{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### **Description**\n", "\n", "This recipe shows how to extract scalar and vector variables along the boundary of a regional subdomain using annual-mean outputs from the National Oceanography Centre Near-Present-Day global eORCA1 configuration of NEMO forced using JRA55-do from 1976-2024.\n", "\n", "For more details on this model configuration and the available outputs, users can explore the Near-Present-Day documentation [here](https://noc-msm.github.io/NOC_Near_Present_Day/).\n", "\n", "---\n", "### **Background**\n", "When analysing ocean models, it is often useful to diagnose fluxes across and properties along the boundary of a geographically defined region (e.g., a semi-enclosed sea, shelf, etc.).\n", "\n", "In NEMO, scalar quantities, such as temperature, salinity, and sea ice concentration are defined at grid cell centres (**T**-points), while velocity components are staggered on cell faces (**U**- and **V**-grid points). This staggering means that extracting a boundary around a masked region requires us to form the set of grid cell faces where normal velocities are defined before interpolating scalar quantities onto these oriented boundary segments.\n", "\n", "In this recipe, we show how to use the `extract_mask_boundary()` method of the `NEMODataTree` to extract properties and normal velocities across a closed boundary of a masked region within a NEMO ocean model domain.\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# -- Import required packages -- #\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import xarray as xr\n", "\n", "import nemo_cookbook as ncb\n", "from nemo_cookbook import NEMODataTree\n", "\n", "xr.set_options(display_style=\"text\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Using Dask**\n", "\n", "**Optional: Connect Client to Dask Local Cluster to run analysis in parallel.**\n", "\n", "Note that, although using Dask is not strictly necessary for this simple example using eORCA1, if we wanted to generalise this recipe to eORCA025 or eORCA12 outputs, using Dask would be essential to avoid unnecessary slow calculations using only a single process." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# -- Initialise Dask Local Cluster -- #\n", "import os\n", "\n", "import dask\n", "from dask.distributed import Client, LocalCluster\n", "\n", "# Update temporary directory for Dask workers:\n", "dask.config.set({'temporary_directory': f\"{os.getcwd()}/dask_tmp\",\n", " 'local_directory': f\"{os.getcwd()}/dask_tmp\"\n", " })\n", "\n", "# Create Local Cluster:\n", "cluster = LocalCluster(n_workers=4, threads_per_worker=3, memory_limit='5GB')\n", "client = Client(cluster)\n", "client" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Accessing IHO World Seas polygons**\n", "**Let's begin by loading the polygons defining the IHO World Seas regions**. \n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

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	ID	Name	MRGID	Longitudes	Latitudes
0	0	Rio de La Plata	4325	[[-54.943023652717045, -54.978746687192626, -5...	[[-34.947906883078645, -34.97439280639835, -35...
1	1	Bass Strait	4366	[[149.90464234356938, 149.9049998519617, 149.9...	[[-37.54324781853184, -37.54805552943908, -37....
2	2	Great Australian Bight	4276	[[143.53250818354263, 143.54855731580784, 143....	[[-38.855345058560204, -38.89580867390668, -38...
3	3	Tasman Sea	4365	[[159.03333000000018, 159.03983414634163, 159....	[[-29.999999999999986, -30.043495934959335, -3...
4	4	Mozambique Channel	4261	[[43.38217926066437, 43.426910578414024, 43.47...	[[-11.370205640977488, -11.374667237992885, -1...
...	...	...	...	...	...
96	96	Laccadive Sea	4269	[[79.19056582495296, 79.20560240772511, 79.205...	[[9.28162992038466, 9.280296445224849, 9.28018...
97	97	Skagerrak	2379	[[10.66160702664314, 10.662945270907699, 10.66...	[[59.91287636715083, 59.910394549469004, 59.90...
98	98	Norwegian Sea	2353	[[16.72106314339885, 16.78890655530549, 16.856...	[[76.5645473926769, 76.50603497544391, 76.4475...
99	99	Ligurian Sea	3363	[[9.834412487214706, 9.835301503777828, 9.8349...	[[44.0485148685363, 44.04729517147973, 44.0472...
100	100	Gulf of Guinea	4286	[[8.975150969002156, 8.974166710829394, 8.9742...	[[-0.935921075698605, -0.9360325715092586, -0....

\n", "

101 rows × 5 columns

\n", "

" ], "text/plain": [ " ID Name MRGID \\\n", "0 0 Rio de La Plata 4325 \n", "1 1 Bass Strait 4366 \n", "2 2 Great Australian Bight 4276 \n", "3 3 Tasman Sea 4365 \n", "4 4 Mozambique Channel 4261 \n", ".. ... ... ... \n", "96 96 Laccadive Sea 4269 \n", "97 97 Skagerrak 2379 \n", "98 98 Norwegian Sea 2353 \n", "99 99 Ligurian Sea 3363 \n", "100 100 Gulf of Guinea 4286 \n", "\n", " Longitudes \\\n", "0 [[-54.943023652717045, -54.978746687192626, -5... \n", "1 [[149.90464234356938, 149.9049998519617, 149.9... \n", "2 [[143.53250818354263, 143.54855731580784, 143.... \n", "3 [[159.03333000000018, 159.03983414634163, 159.... \n", "4 [[43.38217926066437, 43.426910578414024, 43.47... \n", ".. ... \n", "96 [[79.19056582495296, 79.20560240772511, 79.205... \n", "97 [[10.66160702664314, 10.662945270907699, 10.66... \n", "98 [[16.72106314339885, 16.78890655530549, 16.856... \n", "99 [[9.834412487214706, 9.835301503777828, 9.8349... \n", "100 [[8.975150969002156, 8.974166710829394, 8.9742... \n", "\n", " Latitudes \n", "0 [[-34.947906883078645, -34.97439280639835, -35... \n", "1 [[-37.54324781853184, -37.54805552943908, -37.... \n", "2 [[-38.855345058560204, -38.89580867390668, -38... \n", "3 [[-29.999999999999986, -30.043495934959335, -3... \n", "4 [[-11.370205640977488, -11.374667237992885, -1... \n", ".. ... \n", "96 [[9.28162992038466, 9.280296445224849, 9.28018... \n", "97 [[59.91287636715083, 59.910394549469004, 59.90... \n", "98 [[76.5645473926769, 76.50603497544391, 76.4475... \n", "99 [[44.0485148685363, 44.04729517147973, 44.0472... \n", "100 [[-0.935921075698605, -0.9360325715092586, -0.... \n", "\n", "[101 rows x 5 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Open IHO World Seas polygons from GitHub as Pandas DataFrame:\n", "filepaths = ncb.examples.get_filepaths(\"IHO\")\n", "\n", "df_IHO_World_Seas = pd.read_parquet(filepaths['IHO_World_Seas_v3_polygons.parquet'])\n", "df_IHO_World_Seas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Accessing NEMO Model Data**\n", "**Let's begin by loading the grid variables for our eORCA1 NEMO model from the [JASMIN Object Store](https://help.jasmin.ac.uk/docs/short-term-project-storage/using-the-jasmin-object-store/)**. \n", "\n", "**Alternatively, you can replace the ``domain_filepath`` below with a file path to your domain_cfg.nc file and read this with xarray's ``open_dataset()`` function.**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.Dataset> Size: 709MB\n",
