{ "cells": [ { "cell_type": "markdown", "id": "e66cc92f-add7-48dc-a6e9-1e8862765a2e", "metadata": {}, "source": [ "# Create Mask for ECCO Modeling Utilities (EMU)" ] }, { "cell_type": "markdown", "id": "329c9750-bd42-4868-b34b-ac549e496808", "metadata": {}, "source": [ "This notebook describes how to create a mask for EMU.\n", "\n", "- [Example 1](#example-1-create-a-2d-mask-for-the-box-mean-ssh-in-the-nino-34-box-weighted-by-grid-cell-area) Create a 2d mask for the box-mean SSH in the NINO 3.4 box, weighted by grid cell area\n", "\n", "- [Example 2](#example-2-create-a-very-similar-mask-but-for-ssh-anomaly-relative-to-the-global-mean-sea-level) Create a very similar mask but for ***SSH anomaly relative to the global mean sea level***\n", "\n", "- [Example 3](#example-3-create-a-3d-mask-for-the-box-mean-theta-in-the-nino-34-box-between-20-and-60-meters-weighted-by-grid-cell-volume) Create a 3D mask for the box-mean THETA in the NINO 3.4 box between 20 and 60 meters, weighted by grid cell volume\n", "\n", "- [Example 4](#example-4-create-transect-masks-for-transport) Create transect masks for transport\n", "\n", "- [Example 5](#example-5-create-a-basin-mask-for-pacific) Create a basin mask for Pacific" ] }, { "cell_type": "markdown", "id": "4f806aaf-6da0-465d-b48a-9365dfa1a9cf", "metadata": {}, "source": [ "## Load modules " ] }, { "cell_type": "code", "execution_count": 1, "id": "a9f74f87-bcf5-46ea-8b32-f4297af2f7c0", "metadata": {}, "outputs": [], "source": [ "import sys\n", "from os.path import join,expanduser\n", "import xarray as xr\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "#user_home_dir = expanduser('~')\n", "#sys.path.append(join(user_home_dir,'ECCOv4-py'))\n", "import ecco_v4_py as ecco\n", "#sys.path.append(join(user_home_dir,'ECCO-v4-Python-Tutorial'))\n", "#import ecco_access as ea" ] }, { "cell_type": "markdown", "id": "c2189d9b-7805-448f-8812-98cf2e4825fc", "metadata": {}, "source": [ "## Load grid" ] }, { "cell_type": "code", "execution_count": 2, "id": "06090159-b7c3-4b03-96fb-6b14f3f3fa72", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 89MB\n",
       "Dimensions:  (i: 90, i_g: 90, j: 90, j_g: 90, k: 50, k_u: 50, k_l: 50,\n",
       "              k_p1: 51, tile: 13, nb: 4, nv: 2)\n",
       "Coordinates: (12/20)\n",
       "  * i        (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * i_g      (i_g) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j        (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j_g      (j_g) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * k        (k) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
       "  * k_u      (k_u) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
       "    ...       ...\n",
       "    Zp1      (k_p1) float32 204B 0.0 -10.0 -20.0 ... -5.678e+03 -6.134e+03\n",
       "    Zu       (k_u) float32 200B -10.0 -20.0 -30.0 ... -5.678e+03 -6.134e+03\n",
       "    Zl       (k_l) float32 200B 0.0 -10.0 -20.0 ... -5.244e+03 -5.678e+03\n",
       "    XC_bnds  (tile, j, i, nb) float32 2MB -115.0 -115.0 -107.9 ... -115.0 -108.5\n",
       "    YC_bnds  (tile, j, i, nb) float32 2MB -88.18 -88.32 -88.3 ... -88.18 -88.16\n",
       "    Z_bnds   (k, nv) float32 400B 0.0 -10.0 -10.0 ... -5.678e+03 -6.134e+03\n",
       "Dimensions without coordinates: nb, nv\n",
       "Data variables: (12/21)\n",
       "    CS       (tile, j, i) float32 421kB 0.06158 0.06675 ... -0.9854 -0.9984\n",
       "    SN       (tile, j, i) float32 421kB -0.9981 -0.9978 ... -0.1705 -0.05718\n",
       "    rA       (tile, j, i) float32 421kB 3.623e+08 3.633e+08 ... 3.611e+08\n",
       "    dxG      (tile, j_g, i) float32 421kB 1.558e+04 1.559e+04 ... 2.314e+04\n",
       "    dyG      (tile, j, i_g) float32 421kB 2.321e+04 2.327e+04 ... 1.558e+04\n",
       "    Depth    (tile, j, i) float32 421kB 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    ...       ...\n",
