---
title: "1. Introduction to Oce"
author: "Dan Kelley (https://orcid.org/0000-0001-7808-5911) and Clark Richards (https://orcid.org/0000-0002-7833-206X)"
date: "`r Sys.Date()`"
output:
  rmarkdown::html_vignette:
    toc: true
    number_sections: true
    fig_caption: yes
    fig_width: 3.5
    fig_height: 3.5
    dpi: 72
    dev.args: list(pointsize=11)
vignette: >
  %\VignetteIndexEntry{1. Introduction to Oce}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options:
  markdown:
    wrap: 72
---

<!-- HOW TO BUILD AND VIEW THE OCE VIGNETTES. -->

<!-- 1. Edit this file in vignettes/oce.Rmd -->

```{=html}
<!-- 2. Do not bother trying to build vignettes in RStudio. Instead, ->
<!--    in a unix shell, type the following: -->
```
<!--        R CMD build --compact-vignettes="qpdf" oce -->

<!--        R CMD install oce_1.1-2.tar.gz -->

<!--    where the version number may need adjustment in the second command. -->

<!-- 3. To see the results, type the following in an R console: -->

<!--       browseVignettes("oce") -->

```{r, echo = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
```

**Abstract.** The `oce` package makes it easy to read, summarize and plot data
from a variety of Oceanographic instruments, isolating the researcher from the
quirky data formats that are common in this field. It also provides functions
for working with basic seawater properties such as the equation of state, and
with derived quantities such as the buoyancy frequency. Although simple enough
to be used in a teaching context, `oce` is powerful enough for a research
setting. These things are illustrated here in a general context, with further
detail being provided by other vignettes, by the documentation for the
package's functions and datasets, in Kelley (2018) and in Kelley et al. (2022).

# Introduction

Oceanographers must deal with measurements made by a wide variety of
instruments, a task that is complicated by a tendency of instrument
manufacturers to invent new data formats to suite new measurement capabilities.
Many manufacturers provide software for scanning data files and producing
overview plots, but it is of somewhat limited use to researchers who work with
several instrument types at the same time, and who need to move beyond
standardized engineering plots to specialized scientific plots and statistical
analyses.

# Architecture of oce objects

![**Figure 1.** Basic layout of a CTD object. All `oce` objects contain
slots named `data`, `metadata`, and `processingLog`, with the contents
depending on the type of data.](ctd-object.png)

The need to scan diverse data files was one motivation for the creation of
`oce`, but an equal goal was to make it easy to work with the data once they
are in the system. This was accomplished partly by the provision of specialized
and generic (overloaded) functions to work with the data, and partly by
providing accessor methods that make it convenient to reach inside the data
objects (see next section).

As illustrated in Figure 1, each `oce` object contains three slots:

- `data`, a list containing the actual data, e.g., for a CTD object (see also
  the [vignette about CTD data](B_ctd.html)), this will contain `pressure`,
  `temperature`, etc.
- `metadata`, a list containing information about the data, such as units,
  data-quality flags, sampling locations, etc.
- `processingLog`, a list that documents how the object was created and
  possibly changed thereafter.

# Accessing data in oce objects

For detailed analysis, users may want to access data *within* oce objects.
While it is possible to descend through the object using the "slot" and "list"
notation (e.g. `d@data$salinity` yields the salinity within an oce object named
`d`), this approach is not recommended, for two reasons.  First, direct access
makes a user's code brittle to changes in the object structure (which are
sometimes necessary to track changes in measurement technologies, or in
analysis methods).  And, second, direct access is limited to what is actually
stored in the file, and this might not be what the user wants -- for example, a
record might contain electrical conductivity but not salinity.

The preferred approach is to use the `[[` notation, which derives from the
generic "Extract" method for accessing parts of an object. It permits accessing
not just the data stored in the object, but also quantities that can be
computed from such data (e.g. salinity, if temperature, pressure and
conductivity are stored).

A general introduction is provided by

```{r eval=FALSE}
?`[[,oce-method`
```

and details are provided for individual object classes with e.g.

```{r eval=FALSE}
?`[[,ctd-method`
```

for the `ctd` class.

The notation is simple. For example, suppose that `d` is an object that stores
`salinity` in either its `data` slot or `metadata` slot. Then,

```{r eval=FALSE}
S <- d[["salinity"]]
```

will extract the salinity if it is present in the object, *or* it will compute
salinity, if the object's contents are sufficient to compute salinity.