       "Dimensions:       (y: 331, x: 360, nav_lev: 75)\n",
       "Coordinates:\n",
       "  * y             (y) int64 3kB 0 1 2 3 4 5 6 7 ... 324 325 326 327 328 329 330\n",
       "  * x             (x) int64 3kB 0 1 2 3 4 5 6 7 ... 353 354 355 356 357 358 359\n",
       "  * nav_lev       (nav_lev) int64 600B 0 1 2 3 4 5 6 7 ... 68 69 70 71 72 73 74\n",
       "Data variables: (12/49)\n",
       "    atlmsk        (y, x) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    bathy_metry   (y, x) float32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    bottom_level  (y, x) int32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    e1f           (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    e1t           (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    e1u           (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    ...            ...\n",
       "    top_level     (y, x) int32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    umask         (nav_lev, y, x) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>\n",
       "    umaskutil     (y, x) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    vmask         (nav_lev, y, x) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>\n",
       "    vmaskutil     (y, x) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    wmask         (nav_lev, y, x) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>\n",
       "Attributes:\n",
       "    CfgName:    UNKNOWN\n",
       "    CfgIndex:   -999\n",
       "    Iperio:     1\n",
       "    Jperio:     0\n",
       "    NFold:      1\n",
       "    NFtype:     F\n",
       "    VertCoord:  zps\n",
       "    IsfCav:     0\n",
       "    file_name:  mesh_mask.nc\n",
       "    TimeStamp:  01/03/2025 22:19:49 -0000

" ], "text/plain": [ " Size: 709MB\n", "Dimensions: (y: 331, x: 360, nav_lev: 75)\n", "Coordinates:\n", " * y (y) int64 3kB 0 1 2 3 4 5 6 7 ... 324 325 326 327 328 329 330\n", " * x (x) int64 3kB 0 1 2 3 4 5 6 7 ... 353 354 355 356 357 358 359\n", " * nav_lev (nav_lev) int64 600B 0 1 2 3 4 5 6 7 ... 68 69 70 71 72 73 74\n", "Data variables: (12/49)\n", " atlmsk (y, x) int8 119kB dask.array\n", " bathy_metry (y, x) float32 477kB dask.array\n", " bottom_level (y, x) int32 477kB dask.array\n", " e1f (y, x) float64 953kB dask.array\n", " e1t (y, x) float64 953kB dask.array\n", " e1u (y, x) float64 953kB dask.array\n", " ... ...\n", " top_level (y, x) int32 477kB dask.array\n", " umask (nav_lev, y, x) int8 9MB dask.array\n", " umaskutil (y, x) int8 119kB dask.array\n", " vmask (nav_lev, y, x) int8 9MB dask.array\n", " vmaskutil (y, x) int8 119kB dask.array\n", " wmask (nav_lev, y, x) int8 9MB dask.array\n", "Attributes:\n", " CfgName: UNKNOWN\n", " CfgIndex: -999\n", " Iperio: 1\n", " Jperio: 0\n", " NFold: 1\n", " NFtype: F\n", " VertCoord: zps\n", " IsfCav: 0\n", " file_name: mesh_mask.nc\n", " TimeStamp: 01/03/2025 22:19:49 -0000" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define directory path to ancillary files:\n", "domain_url = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-jra55v1/domain_cfg\"\n", "\n", "# Open eORCA1 NEMO model domain_cfg:\n", "ds_domain = xr.open_zarr(domain_url, consolidated=True, chunks={})\n", "\n", "ds_domain" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Next, we need to import the conservative temperature and absolute salinity stored at T-points in a single dataset.**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.Dataset> Size: 17GB\n",
       "Dimensions:                (time_counter: 21, y: 331, x: 360, deptht: 75,\n",
       "                            axis_nbounds: 2)\n",
       "Coordinates:\n",
       "  * time_counter           (time_counter) datetime64[ns] 168B 2000-07-02 ... ...\n",
       "    time_centered          (time_counter) datetime64[ns] 168B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "    nav_lat                (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    nav_lon                (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "  * deptht                 (deptht) float32 300B 0.5058 1.556 ... 5.902e+03\n",
       "Dimensions without coordinates: y, x, axis_nbounds\n",
       "Data variables: (12/74)\n",
       "    berg_latent_heat_flux  (time_counter, y, x) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    deptht_bounds          (deptht, axis_nbounds) float32 600B dask.array<chunksize=(25, 2), meta=np.ndarray>\n",
       "    e3t                    (time_counter, deptht, y, x) float32 751MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    empmr                  (time_counter, y, x) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    evs                    (time_counter, y, x) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    ficeberg               (time_counter, y, x) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    ...                     ...\n",
       "    ttrd_qns_li            (time_counter, y, x) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    vohfcisf               (time_counter, deptht, y, x) float32 751MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    vohflisf               (time_counter, deptht, y, x) float32 751MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    vowflisf               (time_counter, deptht, y, x) float32 751MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    zos                    (time_counter, y, x) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    zossq                  (time_counter, y, x) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "Attributes:\n",
       "    name:         OUTPUT/eORCA1_1y_grid_T\n",
       "    description:  ocean T grid variables\n",
       "    title:        ocean T grid variables\n",
       "    Conventions:  CF-1.6\n",
       "    timeStamp:    2024-Dec-09 10:28:01 GMT\n",
       "    uuid:         89824b34-5d44-4531-8344-fe1ce39fbb7b

