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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bad21046",
+   "metadata": {},
+   "source": [
+    "# ADCP Transect Plotting\n",
+    "\n",
+    "This notebook demonstrates a simple plotting exercise for ADCP data across a transect, using the output of a VirtualShip expedition. There are example plots embedded at the end, but these will ultimately be replaced by your own versions as you work through the notebook.\n",
+    "\n",
+    "The plot(s) we will produce are simple plots which follow the trajectory of the expedition as a function of distance from the start, and are intended to be a starting point for your analysis. Because the `ADCP` instrument is an underway/onboard instrument, this means we benefit from continuous recordings across the length of the ship's track (unlike overboard instruments such as CTDs which have to deployed at individual sampling sites).\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\"> \n",
+    "<b>Note:</b> This notebook assumes that each point along the expedition track is further from the start than the previous point. The code will still work if not, but the resultant plots might not be very intuitive.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "233041ec",
+   "metadata": {},
+   "source": [
+    "## Set up\n",
+    "\n",
+    "#### Imports\n",
+    "\n",
+    "The first step is to import the Python packages required for post-processing the data and plotting. \n",
+    "\n",
+    "<div class=\"alert alert-block alert-success\"> \n",
+    "<b>Tip:</b> You may need to set the Kernel to the relevant (Conda) environment in the top right of this notebook to access the required packages! \n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f6c87472",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cmocean.cm as cmo\n",
+    "import matplotlib.colors as mcolors\n",
+    "import matplotlib.patches as mpatches\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "360f84ce",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Data directory\n",
+    "\n",
+    "Next, you should set `data_dir` to be the path to your expedition results in the code block below. You should replace `\"/path/to/EXPEDITION/results/\"` with the path for your machine.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-success\"> \n",
+    "<b>Tip:</b> You can get the path to your expedition results by navigating to the `results` folder in Terminal (using `cd`) and then using the `pwd` command. This will print your working directory which you can copy to the `data_dir` variable in this notebook. Don't forget to keep it as a string (in \"quotation\" marks)!\n",
+    "</div>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0cb630f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dir = \"/path/to/EXPEDITION/results/\"  # set this to be where your expedition output data is located on your (virtual) machine"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5930b00",
+   "metadata": {},
+   "source": [
+    "## Load data\n",
+    "\n",
+    "We are now ready to read in the data. You can carry on executing the next cells without making changes to the code..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "654fb036",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load ADCP data\n",
+    "adcp_ds = xr.open_dataset(f\"{data_dir}/adcp.zarr\")\n",
+    "if adcp_ds[\"obs\"].size <= 1:\n",
+    "    raise ValueError(\"Number of waypoints must be > 1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1221167e",
+   "metadata": {},
+   "source": [
+    "## Data post-processing\n",
+    "\n",
+    "Before we can continue, we need to do some post-processing to get it ready for plotting. Below are various helper functions which perform tasks such as calculating the ship's distance from the start of the transect at each point and calculating the various velocity components from the ADCP data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0aa1f8f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# utility functions\n",
+    "\n",
+    "\n",
+    "def haversine(lon1, lat1, lon2, lat2):\n",
+    "    \"\"\"Great-circle distance (meters) between two points.\"\"\"\n",
