boundary at x = 1. This extreme nonuniformity in error is the point of this article.

Fig. 2.
Fig. 2.

Cross section along the line segment y = 1/2, x ∈ [1/2, 1] of the absolute value of the error in approximating the solution of the Poisson equation when forced by a typical inhomogeneous function, f = fcheckerboard (x, y) where the “checkerboard” function is defined later in the paper, by an unaccelerated bivariate Fourier series truncated at N = 160. [The analytical solution is (6) to (9) plus (14).]

Citation: Monthly Weather Review 141, 11; 10.1175/MWR-D-13-00074.1

In the rest of this article, we shall prove the higher rate of convergence in the interior in one dimension, demonstrate this in two dimensions, and describe how the order of convergence can be raised by simple modifications to the standard Fourier procedure. We concentrate primarily upon solving the Poisson equation with homogeneous boundary conditions. We shall show in the next-to-last section that when a boundary strip is deleted, the analogous Fourier series that solves the Laplace equation converges exponentially fast.

2. Rates of convergence

Why is the convergence so slow?

In one dimension, the usual Fourier coefficient integral
e11
can be written without approximation by integration by parts as
e12
If f(x) is sufficiently smooth, we may integrate by parts again. However, unless f(0) = f(1) = 0, we are stuck with the boundary terms which decay only as slowly as O(1/n). The sum of the sine series must be zero at x = 0 and x = 1 because every term in the series is individually zero at these points. If f(0) and f(1) are nonzero, then the Fourier series must approximate a function that jumps discontinuously at these endpoints, giving rise to the Gibbs Phenomenon. Classical textbook analysis shows that in thin layers near these endpoints, the error is O(1) independent of the series truncation N—note that the maximum pointwise error of the sum is larger by a factor of N than the Nth coefficient, which is O(1/N). The partial sum overshoots near the jump by about 17% of the jump, and this does not diminish with increasing series truncation N (Boyd 2001; Jerri 2011).
Sköllermo extended this analysis to two dimensions. She integrates the Fourier coefficients integrals by parts, once in each coordinate, to obtain
e13
If all the boundary terms are zero, then the two-dimensional integral can be integrated by parts again. Each additional integration by parts inserts additional negative powers of m and n in front of the transformed integral. The boundedness of the integral then implies that fmn must decay asymptotically as fast as all the negative powers in front of the integral. This is a standard argument for asymptotically estimating Fourier coefficients (Sköllermo 1975; Boyd 2009). However, unless f(x, y) satisfies compatibility conditions on the boundaries such that all the boundary terms in (13) sum to zero, the leading term (which is the sum of the boundary contributions) will be nonzero, implying that fmn will decay only as O[1/(mn)]. A concrete example is f(x, y) ≡ 1 on the unit square, which has the following series:
e14
Because the sines are antisymmetric with respect to both x = 0 and x = π, that is, sin(−mπx) = −sin(mπx) for all x, and similarly in y, the sine series above converges to ±1. (This is called the “checkerboard function” because if one visualizes a western style checkerboard/chessboard with squares of unit area, the function is equal to one on the white squares and minus one on the black.) Because the checkerboard function is discontinuous everywhere on the boundary of the unit square, its series exhibits a two-dimensional Gibbs Phenomenon (Boyd 2009; Jerri 2011). The discontinuities create a global pollution in the sense that the error is O(1/N) everywhere, but rises to O(1) in a layer of thickness O(1/N) at the boundary. The solution to
e15
has coefficients converging faster than fcheckerboard by a factor of (m2 + n2), but the error in ucheckerboard shows the same concentration near the boundaries.
Figure 3 plots the errors versus truncation N where
e16
e17
where D > 0 is a user-choosable parameter. The discarded buffer strip error ignores all points that lie within a distance D of the boundary of the unit square. The second-order convergence for the square versus third-order for the square with discarded strip is clear.
Fig. 3.
Fig. 3.

Maximum error over the unit square for the solution of uxx + uyy = fcheckerboard (top thin curve with black squares). As in top curve, but that errors at points within D = 1/5 of the boundary are excluded (bottom thick curve with black circles). The dashed lines are guidelines with slopes of 1/N2 and 1/N3, respectively.