" ], "text/plain": [ " Size: 17GB\n", "Dimensions: (time_counter: 21, y: 331, x: 360, deptht: 75,\n", " axis_nbounds: 2)\n", "Coordinates:\n", " * time_counter (time_counter) datetime64[ns] 168B 2000-07-02 ... ...\n", " time_centered (time_counter) datetime64[ns] 168B dask.array\n", " nav_lat (y, x) float64 953kB dask.array\n", " nav_lon (y, x) float64 953kB dask.array\n", " * deptht (deptht) float32 300B 0.5058 1.556 ... 5.902e+03\n", "Dimensions without coordinates: y, x, axis_nbounds\n", "Data variables: (12/74)\n", " berg_latent_heat_flux (time_counter, y, x) float32 10MB dask.array\n", " deptht_bounds (deptht, axis_nbounds) float32 600B dask.array\n", " e3t (time_counter, deptht, y, x) float32 751MB dask.array\n", " empmr (time_counter, y, x) float32 10MB dask.array\n", " evs (time_counter, y, x) float32 10MB dask.array\n", " ficeberg (time_counter, y, x) float32 10MB dask.array\n", " ... ...\n", " ttrd_qns_li (time_counter, y, x) float32 10MB dask.array\n", " vohfcisf (time_counter, deptht, y, x) float32 751MB dask.array\n", " vohflisf (time_counter, deptht, y, x) float32 751MB dask.array\n", " vowflisf (time_counter, deptht, y, x) float32 751MB dask.array\n", " zos (time_counter, y, x) float32 10MB dask.array\n", " zossq (time_counter, y, x) float32 10MB dask.array\n", "Attributes:\n", " name: OUTPUT/eORCA1_1y_grid_T\n", " description: ocean T grid variables\n", " title: ocean T grid variables\n", " Conventions: CF-1.6\n", " timeStamp: 2024-Dec-09 10:28:01 GMT\n", " uuid: 89824b34-5d44-4531-8344-fe1ce39fbb7b" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define directory path to model output files:\n", "gridT_url = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-jra55v1/T1y\"\n", "\n", "# Construct NEMO model T-grid dataset, including sea surface temperature (degC) and sea surface salinity (g/kg):\n", "ds_gridT = xr.open_zarr(gridT_url, consolidated=True, chunks={}).sel(time_counter=slice('2000-01', '2020-12'))\n", "\n", "ds_gridT" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Next, we need to import the zonal velocity stored at U-points in a single dataset.**" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.Dataset> Size: 4GB\n",
       "Dimensions:               (depthu: 75, axis_nbounds: 2, time_counter: 21,\n",
       "                           y: 331, x: 360)\n",
       "Coordinates:\n",
       "  * depthu                (depthu) float32 300B 0.5058 1.556 ... 5.902e+03\n",
       "  * time_counter          (time_counter) datetime64[ns] 168B 2000-07-02 ... 2...\n",
       "    time_centered         (time_counter) datetime64[ns] 168B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "    nav_lat               (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    nav_lon               (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "Dimensions without coordinates: axis_nbounds, y, x\n",
       "Data variables: (12/14)\n",
       "    depthu_bounds         (depthu, axis_nbounds) float32 600B dask.array<chunksize=(25, 2), meta=np.ndarray>\n",
       "    e3u                   (time_counter, depthu, y, x) float32 751MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    hfx                   (time_counter, y, x) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    hfx_adv               (time_counter, y, x) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    hfx_diff              (time_counter, y, x) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    sozosatr              (time_counter, y, x) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    ...                    ...\n",
       "    time_counter_bounds   (time_counter, axis_nbounds) datetime64[ns] 336B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "    u2o                   (time_counter, depthu, y, x) float32 751MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    umo                   (time_counter, depthu, y, x) float32 751MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    umo_vint              (time_counter, y, x) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    uo                    (time_counter, depthu, y, x) float32 751MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    uo_eiv                (time_counter, depthu, y, x) float32 751MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "Attributes:\n",
       "    name:         OUTPUT/eORCA1_1y_grid_U\n",
       "    description:  ocean U grid variables\n",
       "    title:        ocean U grid variables\n",
       "    Conventions:  CF-1.6\n",
       "    timeStamp:    2024-Dec-09 10:18:04 GMT\n",
       "    uuid:         8156ec68-1106-4f56-9f8c-d8798e1bd32f

" ], "text/plain": [ " Size: 4GB\n", "Dimensions: (depthu: 75, axis_nbounds: 2, time_counter: 21,\n", " y: 331, x: 360)\n", "Coordinates:\n", " * depthu (depthu) float32 300B 0.5058 1.556 ... 5.902e+03\n", " * time_counter (time_counter) datetime64[ns] 168B 2000-07-02 ... 2...\n", " time_centered (time_counter) datetime64[ns] 168B dask.array\n", " nav_lat (y, x) float64 953kB dask.array\n", " nav_lon (y, x) float64 953kB dask.array\n", "Dimensions without coordinates: axis_nbounds, y, x\n", "Data variables: (12/14)\n", " depthu_bounds (depthu, axis_nbounds) float32 600B dask.array\n", " e3u (time_counter, depthu, y, x) float32 751MB dask.array\n", " hfx (time_counter, y, x) float32 10MB dask.array\n", " hfx_adv (time_counter, y, x) float32 10MB dask.array\n", " hfx_diff (time_counter, y, x) float32 10MB dask.array\n", " sozosatr (time_counter, y, x) float32 10MB dask.array\n", " ... ...\n", " time_counter_bounds (time_counter, axis_nbounds) datetime64[ns] 336B dask.array\n", " u2o (time_counter, depthu, y, x) float32 751MB dask.array\n", " umo (time_counter, depthu, y, x) float32 751MB dask.array\n", " umo_vint (time_counter, y, x) float32 10MB dask.array\n", " uo (time_counter, depthu, y, x) float32 751MB dask.array\n", " uo_eiv (time_counter, depthu, y, x) float32 751MB dask.array\n", "Attributes:\n", " name: OUTPUT/eORCA1_1y_grid_U\n", " description: ocean U grid variables\n", " title: ocean U grid variables\n", " Conventions: CF-1.6\n", " timeStamp: 2024-Dec-09 10:18:04 GMT\n", " uuid: 8156ec68-1106-4f56-9f8c-d8798e1bd32f" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define directory path to model output files:\n", "gridU_url = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-jra55v1/U1y\"\n", "\n", "# Construct NEMO model U-grid dataset, including zonal seawater velocity (m/s):\n", "ds_gridU = xr.open_zarr(gridU_url, consolidated=True, chunks={}).sel(time_counter=slice('2000-01', '2020-12'))\n", "\n", "ds_gridU" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Next, we need to import the meridional velocity stored at V-points in a single dataset.**" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.Dataset> Size: 4GB\n",