+    "    lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\n",
+    "    dlon, dlat = lon2 - lon1, lat2 - lat1\n",
+    "    a = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2\n",
+    "    c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))\n",
+    "    return 6371000 * c\n",
+    "\n",
+    "\n",
+    "def distance_from_start(ds):\n",
+    "    \"\"\"Array of meters from first waypoint.\"\"\"\n",
+    "    lon0, lat0 = ds.isel(obs=0)[\"lon\"].values, ds.isel(obs=0)[\"lat\"].values\n",
+    "    d = np.zeros_like(ds[\"lon\"].values, dtype=float)\n",
+    "    for ob, (lon, lat) in enumerate(zip(ds[\"lon\"], ds[\"lat\"], strict=False)):\n",
+    "        d[ob] = haversine(lon, lat, lon0, lat0)\n",
+    "    return d\n",
+    "\n",
+    "\n",
+    "def calc_velocities(ds):\n",
+    "    \"\"\"Calculate absolute, parallel and perpendicular (to the ship trajectory) velocities, as well as (compass) direction of flow.\"\"\"\n",
+    "    Uabs = np.sqrt(ds[\"U\"] ** 2 + ds[\"V\"] ** 2)\n",
+    "    ds_surface = ds.isel(trajectory=0)\n",
+    "    dlon = np.deg2rad(ds_surface[\"lon\"].differentiate(\"obs\"))\n",
+    "    dlat = np.deg2rad(ds_surface[\"lat\"].differentiate(\"obs\"))\n",
+    "    lat = np.deg2rad(ds_surface[\"lat\"])\n",
+    "    alpha = np.arctan(dlat / (dlon * np.cos(lat))).mean(\"obs\")  # cruise direction angle\n",
+    "    Uparallel = np.cos(alpha) * ds[\"U\"] + np.sin(alpha) * ds[\"V\"]\n",
+    "    Uperp = -np.sin(alpha) * ds[\"U\"] + np.cos(alpha) * ds[\"V\"]\n",
+    "    direction_rad = np.arctan2(\n",
+    "        ds[\"U\"], ds[\"V\"]\n",
+    "    )  # direction of flow [degrees from north]\n",
+    "    direction_deg = (np.degrees(direction_rad) + 360) % 360\n",
+    "\n",
+    "    return Uabs, Uparallel, Uperp, direction_deg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88062e97",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Now we will execute the various post-processing calculations, plus define some extra useful arrays to be used for the plotting..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6433742a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# distance from start as 1d array\n",
+    "distance_1d = distance_from_start(adcp_ds.isel(trajectory=0))\n",
+    "\n",
+    "# calculate velocity components and direction\n",
+    "Uabs, Uparallel, Uperp, direction = calc_velocities(adcp_ds)\n",
+    "\n",
+    "# land / sea bed mask\n",
+    "landmask = xr.where(((adcp_ds[\"U\"] == 0) & (adcp_ds[\"V\"] == 0)), 1, np.nan)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd613a45",
+   "metadata": {},
+   "source": [
+    "## Plotting\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\"> \n",
+    "<b>Note:</b> The plots produced next are a starting point for your analysis. You are encouraged to make adjustments, for example axis limits and scaling if the defaults not best suited to your specific data. Use your preferred AI coding assistant for help!\n",
+    "</div>\n",
+    "\n",
+    "We are now ready to plot our transect data. We will use distance from the start of the transect/expedition for the x-axis, and water column depth for the y-axis. The ADCP data will then be plotted according to the colour map for diagnostic. The profiles across the transect are likely to be different depths because some parts of the ocean are of course shallower than others."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "93693258",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x960 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "PLOT_DICT = {\n",
+    "    \"Uabs\": {\n",
+    "        \"data\": Uabs,\n",
+    "        \"cmap\": cmo.speed,\n",
+    "        \"label\": \"Absolute velocity\",\n",
+    "        \"norm\": None,\n",
+    "    },\n",
+    "    \"Uparallel\": {\n",
+    "        \"data\": Uparallel,\n",
+    "        \"cmap\": cmo.balance,\n",
+    "        \"label\": \"Along-track velocity (positive with flow)\",\n",
+    "        \"norm\": mcolors.CenteredNorm(vcenter=0),\n",
+    "    },\n",
+    "    \"Uperp\": {\n",
+    "        \"data\": Uperp,\n",
+    "        \"cmap\": cmo.balance,\n",