The `[[` method first looks in the `metadata` slot, but if the item is not
found there, it proceeds to look in the `data` slot. This two-step scheme is
helpful because it frees the user from having to know where data are stored.
For example, in a `ctd` object, `latitude` might be stored in the `metadata`
(for a CTD profile) or in the `data` slot (for the slantwise sampling of a
glider).

The `[[` notation is helpful for several reasons, among which two are
prominent.

1. It can give valuable performance enhancements in some cases, e.g. the data
   in the `landsat` class are stored in two byte-level arrays, which yields a
   marked improvement over the 8-byte arrays that R generally uses on a 64-bit
   machine. The `[[` assembles these byte-scale chunks into a conventional R
   numerical matrix, which can be quite convenient for users who wish to
   operate on the data without learning how to assemble the bytes.

2. It provides a uniformity of notation that can be helpful to users. In `oce`,
   objects derived from data files tend to hold just the information in those
   files, not derived information. For example, For example, CTD datasets often
   provide in-situ temperature but not potential temperature (since the latter
   is not measured). The in-situ temperature is found with e.g.
   `d[["temperature"]]`, and so it seems natural to write `d[["theta"]]` to get
   the potential temperature. If this is done, then the `ctd` version of `[[`
   first looks in the data slot, and returns `theta` if it is found there, as
   may sometimes be the case (depending on the choice of the person who created
   the dataset). However, if it is not found, then `[[` calls `swTheta()` to
   calculate the value, and returns the result.

Finally, it is also possible to extract the entirety of either the
`metadata` or `data` slot, e.g.

```{r eval=FALSE}
data <- d[["data"]]
```

yields the full data slot, which is a list with elements that can be
accessed in the conventional way, e.g. for a `ctd` object, and

```{r eval=FALSE}
data$temperature
```

retrieves the temperature.

In addition to the above, `[[` has a special entry that can reveal not just the
data in the object, but also the data that can be computed from the object. It
returns a list with items named `metadata`, `metadataDerived`, `data` and
`dataDerived`.  The `Derived` items are specific not just to the object class,
but also to the data stored within the particular object.  For example,
```{r eval=FALSE}
library(oce)
data(ctd)
ctd[["?"]]
```
reveals that the square of the buoyancy frequency can be retrieved with
`ctd[["N2"]]`, but if say the temperature were removed, then `"N2"` would not
have been listed.

**Exercise 1.** Show that removing temperature from the built-in `"ctd"`
dataset makes `[["?"]]` indicate that N2 is no longer available.

# Modifying data in oce objects

There are several schemes for modifying the data within `oce` objects. For
example, and by analogy with the `[[` notation of the previous section, the
following

```{r eval=FALSE}
data(ctd)
ctd[["temperatureAboveFreezing"]] <- ctd[["temperature"]] - swTFreeze(ctd)
```

will store the excess over freezing temperature into the `ctd` object.

Further information on this notation is provided by

```{r eval=FALSE}
?"[[<-,oce-method"
```

The above works only within the `data` slot. To store within the
`metadata` slot, consider using e.g.

```{r extractExample, eval=FALSE}
ctd[["metadata"]]$scientist <- "Dalhousie Oceanography 4120/5120 Class of 2003"
```

sets the "scientist".

For archival work, it is important to store reasons for changing things
in an object. Two functions are provided for this purpose: `oceSetData`
and `oceSetMetadata`. For example, a better way to change the scientist
might be to write

```{r eval=FALSE}
ctd <- oceSetMetadata(ctd,
    name = "scientist",
    value = "Dalhousie Oceanography 4120/5120 Class of 2003",
    note = "give credit where it's due"
)
```

and a better way to store `temperatureAboveFreezing` would be

```{r eval=FALSE}
ctd <- oceSetData(ctd,
    name = "temperatureAboveFreezing",
    value = ctd[["temperature"]] - swTFreeze(ctd),
    unit = list(unit = expression(degree * C), scale = "ITS-90"),
    originalName = "-",
    note = "add temperatureAboveFreezing, for ice-related calculations"
)
```

which illustrates that this notation, as opposed to the `[[<-` notation,
permits the specification of a unit and an `originalName`, both of
which, together with the `note`, are displayed by `summary(ctd)`.