       "Dimensions:               (depthv: 75, axis_nbounds: 2, time_counter: 21,\n",
       "                           y: 331, x: 360)\n",
       "Coordinates:\n",
       "  * depthv                (depthv) float32 300B 0.5058 1.556 ... 5.902e+03\n",
       "  * time_counter          (time_counter) datetime64[ns] 168B 2000-07-02 ... 2...\n",
       "    time_centered         (time_counter) datetime64[ns] 168B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "    nav_lat               (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "    nav_lon               (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "Dimensions without coordinates: axis_nbounds, y, x\n",
       "Data variables: (12/13)\n",
       "    depthv_bounds         (depthv, axis_nbounds) float32 600B dask.array<chunksize=(25, 2), meta=np.ndarray>\n",
       "    e3v                   (time_counter, depthv, y, x) float32 751MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    hfy                   (time_counter, y, x) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    hfy_adv               (time_counter, y, x) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    hfy_diff              (time_counter, y, x) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    somesatr              (time_counter, y, x) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "    ...                    ...\n",
       "    time_centered_bounds  (time_counter, axis_nbounds) datetime64[ns] 336B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "    time_counter_bounds   (time_counter, axis_nbounds) datetime64[ns] 336B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "    v2o                   (time_counter, depthv, y, x) float32 751MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    vmo                   (time_counter, depthv, y, x) float32 751MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    vo                    (time_counter, depthv, y, x) float32 751MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "    vo_eiv                (time_counter, depthv, y, x) float32 751MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "Attributes:\n",
       "    name:         OUTPUT/eORCA1_1y_grid_V\n",
       "    description:  ocean V grid variables\n",
       "    title:        ocean V grid variables\n",
       "    Conventions:  CF-1.6\n",
       "    timeStamp:    2024-Dec-09 10:18:04 GMT\n",
       "    uuid:         b77cb501-a564-4451-8908-d66fe201e54a

" ], "text/plain": [ " Size: 4GB\n", "Dimensions: (depthv: 75, axis_nbounds: 2, time_counter: 21,\n", " y: 331, x: 360)\n", "Coordinates:\n", " * depthv (depthv) float32 300B 0.5058 1.556 ... 5.902e+03\n", " * time_counter (time_counter) datetime64[ns] 168B 2000-07-02 ... 2...\n", " time_centered (time_counter) datetime64[ns] 168B dask.array\n", " nav_lat (y, x) float64 953kB dask.array\n", " nav_lon (y, x) float64 953kB dask.array\n", "Dimensions without coordinates: axis_nbounds, y, x\n", "Data variables: (12/13)\n", " depthv_bounds (depthv, axis_nbounds) float32 600B dask.array\n", " e3v (time_counter, depthv, y, x) float32 751MB dask.array\n", " hfy (time_counter, y, x) float32 10MB dask.array\n", " hfy_adv (time_counter, y, x) float32 10MB dask.array\n", " hfy_diff (time_counter, y, x) float32 10MB dask.array\n", " somesatr (time_counter, y, x) float32 10MB dask.array\n", " ... ...\n", " time_centered_bounds (time_counter, axis_nbounds) datetime64[ns] 336B dask.array\n", " time_counter_bounds (time_counter, axis_nbounds) datetime64[ns] 336B dask.array\n", " v2o (time_counter, depthv, y, x) float32 751MB dask.array\n", " vmo (time_counter, depthv, y, x) float32 751MB dask.array\n", " vo (time_counter, depthv, y, x) float32 751MB dask.array\n", " vo_eiv (time_counter, depthv, y, x) float32 751MB dask.array\n", "Attributes:\n", " name: OUTPUT/eORCA1_1y_grid_V\n", " description: ocean V grid variables\n", " title: ocean V grid variables\n", " Conventions: CF-1.6\n", " timeStamp: 2024-Dec-09 10:18:04 GMT\n", " uuid: b77cb501-a564-4451-8908-d66fe201e54a" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define directory path to model output files:\n", "gridV_url = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-jra55v1/V1y\"\n", "\n", "# Construct NEMO model V-grid dataset, including meridional seawater velocity (m/s):\n", "ds_gridV = xr.open_zarr(gridV_url, consolidated=True, chunks={}).sel(time_counter=slice('2000-01', '2020-12'))\n", "\n", "ds_gridV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Creating a NEMODataTree**\n", "\n", "**Using our outputs, let's create a NEMODataTree to store our domain and T-grid variables for the eORCA1 model.**" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.DataTree 'NEMO model'>\n",
       "Group: /\n",
       "│   Dimensions:               (time_counter: 21, axis_nbounds: 2)\n",
       "│   Coordinates:\n",
       "│     * time_counter          (time_counter) datetime64[ns] 168B 2000-07-02 ... 2...\n",
       "│       time_centered         (time_counter) datetime64[ns] 168B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "│   Dimensions without coordinates: axis_nbounds\n",
       "│   Data variables:\n",
       "│       time_centered_bounds  (time_counter, axis_nbounds) datetime64[ns] 336B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "│       time_counter_bounds   (time_counter, axis_nbounds) datetime64[ns] 336B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "│   Attributes:\n",
       "│       nftype:   F\n",
       "│       iperio:   True\n",
       "├── Group: /gridT\n",
       "│       Dimensions:                (time_counter: 21, j: 331, i: 360, k: 75,\n",
       "│                                   axis_nbounds: 2)\n",
       "│       Coordinates:\n",
       "│           time_centered          (time_counter) datetime64[ns] 168B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "│         * j                      (j) int64 3kB 1 2 3 4 5 6 ... 326 327 328 329 330 331\n",
       "│         * i                      (i) int64 3kB 1 2 3 4 5 6 ... 355 356 357 358 359 360\n",
       "│           gphit                  (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           glamt                  (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│         * k                      (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75\n",
       "│         * deptht                 (k) float32 300B 0.5058 1.556 ... 5.698e+03 5.902e+03\n",