+    "        \"label\": \"Cross-track velocity (positive with flow to the left)\",\n",
+    "        \"norm\": mcolors.CenteredNorm(vcenter=0),\n",
+    "    },\n",
+    "    \"direction\": {\n",
+    "        \"data\": direction,\n",
+    "        \"cmap\": cmo.phase,\n",
+    "        \"label\": \"Flow direction (0=N, 90=E, 180=S, 270=W)\",\n",
+    "        \"norm\": None,\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "# fig\n",
+    "fig, axs = plt.subplots(\n",
+    "    len(PLOT_DICT), 1, figsize=(10, 10), dpi=96, sharex=True, sharey=True\n",
+    ")\n",
+    "\n",
+    "for idx, ((key, var), ax) in enumerate(zip(PLOT_DICT.items(), axs, strict=False)):\n",
+    "    # adcp data\n",
+    "    mesh = ax.pcolormesh(\n",
+    "        distance_1d / 1000,\n",
+    "        adcp_ds[\"z\"],\n",
+    "        var[\"data\"],\n",
+    "        cmap=var[\"cmap\"],\n",
+    "        norm=var[\"norm\"] if var[\"norm\"] is not None else None,\n",
+    "    )\n",
+    "\n",
+    "    # seabed\n",
+    "    ax.pcolormesh(\n",
+    "        distance_1d / 1000,  # distance in km\n",
+    "        adcp_ds[\"z\"],\n",
+    "        landmask,\n",
+    "        cmap=mcolors.ListedColormap([mcolors.to_rgba(\"tan\"), mcolors.to_rgba(\"white\")]),\n",
+    "    )\n",
+    "\n",
+    "    # title\n",
+    "    ax.set_title(var[\"label\"])\n",
+    "\n",
+    "    # colorbar\n",
+    "    if key == \"direction\":\n",
+    "        cbar = fig.colorbar(\n",
+    "            mesh,\n",
+    "            ax=ax,\n",
+    "            label=\"Degrees from North\",\n",
+    "            ticks=[0, 90, 180, 270],\n",
+    "        )\n",
+    "    else:\n",
+    "        cbar = fig.colorbar(\n",
+    "            mesh,\n",
+    "            ax=ax,\n",
+    "            label=r\"m s$^{-1}$\",\n",
+    "        )\n",
+    "\n",
+    "    # axis labels\n",
+    "    ax.set_ylabel(\"Depth (m)\")\n",
+    "    if idx == len(axs) - 1:  # bottom panel only for single column of subplots\n",
+    "        ax.set_xlabel(\"Distance from start (km)\")\n",
+    "\n",
+    "# legend for sea bed\n",
+    "tan_patch = mpatches.Patch(color=mcolors.to_rgba(\"tan\"), label=\"Land / sea bed\")\n",
+    "axs[-1].legend(handles=[tan_patch], loc=\"lower right\")\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2ada2b1",
+   "metadata": {},
+   "source": [
+    "The resultant figure shows various components of the velocity field, derived from ADCP data.\n",
+    "\n",
+    "1) Absolute velocity\n",
+    "2) Along-track velocity (where positive values indicate flow in the overall direction of the ship's track across the the transect)\n",
+    "3) Cross-track velocity (where postive values indicate flow to the left of the ship's direction).\n",
+    "4) The direction of the flow, expressed as degrees from North.\n",
+    "\n",
+    "You can use these plots as a starting point to consider how flow dynamics vary over the cross-section. You may find some diagnostics more useful than others, depending on your specific aims!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ship",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/user-guide/tutorials/CTD_transects.ipynb b/docs/user-guide/tutorials/CTD_transects.ipynb
new file mode 100644
index 00000000..63d8ad00
--- /dev/null
+++ b/docs/user-guide/tutorials/CTD_transects.ipynb
@@ -0,0 +1,496 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cca80169",
+   "metadata": {},
+   "source": [
+    "# CTD Transect Plotting\n",
+    "\n",
+    "This notebook demonstrates a simple plotting exercise for CTD data across a transect, using the output of a VirtualShip expedition. There are example plots embedded at the end, but these will ultimately be replaced by your own versions as you work through the notebook.\n",
+    "\n",
+    "We can plot physical (temperature, salinity) or biogeochemical data (oxygen, chlorophyll, primary production, phyto/zoo-plankton, nutrients, pH) as measured by the VirtualShip `CTD` and `CTD_BGC` instruments, respectively.\n",
+    "\n",
+    "The plot(s) we will produce are simple plots which follow the trajectory of the expedition as a function of distance from the first waypoint, and are intended to be a starting point for your analysis. \n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\"> \n",