# Oce functions

The uniformity of the various `oce` objects helps users build skill in
examining and modifying objects. Fine-scale control is provided
throughout `oce`, but the best way to learn is to start with simplest
tools and their defaults. For example, the following will read a CTD
file named `"station1.cnv"`, summarize the contents, and plot an
overview of the data, with profiles, a TS diagram, and a map (Figure 2).

```{r eval=FALSE}
library(oce)
d <- read.oce("station1.cnv")
summary(d)
plot(d)
```

The reader should stop now and try this on a file of their own. The
pattern will work with a fairly wide variety of file types, because
`read.oce()` examines the file name and contents to try to discover
what it is. For an example, if `read.oce()` is given the name of a
file created by an Acoustic Doppler Profiler, it will return an object
inheriting from class `"adp"`, so the `summary()` and `plot()` calls
will be tailored to that type, e.g. the graph will show images of
time-distance variation in each of the measured velocity components.

**Notes on oce function names.**

1. As just illustrated, the general function to read a dataset ends in `.oce`,
   and the name is a signal that the returned object is of class `oce`.
   Depending on the file contents, `d` will also have an additional class, e.g.
   if the file contains CTD data, then the object would inherit from two
   classes, `oce` and `ctd`, with the second being used to tailor the graphics
   by passing control to the `ctd` variant of the generic `plot` function (use
   `help("plot,ctd-method")` to learn more).

2. Generally, `oce` functions employ a "camel case" naming convention, in which
   a function that is described by several words is named by stringing the
   words together, capitalizing the first letter of second and subsequent
   words. For example, `ctdFindProfiles()` locates individual profiles within a
   `ctd` object that holds data acquired during a cyclic raising and lowering
   of the CTD instrument.

3. Function names begin with `oce` in cases where a more natural name would be
   in conflict with a function in the base R system or a package commonly used
   by Oceanographers. For example, `oceContour()` is a function that provides
   an alternative to `contour()` in the `graphics` package.

# Calculation of seawater properties

The `oce` package provides many functions for dealing with seawater properties.
Perhaps the most used is `swRho(S,T,p)`, which computes seawater density
$\rho=\rho(S, T, p)$, where $S$ is salinity, $T$ is *in-situ* temperature in
$^\circ$C (on the ITS-90 scale), and $p$ is seawater pressure, i.e. the excess
over atmospheric pressure, in dbar. (This and similar functions starts with the
letters `sw` to designate that they relate to seawater properties.) The result
is a number in the order of $1000$kg/m$^3$. For many purposes, Oceanographers
prefer to use the density anomaly $\sigma=\rho-1000$kg/m$^3$, provided with
`swSigma(salinity,temperature,pressure)`, or its adiabatic cousin
$\sigma_\theta$, provided with `swSigmaTheta()`.

Most of the functions can use either the UNESCO or GSW (TEOS-10) formulation of
seawater properties, with the choice set by an argument called `eos`. It should
be noted that `oce` uses the `gsw` package for GSW calculations. **Caution:**
the results obtained with `eos="gsw"` in `oce` functions may differ from the
results obtained when using the `gsw` functions directly, due to unit
conventions. For example, `swCSTp(..., eos="gsw")` reports conductivity ratio
for consistency with the UNESCO formulation, however the underlying `gsw`
function `gsw::gsw_C_from_SP()` reports conductivity in mS/cm.

A partial list of seawater functions is as follows:

-   `swAlpha()` for thermal expansion coefficient
    $\alpha=-\rho_0^{-1}\partial\rho/\partial T$
-   `swAlphaOverBeta()` for $\alpha/\beta$
-   `swBeta()` for haline compression coefficient
    $\beta=\rho_0^{-1}\partial\rho/\partial S$
-   `swConductivity()` for conductivity from $S$, $T$ and $p$
-   `swDepth()` for depth from $p$ and latitude
-   `swDynamicHeight()` for dynamic height
-   `swLapseRate()` for adiabatic lapse rate
-   `swN2()` for buoyancy frequency
-   `swRho()` for density $\rho$ from $S$, $T$ and $p$
-   `swSCTp()` for salinity from conductivity, temperature and pressure
-   `swSTrho()` for salinity from temperature and density
-   `swSigma()` for $\rho-1000$,kg/m$^3$
-   `swSigmaT()` for $\sigma$ with $p$ set to zero and temperature
    unaltered
-   `swSigmaTheta()` for$\sigma$ with $p$ set to zero and temperature
    altered adiabatically
-   `swSoundSpeed()` for speed of sound
-   `swSpecificHeat()` for specific heat
-   `swSpice()`, `spiciness0()`, `spiciness1()` and `spiciness2()` for quantities used in watermass research
-   `swTFreeze()` for freezing temperature
-   `swTSrho()` for temperature from salinity and density
-   `swTheta()` for potential temperature $\theta$
-   `swViscosity()` for viscosity

Details and examples are provided in the documentation of these functions.

