# A perfect CTD profile

I love the $$\tanh$$ function. A lot. It’s such a perfect model for a density interface in the ocean, that it is commonly used in theoretical and numerical models and I regularly used it for both research and demonstration/example purposes. Behold, a $$\tanh$$ interface:

$T(z) = T_0 + \delta T \tanh \left( \frac{z-z_0}{dz} \right)$

T <- function(z, T0=10, dT=5, z0=-25, dz=5) T0 + dT*tanh((z - z0)/dz)
z <- seq(0, -60)
plot(T(z), z) But whenever I use it, especially for teaching, I’m always saying how it’s idealized and really doesn’t represent what an ocean interface actually looks like. UNTIL NOW.

library(oce)
ctd <- read.oce('D19002034.ODF')
## Warning in read.odf(file = file, columns = columns, exclude = exclude, debug =
## debug - : "CRAT_01" should be unitless, but the file states the unit as "S/m" so
## that is retained in the object metadata. This will likely cause problems. See ?
## read.odf for an example of rectifying this unit error.
par(mfrow=c(1, 2))
plot(ctd, which=1, type='l')
plot(ctd, which=5) Yes, this is real data.

Just how close to a $$\tanh$$ is it?1

z <- -ctd[["depth"]]
T <- ctd[["temperature"]]
m <- nls(T~a+b*tanh((z-z0)/dz), start=list(a=3, b=1, z0=-10, dz=5))
m
## Nonlinear regression model
##   model: T ~ a + b * tanh((z - z0)/dz)
##    data: parent.frame()
##        a        b       z0       dz
##   3.7251   0.9516 -25.0448  -2.7648
##  residual sum-of-squares: 0.05222
##
## Number of iterations to convergence: 16
## Achieved convergence tolerance: 4.394e-06
plot((T-predict(m))/T * 100, z, type="o", xlab='Percent error') 1. My PhD advisor, who also taught me introductory physical oceanography, once said to a class of students while using tanh to describe an idealized interface: “tanh – it’s like ‘lunch’, only better!”↩︎