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Added ROMS ocean model example notebook (pydata#3116)
* change name of test env to xarray-tests (pydata#3110) * ROMS_ocean_model example added * Allow other tutorial filename extensions (pydata#3121) * switching out examples to use nbsphinx * added jupyter_client to doc env * allow non netcdf tutorial files * Changed load to xr.tutorial.open_dataset(), and added some extra documentation. * change name of test env to xarray-tests (pydata#3110) * ROMS_ocean_model example added * Changed load to xr.tutorial.open_dataset(), and added some extra documentation. * fixed colormap issues leftover from cmocean import * Added intro paragraph to ROMS example notebook, removed comments, and added citation in whats-new.
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# ROMS Ocean Model Example" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "The Regional Ocean Modeling System ([ROMS](http://myroms.org)) is an open source hydrodynamic model that is used for simulating currents and water properties in coastal and estuarine regions. ROMS is one of a few standard ocean models, and it has an active user community.\n", | ||
| "\n", | ||
| "ROMS uses a regular C-Grid in the horizontal, similar to other structured grid ocean and atmospheric models, and a stretched vertical coordinate (see [the ROMS documentation](https://www.myroms.org/wiki/Vertical_S-coordinate) for more details). Both of these require special treatment when using `xarray` to analyze ROMS ocean model output. This example notebook shows how to create a lazily evaluated vertical coordinate, and make some basic plots. The `xgcm` package is required to do analysis that is aware of the horizontal C-Grid." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import numpy as np\n", | ||
| "import cartopy.crs as ccrs\n", | ||
| "import cartopy.feature as cfeature\n", | ||
| "import matplotlib.pyplot as plt\n", | ||
| "%matplotlib inline\n", | ||
| "\n", | ||
| "import xarray as xr" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Load a sample ROMS file. This is a subset of a full model available at \n", | ||
| "\n", | ||
| " http://barataria.tamu.edu/thredds/catalog.html?dataset=txla_hindcast_agg\n", | ||
| " \n", | ||
| "The subsetting was done using the following command on one of the output files:\n", | ||
| "\n", | ||
| " #open dataset\n", | ||
| " ds = xr.open_dataset('/d2/shared/TXLA_ROMS/output_20yr_obc/2001/ocean_his_0015.nc')\n", | ||
| " \n", | ||
| " # Turn on chunking to activate dask and parallelize read/write.\n", | ||
| " ds = ds.chunk({'ocean_time': 1})\n", | ||
| " \n", | ||
| " # Pick out some of the variables that will be included as coordinates\n", | ||
| " ds = ds.set_coords(['Cs_r', 'Cs_w', 'hc', 'h', 'Vtransform'])\n", | ||
| " \n", | ||
| " # Select a a subset of variables. Salt will be visualized, zeta is used to \n", | ||
| " # calculate the vertical coordinate\n", | ||
| " variables = ['salt', 'zeta']\n", | ||
| " ds[variables].isel(ocean_time=slice(47, None, 7*24), \n", | ||
| " xi_rho=slice(300, None)).to_netcdf('ROMS_example.nc', mode='w')\n", | ||
| "\n", | ||
| "So, the `ROMS_example.nc` file contains a subset of the grid, one 3D variable, and two time steps." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Load in ROMS dataset as an xarray object" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# load in the file\n", | ||
| "ds = xr.tutorial.open_dataset('ROMS_example.nc', chunks={'ocean_time': 1})\n", | ||
| "\n", | ||
| "# This is a way to turn on chunking and lazy evaluation. Opening with mfdataset, or \n", | ||
| "# setting the chunking in the open_dataset would also achive this.\n", | ||
| "ds" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Add a lazilly calculated vertical coordinates\n", | ||
| "\n", | ||
| "Write equations to calculate the vertical coordinate. These will be only evaluated when data is requested. Information about the ROMS vertical coordinate can be found (here)[https://www.myroms.org/wiki/Vertical_S-coordinate]\n", | ||
| "\n", | ||
| "In short, for `Vtransform==2` as used in this example, \n", | ||
| "\n", | ||
| "$Z_0 = (h_c \\, S + h \\,C) / (h_c + h)$\n", | ||
| "\n", | ||
| "$z = Z_0 (\\zeta + h) + \\zeta$\n", | ||
| "\n", | ||
| "where the variables are defined as in the link above." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "if ds.Vtransform == 1:\n", | ||
| " Zo_rho = ds.hc * (ds.s_rho - ds.Cs_r) + ds.Cs_r * ds.h\n", | ||
| " z_rho = Zo_rho + ds.zeta * (1 + Zo_rho/ds.h)\n", | ||
| "elif ds.Vtransform == 2:\n", | ||
| " Zo_rho = (ds.hc * ds.s_rho + ds.Cs_r * ds.h) / (ds.hc + ds.h)\n", | ||
| " z_rho = ds.zeta + (ds.zeta + ds.h) * Zo_rho\n", | ||
| "\n", | ||
| "ds.coords['z_rho'] = z_rho.transpose() # needing transpose seems to be an xarray bug\n", | ||
| "ds.salt" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### A naive vertical slice\n", | ||
| "\n", | ||
| "Create a slice using the s-coordinate as the vertical dimension is typically not very informative." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "scrolled": false | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "ds.salt.isel(xi_rho=50, ocean_time=0).plot()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "We can feed coordinate information to the plot method to give a more informative cross-section that uses the depths. Note that we did not need to slice the depth or longitude information separately, this was done automatically as the variable was sliced." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "section = ds.salt.isel(xi_rho=50, eta_rho=slice(0, 167), ocean_time=0)\n", | ||
| "section.plot(x='lon_rho', y='z_rho', figsize=(15, 6), clim=(25, 35))\n", | ||
| "plt.ylim([-100, 1]);" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### A plan view\n", | ||
| "\n", | ||
| "Now make a naive plan view, without any projection information, just using lon/lat as x/y. This looks OK, but will appear compressed because lon and lat do not have an aspect constrained by the projection." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "ds.salt.isel(s_rho=-1, ocean_time=0).plot(x='lon_rho', y='lat_rho')" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "And let's use a projection to make it nicer, and add a coast." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "proj = ccrs.LambertConformal(central_longitude=-92, central_latitude=29)\n", | ||
| "fig = plt.figure(figsize=(15, 5))\n", | ||
| "ax = plt.axes(projection=proj)\n", | ||
| "ds.salt.isel(s_rho=-1, ocean_time=0).plot(x='lon_rho', y='lat_rho', \n", | ||
| " transform=ccrs.PlateCarree())\n", | ||
| "\n", | ||
| "coast_10m = cfeature.NaturalEarthFeature('physical', 'land', '10m',\n", | ||
| " edgecolor='k', facecolor='0.8')\n", | ||
| "ax.add_feature(coast_10m)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "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": "3.6.7" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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