The following exercise may be of help to readers who prefer to learn by doing.
(Answers are provided at the end of this document.)

**Exercise 2.** a. What is the density of a seawater parcel at pressure 100
dbar, with salinity 34 PSU and temperature 10$^\circ$C? b. What temperature
would the parcel have if raised adiabatically to the surface? c. What density
would it have if raised adiabatically to the surface? d. What density would it
have if lowered about 100m, increasing the pressure to 200 dbar? e. Draw a
blank $T$-$S$ diagram with $S$ from 30 to 40 PSU and $T$ from -2 to
20$^\circ$C.

# CTD data

The `read.oce` function recognizes a wide variety of CTD data formats, and the
associated `plot` function can produce many types of graphical display. In
addition, there are several functions that aid in the analysis of such data.
See the [ctd vignette](B_ctd.html) for more on dealing with CTD data.

# Section plots

The commands

```{r eval=FALSE}
data(section)
plot(section, which = c(1, 2, 3, 99))
```

will plot a summary diagram containing sections of $T$, $S$, and
$\sigma_\theta$, along with a chart indicating station locations. In addition
to such overview diagrams, the `section` variant of the generic `plot` function
can also create individual plots of individual properties (use
`help("plot,section-method")` to learn more).

Some section datasets are supplied in a pre-gridded format, but it is also
common to have different pressure levels at each station. For such cases,
`sectionGrid()` may be used, e.g. the following produces Figure 4. The ship was
travelling westward from the Mediterranean towards North America, taking 124
stations in total; the `stationId` value selects the last few stations of the
section, during which the ship headed toward the northwest, crossing isobaths
(and perhaps, the Gulf Stream) at nearly right angles.

```{r fig.cap="**Figure 4.** Portion of the CTD section designated A03, showing the Gulf Sream region.  The square on the cruise track corresponds to zero distance on the section.", fig.width=4, fig.height=4, dpi=72, dev.args=list(pointsize=12)}
library(oce)
data(section)
GS <- subset(section, 102 <= stationId & stationId <= 124)
GSg <- sectionGrid(GS, p = seq(0, 1600, 25))
plot(GSg, which = c(1, 99), map.xlim = c(-85, -(64 + 13 / 60)))
```

**Exercise 3.** Draw a $T$-$S$ diagram for the section data, using black
symbols to the east of 30W and gray symbols to the west, thus highlighting
Mediterranean-derived waters. Use `handleFlags()` (see [using data-quality
flags](E_flags.html)) to discard questionable data, and use the accessor
function `[[`.

**Exercise 4.** Plot dynamic height and geostrophic velocity across the Gulf
Stream. (Hint: use the `swDynamicHeight()` function.)

# Maps

Oce has several functions that facilitate the drawing of maps. A variety of
projections are provided, with the mathematics of projection being handled
behind the scenes with the `sf` package. An introduction to drawing maps is
provided with `?mapPlot`, and the [map projection
vignette](D_map_projections.html) provides much more detail.

# Sea-level data

The following code graphs a built-in dataset of sea-level time series (Figure
9). The top panel provides an overview of the entire data set. The second panel
is narrowed to the most recent month, which should reveal spring-neap cycles if
the tide is mixed. The third panel is a log spectrum, with a few tidal
constituents indicated. The `section` variant of the generic `plot` function
provides other possibilities for plotting, including a cumulative spectrum that
can be quite informative (use `help("plot,sealevel-method")` to learn more).

```{r fig.cap="**Figure 9.** Sea-level timeseries measured in 2003 in Halifax Harbour.", fig.width=5, fig.height=4, dpi=72, dev.args=list(pointsize=16)}
library(oce)
data(sealevel)
plot(sealevel)
```

**Exercise 5.** Illustrate Halifax sea-level variations during Hurricane Juan,
near the end of September, 2003.

A preliminary version of tidal analysis is provided by the `tidem` function
provided in this version of the package, but readers are cautioned that the
results are certain to change in a future version. (The problems involve phase
and the inference of satellite nodes.)

