Introduction to G-RSM (Spectral method for dummies) Masao Kanamitsu Scripps Institution of...
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Transcript of Introduction to G-RSM (Spectral method for dummies) Masao Kanamitsu Scripps Institution of...
Introduction to G-RSM (Spectral method for dummies)
Masao Kanamitsu
Scripps Institution of Oceanography
University of California, San Diego
How to digitize a field
Values on grid points □ Geographical location is given□Discrete representation
Easy to understand.No computation necessary.Computer display utilize this method.
Will be referred to as physical space==========================
Notation in this presentation:Physical space in RED
Trivial 1-D example
Physical space:f(x) is expressed by 7 numbers
(-2.0, -1.33, -0.67, 0., +0.67, +1.33, +2.0)
Any other method to digitize fields?
f(x)=ax+ba=0.67b=0.
f(x) is expressed by 2 numbers (0.67, 0.)
Note Continuous representation
==========================Notation in this presentation:
Functional space in BLUE
Can we do similar procedure for more general field distributions?
Fourier Series•Combination (or summation) of sine and cosine waves with different wave length.
Born March 21, 1768Auxerre, Yonne, France
Died May 16, 1830 (aged 62)Paris, France
Nationality French Field Mathematician, physicist, and historianInstitutions École Normale
École PolytechniquePolytechniqueAcademic advisor Joseph Lagrange
Joseph Fourier
Fourier’s discovery
He claims that any function of a variable, whether continuous or discontinuous, can be expanded in a series of sines of multiples of the variable.
Though this result is not correct, Fourier's observation that some discontinuous functions are the sum of infinite series was a breakthrough. The question of determining when a function is the sum of its Fourier series has been fundamental for centuries. Joseph Louis Lagrange had given particular cases of this (false) theorem, and had implied that the method was general, but he had not pursued the subject. Johann Dirichlet was the first to give a satisfactory demonstration of it with some restrictive conditions. A more subtle, but equally fundamental, contribution is the concept of dimensional homogeneity in equations; i.e. an equation can only be formally correct if the dimensions match on either side of the equality.
More general example
Physical space:f(x) is expressed by 7 numbers
(2.0, 0.0, -2.0, -0.5, 0.5, 0.0, 2.0)
Wave space:
If we use the following series of functions:
f0(x)=constantf1(x)=sin(2B/L•x), f2(x)=cos(2B/L•x),f3(x)=sin(2B/L•2x), f4(x)=cos(2B/L•2x),f5(x)=sin(2B/L•3x), f5(x)=cos(2Bx/L•3x)
then,
f(x) = 0.000 -0.772• f1(x)+1.083 • f2(x)+0.722 • f3(x)+ 0.750 • f4(x)+ 0.000 • f5(x) +0.167 • f6(x)
Now, f(x) is expressed by 7 different numbers!!
(0.000, -0.772, 1.083, 0.722, 0.750, 0.00, 0.167)
Wave space
Values of coefficients of a series of functions
Will be referred to as wave space
No geographical locationKnown set of mathematical functionsContinuous representationSpace derivatives can be computed analytically.
Not possible to visualize
grid point space
Values on grid points.
Will be referred to as grid point spaceGeographical location specifiedPhysical values themselvesDiscrete representationSpace derivatives computation requires finite difference approximation.
Easy to visualize
Derivatives Continuous vs. discrete representation
• (F/ x)x=60=(F80-F40)/(80-40)
– Can be defined only at the grid point
• (F/ x)x=60= -0.772*2/L*cos(2/Lx)- 1.083* 2/L*sin(2/Lx)+……..