       "    hFacC    (k, tile, j, i) float32 21MB 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    hFacW    (k, tile, j, i_g) float32 21MB 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    hFacS    (k, tile, j_g, i) float32 21MB 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    maskC    (k, tile, j, i) bool 5MB False False False ... False False False\n",
       "    maskW    (k, tile, j, i_g) bool 5MB False False False ... False False False\n",
       "    maskS    (k, tile, j_g, i) bool 5MB False False False ... False False False\n",
       "Attributes: (12/58)\n",
       "    acknowledgement:                 This research was carried out by the Jet...\n",
       "    author:                          Ian Fenty and Ou Wang\n",
       "    cdm_data_type:                   Grid\n",
       "    comment:                         Fields provided on the curvilinear lat-l...\n",
       "    Conventions:                     CF-1.8, ACDD-1.3\n",
       "    coordinates_comment:             Note: the global 'coordinates' attribute...\n",
       "    ...                              ...\n",
       "    references:                      ECCO Consortium, Fukumori, I., Wang, O.,...\n",
       "    source:                          The ECCO V4r4 state estimate was produce...\n",
       "    standard_name_vocabulary:        NetCDF Climate and Forecast (CF) Metadat...\n",
       "    summary:                         This dataset provides geometric paramete...\n",
       "    title:                           ECCO Geometry Parameters for the Lat-Lon...\n",
       "    uuid:                            87ff7d24-86e5-11eb-9c5f-f8f21e2ee3e0
" ], "text/plain": [ " Size: 89MB\n", "Dimensions: (i: 90, i_g: 90, j: 90, j_g: 90, k: 50, k_u: 50, k_l: 50,\n", " k_p1: 51, tile: 13, nb: 4, nv: 2)\n", "Coordinates: (12/20)\n", " * i (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n", " * i_g (i_g) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n", " * j (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n", " * j_g (j_g) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n", " * k (k) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n", " * k_u (k_u) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n", " ... ...\n", " Zp1 (k_p1) float32 204B 0.0 -10.0 -20.0 ... -5.678e+03 -6.134e+03\n", " Zu (k_u) float32 200B -10.0 -20.0 -30.0 ... -5.678e+03 -6.134e+03\n", " Zl (k_l) float32 200B 0.0 -10.0 -20.0 ... -5.244e+03 -5.678e+03\n", " XC_bnds (tile, j, i, nb) float32 2MB -115.0 -115.0 -107.9 ... -115.0 -108.5\n", " YC_bnds (tile, j, i, nb) float32 2MB -88.18 -88.32 -88.3 ... -88.18 -88.16\n", " Z_bnds (k, nv) float32 400B 0.0 -10.0 -10.0 ... -5.678e+03 -6.134e+03\n", "Dimensions without coordinates: nb, nv\n", "Data variables: (12/21)\n", " CS (tile, j, i) float32 421kB 0.06158 0.06675 ... -0.9854 -0.9984\n", " SN (tile, j, i) float32 421kB -0.9981 -0.9978 ... -0.1705 -0.05718\n", " rA (tile, j, i) float32 421kB 3.623e+08 3.633e+08 ... 3.611e+08\n", " dxG (tile, j_g, i) float32 421kB 1.558e+04 1.559e+04 ... 2.314e+04\n", " dyG (tile, j, i_g) float32 421kB 2.321e+04 2.327e+04 ... 1.558e+04\n", " Depth (tile, j, i) float32 421kB 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n", " ... ...\n", " hFacC (k, tile, j, i) float32 21MB 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n", " hFacW (k, tile, j, i_g) float32 21MB 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n", " hFacS (k, tile, j_g, i) float32 21MB 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n", " maskC (k, tile, j, i) bool 5MB False False False ... False False False\n", " maskW (k, tile, j, i_g) bool 5MB False False False ... False False False\n", " maskS (k, tile, j_g, i) bool 5MB False False False ... False False False\n", "Attributes: (12/58)\n", " acknowledgement: This research was carried out by the Jet...\n", " author: Ian Fenty and Ou Wang\n", " cdm_data_type: Grid\n", " comment: Fields provided on the curvilinear lat-l...\n", " Conventions: CF-1.8, ACDD-1.3\n", " coordinates_comment: Note: the global 'coordinates' attribute...\n", " ... ...\n", " references: ECCO Consortium, Fukumori, I., Wang, O.,...