**Exercise 6.** Plot the de-tided Halifax sea level during Autumn 2003, to see
whether Hurricane Juan is visible. (Hint: use `predict` with the results from
`tidem`.)

# Acoustic Doppler Profiler data

The following commands produce Figure 10, showing one velocity component of
currents measured in the St Lawrence Estuary Internal Wave Experiment. This
plot type is just one of many provided by the `adp` variant of the generic
`plot` function (see `?"plot,adp-method"`). See [the adp vignette](C_adp.html)
for much more on acoustic Doppler profiler data.

```{r fig.cap="**Figure 10.** Measurements made with a bottom-mounted ADP in the St Lawrence Estuary. The line near the surface indicates pressure measured by the ADP.", fig.width=6, fig.height=2, dpi=72}
library(oce)
data(adp)
plot(adp, which = 1)
lines(adp[["time"]], adp[["pressure"]], lwd = 2)
```

# Handling data-quality flags

Archives of CTD/bottle and Argo drifter measurements commonly supply
data-quality flags that provide an indication of the trust to be put in
individual data points. Oce has a flexible scheme for dealing with such flags,
and also for inserting flags into these or any data type; see the [using
data-quality flags vignette](E_flags.html) for more..

# Working in non-English languages

Many of the `oce` plotting functions produce axis labels that can be displayed
in languages other than English. At the time of writing, French, German,
Spanish, and Mandarin are supported in at least a rudimentary way. Setting the
language can be done at the general system level, or within R, as indicated
below (results not shown).

```{r fig.keep="none"}
library(oce)
Sys.setenv(LANGUAGE = "fr")
data(ctd)
plot(ctd)
```

```{r echo=FALSE}
Sys.setenv(LANGUAGE = "en")
```

Most of the translated items were found by online dictionaries, and so they may
be incorrect for oceanographic usage. Readers can help out in the translation
effort, if they have knowledge of how nautical words such as *Pitch* and *Roll*
and technical terms such as *Absolute Salinity* and *Potential Temperature*
should be written in various languages.

# Extending oce

The oce object structure can be used as a basis for new object types. This has
the advantage the basic operations of oce will carry over to the new types. For
example, the accessing operator `[[` and `summary` function will work as
expected, and so will such aspects as the handling of data-quality flags and
units. More details on setting up classes that inherit from oce are provided in
[the subclassing vignette](F_subclassing.html).

# The future of oce

The present version of `oce` can only handle data types that the authors have
been using in their research. New data types will be added as the need arises
in that work, but the authors would also be happy to add other data types that
are likely to prove useful to the Oceanographic community. (The data types need
not be restricted to Physical Oceanography, but the authors will need some help
in dealing with other types of data, given their research focus.)

Two principles will guide the addition of data types and functions: (a) the
need, as perceived by the authors or by other contributors and (b) the ease
with which the additions can be made. One might call this
development-by-triage, by analogy to the scheme used in Emergency Rooms to
organize medical effort efficiently.

# Development website

The site `https://github.com/dankelley/oce` provides a window on the
development that goes on between the CRAN releases of the package. Readers are
requested to visit the site to report bugs, to suggest new features, or just to
see how `oce` development is coming along. Note that the `development` branch
is used by the authors in their work, and is updated so frequently that it must
be considered unstable, at least in those spots being changed on a given day.
Official CRAN releases derive from the `master` branch, and are done when the
code is considered to be of reasonable stability and quality. This is all in a
common pattern for open-source software.

# Solutions to exercises

**Exercise 1.** Show that removing temperature from the built-in `"ctd"`
dataset makes `[["?"]]` indicate that N2 is no longer available.

This may be demonstrated as below.
```{r}
library(oce)
data(ctd)
"N2" %in% ctd[["?"]]$dataDerived
ctd@data$temperature <- NULL # erase temperature
"N2" %in% ctd[["?"]]$dataDerived
```



**Exercise 2.** Seawater properties. In the UNESCO system we may write

```{r fig.width=5, fig.height=5, fig.keep="none"}
library(oce)
swRho(34, 10, 100, eos = "unesco")
swTheta(34, 10, 100, eos = "unesco")
swRho(34, swTheta(34, 10, 100, eos = "unesco"), 0, eos = "unesco")
swRho(34, swTheta(34, 10, 100, 200, eos = "unesco"), 200, eos = "unesco")
plotTS(as.ctd(c(30, 40), c(-2, 20), rep(0, 2)), eos = "unesco", grid = TRUE, col = "white")
```

and in the Gibbs SeaWater system, we use `eos="gws"` and must supply
`longitude` and `latitude` arguments to the `sw*()` calls and also to
the `as.ctd()` call.