       "│       Dimensions without coordinates: axis_nbounds\n",
       "│       Data variables: (12/78)\n",
       "│           berg_latent_heat_flux  (time_counter, j, i) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           deptht_bounds          (k, axis_nbounds) float32 600B dask.array<chunksize=(25, 2), meta=np.ndarray>\n",
       "│           e3t                    (time_counter, k, j, i) float32 751MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "│           empmr                  (time_counter, j, i) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           evs                    (time_counter, j, i) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           ficeberg               (time_counter, j, i) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           ...                     ...\n",
       "│           zos                    (time_counter, j, i) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           zossq                  (time_counter, j, i) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           e1t                    (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           e2t                    (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           tmask                  (k, j, i) bool 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>\n",
       "│           tmaskutil              (j, i) bool 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           nftype:   F\n",
       "│           iperio:   True\n",
       "├── Group: /gridU\n",
       "│       Dimensions:               (k: 75, axis_nbounds: 2, time_counter: 21, j: 331,\n",
       "│                                  i: 360)\n",
       "│       Coordinates:\n",
       "│         * k                     (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75\n",
       "│         * depthu                (k) float32 300B 0.5058 1.556 ... 5.698e+03 5.902e+03\n",
       "│           time_centered         (time_counter) datetime64[ns] 168B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "│         * j                     (j) int64 3kB 1 2 3 4 5 6 ... 326 327 328 329 330 331\n",
       "│         * i                     (i) float64 3kB 1.5 2.5 3.5 4.5 ... 358.5 359.5 360.5\n",
       "│           gphiu                 (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           glamu                 (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│       Dimensions without coordinates: axis_nbounds\n",
       "│       Data variables: (12/18)\n",
       "│           depthu_bounds         (k, axis_nbounds) float32 600B dask.array<chunksize=(25, 2), meta=np.ndarray>\n",
       "│           e3u                   (time_counter, k, j, i) float32 751MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "│           hfx                   (time_counter, j, i) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           hfx_adv               (time_counter, j, i) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           hfx_diff              (time_counter, j, i) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           sozosatr              (time_counter, j, i) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           ...                    ...\n",
       "│           uo                    (time_counter, k, j, i) float32 751MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "│           uo_eiv                (time_counter, k, j, i) float32 751MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "│           e1u                   (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           e2u                   (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           umask                 (k, j, i) bool 9MB False False False ... False False\n",
       "│           umaskutil             (j, i) bool 119kB False False False ... False False\n",
       "│       Attributes:\n",
       "│           name:         OUTPUT/eORCA1_1y_grid_U\n",
       "│           description:  ocean U grid variables\n",
       "│           title:        ocean U grid variables\n",
       "│           Conventions:  CF-1.6\n",
       "│           timeStamp:    2024-Dec-09 10:18:04 GMT\n",
       "│           uuid:         8156ec68-1106-4f56-9f8c-d8798e1bd32f\n",
       "│           nftype:       F\n",
       "│           iperio:       True\n",
       "├── Group: /gridV\n",
       "│       Dimensions:               (k: 75, axis_nbounds: 2, time_counter: 21, j: 331,\n",
       "│                                  i: 360)\n",
       "│       Coordinates:\n",
       "│         * k                     (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75\n",
       "│         * depthv                (k) float32 300B 0.5058 1.556 ... 5.698e+03 5.902e+03\n",
       "│           time_centered         (time_counter) datetime64[ns] 168B dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "│         * j                     (j) float64 3kB 1.5 2.5 3.5 4.5 ... 329.5 330.5 331.5\n",
       "│         * i                     (i) int64 3kB 1 2 3 4 5 6 ... 355 356 357 358 359 360\n",
       "│           gphiv                 (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           glamv                 (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│       Dimensions without coordinates: axis_nbounds\n",
       "│       Data variables: (12/17)\n",
       "│           depthv_bounds         (k, axis_nbounds) float32 600B dask.array<chunksize=(25, 2), meta=np.ndarray>\n",
       "│           e3v                   (time_counter, k, j, i) float32 751MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "│           hfy                   (time_counter, j, i) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           hfy_adv               (time_counter, j, i) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           hfy_diff              (time_counter, j, i) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           somesatr              (time_counter, j, i) float32 10MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>\n",
       "│           ...                    ...\n",
       "│           vo                    (time_counter, k, j, i) float32 751MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "│           vo_eiv                (time_counter, k, j, i) float32 751MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>\n",