\n", " source: The ECCO V4r4 state estimate was produce...\n", " standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metadat...\n", " summary: This dataset provides geometric paramete...\n", " title: ECCO Geometry Parameters for the Lat-Lon...\n", " uuid: 87ff7d24-86e5-11eb-9c5f-f8f21e2ee3e0" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load grid \n", "#ShortNames_list = [\"ECCO_L4_GEOMETRY_LLC0090GRID_V4R4\"]\n", "#ecco_grid = xr.open_dataset(ShortNames_list[0])\n", "ecco_grid = xr.open_dataset('/efs_ecco/ECCO/V4/r4/ECCO_L4_GEOMETRY_LLC0090GRID_V4R4/GRID_GEOMETRY_ECCO_V4r4_native_llc0090.nc')\n", "ecco_grid.load()" ] }, { "cell_type": "markdown", "id": "29c0f073-e74f-4f94-b86e-df7a3b9d36ce", "metadata": {}, "source": [ "### Save some fields to numpy arrarys" ] }, { "cell_type": "code", "execution_count": 3, "id": "eb08d286-ab2e-4f6c-bd87-04ca347fb565", "metadata": {}, "outputs": [], "source": [ "# Get the model grid longitude and latitude\n", "XC = ecco_grid.XC.values # modle grid longitude (13x90x90) \n", "YC = ecco_grid.YC.values # latitudes\n", "# Also grid cell areaa in (m^2)\n", "rA = ecco_grid.rA.values\n", "maskC = ecco_grid.maskC.values # 3d mask (0/1) for model grid (tracer point)\n", "maskCSurf = maskC[0] # maskC at surface level (level = 1)\n", "hFacC = ecco_grid.hFacC.values # 3d non-dim factors (0-1) reflecing model cell geometry in vertical direction. Partial cells have values >0 but <1.\n", "Zl = ecco_grid.Zl.values # depth of vertical cell face: between 0 and -5678 meters\n", "Z = ecco_grid.Z.values # depth of vertical cell ceter: between -10 and -5906 meters\n", "drF = ecco_grid.drF.values # layer thickness (m); 1d with 50 elements\n", "\n", "# More fields for the western and southern faces of grid cells. U is on the west face and V is on the southern face.\n", "# They are the same as the fields above (C for tracer points at the center of grid cells).\n", "# We need these fields for creating transport masks\n", "maskW = ecco_grid.maskW.values # 3d mask (0/1) for model grid (U point)\n", "maskWSurf = maskW[0] # maskW at surface level (level = 1)\n", "hFacW = ecco_grid.hFacW.values \n", "\n", "maskS = ecco_grid.maskS.values # 3d mask (0/1) for model grid (V point)\n", "maskSSurf = maskS[0] # maskS at surface level (level = 1)\n", "hFacS = ecco_grid.hFacS.values \n", "\n", "# dxG and dyG are the horizontal grid spacing (meters) for the western and southern faces, respectively.\n", "dxG = ecco_grid.dxG.values\n", "dyG = ecco_grid.dyG.values" ] }, { "cell_type": "markdown", "id": "27cee837-d732-44fe-81d1-8611cc7b7f9d", "metadata": {}, "source": [ "## Example 1: Create a 2d mask for the box-mean SSH in the NINO 3.4 box, weighted by grid cell area" ] }, { "cell_type": "markdown", "id": "3f20dccc-8efb-4469-afd1-09373a641f32", "metadata": {}, "source": [ "### Specify latitude and longitude ranges" ] }, { "cell_type": "code", "execution_count": 4, "id": "501d2630-b2e0-499e-bb43-6bb7b0488977", "metadata": {}, "outputs": [], "source": [ "# Example: Create a 2d mask for the box-mean SSH in the NINO 3.4 box, weighted by grid cell area\n", "# Specify latitude and longitude ranges\n", "lat1 = -5\n", "lat2 = 5\n", "lon1 = -170\n", "lon2 = -120" ] }, { "cell_type": "markdown", "id": "1e8f097e-ac46-4c58-88bd-15b55f841178", "metadata": {}, "source": [ "### Create a temporary mask that contains 0 for points outside the box and 1 for points inside." ] }, { "cell_type": "code", "execution_count": 5, "id": "22fc3803-4442-4722-8a9d-d03e3804aff5", "metadata": {}, "outputs": [], "source": [ "tmp_msk = (XC>=lon1) & (XC=lat1) & (YC,\n", " array([[nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " ...,\n", " [nan, nan, nan, ..., 0., 0., 0.],\n", " [nan, nan, nan, ..., 0., 0., 0.],\n", " [nan, nan, nan, ..., 0., 0., 0.]]))" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mask_tmpplot = np.copy(mask)\n", "mask_tmpplot[maskCSurf==False] = np.nan\n", "ecco.plot_tiles(mask_tmpplot, rotate_to_latlon=True, layout='latlon', show_tile_labels=False, show_colorbar=True, less_output=True)" ] }, { "cell_type": "markdown", "id": "f7a0b9b6-ab44-4015-9bc6-0cdf121fddb3", "metadata": {}, "source": [ "### Check the sum of mask and a point outside the NINO 3.4 box\n", "In this case, the sum of the mask should be equal to 1. The mask outside the box is zero. " ] }, { "cell_type": "code", "execution_count": 8, "id": "353f7258-052b-4c33-bc7a-0722da5b256c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mask at 1,45,50 (tile, j, i): 0.0\n", "Sum of mask: 1.0\n" ] } ], "source": [ "print(f'mask at 1,45,50 (tile, j, i): {mask[1,45,45]}')\n", "print(f'Sum of mask: {np.sum(mask)}')" ] }, { "cell_type": "markdown", "id": "fd8e968e-319f-4728-85c4-a673e29785c7", "metadata": {}, "source": [ "### Write mask to a file for EMU to use" ] }, { "cell_type": "code", "execution_count": 9, "id": "df81f0eb-483c-4dc9-a768-f8aa7a638852", "metadata": {}, "outputs": [], "source": [ "# Write out the mask to a file to be used by EMU\n", "# First convert the 13x90x90 to a compact formt 1170x90, which is the format that EMU expects for input files.\n", "mask_c = ecco.llc_tiles_to_compact(mask, less_output=True)\n", "\n", "# Assume you have an output directory (repalce USERNAME with your own username). If not, create one. \n", "# output_dir = '/efs_ecco/USERNAME/mask'\n", "output_dir = '/efs_ecco/owang/EMU/mask'\n", "mask_fn = f'mask2d_{lon1:.1f}_{lon2:.1f}_{lat1:.1f}_{lat2:.1f}.bin'\n", "\n", "# Output the mask\n", "mask_c.astype('>f4').tofile(output_dir+'/'+mask_fn)" ] }, { "cell_type": "markdown", "id": "50c05704-edb4-4d02-b7c0-f2332dacc54a", "metadata": {}, "source": [ "## Example 2: Create a very similar mask but for ***SSH anomaly relative to the global mean sea level***\n", "This mask is the same as the previous example, except that SSH now is anomlay relative to the global mean sea level. So, we are going to create the mask for the box-mean ***SSH anomaly***, relative to the global mean sea level, in the NINO 3.4 box, weighted by grid cell area. We will use many variables that were computed in the previous example." ] }, { "cell_type": "markdown", "id": "73485e9e-79af-4b1b-8734-895b54e0718c", "metadata": {}, "source": [ "### Create mask" ] }, { "cell_type": "code", "execution_count": 10, "id": "ba013e5f-bd32-4296-80bb-9234600f10d6", "metadata": {}, "outputs": [], "source": [ "# global surface ocean area \n", "gso_area_sum = np.sum(rA*maskCSurf)\n", "\n", "# Note the second term on LHS, which is needed to remove the global mean sea level.\n", "mask_ano = rA*tmp_msk*maskCSurf/area_sum - rA*maskCSurf/gso_area_sum" ] }, { "cell_type": "markdown", "id": "f55b1033-16da-497b-9e27-41735f3f2409", "metadata": {}, "source": [ "### Plot the mask\n", "There are non-zero values outside the NINO 3.4 box. For plotting purposes, we again set land points to NaN so they appear as white regions." ] }, { "cell_type": "code", "execution_count": 11, "id": "6526f17c-e7af-4eab-88e0-1a57dd05f678", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", " array([[nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " ...,\n", " [nan, nan, nan, ..., 0., 0., 0.],\n", " [nan, nan, nan, ..., 0., 0., 0.],\n", " [nan, nan, nan, ..., 0., 0., 0.]]))" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot mask to check by using ECCOv4-py plot_tiles\n", "mask_tmpplot_ano = np.copy(mask_ano)\n", "mask_tmpplot_ano[maskCSurf==False] = np.nan\n", "ecco.plot_tiles(mask_tmpplot_ano, rotate_to_latlon=True, layout='latlon', show_tile_labels=False, show_colorbar=True, less_output=True)" ] }, { "cell_type": "markdown", "id": "7119de59-97f3-4622-91ec-a89233f843ca", "metadata": {}, "source": [ "### Check the mask for a point outside of the NINO 3.4 box\n", "The mask outside the box is not zero but contains small values reflecting the local SSH contribution to the global mean sea level." ] }, { "cell_type": "code", "execution_count": 12, "id": "64ea7758-32a8-4f82-b4dd-0fc7ea7f1ca2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mask at 1,45,50 (tile, j, i): -2.90e-05\n" ] } ], "source": [ "print(f'mask at 1,45,50 (tile, j, i): {mask_ano[1,45,45]:.2e}')" ] }, { "cell_type": "markdown", "id": "c4a8de64-6b35-46de-8592-cc19037c9695", "metadata": {}, "source": [ "### Write mask to a file for EMU to use" ] }, { "cell_type": "code", "execution_count": 13, "id": "b39f443d-be03-40ef-92a3-ff1b25f25ab5", "metadata": {}, "outputs": [], "source": [ "# Write out the mask to a file to be used by EMU\n", "# First convert the 13x90x90 to a compact formt 1170x90, which is the format that EMU expects for input files.