The climate blogging scene seems to be running out of steam. WUWT is mainly the same old posters with the same stories. Climate Audit blogs about football aeration. Lucia blogs about just about anything. So it seems an alternate topic is needed. I'll keep blogging about climate science, of course, but also maybe a bit about maths. It won't be click bait, but I hope there will be things worth discussing.

The maths for today is at least something I've been using, and I'll post a file which may be useful. I'm talking about

spherical harmonics. These are functions orthogonal on a sphere, which can be used in the same way as you might use Fourier series. I use them for

smoothing, and I'll be using them in future for integration.

I'll talk a little bit about where they come from - you can skip if you like. Then I'll show a method I now use for calculating them, with an associated table of coefficients that you can download.

The reason I've been looking at the methods aspect is that I wanted to do a Javascript implementation, for interactive use. I have long had a R implementation, which I've used for TempLS output. I use recurrence relations, to be described. It's not particularly hard to program, but easy to make mistakes. So, as I often do nowadays, I thought to pack the logic into a table of numbers which could be used with a simple mechanical program in any platform. I'll link the table. In the section below, you can skip all the math, which is not compulsory, and go straight to usage.

####
Origins

Spherical harmonics are eigenfunctions of the Laplacian on the sphere. That is, they are solutions of

∇

^{2}Ψ = λΨ for some λ, on the whole sphere (no boundaries).

The reason they are orthogonal is similar to the orthogonality of eigenvectors of symmetric matrices (the Laplacian is self-adjoint). If

∇

^{2}Ψ

_{1} = λ

_{1}Ψ

_{1} and ∇

^{2}Ψ

_{2} = λ

_{2}Ψ

_{2}, then integrating over the sphere

λ

_{1}∫ Ψ

_{1} Ψ

_{2} =
∫ ∇

^{2}Ψ

_{1} Ψ

_{2}
= -∫ ∇Ψ

_{1} . ∇ Ψ

_{2} integrating by parts

= ∫ Ψ

_{1} ∇

^{2}Ψ

_{2}
= λ

_{2}∫ Ψ

_{1} Ψ

_{2}
So if λ

_{1} != λ

_{2}, then

∫ Ψ

_{1} Ψ

_{2} = 0

####
Separation of variables

Using a coordinating system of the sphere consisting of latitude θ and longitude φ, we can use

separation of variables to get two separate ordinary differential equations, for which θ and φ are eigenfunctions. The equation for φ is just the simple harmonic with trig function solutions. The equation for θ is a Legendre equation, for which the solutions are

Associated Legendre polynomials P

_{l}^{m}(sin(θ)). Together, the spherical harmonics are

Y

_{l}^{m}=N P

_{l}^{m}(sin(θ)) exp(i m φ) for l=0,1,2..., m=-l,...l, N a normalizing factor

Wiki displays them thus (

attribution)

*Visual representations of the first few real spherical harmonics. Blue portions represent regions where the function is positive, and yellow portions represent where it is negative. The distance of the surface from the origin indicates the value of in angular direction (Wiki)*
You can think of it as a table, with Y

_{0}^{0}=1 at the top, m as the x variable and l as -y. This is the table that I compute.

A comment on the symmetries. As l increases, down the centre, each row brings an extra layer on the axis. As you go from that axis, you get one extra period around the equator, and one less along the axis. So the fourth row (l=3) end has three periods around the equator, and none along the polar axis. This increase of oscillations translates into ability to resolve variation. If you go to row 13, the period is about 30 degrees, so you can resolve variation on that scale.
####
Recurrence relations and array

Being essentially hypergeometric, there is a rich collection of

three-term recurrence relations. I number the table by increasing m and then by L, ie by rows. I can compute any term provided I have a recurrence relation connecting it to two already known values. As an integer array, I can take any value out of the displayed cone (l≥0, |m|≤l) to be zero.

For most, I use the relation connecting three consecutive l values (x=sin(θ)):

But this fails when m=l+1 - ie at end of rows. So then I use

relying on the last term being zero.

There are some variations. I don't use complex values, but rather the trig component cos(mφ) for m>0, and sin(mφ) for m<0. I calculate the trig formulae by summation, so that makes the edge formulae three-term again. And I modify the reduced relation to

P

_{m+1}^{m+1} = -cos(θ) P

_{m}^{m}
absorbing the (2m+1) factor into the normalizer N.

####
Usage

So after all that, I assemble a table that I call hm, with 9 rows, and as many columns as harmonics required. If you include n rows from the table, there are n

^{2} terms, and that number of columns required. The 9 rows are:

- l
- m
- i2 pointer to recurrence location 3
- i1 loc 2
- j whether to use sin(θ) etc in recurrence
- c2 third coef in reccurence
- c1 second
- c0 first
- N normalizer.

So the implementation is just, successively, column i by column, H(1)=1,

c0 H(i)= s(j) c1 H(i1) + s(j+1) c2 H(i2)

s is an array consisting of (sin(θ), 1, cos(θ)cos(φ), cos(θ)sin(φ)), so j will be 1 or 3 (for row ends).

Here is a csv file of that table (transposed), with 441 columns, equivalent to 21 rows.

Here is an R usage:

hm=read.csv("https://s3-us-west-1.amazonaws.com/www.moyhu.org/blog/math/SHcoef.csv")
makeSH=function(la,lo,N){#Forms spherical harmonics YLM by implementing HM.
# la, lo are lat/lon in degrees
n2=(0:N+1)^2
N1=max(n2)
H=matrix(1,length(la),N1) # will be SH array
p2=pi/180
ss=cbind(sin(la*p2),1,cos(la*p2)*sin(lo*p2),cos(la*p2)*cos(lo*p2)); #
for(i in 2:N1){
m=hm[,i]; # hm is the table of coefs
H[,i]=(H[,m[4]]*m[7]*ss[,m[5]]+H[,m[3]]*m[6]*ss[,m[5]+1])/m[8]
}
for(i in 1:N1)H[,i]=H[,i]*hm[9,i]
H
}

You supply a set of lat/lon in degrees, and N+1 for number of rows, and you get an array H of orthonormal harmonics.

####
More usage

So what to do with it? Given any set of points (stations) on the sphere, and values v, you can project, regression wise:

b = (H

^{T}H)

^{-1}(H

^{T}v)

for the coefficients, and then

V = H b

is the SH approximation. You can pick out terms or just display the smoothed approx. I am more enthusiastic now about using this for integration, when the coefficient b

_{1} is the approximate integral (since H

_{1} normalized is 1/4π and all other terms have zero integral. I think this method is almost as good as the mesh methods I use, and a lot faster (if stored weights are unavailable).