       "│           e1v                   (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           e2v                   (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           vmask                 (k, j, i) bool 9MB False False False ... False False\n",
       "│           vmaskutil             (j, i) bool 119kB False False False ... False False\n",
       "│       Attributes:\n",
       "│           name:         OUTPUT/eORCA1_1y_grid_V\n",
       "│           description:  ocean V grid variables\n",
       "│           title:        ocean V grid variables\n",
       "│           Conventions:  CF-1.6\n",
       "│           timeStamp:    2024-Dec-09 10:18:04 GMT\n",
       "│           uuid:         b77cb501-a564-4451-8908-d66fe201e54a\n",
       "│           nftype:       F\n",
       "│           iperio:       True\n",
       "├── Group: /gridW\n",
       "│       Dimensions:       (j: 331, i: 360, k: 75)\n",
       "│       Coordinates:\n",
       "│         * j             (j) int64 3kB 1 2 3 4 5 6 7 8 ... 325 326 327 328 329 330 331\n",
       "│         * i             (i) int64 3kB 1 2 3 4 5 6 7 8 ... 354 355 356 357 358 359 360\n",
       "│           gphiw         (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           glamw         (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│         * k             (k) float64 600B 0.5 1.5 2.5 3.5 4.5 ... 71.5 72.5 73.5 74.5\n",
       "│       Data variables:\n",
       "│           e1w           (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           e2w           (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "│           wmask         (k, j, i) bool 9MB False False False ... False False False\n",
       "│           wmaskutil     (j, i) bool 119kB False False False ... False False False\n",
       "│       Attributes:\n",
       "│           nftype:   F\n",
       "│           iperio:   True\n",
       "└── Group: /gridF\n",
       "        Dimensions:       (j: 331, i: 360, k: 75)\n",
       "        Coordinates:\n",
       "          * j             (j) float64 3kB 1.5 2.5 3.5 4.5 ... 328.5 329.5 330.5 331.5\n",
       "          * i             (i) float64 3kB 1.5 2.5 3.5 4.5 ... 357.5 358.5 359.5 360.5\n",
       "            gphif         (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "            glamf         (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "          * k             (k) int64 600B 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75\n",
       "        Data variables:\n",
       "            e1f           (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "            e2f           (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>\n",
       "            fmask         (k, j, i) bool 9MB False False False ... False False False\n",
       "            fmaskutil     (j, i) bool 119kB False False False ... False False False\n",
       "        Attributes:\n",
       "            nftype:   F\n",
       "            iperio:   True

" ], "text/plain": [ "\n", "Group: /\n", "│ Dimensions: (time_counter: 21, axis_nbounds: 2)\n", "│ Coordinates:\n", "│ * time_counter (time_counter) datetime64[ns] 168B 2000-07-02 ... 2...\n", "│ time_centered (time_counter) datetime64[ns] 168B dask.array\n", "│ Dimensions without coordinates: axis_nbounds\n", "│ Data variables:\n", "│ time_centered_bounds (time_counter, axis_nbounds) datetime64[ns] 336B dask.array\n", "│ time_counter_bounds (time_counter, axis_nbounds) datetime64[ns] 336B dask.array\n", "│ Attributes:\n", "│ nftype: F\n", "│ iperio: True\n", "├── Group: /gridT\n", "│ Dimensions: (time_counter: 21, j: 331, i: 360, k: 75,\n", "│ axis_nbounds: 2)\n", "│ Coordinates:\n", "│ time_centered (time_counter) datetime64[ns] 168B dask.array\n", "│ * j (j) int64 3kB 1 2 3 4 5 6 ... 326 327 328 329 330 331\n", "│ * i (i) int64 3kB 1 2 3 4 5 6 ... 355 356 357 358 359 360\n", "│ gphit (j, i) float64 953kB dask.array\n", "│ glamt (j, i) float64 953kB dask.array\n", "│ * k (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75\n", "│ * deptht (k) float32 300B 0.5058 1.556 ... 5.698e+03 5.902e+03\n", "│ Dimensions without coordinates: axis_nbounds\n", "│ Data variables: (12/78)\n", "│ berg_latent_heat_flux (time_counter, j, i) float32 10MB dask.array\n", "│ deptht_bounds (k, axis_nbounds) float32 600B dask.array\n", "│ e3t (time_counter, k, j, i) float32 751MB dask.array\n", "│ empmr (time_counter, j, i) float32 10MB dask.array\n", "│ evs (time_counter, j, i) float32 10MB dask.array\n", "│ ficeberg (time_counter, j, i) float32 10MB dask.array\n", "│ ... ...\n", "│ zos (time_counter, j, i) float32 10MB dask.array\n", "│ zossq (time_counter, j, i) float32 10MB dask.array\n", "│ e1t (j, i) float64 953kB dask.array\n", "│ e2t (j, i) float64 953kB dask.array\n", "│ tmask (k, j, i) bool 9MB dask.array\n", "│ tmaskutil (j, i) bool 119kB dask.array\n", "│ Attributes:\n", "│ nftype: F\n", "│ iperio: True\n", "├── Group: /gridU\n", "│ Dimensions: (k: 75, axis_nbounds: 2, time_counter: 21, j: 331,\n", "│ i: 360)\n", "│ Coordinates:\n", "│ * k (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75\n", "│ * depthu (k) float32 300B 0.5058 1.556 ... 5.698e+03 5.902e+03\n", "│ time_centered (time_counter) datetime64[ns] 168B dask.array\n", "│ * j (j) int64 3kB 1 2 3 4 5 6 ... 326 327 328 329 330 331\n", "│ * i (i) float64 3kB 1.5 2.5 3.5 4.5 ... 358.5 359.5 360.5\n", "│ gphiu (j, i) float64 953kB dask.array\n", "│ glamu (j, i) float64 953kB dask.array\n", "│ Dimensions without coordinates: axis_nbounds\n", "│ Data variables: (12/18)\n", "│ depthu_bounds (k, axis_nbounds) float32 600B dask.array\n", "│ e3u (time_counter, k, j, i) float32 751MB dask.array\n", "│ hfx (time_counter, j, i) float32 10MB dask.array\n", "│ hfx_adv (time_counter, j, i) float32 10MB dask.array\n", "│ hfx_diff (time_counter, j, i) float32 10MB dask.array\n", "│ sozosatr (time_counter, j, i) float32 10MB dask.array\n", "│ ... ...\n", "│ uo (time_counter, k, j, i) float32 751MB dask.array\n", "│ uo_eiv (time_counter, k, j, i) float32 751MB dask.array\n", "│ e1u (j, i) float64 953kB dask.array\n", "│ e2u (j, i) float64 953kB dask.array\n", "│ umask (k, j, i) bool 9MB False False False ... False False\n", "│ umaskutil (j, i) bool 119kB False False False ... False False\n", "│ Attributes:\n", "│ name: OUTPUT/eORCA1_1y_grid_U\n", "│ description: ocean U grid variables\n", "│ title: ocean U grid variables\n", "│ Conventions: CF-1.6\n", "│ timeStamp: 2024-Dec-09 10:18:04 GMT\n", "│ uuid: 8156ec68-1106-4f56-9f8c-d8798e1bd32f\n", "│ nftype: F\n", "│ iperio: True\n", "├── Group: /gridV\n", "│ Dimensions: (k: 75, axis_nbounds: 2, time_counter: 21, j: 331,\n", "│ i: 360)\n", "│ Coordinates:\n", "│ * k (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75\n", "│ * depthv (k) float32 300B 0.5058 1.556 ... 