+    "<b>Note:</b> This notebook assumes that each waypoint in the expedition is further from the start than the last waypoint. The code will still work if not, but the resultant plots might not be very intuitive.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aad20bd7",
+   "metadata": {},
+   "source": [
+    "## Set up\n",
+    "\n",
+    "#### Imports\n",
+    "\n",
+    "The first step is to import the Python packages required for post-processing the data and plotting. \n",
+    "\n",
+    "<div class=\"alert alert-block alert-success\"> \n",
+    "<b>Tip:</b> You may need to set the Kernel to the relevant (Conda) environment in the top right of this notebook to access the required packages! \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "c7f9f2ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cmocean.cm as cmo\n",
+    "import matplotlib.colors as mcolors\n",
+    "import matplotlib.patches as mpatches\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f387780",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Data directory\n",
+    "\n",
+    "Next, you should set `data_dir` to be the path to your expedition results in the code block below. You should replace `\"/path/to/EXPEDITION/results/\"` with the path for your machine.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-success\"> \n",
+    "<b>Tip:</b> You can get the path to your expedition results by navigating to the `results` folder in Terminal (using `cd`) and then using the `pwd` command. This will print your working directory which you can copy to the `data_dir` variable in this notebook. Don't forget to keep it as a string (in \"quotation\" marks)!\n",
+    "</div>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf497101",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dir = \"/path/to/EXPEDITION/results/\"  # set this to be where your expedition output data is located on your (virtual) machine"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a499ebe2",
+   "metadata": {},
+   "source": [
+    "#### Variable choice\n",
+    "\n",
+    "You should now consider which variable from your CTD casts you would like to plot. Which ones are available to you will depend on whether you have used the `CTD` (physical variables) or `CTD_BGC` (biogeochemical) instrument, or both. Below is a list of all valid variable choices for both instruments...\n",
+    "\n",
+    "`CTD` (physical):\n",
+    "- \"temperature\"\n",
+    "- \"salinity\"\n",
+    "\n",
+    "`CTD_BGC` (biogeochemical):\n",
+    "- \"oxygen\"\n",
+    "- \"nitrate\"\n",
+    "- \"phosphate\"\n",
+    "- \"ph\"\n",
+    "- \"zooplankton\"\n",
+    "- \"phytoplankton\"\n",
+    "- \"primary_production\"\n",
+    "- \"chlorophyll\"\n",
+    "\n",
+    "Copy one of the above to `plot_variable` below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "8de8b4ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_variable = \"temperature\"  # change this to your chosen variable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a05fad14",
+   "metadata": {},
+   "source": [
+    "\n",
+    "We also define the `VARIABLES` dictionary here, which we use to store some parameters for the plots related to each variable choice (e.g. labels, what units each is in, and which colour map we should use for the plots).\n",
+    "\n",
+    "<div class=\"alert alert-block alert-success\"> \n",
+    "<b>Tip:</b> You don't need to change anything here, but should you wish to change the colour scheme (`cmap`) for any CTD variable you can do so. At the moment it's set to use relevant cmaps from the cmocean Python package, which has developed specialist colour schemes for oceanographic data applications.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "b32d2730",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "VARIABLES = {\n",
+    "    \"temperature\": {\n",
+    "        \"cmap\": cmo.thermal,\n",
+    "        \"label\": \"Temperature (°C)\",\n",
+    "        \"ds_name\": \"temperature\",\n",
+    "    },\n",
+    "    \"salinity\": {\n",
+    "        \"cmap\": cmo.haline,\n",