Can be defined everywhere (continuous)
How to select “(series of) functions”
• Requirements:– Satisfy boundary condition– Orthogonal– Solution of a linearized forecast equation
• Examples:– 1-D periodic ==> Sinusoidal (Fourier)
– 2-D periodic over plane ==> Double Fourier
– 2-D wall (zero) ==> Sine only series
– 2-D symmetric ==> Cosine only series
– 2-D on sphere ==> Associated Legendre polynomial
Fourier Transform
Transformation between physical and wave space
1-D example:
where,
λ=2π/L
f a a m b mm
m
m
m
( ) cos sin
0
1 1
Conversion from wave space to physical space
cos sinm i m e im
f F m e im
m
( ) ( )
F mb
iam m( )
2 2
Complex notation is more convenient :
f(λ) is physical spaceF(m) is wave space
where
then
Conversion from physical space to wave space
F m f e dim( ) ( ) 1
20
2
Note that F(m) is not a function of space (λ)
f(λ) is physical spaceF(m) is wave space
Note on ‘scale’
Wavenumber “m” relates to scale (spectral)
small “m” ==> large scale
large “m” ==> small scale
Think as “number of troughs and ridges around the latitude circle”
Skip spectral representation on sphere.
Please refer to the
http://g-rsm.wikispaces.com/Short+Courses
page
Spectral method and Grid-point method
A method to numerically solve linear and nonlinear (partial) differential equations.
We may have:
Spectral quasi-geostrophic model
Spectral non-hydrostatic model
Also used in pure physics and other applications
Spectral forecast equation
• Grid-point method => predict values on grids
• Spectral method => predict coefficients
Spectral Forecast Equation1-D linear equation example
Grid-point method
2211
11 ti
ti
ti
ti uu
ct
uu
Spectral Forecast Equation1-D linear equation example
Spectral Method
12
0
2
( ) e dim
Apply the following operator to both sides of the equation
like the following:
which leads to:
deu
cdet
u imim
2
0
2
0
Spectral Forecast Equation1-D linear equation example
Spectral Method
)()(
mimcUt
mU
or, using finite differencing in time,
)(2
)()( 11
mimcUt
mUmU ttt
Spectral Forecast Equation1-D non-linear equation example
Grid-point method
2211
11 ti
tit
i
ti
ti uu
ut
uu
Spectral Forecast Equation1-D non-linear equation example
Spectral Method
deu
udet
u imim
2
0
2
0
LHS:
t
mU
)(
Spectral Forecast Equation1-D non-linear equation example
Spectral Method
k
kmUkikUt
mU)()(
)(
Note that his equation shows nonlinear interaction between the waves.For example, U(3) is generated by U(1) and U(2) {m=3,k=1}(or many other combinations of m and k)
Introduction to Transform Method
Question
How many grid-points or spectral coefficients are required to represent a given field?
Ans.
Wave truncation M ==> requires 2M grid points(Or sin/cos coefficients)
QuestionHow many grid points are required to obtain ‘mathematically correct’
nonlinear term ?
Qualitative ans.Representation of u requires 2M grid pointsRepresentation of requires 2M grid pointstherefore, requires 4M grid points.
However, we have a selection rule, which states that only special combination of u and creates waves within the truncation limit, i.
e., U(k) and U(m-k). This requirement reduces the number of combinations by M-1,
x
u
x
uu
x
u
x
uuthus requires 3M+1 grid points.
Advantage of the spectral method
1. No space truncation error
2. No phase speed error
3. Satisfies conservation properties
4. No pole problem
5. Physically clean
6. No overhead for semi-implicit scheme
No-overhead Example :
Semi-implicit scheme often requires solution of the following Poisson equation :
2
For Grid point method we need to solve:
i j i j i j i j i j
i jx y
1 1 1 1 4, , , , ,,
(ζknown)
This requires relaxation method or matrix solver.
Disadvantages of the spectral method
1. Restricted by boundary condition.
2. Difficulties in handling discontinuity and
positive definite quantities
==>Gibbs phenomena
3. For very high resolution (>T1000), efficiency may become a problem.
The Regional Spectral Model
Juang, H.-M. and M. Kanamitsu, 1994: The NMC nested regional spectral model.
Mon. Wea. Rev., 122, 3-26.
RSM Basics (1)
• The most serious question is “HOW TO DEAL WITH LATERAL BOUNDARY CONDITION?”– Assume cyclic .... Hilam– Assume zero .... Tatsumi– (Non-zero boundary condition also causes serio
us difficulties when semi-implicit scheme is used.)