5.698e+03 5.902e+03\n", "│ time_centered (time_counter) datetime64[ns] 168B dask.array\n", "│ * j (j) float64 3kB 1.5 2.5 3.5 4.5 ... 329.5 330.5 331.5\n", "│ * i (i) int64 3kB 1 2 3 4 5 6 ... 355 356 357 358 359 360\n", "│ gphiv (j, i) float64 953kB dask.array\n", "│ glamv (j, i) float64 953kB dask.array\n", "│ Dimensions without coordinates: axis_nbounds\n", "│ Data variables: (12/17)\n", "│ depthv_bounds (k, axis_nbounds) float32 600B dask.array\n", "│ e3v (time_counter, k, j, i) float32 751MB dask.array\n", "│ hfy (time_counter, j, i) float32 10MB dask.array\n", "│ hfy_adv (time_counter, j, i) float32 10MB dask.array\n", "│ hfy_diff (time_counter, j, i) float32 10MB dask.array\n", "│ somesatr (time_counter, j, i) float32 10MB dask.array\n", "│ ... ...\n", "│ vo (time_counter, k, j, i) float32 751MB dask.array\n", "│ vo_eiv (time_counter, k, j, i) float32 751MB dask.array\n", "│ e1v (j, i) float64 953kB dask.array\n", "│ e2v (j, i) float64 953kB dask.array\n", "│ vmask (k, j, i) bool 9MB False False False ... False False\n", "│ vmaskutil (j, i) bool 119kB False False False ... False False\n", "│ Attributes:\n", "│ name: OUTPUT/eORCA1_1y_grid_V\n", "│ description: ocean V grid variables\n", "│ title: ocean V grid variables\n", "│ Conventions: CF-1.6\n", "│ timeStamp: 2024-Dec-09 10:18:04 GMT\n", "│ uuid: b77cb501-a564-4451-8908-d66fe201e54a\n", "│ nftype: F\n", "│ iperio: True\n", "├── Group: /gridW\n", "│ Dimensions: (j: 331, i: 360, k: 75)\n", "│ Coordinates:\n", "│ * j (j) int64 3kB 1 2 3 4 5 6 7 8 ... 325 326 327 328 329 330 331\n", "│ * i (i) int64 3kB 1 2 3 4 5 6 7 8 ... 354 355 356 357 358 359 360\n", "│ gphiw (j, i) float64 953kB dask.array\n", "│ glamw (j, i) float64 953kB dask.array\n", "│ * k (k) float64 600B 0.5 1.5 2.5 3.5 4.5 ... 71.5 72.5 73.5 74.5\n", "│ Data variables:\n", "│ e1w (j, i) float64 953kB dask.array\n", "│ e2w (j, i) float64 953kB dask.array\n", "│ wmask (k, j, i) bool 9MB False False False ... False False False\n", "│ wmaskutil (j, i) bool 119kB False False False ... False False False\n", "│ Attributes:\n", "│ nftype: F\n", "│ iperio: True\n", "└── Group: /gridF\n", " Dimensions: (j: 331, i: 360, k: 75)\n", " Coordinates:\n", " * j (j) float64 3kB 1.5 2.5 3.5 4.5 ... 328.5 329.5 330.5 331.5\n", " * i (i) float64 3kB 1.5 2.5 3.5 4.5 ... 357.5 358.5 359.5 360.5\n", " gphif (j, i) float64 953kB dask.array\n", " glamf (j, i) float64 953kB dask.array\n", " * k (k) int64 600B 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75\n", " Data variables:\n", " e1f (j, i) float64 953kB dask.array\n", " e2f (j, i) float64 953kB dask.array\n", " fmask (k, j, i) bool 9MB False False False ... False False False\n", " fmaskutil (j, i) bool 119kB False False False ... False False False\n", " Attributes:\n", " nftype: F\n", " iperio: True" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define dictionary of grid datasets defining eORCA1 parent model domain with no child/grand-child nests:\n", "# Note: domain_cfg z-dimension is expected to be named 'nav_lev'.\n", "datasets = {\"parent\": {\"domain\": ds_domain, \"gridT\": ds_gridT, \"gridU\": ds_gridU, \"gridV\": ds_gridV}}\n", "\n", "# Initialise a new NEMODataTree whose parent domain is zonally periodic & north-folding on F-points:\n", "nemo = NEMODataTree.from_datasets(datasets=datasets, iperio=True, nftype=\"F\", read_mask=False)\n", "\n", "nemo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Now, we will use the IHO World Seas polygons to extract the Barents Sea from the eORCA1 model domain.**" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Define IHO World Seas Barents Sea polygon:\n", "lon_poly = df_IHO_World_Seas[df_IHO_World_Seas['Name'] == 'Barentsz Sea']['Longitudes'].item()[0]\n", "lat_poly = df_IHO_World_Seas[df_IHO_World_Seas['Name'] == 'Barentsz Sea']['Latitudes'].item()[0]\n", "\n", "# Define boolean mask for NEMO model T-point contained within the Barents Sea:\n", "mask = nemo.mask_with_polygon(grid='gridT', lon_poly=lon_poly, lat_poly=lat_poly)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Next, let's plot the mean sea surface temperature of our eORCA1 model within the IHO World Seas polygons for the Barents Sea:**" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/z2/j_dr250s42x34hk63_rp4bm80000gq/T/ipykernel_10946/3108725572.py:4: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh.\n", " plt.pcolormesh(nemo['gridT/glamt'], nemo['gridT/gphit'], nemo['gridT/tos_con'].where(mask).mean(dim='time_counter'), cmap='RdBu_r')\n" ] }, { "data": { "text/plain": [ "(60.0, 90.0)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# -- Plot time-mean sea surface temperature (°C) in the Barents Sea, with IHO World Seas polygon overlaid -- #\n", "plt.figure(figsize=(8, 6))\n", "\n", "plt.pcolormesh(nemo['gridT/glamt'], nemo['gridT/gphit'], nemo['gridT/tos_con'].where(mask).mean(dim='time_counter'), cmap='RdBu_r')\n", "plt.plot(lon_poly, lat_poly, color='0.1', lw=2)\n", "plt.colorbar(label='Sea Surface Temperature (°C)')\n", "\n", "# Axes labels:\n", "plt.title(\"eORCA1 JRA55v1:\\nBarents Sea Mean Sea Surface Temperature\", fontdict={\"size\": 12, \"weight\": \"bold\"})\n", "plt.xlabel('Longitude', fontdict={\"size\": 10, \"weight\": \"bold\"})\n", "plt.ylabel('Latitude', fontdict={\"size\": 10, \"weight\": \"bold\"})\n", "plt.xlim([0, 80])\n", "plt.ylim([60, 90])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Extracting velocities and properties along the boundary of the Barents Sea**\n", "\n", "**Now we have explored our `NEMODataTree`, let's extract the normal velocities and conservative temperature along the boundary of the Barent Sea region in our eORCA1 domain.**\n", "\n", "We must define the names of the zonal and meridional velocity components as a list `uv_vars` and the names of any scalar variables defined on **T**-grid points (`vars`) we would like to extract along the boundary." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

<xarray.Dataset> Size: 5MB\n",
       "Dimensions:       (bdy: 140, time_counter: 21, k: 75)\n",
       "Coordinates:\n",
       "  * bdy           (bdy) int64 1kB 0 1 2 3 4 5 6 ... 133 134 135 136 137 138 139\n",
       "    glamb         (bdy) float64 1kB 68.38 66.5 65.69 64.9 ... 67.12 68.21 69.25\n",
       "    gphib         (bdy) float64 1kB 76.92 76.88 76.63 ... 77.58 77.37 77.16\n",