+    "        \"label\": \"Salinity (psu)\",\n",
+    "        \"ds_name\": \"salinity\",\n",
+    "    },\n",
+    "    \"oxygen\": {\n",
+    "        \"cmap\": cmo.oxy,\n",
+    "        \"label\": r\"Dissolved oxygen (mmol m$^{-3}$)\",\n",
+    "        \"ds_name\": \"o2\",\n",
+    "    },\n",
+    "    \"nitrate\": {\n",
+    "        \"cmap\": cmo.matter,\n",
+    "        \"label\": r\"Nitrate (mmol m$^{-3}$)\",\n",
+    "        \"ds_name\": \"no3\",\n",
+    "    },\n",
+    "    \"phosphate\": {\n",
+    "        \"cmap\": cmo.matter,\n",
+    "        \"label\": r\"Phosphate (mmol m$^{-3}$)\",\n",
+    "        \"ds_name\": \"po4\",\n",
+    "    },\n",
+    "    \"ph\": {\n",
+    "        \"cmap\": cmo.balance,\n",
+    "        \"label\": \"pH\",\n",
+    "        \"ds_name\": \"ph\",\n",
+    "    },\n",
+    "    \"zooplankton\": {\n",
+    "        \"cmap\": cmo.algae,\n",
+    "        \"label\": r\"Total zooplankton (mmol m$^{-3}$)\",\n",
+    "        \"ds_name\": \"zooc\",\n",
+    "    },\n",
+    "    \"phytoplankton\": {\n",
+    "        \"cmap\": cmo.algae,\n",
+    "        \"label\": r\"Total phytoplankton (mmol m$^{-3}$)\",\n",
+    "        \"ds_name\": \"phyc\",\n",
+    "    },\n",
+    "    \"primary_production\": {\n",
+    "        \"cmap\": cmo.matter,\n",
+    "        \"label\": r\"Total primary production of phytoplankton (mg m$^{-3}$ day$^{-1}$)\",\n",
+    "        \"ds_name\": \"nppv\",\n",
+    "    },\n",
+    "    \"chlorophyll\": {\n",
+    "        \"cmap\": cmo.algae,\n",
+    "        \"label\": r\"Chlorophyll (mg m$^{-3}$)\",\n",
+    "        \"ds_name\": \"chl\",\n",
+    "    },\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f9a5afb",
+   "metadata": {},
+   "source": [
+    "## Load data\n",
+    "\n",
+    "We are now ready to read in the data. You can carry on executing the next cells without making changes to the code..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "13f4664b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load CTD data\n",
+    "filename = (\n",
+    "    \"ctd.zarr\" if plot_variable in [\"temperature\", \"salinity\"] else \"ctd_bgc.zarr\"\n",
+    ")\n",
+    "ctd_ds = xr.open_dataset(f\"{data_dir}/{filename}\")\n",
+    "if ctd_ds[\"trajectory\"].size <= 1:\n",
+    "    raise ValueError(\"Number of waypoints must be > 1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8201b14",
+   "metadata": {},
+   "source": [
+    "## Data post-processing\n",
+    "\n",
+    "Before we can continue, we need to do some post-processing to get it ready for plotting. Below are various helper functions which perform tasks such as calculating the distance of each waypoint from the start, capturing only the downcasts of the CTD casts, as well as some other utility methods. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "785b2b35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# utility functions\n",
+    "\n",
+    "\n",
+    "def haversine(lon1, lat1, lon2, lat2):\n",
+    "    \"\"\"Great-circle distance (meters) between two points.\"\"\"\n",
+    "    lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\n",
+    "    dlon, dlat = lon2 - lon1, lat2 - lat1\n",
+    "    a = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2\n",
+    "    c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))\n",
+    "    return 6371000 * c\n",
+    "\n",
+    "\n",
+    "def distance_from_start(ds):\n",
+    "    \"\"\"Add 'distance' variable: meters from first waypoint.\"\"\"\n",
+    "    lon0, lat0 = (\n",
+    "        ds.isel(trajectory=0)[\"lon\"].values[0],\n",
+    "        ds.isel(trajectory=0)[\"lat\"].values[0],\n",
+    "    )\n",
+    "    d = np.zeros_like(ds[\"lon\"].values, dtype=float)\n",
+    "    for ob, (lon, lat) in enumerate(zip(ds[\"lon\"], ds[\"lat\"], strict=False)):\n",
+    "        d[ob] = haversine(lon, lat, lon0, lat0)\n",
+    "    ds[\"distance\"] = xr.DataArray(\n",
+    "        d,\n",
+    "        dims=ds[\"lon\"].dims,\n",
+    "        attrs={\"long_name\": \"distance from first waypoint\", \"units\": \"m\"},\n",
+    "    )\n",
+    "    return ds\n",
+    "\n",
+    "\n",
+    "def descent_only(ds, variable):\n",