\n", "mask_ano_c = ecco.llc_tiles_to_compact(mask_ano, less_output=True)\n", "\n", "mask_ano_fn = f'mask2d_regional_ano_{lon1:.1f}_{lon2:.1f}_{lat1:.1f}_{lat2:.1f}.bin'\n", "\n", "# Output the mask\n", "mask_ano_c.astype('>f4').tofile(output_dir+'/'+mask_ano_fn)" ] }, { "cell_type": "markdown", "id": "070ad36f-d68f-44e1-b1e8-29dc92bf3081", "metadata": {}, "source": [ "## Example 3: Create a 3D mask for the box-mean THETA in the NINO 3.4 box between 20 and 60 meters, weighted by grid cell volume" ] }, { "cell_type": "markdown", "id": "578f67b5-9291-4129-b37f-e9b90041882b", "metadata": {}, "source": [ "### Depth range \n", "The longitude and latitude ranges are the same as Examples 1 and 2." ] }, { "cell_type": "code", "execution_count": 14, "id": "1e5f9ba4-3c69-4fa8-96a3-6a8197558fb9", "metadata": {}, "outputs": [], "source": [ "# Depth range in meters. Note that z1 is for the deeper layer and z2 is for the shallower layer, i.e., z1>=z2.\n", "z1 = 60\n", "z2 = 10" ] }, { "cell_type": "markdown", "id": "ee6f1b14-fed1-4695-8190-20e8c3995712", "metadata": {}, "source": [ "### Create a temporary 3d mask that contains 0 for points outside the box and 1 for points inside." ] }, { "cell_type": "code", "execution_count": 15, "id": "75ced0b5-207c-4d0b-9a12-1324ec558ac9", "metadata": {}, "outputs": [], "source": [ "# We will use the 2d 0/1 mask for the NINO 3.4 box. First populate a 3d array with the 2d mask.\n", "tmp_msk3d = np.tile(tmp_msk, (len(drF), 1, 1, 1))\n", "# Vertical range\n", "idxz = (np.abs(Zl)=z2) \n", "# mask out vertical levels outside the vertical range\n", "tmp_msk3d[~idxz,:] = 0." ] }, { "cell_type": "code", "execution_count": 16, "id": "8ede48bb-5b68-4033-a71e-03efe4130a07", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the vertical mask to check\n", "plt.figure(figsize=(8,8))\n", "plt.plot(idxz, Z, '-o')\n", "plt.title('Vertical Mask vs. Depth')\n", "plt.xlabel('Mask')\n", "plt.ylabel('Depth (m)') \n", "plt.ylim(-z1*2, 0)\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "9b0ded38-d7dc-4740-91d6-76e28b5860da", "metadata": {}, "source": [ "### Calculate 3d mask" ] }, { "cell_type": "code", "execution_count": 17, "id": "ee0a60bc-2250-4295-b8ba-d6ba167d836e", "metadata": {}, "outputs": [], "source": [ "# Apply grid cell volume weight\n", "# Create a 3d array for grid cell volume\n", "vol3d = rA*hFacC*np.expand_dims(drF,axis=(1,2,3)) # product of horizontal area, layer thickness, and layer thickness geometry \n", "# Calculate the total volume of wet points in the box.\n", "vol3d_boxsum = np.sum(vol3d*tmp_msk3d)\n", "# Volume weighted mask for the block is just \n", "mask3d = vol3d*tmp_msk3d/vol3d_boxsum" ] }, { "cell_type": "markdown", "id": "8fb8b13c-d097-4728-ae65-cafd6ea6bff2", "metadata": {}, "source": [ "### Plot the mask" ] }, { "cell_type": "code", "execution_count": 18, "id": "4ff07a81-fb8f-40fb-9bd6-4b659f7cafe8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0.98, '3d mask for level 3')" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot mask to check by using ECCOv4-py plot_tiles\n", "# Create a temporary mask for plotting and set land points to NaN\n", "mask_tmpplot3d = np.copy(mask3d)\n", "mask_tmpplot3d[maskC==False] = np.nan\n", "# layer 3 \n", "level = 3\n", "fig=ecco.plot_tiles(mask_tmpplot3d[level-1], rotate_to_latlon=True, layout='latlon', show_tile_labels=False, show_colorbar=True, less_output=True)\n", "fig[0].suptitle(f'3d mask for level {level}')" ] }, { "cell_type": "markdown", "id": "9ccf7882-8aa7-4b07-85d3-a9ec6c825f42", "metadata": {}, "source": [ "### Write the 3d mask to a file for EMU to use" ] }, { "cell_type": "code", "execution_count": 19, "id": "778c97bc-b4c0-4564-b06e-b59dd0acd630", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "llc_faces_to_compact: data_compact array shape (50, 1170, 90)\n" ] } ], "source": [ "# Write out the mask to a file to be used by EMU\n", "# First convert the 13x90x90 tile format to the compact formt 1170x90, which is the format that EMU expects for input files.