       "    depthb        (k, bdy) float64 84kB 0.5058 0.5058 ... 5.902e+03 5.902e+03\n",
       "  * time_counter  (time_counter) datetime64[ns] 168B 2000-07-02 ... 2020-07-02\n",
       "  * k             (k) int64 600B 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75\n",
       "Data variables:\n",
       "    i_bdy         (bdy) float64 1kB 294.5 294.5 295.0 ... 293.0 293.5 294.0\n",
       "    j_bdy         (bdy) float64 1kB 329.0 328.0 327.5 ... 328.5 329.0 329.5\n",
       "    flux_type     (bdy) <U1 560B 'U' 'U' 'V' 'U' 'U' 'U' ... 'V' 'V' 'V' 'U' 'V'\n",
       "    flux_dir      (bdy) int64 1kB 1 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 -1 1\n",
       "    velocity      (time_counter, k, bdy) float64 2MB dask.array<chunksize=(21, 75, 140), meta=np.ndarray>\n",
       "    bmask         (k, bdy) float64 84kB dask.array<chunksize=(75, 140), meta=np.ndarray>\n",
       "    e1b           (bdy) float64 1kB dask.array<chunksize=(140,), meta=np.ndarray>\n",
       "    e3b           (time_counter, k, bdy) float64 2MB dask.array<chunksize=(21, 75, 140), meta=np.ndarray>\n",
       "    thetao_con    (time_counter, k, bdy) float64 2MB dask.array<chunksize=(21, 75, 140), meta=np.ndarray>

" ], "text/plain": [ " Size: 5MB\n", "Dimensions: (bdy: 140, time_counter: 21, k: 75)\n", "Coordinates:\n", " * bdy (bdy) int64 1kB 0 1 2 3 4 5 6 ... 133 134 135 136 137 138 139\n", " glamb (bdy) float64 1kB 68.38 66.5 65.69 64.9 ... 67.12 68.21 69.25\n", " gphib (bdy) float64 1kB 76.92 76.88 76.63 ... 77.58 77.37 77.16\n", " depthb (k, bdy) float64 84kB 0.5058 0.5058 ... 5.902e+03 5.902e+03\n", " * time_counter (time_counter) datetime64[ns] 168B 2000-07-02 ... 2020-07-02\n", " * k (k) int64 600B 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75\n", "Data variables:\n", " i_bdy (bdy) float64 1kB 294.5 294.5 295.0 ... 293.0 293.5 294.0\n", " j_bdy (bdy) float64 1kB 329.0 328.0 327.5 ... 328.5 329.0 329.5\n", " flux_type (bdy) \n", " bmask (k, bdy) float64 84kB dask.array\n", " e1b (bdy) float64 1kB dask.array\n", " e3b (time_counter, k, bdy) float64 2MB dask.array\n", " thetao_con (time_counter, k, bdy) float64 2MB dask.array" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Extract mask boundary as an xarray.Dataset, including geographical coordinates and grid scale factors along the boundary:\n", "ds_bdy = nemo.extract_mask_boundary(mask=mask, uv_vars=[\"uo\", \"vo\"], vars=[\"thetao_con\"], dom=\".\")\n", "\n", "ds_bdy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Taking a closer look at our boundary dataset, we can see it is structured analogously to a NEMO model grid node within our `NEMODataTree`:**\n", "\n", "* Inherited NEMO Grid Dimension: `k`\n", "\n", "* New Along-Boundary Grid Dimension: `bdy`, such that `i_bdy(bdy)`, `j_bdy(bdy)`\n", "\n", "* Geographical Coordinates: `glamb` (longitude), `gphib` (latitude).\n", "\n", "* Horizontal Grid Scale Factors: `e1b`\n", "\n", "* Vertical Grid Scale Factors: `e3b`\n", "\n", "* Land-Sea Mask: `bmask`\n", "\n", "The `xarray.Dataset` also includes `flux_type` which defines the type of vector point the grid cell faces is and `flux_dir` which specifies the sign of each normal velocity component.\n", "\n", "The `velocity` variable therefore represents a combination of zonal (**U**) and meridional (**V**) velocities to which the appropriate `flux_dir` has **already** been applied." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Visualising the geographical locations of the Barents Sea boundary indexes**\n", "\n", "**Next, let's visualise the locations of the NEMO model U/V-grid points which form the closed boundary around the Barents Sea:**\n", "\n", "We can see that the boundary index `bdy` increases clockwise to form a closed loop around the boundary." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(290.0, 331.0)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# -- Plot time-mean sea surface temperature (°C) & Barents Sea boundary -- #\n", "plt.figure(figsize=(8, 5))\n", "\n", "# Plot time-mean sea surface temperature (°C) in the Barents Sea:\n", "nemo['gridT/tos_con'].masked.mean(dim='time_counter').plot()\n", "\n", "# Plot mask boundary points coloured by their boundary index:\n", "scatter = plt.scatter(ds_bdy['i_bdy'], ds_bdy['j_bdy'], c=ds_bdy['bdy'], s=10)\n", "plt.colorbar(scatter, label='Boundary Index')\n", "\n", "# Add axes limits:\n", "plt.title(\"eORCA1 JRA55v1:\\nBarents Sea Mean Sea Surface Temperature & Mask Boundary\", fontdict={\"size\": 12, \"weight\": \"bold\"})\n", "plt.xlabel('i indices of NEMO model T-points', fontdict={\"size\": 10, \"weight\": \"bold\"})\n", "plt.ylabel('j indices of NEMO model T-points', fontdict={\"size\": 10, \"weight\": \"bold\"})\n", "plt.xlim([275, 315])\n", "plt.ylim([290, 331])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Visualising conservative temperature along the Barents Sea boundary**\n", "\n", "**Finally, let's visualise a snapshot of the conservative temperature field along the boundary of the Barents Sea:**\n", "\n", "Since the boundary is defined on staggered **U** and **V**-grid points, scalar variables are linearly interpolated onto the NEMO model vector grid points which form the closed boundary within the `extract_mask_boundary()` method.\n", "\n", "This also means that we can calculate tracer transports across the boundary as the product of the chosen tracer variable (e.g., `thetao_con`), the normal velocity (`velocity`) and the boundary grid scale factors (`e1b` and `e3b`) stored in the boundary `xarray.Dataset`." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(550.0, 0.0)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# -- Plot scalar property interpolated along the Barents Sea boundary -- #\n", "plt.figure(figsize=(8, 5))\n", "\n", "# Plot conservative temperature (°C) along the Barents Sea boundary:\n", "ds_bdy['thetao_con'].isel(time_counter=0).plot(y='depthb', yincrease=False)\n", "\n", "# Axes labels:\n", "plt.title(\"eORCA1 JRA55v1:\\nConservative Temperature along Barents Sea Boundary\", fontdict={\"size\": 12, \"weight\": \"bold\"})\n", "plt.xlabel(\"Boundary Index\", fontdict={\"size\": 10, \"weight\": \"bold\"})\n", "plt.ylabel(\"Depth (m)\", fontdict={\"size\": 10, \"weight\": \"bold\"})\n", "plt.ylim([550, 0])" ] } ], "metadata": { "kernelspec": { "display_name": "dev", "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": "3.13.12" } }, "nbformat": 4, "nbformat_minor": 2 }