+    "    \"\"\"Extract descending CTD data (downcast), pad with NaNs for alignment.\"\"\"\n",
+    "    min_z_idx = ds[\"z\"].argmin(\"obs\")\n",
+    "    da_clean = []\n",
+    "    for i, traj in enumerate(ds[\"trajectory\"].values):\n",
+    "        idx = min_z_idx.sel(trajectory=traj).item()\n",
+    "        descent_vals = ds[variable][\n",
+    "            i, : idx + 1\n",
+    "        ]  # take values from surface to min_z_idx (inclusive)\n",
+    "        da_clean.append(descent_vals)\n",
+    "    max_len = max(len(arr[~np.isnan(arr)]) for arr in da_clean)\n",
+    "    da_padded = np.full((ds[\"trajectory\"].size, max_len), np.nan)\n",
+    "    for i, arr in enumerate(da_clean):\n",
+    "        da_dropna = arr[~np.isnan(arr)]\n",
+    "        da_padded[i, : len(da_dropna)] = da_dropna\n",
+    "    return xr.DataArray(\n",
+    "        da_padded,\n",
+    "        dims=[\"trajectory\", \"obs\"],\n",
+    "        coords={\"trajectory\": ds[\"trajectory\"], \"obs\": np.arange(max_len)},\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "def build_masked_array(data_up, profile_indices, n_profiles):\n",
+    "    arr = np.full((n_profiles, data_up.shape[1]), np.nan)\n",
+    "    for i, idx in enumerate(profile_indices):\n",
+    "        if idx is not None:\n",
+    "            arr[i, :] = data_up.values[idx, :]\n",
+    "    return arr\n",
+    "\n",
+    "\n",
+    "def get_profile_indices(distance_1d):\n",
+    "    \"\"\"\n",
+    "    Returns regular distance bins and profile indices for CTD transect plotting.\n",
+    "\n",
+    "    Bin size is set to one order of magnitude lower than max distance.\n",
+    "    \"\"\"\n",
+    "    dist_min, dist_max = float(distance_1d.min()), float(distance_1d.max())\n",
+    "    if dist_max > 1e6:\n",
+    "        dist_step = 1e5\n",
+    "    elif dist_max > 1e5:\n",
+    "        dist_step = 1e4\n",
+    "    elif dist_max > 1e4:\n",
+    "        dist_step = 1e3\n",
+    "    else:\n",
+    "        dist_step = 1e2  # fallback for very short transects\n",
+    "\n",
+    "    distance_regular = np.arange(dist_min, dist_max + dist_step, dist_step)\n",
+    "    threshold = dist_step / 2\n",
+    "    profile_indices = [\n",
+    "        np.argmin(np.abs(distance_1d.values - d))\n",
+    "        if np.min(np.abs(distance_1d.values - d)) < threshold\n",
+    "        else None\n",
+    "        for d in distance_regular\n",
+    "    ]\n",
+    "    return profile_indices, distance_regular"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2bdf98e6",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Now we will execute the utility functions, plus define some extra useful arrays to be used for the plotting..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "f59824a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add distance from start\n",
+    "ctd_distance = distance_from_start(ctd_ds)\n",
+    "\n",
+    "# exract descent-only data\n",
+    "z_up = descent_only(ctd_distance, \"z\")\n",
+    "d_up = descent_only(ctd_distance, \"distance\")\n",
+    "var_up = descent_only(ctd_distance, VARIABLES[plot_variable][\"ds_name\"])\n",
+    "\n",
+    "# 1d array of depth dimension (from deepest trajectory)\n",
+    "traj_idx, obs_idx = np.where(z_up == np.nanmin(z_up))\n",
+    "z1d = z_up.values[traj_idx[0], :]\n",
+    "\n",
+    "# distance as 1d array\n",
+    "distance_1d = d_up.isel(obs=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17745cf1",
+   "metadata": {},
+   "source": [
+    "## Plotting\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\"> \n",
+    "<b>Note:</b> The plots produced next are a starting point for your analysis. You are encouraged to make adjustments, for example axis limits and scaling if the defaults not best suited to your specific data. Use your preferred AI coding assistant for help!\n",
+    "</div>\n",
+    "\n",
+    "We are now ready to plot our transect data. We will use distance from the first waypoint/CTD cast for the x-axis, and water column depth for the y-axis. The data for the chosen variable will then be plotted according to the colour map. The CTD casts are likely to be different depths because some parts of the ocean are of course shallower than others.\n",