\n", "mask3d_c = ecco.llc_tiles_to_compact(mask3d, less_output=True)\n", "\n", "# Specify output filename\n", "mask3d_fn = f'mask3d_{lon1:.1f}_{lon2:.1f}_{lat1:.1f}_{lat2:.1f}_{z1:.1f}_{z2:.1f}.bin'\n", "\n", "# Output the mask\n", "mask3d_c.astype('>f4').tofile(output_dir+'/'+mask3d_fn)" ] }, { "cell_type": "markdown", "id": "e20e4665-f2d9-42e4-9cf7-feb8de86426e", "metadata": {}, "source": [ "## Example 4: Create transect masks for transport\n", "\n", "This example describes how to create mask files for a transect that can be used by EMU to compute surface-to-bottom transport. The user specifies the latitude and longitude of two points. The example creates two mask files, one for each of the horizontal velocity components, for the transect with the specified latitude and longitude of the two points." ] }, { "cell_type": "code", "execution_count": 20, "id": "df695c4d-a65c-4a38-8edb-7a1dc4884a6a", "metadata": {}, "outputs": [], "source": [ "# Specify longitude and latitude of two end points across the North Atlantic between Greenland and Europe\n", "pt1 = [-20, 75] # longitude, latitude\n", "pt2 = [15, 60]" ] }, { "cell_type": "markdown", "id": "a97c3cc0-77c3-42cc-8227-8b157faf8a59", "metadata": {}, "source": [ "We use ECCOv4-py's ecco.get_section_line_masks to find three masks as 13x90x90 arrays.\n", "Two masks are for the two horizontal velocity components: trxW for U and trxS for V.\n", "The sign convention for trxW and trxS is as follows: if you stand at pt1 and face pt2, \n", "positive values are to the left of the transect from pt1 to pt2, and negative values are to the right.\n", "There is a third mask, trxC, for tracer points, which is not needed to calcualte transport. But some of the EMU\n", "tools, like the Adjoint Tool, still need it. " ] }, { "cell_type": "code", "execution_count": 21, "id": "0f5c8307-d3c3-434e-a775-51c361927c92", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/srv/conda/envs/notebook/lib/python3.11/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n", " out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg\n", "/srv/conda/envs/notebook/lib/python3.11/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n", " out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg\n", "/srv/conda/envs/notebook/lib/python3.11/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n", " out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg\n", "/srv/conda/envs/notebook/lib/python3.11/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n", " out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg\n" ] } ], "source": [ "# Find mask by specifying the lon/lat of the two end points\n", "trxC_ds, trxW_ds, trxS_ds = ecco.get_section_line_masks(pt1, pt2, ecco_grid)\n", "# Save to numpy arries\n", "trxC = trxC_ds.values\n", "trxW = trxW_ds.values\n", "trxS = trxS_ds.values" ] }, { "cell_type": "markdown", "id": "238406f2-776c-44de-8ff2-583f33e7882a", "metadata": {}, "source": [ "### Plot the masks (not area-weighted) to check\n", "We first make a temporary copy and flag land points with NaN for better visualization. We also make the plot layout match the model x- and y-directions, where the positive x-direction runs from left to right and the positive y-direction runs from bottom to top. This layout helps you check whether the sign is as expected." ] }, { "cell_type": "code", "execution_count": 22, "id": "670559ce-ee58-41b5-ac28-105511f7b76c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0.98, 'Mask for C')" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# make a copy and flag land points with NaN for better visualization\n", "tmpW = np.copy(trxW).astype('>f4')\n", "tmpW[maskWSurf==0]=np.nan\n", "fig=ecco.plot_tiles(tmpW, rotate_to_latlon=True, show_tile_labels=False, show_colorbar=True, less_output=True)\n", "fig[0].suptitle(f'Mask for U', fontsize = 16)\n", "\n", "tmpS = np.copy(trxS).astype('>f4')\n", "tmpS[maskSSurf==0]=np.nan\n", "fig=ecco.plot_tiles(tmpS, rotate_to_latlon=True, show_tile_labels=False, show_colorbar=True, less_output=True)\n", "fig[0].suptitle(f'Mask for V', fontsize = 16)\n", "\n", "tmpC = np.copy(trxC).astype('>f4')\n", "tmpC[maskCSurf==0]=np.nan\n", "fig=ecco.plot_tiles(tmpC, rotate_to_latlon=True, show_tile_labels=False, show_colorbar=True, less_output=True)\n", "fig[0].suptitle(f'Mask for C', fontsize = 16)" ] }, { "cell_type": "markdown", "id": "1887b49a-be01-4fcf-bcbe-0960928a27a4", "metadata": {}, "source": [ "### Apply area weighting" ] }, { "cell_type": "code", "execution_count": 23, "id": "02d587c9-6afe-40b5-8217-378e0f1aeacd", "metadata": {}, "outputs": [], "source": [ "# Area across W and S faces\n", "areaW = dyG*hFacW*np.expand_dims(drF,axis=(1,2,3))\n", "areaS = dxG*hFacS*np.expand_dims(drF,axis=(1,2,3))\n", "\n", "trxWa = trxW * areaW # trxWa in m^2\n", "trxSa = trxS * areaS" ] }, { "cell_type": "markdown", "id": "ae974a16-59b1-4792-b5b7-2019825d770f", "metadata": {}, "source": [ "### Plot the area-weighted masks" ] }, { "cell_type": "code", "execution_count": 24, "id": "7f862083-6d4d-42ff-b319-afb1dde8a0d5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0.98, 'Area-weighted Mask for V (m²): level 1')" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# make a copy and flag land points with NaN for better visualization\n", "# plot the first level \n", "level = 1 \n", "\n", "tmpWa = np.copy(trxWa).astype('>f4')\n", "tmpWa[maskW==0]=np.nan\n", "\n", "fig=ecco.plot_tiles(tmpWa[level-1], rotate_to_latlon=True, show_tile_labels=False, show_colorbar=True, less_output=True)\n", "fig[0].suptitle(f'Area-weighted Mask for U (m\\u00b2): level {level}', fontsize = 16)\n", "\n", "tmpSa = np.copy(trxSa).astype('>f4')\n", "tmpSa[maskS==0]=np.nan\n", "fig=ecco.plot_tiles(tmpSa[level-1], rotate_to_latlon=True, show_tile_labels=False, show_colorbar=True, less_output=True)\n", "fig[0].suptitle(f'Area-weighted Mask for V (m\\u00b2): level {level}', fontsize = 16)\n" ] }, { "cell_type": "markdown", "id": "71703214-c5b4-4ea1-a290-c1bee1a1eb68", "metadata": {}, "source": [ "### Write masks to files for EMU to use" ] }, { "cell_type": "code", "execution_count": 25, "id": "37ff484e-2673-43d3-ad35-11a1a192c6a3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "llc_faces_to_compact: data_compact array shape (50, 1170, 90)\n", "llc_faces_to_compact: data_compact array shape (50, 1170, 90)\n" ] } ], "source": [ "# Write out the mask to a file to be used by EMU\n", "# First convert the 13x90x90 tile format to the compact formt 1170x90, which is the format that EMU expects for input files.\n", "trxWa_c = ecco.llc_tiles_to_compact(trxWa, less_output=True)\n", "trxSa_c = ecco.llc_tiles_to_compact(trxSa, less_output=True)\n", "\n", "# Specify output filename\n", "trxWa_fn = f'trxWa_{pt1[0]:.1f}_{pt2[0]:.1f}_{pt1[1]:.1f}_{pt2[1]:.1f}.bin'\n", "trxSa_fn = f'trxSa_{pt1[0]:.1f}_{pt2[0]:.1f}_{pt1[1]:.1f}_{pt2[1]:.1f}.bin'\n", "\n", "# Output the mask\n", "trxWa_c.astype('>f4').tofile(output_dir+'/'+trxWa_fn)\n", "trxSa_c.astype('>f4').tofile(output_dir+'/'+trxSa_fn)" ] }, { "cell_type": "markdown", "id": "010bdaad-1346-4a58-9f02-d91bd5184fe0", "metadata": {}, "source": [ "## Example 5: Create a basin mask for Pacific\n", "This example creates a 2d area-weighted basin mask for Pacific. The procedure is very similar to Example 1, except that we will find the model mask for the Pacific Ocean by using a pre-defined basin index (in ECCOv4-py under the directory binary_data), instead of using user-specified spatial ranges." ] }, { "cell_type": "markdown", "id": "0046b774-84d5-49b7-a853-b4fca3fe8250", "metadata": {}, "source": [ "### Obtain pre-defined basin index" ] }, { "cell_type": "code", "execution_count": 