RSM Basics (2)
Introduction of the Perturbation
1. Satisfy zero lateral boundary condition2. Better boundary condition for semi-implicit
scheme3. Diffusion can be applied to perturbation only
(does not change large scale).4. Lateral boundary relaxation cleaner.5. Maintain large scale forecast produced by the
global model
RSM Basics (3)
Definition of perturbation
At=Ar+Ag
At: Full field (to be predicted)
Ar: Perturbation (rsm variable, to be predicted)
Ag: Global model field (known at all times)
RSM Basics (4)
Writing equation for Ar is not easy, particularly for nonlinear terms and very nonlinear physical processes.
y
AAvv
x
AAuu
t
A
t
A grgr
grgr
gr)(
)()(
)(
RSM Basics (5)Different approach:
t
At
Compute using At=Ar+Ag, then use
t
A
t
A
t
A gtr
t
At
is computed in a similar manner as regular model.
t
Ag
is known
Step by step computational procedure (1)
1. Run global model. Get global spherical coefficient Ag(n,m) at all times.
2. Get grid point analysis over regional domain At(x,y).
3. Get grid point values of global model.Ag(n,m) ==> Spher. trans. ==> Ag(x,y)
4. Compute grid point perturbation Ar(x,y) =At(x,y) - Ag (x,y)
• Get Fourier coefficient of perturbation.Ar(x,y) ==>Fourier trans. ==> Ar(k,l)
(Now Ar(k,l) satisfies zero b.c.)[Steps 1-5 are preparation at the initial time]
Step by step computational procedure (2)
6. Get grid point value of perturbation and its derivatives.
Ar(k,l) ==>Fourier trans. ==> Ar(x,y)
Ar(k,l) ==>Fourier trans.==>
Ar(k,l) ==>Fourier trans.==>
7. Get grid point value of global field and its derivatives.
Ag(m,n) ==>Spherical tans.==> Ag(x,y)
Ag(m,n) ==>Spectral trans.==>
Ag(m,n) ==>Spectral trans.==>
y
yxAr
),(
x
yxAr
),(
x
yxAg
),(
y
yxAg
),(
Step by step computational procedure (3)
8. Get grid point total field and derivativesAt (x,y)= Ag(x,y) + Ar(x,y)
x
yxA
x
yxA
x
yxA rgt
),(),(),(
y
yxA
y
yxA
y
yxA rgt
),(),(),(
9. Now possible to compute full model tendencies in grid point space
y
AAvv
x
AAuu
t
A grgr
grgr
t)(
)()(
)(
(This is non-zero at the boundary)
Step by step computational procedure (4)
10. Get perturbation tendency
t
A
t
A
t
A gtr
(Note that
t
Ag
is known)
11. Get Fourier coefficient of perturbation tendency
t
lkA
t
A rr
),( (This satisfies boundary condition)
Step by step computational procedure (5)
12. Advance in time
tt
lkAlkAlkA r
ttrttr
2
),(),(),(
13. Go back to step 6
Further note on the perturbation method
Since known fields are Ag and At, and Ar is computed from At and Ag, the equation should be expressed as:
Ar=At – Ag
From pure mathematical point of view, Ag can be arbitrary except that it must satisfy the condition Ar=0 at the boundaries (Tatsumi’s method).
The choice of Ag as a global model field is to reduce the amplitude of the domain scale from Ar and thus spectral filtering does not affect those scales.
Further note on the perturbation method
Since RSM does not directly predict Ar, it may not be appropriate to call it as a perturbation model.
More appropriately, it should be called a perturbation filter model.
Although the global model field is used in the entire domain, it is only applied to reduce the error due to the Fourier transform of the domain scale field. There is no explicit forcing towards global model field in the interior of the regional domain.
The explicit forcing towards the global model fields is achieved by the lateral boundary blending and/or nudging. It is important to note that these lateral boundary treatment is still an essential part of the RSM, as in the grid-point regional model.