Citation: Monthly Weather Review 141, 11; 10.1175/MWR-D-13-00074.1

Figure 4 shows the errors for N = 40. The dashed square shows the boundary of the region used to compute Edeleted,strip. Third-order convergence is obviously better than second order, but can one do better?

Fig. 4.
Fig. 4.

Absolute values of errors in the N = 40 Fourier approximation to the solution of the Poisson equation when forced by fcheckerboard(x, y). The domain of meteorological interest is shown schematically as the interior of the dashed square.

Citation: Monthly Weather Review 141, 11; 10.1175/MWR-D-13-00074.1

3. Raising the order of convergence by boundary subtractions

Gunilla Sköllermo's answer was to split f(x, y) in two, choosing a function B(x, y) which matches f(x, y) everywhere on the boundary of the square, and a second part, . By construction everywhere on the boundary of the square. Sköllermo showed that now converges O(N2) faster than the Fourier series of f(x, y), which lacks the vanish-at-the-boundary property. She constructs the function B(x, y) using what today is called transfinite interpolation (Gordon and Hall 1973):
e18
Sköllermo then analytically solved the Poisson equation with forcing equal to B(x, y). She decided to impose the homogeneous Dirichlet boundary conditions satisfied by the whole solution on each individual part of the response to B(x, y). Unfortunately, uxx + uyy = xy with u = 0 on the boundary can be analytically solved only as a slowly convergent Fourier series—precisely what one was trying to avoid by splitting f(x, y) into parts.
In the Chen–Kuo harmonic-sine LAM decomposition, it is always necessary to solve a Laplace equation with inhomogeneous boundary conditions. In meteorological applications, it therefore creates no complication to obtain a convenient particular solution to uxx + uyy = B without imposition of homogeneous boundary conditions. Define B1(x, y) as the sum of the first four terms of B(x, y):
e19
e20
where
e21
A solution to u1,xx + u1,yy = B1(x, y) is
e22
The solution to the other four subproblems is only slightly harder. For example, to solve
e23
define
e24
We then solve the one-dimensional Poisson equation:
e25
One choice of particular integral is
e26
However, for consistency with the rest of the algorithm, we prefer to write
e27
where
e28
and is the Fourier sine solution to υ5,yy = f(0, y) − w5,yy, accurate O(1/N5) on the interior of [0, 1]. Similarly
e29
where
e30
e31
where
e32
e33
where
e34
The final solution with O(1/N5) accuracy on the interior of the square is
e35
where
e36
and uLap(x, y) is a solution to the Laplace equation,
e37
with the boundary conditions
e38
the boundary of the unit square.
The normal meteorological problem is to solve the Poisson equation with inhomogeneous boundary conditions by the splitting described in the introduction, where u solves the Poisson equation and uLap is a solution to the Laplace equation. If the Poisson solution does not satisfy homogeneous boundary conditions, then it is merely necessary to alter the inhomogeneous boundary conditions of the Laplace solution to
e39
so that the sum of u and uLap is the specified g(x, y) on the boundary of the unit square. The fact that u(x, y), the Poisson solution, does not satisfy homogeneous boundary conditions is of no significance.