26, "id": "947d9a0d-fbfb-4978-8647-3cef06ece67f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "load_binary_array: loading file /efs_ecco/ECCO/V4/r4/ECCOv4-py/binary_data/basins.data\n", "load_binary_array: data array shape (1170, 90)\n", "load_binary_array: data array type >f4\n", "llc_compact_to_faces: dims, llc (1170, 90) 90\n", "llc_compact_to_faces: data_compact array type >f4\n", "llc_faces_to_tiles: data_tiles shape (13, 90, 90)\n", "llc_faces_to_tiles: data_tiles dtype >f4\n", "shape after reading \n", "(13, 90, 90)\n" ] } ], "source": [ "# ecco.get_basin_mask returns an xarray object\n", "msk_pac_uw_ds = ecco.get_basin_mask('pac', ecco_grid.maskC.isel(k=0), basin_path='/efs_ecco/ECCO/V4/r4/ECCOv4-py/binary_data',\n", " less_output=True)\n", "# Save to numpy array: 1 for inside Pacific Ocean and 0 for outside\n", "msk_pac_uw = msk_pac_uw_ds.values" ] }, { "cell_type": "markdown", "id": "40caee6e-d234-45d3-bee1-bded0a67e1a7", "metadata": {}, "source": [ "### Calculate Pacific mask for EMU" ] }, { "cell_type": "code", "execution_count": 27, "id": "56479845-c89a-4b1b-857a-12571d6384ee", "metadata": {}, "outputs": [], "source": [ "# Apply grid cell area weight\n", "# First calculate the total area of wet points in the box, \n", "# where rA is grid cell area, tmp_msk defines the 0/1 mask for the box, and maskCSurf defines wet/dry points.\n", "area_sum_pac = np.sum(rA*msk_pac_uw*maskCSurf)\n", "# The area weighted mask is just \n", "mask_pac = rA*msk_pac_uw*maskCSurf/area_sum_pac" ] }, { "cell_type": "markdown", "id": "ba4a92ad-4c78-4b7b-ad0f-5205c843b103", "metadata": {}, "source": [ "### Plot to check by using ECCOv4-py plot_tiles\n", "For plotting purposes, we set land points to NaN so that they appear as white regions." ] }, { "cell_type": "code", "execution_count": 28, "id": "081f6c3b-27c9-4b06-9a0e-4f1e0fb26947", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", " array([[nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " ...,\n", " [nan, nan, nan, ..., 0., 0., 0.],\n", " [nan, nan, nan, ..., 0., 0., 0.],\n", " [nan, nan, nan, ..., 0., 0., 0.]]))" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mask_tmpplot_pac = np.copy(mask_pac)\n", "mask_tmpplot_pac[maskCSurf==False] = np.nan\n", "ecco.plot_tiles(mask_tmpplot_pac, rotate_to_latlon=True, layout='latlon', show_tile_labels=False, show_colorbar=True, less_output=True)" ] }, { "cell_type": "markdown", "id": "b901e6a0-67e1-42cd-bc0c-f3012d5feed2", "metadata": {}, "source": [ "The values in the Pacific Ocean are not zero, while those outside Pacific are zero. The mask pattern in the Pacific Ocean is the same as the distribution of model grid cell areas, beacuse the mask is area-weighted. " ] }, { "cell_type": "markdown", "id": "c8a4810e-7684-4498-bd30-54873324d4bb", "metadata": {}, "source": [ "### Check the sum of mask and a point outside the Pacific Ocean\n", "In this case, the sum of the mask should be equal to 1. The mask outside the Pacific Ocean is zero. " ] }, { "cell_type": "code", "execution_count": 29, "id": "a15fc65e-eed6-4247-95b4-d3856512e7b0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mask at 1,45,50 (tile, j, i): 0.0\n", "Sum of mask: 1.0\n" ] } ], "source": [ "print(f'mask at 1,45,50 (tile, j, i): {mask_pac[1,45,45]}')\n", "print(f'Sum of mask: {np.sum(mask_pac)}')" ] }, { "cell_type": "markdown", "id": "51e3e3e3-4d95-45ad-8b20-49524238c2b3", "metadata": {}, "source": [ "### Write the mask to files for EMU to use" ] }, { "cell_type": "code", "execution_count": 30, "id": "d4af78a2-82f4-44fc-a14e-0b6606fec28e", "metadata": {}, "outputs": [], "source": [ "# Write out the mask to a file to be used by EMU\n", "# First convert the 13x90x90 to a compact formt 1170x90, which is the format that EMU expects for input files.\n", "mask_pac_c = ecco.llc_tiles_to_compact(mask_pac, less_output=True)\n", "\n", "# output filename \n", "mask_fn_pac = f'mask2d_pac.bin'\n", "\n", "# Output the mask\n", "mask_pac_c.astype('>f4').tofile(output_dir+'/'+mask_fn_pac)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 }