**Exercise 3.** Draw a $T$-$S$ diagram for the section data, using black
symbols to the east of 30W and gray symbols to the west, thus highlighting
Mediterranean-derived waters. Use `handleFlags()` (see [using data-quality
flags](E_flags.html)) to discard questionable data, and use the accessor
function `[[`.

We will use the Gibbs SeaWater system, so `as.ctd()` needs location
information.

```{r fig.width=5, fig.height=5, fig.keep="none"}
library(oce)
data(section)
s <- handleFlags(section, flags = list(c(1, 3:9)))
ctd <- as.ctd(s[["salinity"]], s[["temperature"]], s[["pressure"]],
    longitude = s[["longitude"]], latitude = s[["latitude"]]
)
col <- ifelse(s[["longitude"]] > -30, "black", "gray")
plotTS(ctd, col = col, eos = "gsw")
```

**Exercise 4.** Plot dynamic height and geostrophic velocity across the Gulf
Stream. (Hint: use the `swDynamicHeight` function.)

(Try `?swDynamicHeight` for hints on smoothing.)

```{r fig.width=5, fig.height=3, fig.keep="none"}
library(oce)
data(section)
GS <- subset(section, 102 <= stationId & stationId <= 124)
dh <- swDynamicHeight(GS)
par(mfrow = c(2, 1), mar = c(3, 3, 1, 1), mgp = c(2, 0.7, 0))
plot(dh$distance, dh$height, type = "l", xlab = "", ylab = "Dyn. Height [m]")
grid()
# 1e3 metres per kilometre
latMean <- mean(GS[["latitude"]])
f <- coriolis(latMean)
g <- gravity(latMean)
v <- diff(dh$height) / diff(dh$distance) * g / f / 1e3
plot(dh$distance[-1], v, type = "l", xlab = "Distance [km]", ylab = "Velocity [m/s]")
grid()
abline(h = 0, col = "red")
```

**Exercise 5.** Halifax sea-level during Hurricane Juan, near the end of
September, 2003.

A web search will tell you that Hurricane Juan hit about midnight, 2003-sep-28.
The first author can verify that the strongest winds occurred a bit after
midnight -- that was the time he moved to a room without windows, in fear of
flying glass.

```{r fig.width=7, fig.height=3, fig.keep="none"}
library(oce)
data(sealevel)
# Focus on 2003-Sep-28 to 29th, the time when Hurricane Juan caused flooding
plot(sealevel, which = 1, xlim = as.POSIXct(c("2003-09-24", "2003-10-05"), tz = "UTC"))
abline(v = as.POSIXct("2003-09-29 04:00:00", tz = "UTC"), col = "red")
mtext("Juan", at = as.POSIXct("2003-09-29 04:00:00", tz = "UTC"), col = "red")
```

**Exercise 6.** Plot the de-tided Halifax sea level during Autumn 2003, to see
whether Hurricane Juan is visible. (Hint: use `predict` with the results from
`tidem`.)

```{r results="hide", fig.keep="none"}
library(oce)
data(sealevel)
m <- tidem(sealevel)
oce.plot.ts(sealevel[["time"]], sealevel[["elevation"]] - predict(m),
    ylab = "Detided sealevel [m]",
    xlim = c(as.POSIXct("2003-09-20"), as.POSIXct("2003-10-08"))
)
```

The spike reveals a surge of about 1.5m, on the 29th of September, 2003.


# References

Kelley, Dan E. Oceanographic Analysis with R. 1st ed. 2018. New York, NY:
Springer New York : Imprint: Springer, 2018.
https://doi.org/10.1007/978-1-4939-8844-0.

Kelley, Dan E., Clark Richards, and Chantelle Layton. “Oce: An R Package for
Oceanographic Analysis.” Journal of Open Source Software 7, no. 71 (March 3,
2022): 3594. https://doi.org/10.21105/joss.03594.