+    "\n",
+    "There are a few extra steps below which arrange the CTD casts into regular distance bins, so as to clearly demonstrate where along the transect we made CTD casts and indeed where there are gaps.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce83c3b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x540 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# regularised transect\n",
+    "profile_indices, distance_regular = get_profile_indices(distance_1d)\n",
+    "var_masked = build_masked_array(var_up, profile_indices, len(distance_regular))\n",
+    "\n",
+    "xticks_reg = np.linspace(\n",
+    "    float(distance_regular.min()),\n",
+    "    float(distance_regular.max()),\n",
+    "    len(distance_regular),\n",
+    ")\n",
+    "\n",
+    "# plot regularised transect\n",
+    "fig, ax = plt.subplots(figsize=(10, 6), dpi=90)\n",
+    "\n",
+    "ax.grid(True, which=\"both\", color=\"lightgrey\", linestyle=\"-\", linewidth=0.7, alpha=0.5)\n",
+    "\n",
+    "mesh = ax.pcolormesh(\n",
+    "    distance_regular / 1000,  # distance in km\n",
+    "    z1d,\n",
+    "    var_masked.T,\n",
+    "    cmap=VARIABLES[plot_variable][\"cmap\"],\n",
+    ")\n",
+    "\n",
+    "ax.set_ylabel(\"Depth (m)\")\n",
+    "ax.set_xlabel(\"Distance from start (km)\")\n",
+    "\n",
+    "plt.colorbar(mesh, ax=ax, label=VARIABLES[plot_variable][\"label\"])\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68c5c8c2",
+   "metadata": {},
+   "source": [
+    "In the plot above, we can see that there are gaps in the transects where no CTD casts have been made. After all, it's impossible to take measurements at every point across the transect! There will always be gaps making 10s of deployments across transects 1000s of kms long 🙃 This makes expedition/sampling site planning all the more important...\n",
+    "\n",
+    "We can also also plot a 'filled' version without the distance bins, to give an alternative view of the evolution across the transect which is not dominated by gaps and white space. This time we will also add a 'sea bed' to the plot.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\"> \n",
+    "<b>Note:</b> It is important to remember that the gaps do actually exist in reality and this is a caveat which must be considered when interpreting the transect derived from CTD casts. Indeed, if you look at the x-axis of the plot below you will see that the deployments are not necessarily regularly spaced and some gaps are larger than others.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fcf8a137",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot 'filled' transect (with sea bed visualised as well)\n",
+    "fig, ax = plt.subplots(figsize=(10, 6), dpi=96)\n",
+    "\n",
+    "mesh = ax.pcolormesh(\n",
+    "    distance_1d / 1000,  # distance in km\n",
+    "    z1d,\n",
+    "    var_up.T,\n",
+    "    cmap=VARIABLES[plot_variable][\"cmap\"],\n",
+    ")\n",
+    "\n",
+    "seabed = xr.where(np.isnan(var_up), 1, np.nan)  # sea bed\n",
+    "ax.pcolormesh(\n",
+    "    distance_1d / 1000,  # distance in km\n",
+    "    z1d,\n",
+    "    seabed.T,\n",
+    "    cmap=mcolors.ListedColormap([mcolors.to_rgba(\"tan\"), mcolors.to_rgba(\"white\")]),\n",
+    ")\n",
+    "\n",
+    "tan_patch = mpatches.Patch(color=mcolors.to_rgba(\"tan\"), label=\"Land / sea bed\")\n",
+    "ax.legend(handles=[tan_patch], loc=\"lower right\")\n",
+    "\n",
+    "ax.set_xticks(distance_1d / 1000)\n",
+    "\n",
+    "ax.set_ylabel(\"Depth (m)\")\n",
+    "ax.set_xlabel(\"Distance from start (km)\")\n",
+    "\n",
+    "plt.colorbar(mesh, ax=ax, label=VARIABLES[plot_variable][\"label\"])\n",
+    "plt.tight_layout()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ship",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/user-guide/tutorials/index.md b/docs/user-guide/tutorials/index.md
index 339974ff..9b181aff 100644
--- a/docs/user-guide/tutorials/index.md
+++ b/docs/user-guide/tutorials/index.md
@@ -10,4 +10,6 @@ ADCP_data_tutorial.ipynb
 CTD_data_tutorial.ipynb
 Drifter_data_tutorial.ipynb
 Argo_data_tutorial.ipynb
+CTD_transects.ipynb
+ADCP_transects.ipynb
 ```