The use of Scale Selective Bias Correction Method developed recently by Kanamaru and Kanamitsu considers nudging inside the domain to reduce large systematic error. (To be discussed in other talks)
Little history
1970-‘85: Global spectral modelBourke(1974), Hoskins & Simmons (1975)ECMWF, NMC, JMA
1980's: Regional spectral modelTatsumi(1986)Hoyer (and Simmons) (1987)Juang and Kanamitsu (1994)
Spherical transform
)( mn
immn PeY
mnP
Y nm Spherical harmonic function
m: zonal wavenumbern: total wavenumbern-m: number of zero crossings
is Associated Legendre Polynomial
(Φ is latitude) 2
First few examples of mnP
P
P
P
P
P
P
P
0
0
1
0
1
1
2
0 2
2
1
2
1 2
3
0 3
1
12
3 1
3
3
12
5 3
cos
sin
( cos )
sin cos
sin
( cos cos )
Properties of the
1) Defined as a solution of on sphere.
2) Function of sin2 and cos2.
3) Largest order is ‘n’.
4) for m>n
5) Has n-m zero crossing between the poles.
6) Symmetric w.r.t. equator for even n-m.
7) Antisymmetric w.r.t. equator for odd n-m.
8) Orthogonal function.
mnP
02 f
0mnP
Legendre (or spherical) Transform formula
f F n m P enm
nm
im( , ) ( , ) ( )
0
F n m f e Pimn
m( , ) ( , ) (cos )
14
1
1
Note on ‘scale’
small “m” ==> large zonal scale
large “m” ==> small zonal scale
small “n-m” ==> large meridional scale
large “n-m” ==> small meridional scale
Truncation (model resolution)
• 1-D example:• ‘Maximum m’ or ‘M’ determines the
smallest scale possible.• 2-D spherical example
n
m
N
M
Spectral forecast equation on sphere
Vorticity equation example:
mn
mn
mn
mn
mn
mn
mn VnnnnA
nnt
11)2)(1()1(2)1(
1
Nonlinear terms:
);( 21212
2
1
1nnnmmmLUiU
x
uu m
nmn
This is called “Interaction coefficient method”.This computation requires M5 operations,which is a major disadvantage for lengthy calculations.
“How many grid points are required to obtain ‘mathematically accurate’ nonlinear term?”
- another derivation -
Problem is that sampling interval misinterprets correct wavelength.
Sampled here
f x F S R eS R j
i S RS
j
( )
( )2
21
If we have 2*S gridpoints. We can represent S waves. Suppose, we have a wave with a wavenumber S+R, then the grid point values of this wave on 2*S grid points are expressed as:
The Fourier transform of this grid point value to wavenumber will be preformed as:
F mS
f x ejj
S imS
j( ) ( )
( )
12 1
2 2
21
F mS
F S R ei j
S R m
S
j
S
( ) ( )( ) ( )
1
2
1 22
1
2
This summation is non-zero if:
S+R-m is an integer multiple of 2S or m=-{(2N-1)S-R} where N=1,2,3,4...
When N=1, |m|=S-RN=2, |m|=3S-R (greater than S for R<S thus no need
to consider for N>1)
This indicates that the wave S+R is aliased to S-R. In other word, aliasing occurs as if the wave is folded to a smaller wavenumber at S.
The quadratic term generates 2M wave. If we place a condition to the number of grid points (2S) such that the waves between M+1 and 2M do not aliased into waves less than or equal to M, then we have a condition, S+R=2M and S-R=M, i.e.,
132 MS (+1 to avoid aliasing to M)
thus requires 3M+1 grid points.
For spherical coefficients, number of required E-W grid points are:
(3M+1)
and N-S grid points are:
(3M+1)/2
for triangular truncation.
Note:We choose number of grid points in E-W so that the Fast Fourier Transform
works the best. It requires that the number of points is a multiple of 2, 3, 5. Combination of this restriction and the condition above determines the most efficient model truncation (T21, T42, T63 ...). Note that NCEP model has additional restriction that the wavenumber must be even).Example: Number of grid point = 128 = 2**7
3M+1=128 ==> M=42
There is additional requirement for the non-linear term calculations on sphere!!
N-S grid point placement must satisfy the following equation which makes the numerical error of the integration zero.
f x dx W f xk kk
J
( ) ( )
11
1
ε=0 leads to:
P for triang truncationM( ) ( ) . .3 1
2
0 0
These special latitudes are called Gaussian latitudes