4. Fourier solution of the Laplace equation

The Laplace equation is
e40
Splitting the function g into its components on each of the four sides of the square, the boundary conditions are
e41
where we have used “N” and “S” to denote the top and bottom boundaries of the square and “W” and “E” to denote the sides. The solution splits naturally into four pieces, each piece satisfying homogeneous Dirichlet boundary conditions on three of the four sides of the square. For example, the “northern” piece is derived by expanding the boundary condition along y = 1 as a sine series in x:
e42
Substituting a sine series in x with y-dependent coefficients yields a set of uncoupled, constant coefficient ordinary differential equations for the coefficients; imposing the boundary conditions yields
e43
At the northern boundary, y = 1, the Laplace solution has the poor convergence rate of the expansion of the (generally nonperiodic) function gN(x); the error is O(1) in the Gibbs layers near x = 0 and x = 1. Away from the northern boundary, the fact that sinh(z) ~ (1/2) exp(z) for large z implies
e44
This means that the Laplace solution has an exponential rate of convergence away from the boundaries. We can state this as a general theorem incorporating all four pieces of the solution. The Fourier–sinh solution to Laplace's equation is classical [48–51 in Jackson (1962) and online at http://eqworld.ipmnet.ru/en/solutions/lpde/lpde301.pdf], but the rate-of-convergence theorem is new.
Theorem 1: The Fourier solution to the Laplace equation on the unit square is of the following form:
e45
where and with
e46
and so on. Then on the boundary-strip-deleted domain, (x, y) ∈ [D, 1 − D] ⊗ [D, 1 − D],
e47
for some positive constant D. In other words, the Laplace solution series converges exponentially fast with n.
Because the pertinent Fourier series are one dimensional, the series can be accelerated by a factor of N2 by writing , where (x, y) is a Laplace solution that interpolates the boundary values at the four corners of the unit square:
e48
The Fourier method is then applied to compute with boundary conditions of g = . The series for converge with O(1/N2) error within O(1/N) of the corners and with O(1/N3) error on the interior of the domain.

All this Fourier–Laplace mechanics are well described in appendix B of Chen and Kuo (1992a). The only novelty in this section is the proof of exponential convergence in the inner square of meteorological interest, given in the appendix.

5. Summary

We have shown that for applications to limited-area modeling (LAM), the domain of needed accuracy is not the whole LAM window, here normalized to the unit square, but is rather a smaller square embedded within the larger. In the smaller domain, we show that the error of the bivariate Poisson Fourier solution is O(1/N3). The previously published estimate O(1/N2), a factor of N more pessimistic, is correct only for the entire square including the meteorologically irrelevant boundary buffer.

We also explicitly constructed an improved Fourier algorithm that solves the Poisson equation with fifth-order accuracy. Because we seek only particular solutions, our order-raising procedure is significantly simpler than previously proposed methods.

The Fourier solution to the Laplace equation, also needed for the harmonic-sine LAM analysis of Chen and Kuo (1992a,b), is shown to converge exponentially fast on the square of meteorological interest.

An exponential rate of convergence can be recovered for the Poisson solution, too, in the domain with the deleted strip by applying a series acceleration method (Boyd 2011). The rate of convergence diminishes to nothing as the discontinuities of the boundary of the domain are approached, but again, because a boundary strip is of no interest, the exponential convergence rate of the accelerated series is always nonzero in the region of meteorological interest. [Another oceanic–atmospheric example where series acceleration on only part of the domain is needed—the tropics for Hermite series on the latitudinally infinite equatorial beta plane—is described in Boyd and Moore (1986).] Other periodize-with-windowing procedures appear promising (Boyd 2005; Termonia et al. 2012; Degrauwe et al. 2012). “Boundary subtraction” can be continued to arbitrary order (Averbuch et al. 1997, 1998; Huybrechs 2010; Huybrechs et al. 2011; Adcock and Hansen 2012).

Thus, we do not claim to have spoken the last word or offered the most accurate algorithm. We only claim that our third- and fifth-order Poisson solvers are simple, and sufficiently accurate for most practical LAM applications.

Acknowledgments

The first author was supported by the NSF through Grant OCE 1059703 and by the Distinguished International Visiting Scholar Program of the Chinese Academy of Sciences. The remaining authors are supported by the project from Research Plan 973 (2012CB417201) and the project from the Natural Science Foundation of China (40930950, 41075043).

APPENDIX

A Proof of the Interior Rate of Convergence for the Fourier Series of a Discontinuous Function

Gibbs Phenomenon and the ensuing O(1) error in the Fourier approximation of a general, nonperiodic function are discussed in standard textbooks including the first author's book (Boyd 2001) and especially in the collection of articles entirely on this topic (Jerri 2011). However, it is difficult to find a careful treatment that proves that the error improves to O(1/N) everywhere outside the “Gibbs layers,” which shrink in width inversely proportional to N, so we provide one. Note that in this appendix, the domain is [0, π] instead of [0, 1].

Our error analysis requires a strengthened form of a classic lemma.

a. Theorem 2 [Riemann–Lebesgue (strong form)]

Suppose f(x) is sufficiently nice as to allow one integration by parts in the integrals below. It is sufficient for f(x) to belong to , the class of functions that are continuous with continuous first derivatives on the interval x ∈ [a, b] and both f(x) and df/dx(x) are integrable on the same interval.

Define
ea1
Then for all n > 0,
ea2
ea3
Note that the limits of integration are arbitrary and not restricted to [−π, π].
Proof: Integration by parts gives without approximation
ea4
ea5
The integrals can be bounded by using the rectangle bound on integrals, which asserts that for any integrand g(x), the integral is less than or equal to the area under a rectangle whose height is equal to the maximum absolute value of the integrand and whose width is equal to the interval of integration, . The theorem then follows trivially.
Titchmarsh (1939, p. 402) shows that for any function f(x) with a Fourier series, the partial sum of degree N can be represented without approximation using the Dirichlet integral as
ea6
ea7
Since the partial sum for the trivial function f(x) = 1 is identical to f(x), the identity
ea8
follows. We can use this to write the error in f(x), again without approximation, as
ea9
ea10

As noted in the main body of this article, a function can always be written on x ∈ [0, 1] as the sum of a linear polynomial B(x) that interpolates f(0) and f(1) plus the function . Because , the usual integration-by-parts argument given in section 2 shows that the Fourier coefficients of decrease as O(1/n3). We can then invoke the following lemma.

Lemma 1: Define the error in the Nth partial sum of a Fourier sine series as
ea11
Suppose that
ea12
Then
ea13
Proof: |sin(nx)| ≤ 1 for all real x and n, so each term in the Fourier sum is bounded by |bn|. The specified inequality obeyed by the Fourier coefficients then implies . Define a piecewise function:
ea14
Then . Because 1/(x−1)k > ξ(x; k), it follows that the integral of the 1/(x − 1)k, which can be evaluated analytically, must be larger than the integral of ξ, giving or equivalently
ea15
from which the theorem follows.

The lemma shows that the error in the Nth partial sum of can be no larger than O(1/N2), so the error in the Fourier series of f(x) is dominated by the error in the Fourier series of B(x).

Explicitly,
ea16
The Fourier sine series with this linear polynomial converges to a periodic function , which is a weighted sum of
ea17
where the “periodic sign function” is
ea18
ea19
These are illustrated in Fig. A1.
Fig. A1.
Fig. A1.

(left) The periodic sine function σ(x) approximated by its sine series up to and including sin(19x). (right) As in (left), but for the piecewise linear sawtooth function with partial sums up to sin(20x).

Citation: Monthly Weather Review 141, 11; 10.1175/MWR-D-13-00074.1

From the definition of the sawtooth function, one can easily derive the identities:
ea20
ea21
Similarly, σ(x + y) jumps so that
ea22
ea23
ea24

The limits of integration do not include the points y = 0, π where the denominator sin(y/2) = 0. It follows that for any fixed x on the interior of the interval [0, π], we can invoke the strong form of the Riemann–Lebesgue theorem to prove the following.

b. Theorem 3 (interior rate of convergence)

Suppose f(x) is sufficiently “nice” as to allow one integration by parts in the usual Fourier coefficient integrals below. It is sufficient for f(x) to belong to , the class of functions that are continuous with continuous first derivatives on the interval x ∈ [a, b] and both f(x) and df/dx(x) are integrable on the same interval.

Then for any fixed x on the interior of [0, π],
ea25

There is no contradiction with the classical analysis of Gibbs Phenomenon. At fixed x near a discontinuity, the error is indeed large and O(1) for small N, but as the Gibbs layers shrink with N, the error at fixed x asymptotes to its predicted O(1/N) decay as illustrated in Fig. A2.

Fig. A2.
Fig. A2.

Absolute value of , plotted with logarithmic scales vs N.

Citation: Monthly Weather Review 141, 11; 10.1175/MWR-D-13-00074.1

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