{ "cells": [ { "cell_type": "markdown", "id": "9dc175c8-f744-4655-9037-b313c486c951", "metadata": {}, "source": [ "In this notebook, we will make an interpolation grid that will be used to interpolate 3D fields from the ECCO-Darwin model for use as initial conditions. \n", "\n", "In this notebook, we will also confront two new challenges that were not seen in the previous model examples. First, the regional model grid has different depth levels compared to the ECCO grid, so we will also need to interpolate the variables vertically. Second, the grid is at very high latitude, which means we will need to reproject the fields to an appropriate coordinate system before interpolation. \n", "\n", "First, import packages to visualize the model fields here:" ] }, { "cell_type": "code", "execution_count": 1, "id": "836f57c0-6983-48ce-b8a0-31c5b04463a7", "metadata": {}, "outputs": [], "source": [ "# import modules for computation and plotting\n", "import os\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.interpolate import griddata\n", "from matplotlib.gridspec import GridSpec\n", "import netCDF4 as nc4\n", "import cmocean.cm as cm\n", "\n", "# import pertinent tools from eccoseas\n", "from eccoseas.ecco import pickup\n", "from eccoseas.ecco import grid\n", "from eccoseas.ecco import io\n", "from eccoseas.downscale import horizontal\n", "from eccoseas.downscale import vertical\n", "from eccoseas.toolbox import reprojection\n", "from eccoseas.downscale import interpolation_grid as ecco_interp" ] }, { "cell_type": "markdown", "id": "5ea43d2c-8106-4b7e-9d71-dda71ff556e4", "metadata": {}, "source": [ "## Motivation for an Interpolation Grid\n", "For this model, we will use pickup files from the ECCO-Darwin model to construct our initial conditions. In total, we will need to interpolate separate fields: 11 baseline physical variable, 5 sea ice variables, and 37 variable pertaining to the biogeochemistry. Given that the interpolation can be quite slow, especially in areas with a complex coastline that was not represented in the global model, it is advantangeous to first make and store a grid that identifies when and how the interpolation is completed. In other words, we can keep track of where the grid has been interpolated with bilinear interpolation, and locations where a \"spreading\" routine is used to fill adjacent cells." ] }, { "cell_type": "markdown", "id": "7ce409cc-d417-42f1-83bb-88c1499b6a83", "metadata": {}, "source": [ "## Constructing the Interpolation Grid\n", "\n", "To construct the interpolation grid, we will proceed in the following steps:\n", "1. ensure we have downloaded the necessary ECCO grid files\n", "2. read in the ECCO model grid\n", "3. read in grid for the regional domain\n", "4. prepare the ECCO fields for interpolation\n", "5. interpolate the masks vertically\n", "6. rerproject the model grid\n", "7. create and store the interpolation grid\n", "8. visualize the results" ] }, { "cell_type": "markdown", "id": "7c5328af-a028-439b-a89a-4f39b72a9a65", "metadata": {}, "source": [ "### Step 1: Download the ECCO Files\n", "To begin, ensure that all of the necessary pickup and grid files are obtained for this configuration. These are listed on the Overview page for this model. \n", "\n", "These files are stored in the following directory:" ] }, { "cell_type": "code", "execution_count": 2, "id": "1747b211-af0c-4e11-9352-0ac76399982e", "metadata": {}, "outputs": [], "source": [ "data_folder = '../../../data/alaskan_north_slope'" ] }, { "cell_type": "markdown", "id": "d170a9e7-8862-4e25-b732-1268f2c77c5a", "metadata": {}, "source": [ "### Step 2: Read in the ECCO grid" ] }, { "cell_type": "markdown", "id": "cc7cdf43-6eeb-4e0e-8d5a-fe931dcaccba", "metadata": {}, "source": [ "Next, we will read in the ECCO grid and convert the grid to tiles. This can be done with functions from the `io` and `grid` modules of **eccoseas**:" ] }, { "cell_type": "code", "execution_count": 3, "id": "732661d7-64c5-4689-8e9a-68fffa55ce9f", "metadata": {}, "outputs": [], "source": [ "ecco_XC_faces, ecco_YC_faces, ecco_AngleCS_faces, ecco_AngleSN_faces, ecco_hFacC_faces, ecco_hFacW_faces, ecco_hFacS_faces =\\\n", " io.read_ecco_geometry_to_faces(data_folder, llc=270, Nr=50)" ] }, { "cell_type": "code", "execution_count": 4, "id": "d3774b14-569e-4b03-890b-b2386ef50c21", "metadata": {}, "outputs": [], "source": [ "ecco_XC_tiles = grid.ecco_faces_to_tiles(ecco_XC_faces, llc=270, dim=2)\n", "ecco_YC_tiles = grid.ecco_faces_to_tiles(ecco_YC_faces, llc=270, dim=2)\n", "ecco_AngleCS_tiles = grid.ecco_faces_to_tiles(ecco_AngleCS_faces, llc=270, dim=2)\n", "ecco_AngleSN_tiles = grid.ecco_faces_to_tiles(ecco_AngleSN_faces, llc=270, dim=2)\n", "ecco_hFacC_tiles = grid.ecco_faces_to_tiles(ecco_hFacC_faces, llc=270, dim=3)\n", "ecco_hFacS_tiles = grid.ecco_faces_to_tiles(ecco_hFacS_faces, llc=270, dim=3)\n", "ecco_hFacW_tiles = grid.ecco_faces_to_tiles(ecco_hFacW_faces, llc=270, dim=3)\n", "ecco_RF = np.fromfile(os.path.join(data_folder,'RF.data'), '>f4')\n", "ecco_DRF = np.fromfile(os.path.join(data_folder,'DRF.data'), '>f4')" ] }, { "cell_type": "code", "execution_count": 5, "id": "f3c9e65b-f34e-4a23-9621-2d9213cbdd0b", "metadata": {}, "outputs": [], "source": [ "ecco_Nr = np.size(ecco_DRF)" ] }, { "cell_type": "markdown", "id": "64a135ac-a5bb-4766-83b5-60039dcc0c56", "metadata": {}, "source": [ "### Step 3: Read in the Regional Model Grid and Mask\n", "Since the model grid has already been generated for this model (see note on the Model Grid page for this model), we will read it in from the nc file:" ] }, { "cell_type": "code", "execution_count": 6, "id": "f48b8cc3-eb40-40c3-b61e-1359d986109d", "metadata": {}, "outputs": [], "source": [ "# define the parameters that will be used in the data file\n", "ds = nc4.Dataset('../../../data/alaskan_north_slope/NorthSlope_ncgrid.nc')\n", "XC = ds.variables['XC'][:, :]\n", "YC = ds.variables['YC'][:, :]\n", "bathy = -1*ds.variables['Depth'][:,:]\n", "AngleCS = ds.variables['AngleCS'][:,:]\n", "AngleSN = ds.variables['AngleSN'][:,:]\n", "hFacC = ds.variables['HFacC'][:, :, :]\n", "hFacW = ds.variables['HFacW'][:, :, :]\n", "hFacS = ds.variables['HFacS'][:, :, :]\n", "delR = ds.variables['drF'][:]\n", "ds.close()\n", "Z = np.cumsum(delR)\n", "\n", "# remove the extra row and col from hFacS and hFacW\n", "hFacS = hFacS[:,:-1,:]\n", "hFacW = hFacW[:,:,:-1]" ] }, { "cell_type": "markdown", "id": "1eb82b3d-ffa2-4312-916f-3325a0089c6b", "metadata": {}, "source": [ "The mask is generated by setting all of the non-zero `hFac` points to 1:" ] }, { "cell_type": "code", "execution_count": 7, "id": "d1dcfe0a-3641-43d7-99aa-88a48310eeda", "metadata": {}, "outputs": [], "source": [ "maskC = np.copy(hFacC)\n", "maskC[maskC>0] = 1\n", "\n", "maskW = np.copy(hFacW)\n", "maskW[maskW>0] = 1\n", "\n", "maskS = np.copy(hFacS)\n", "maskS[maskS>0] = 1" ] }, { "cell_type": "markdown", "id": "a37e0e64-13eb-4122-ae3c-0fead095fc9b", "metadata": {}, "source": [ "### Step 4: Prepare the grids for interpolation\n", "At this point, we can use the geometry of both grids to check to see which tiles have the information we need. By assessing the ECCO grid geometry, we discover that we need tiles 7 and 8:" ] }, { "cell_type": "code", "execution_count": 8, "id": "b1787f19", "metadata": {}, "outputs": [], "source": [ "# define the tile list\n", "tile_list = [7,8]" ] }, { "cell_type": "markdown", "id": "144a3587", "metadata": {}, "source": [ "Let's verify that these tiles cover our grid:" ] }, { "cell_type": "code", "execution_count": 9, "id": "076dfc32-ab58-422e-9b5a-47985af1e190", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot the ECCO tile points\n", "for tile in tile_list:\n", " plt.plot(ecco_XC_tiles[tile],ecco_YC_tiles[tile],'k.')\n", "\n", "# plot the boundary of the regional model\n", "plt.plot(XC[:,0],YC[:,0], 'g-')\n", "plt.plot(XC[:,-1],YC[:,-1], 'g-')\n", "plt.plot(XC[0,:],YC[0,:], 'g-')\n", "plt.plot(XC[-1,:],YC[-1,:], 'g-')\n", "\n", "plt.xlabel('Longitude')\n", "plt.ylabel('Latitude')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "b332346e-acea-4d60-9494-8d850824b8f4", "metadata": {}, "source": [ "As we can see, this model boundary (green) is completely surrounded by the points in tile 7 and 8 (black). We also note that there is a lot extraneous information in points outside the regional of our model domain - we will omit these points to speed up our interpolation. Given these observations, now I read in points from just those tiles to use in interpolation:" ] }, { "cell_type": "code", "execution_count": 10, "id": "bfd5f5ec-1a41-4da6-81aa-9d121dfa023a", "metadata": {}, "outputs": [], "source": [ "# determine the number of points in each set\n", "total_points = 0\n", "for tile_number in tile_list:\n", " total_points += np.size(ecco_XC_tiles[tile_number])\n", "\n", "# make empty arrays to fill in\n", "ecco_XC_points = np.zeros((total_points, ))\n", "ecco_YC_points = np.zeros((total_points, ))\n", "ecco_AngleCS_points = np.zeros((total_points, ))\n", "ecco_AngleSN_points = np.zeros((total_points, ))\n", "ecco_maskC_points = np.zeros((ecco_Nr, total_points))\n", "ecco_maskW_points = np.zeros((ecco_Nr, total_points))\n", "ecco_maskS_points = np.zeros((ecco_Nr, total_points))\n", "ecco_hFacW_points = np.zeros((ecco_Nr, total_points))\n", "ecco_hFacS_points = np.zeros((ecco_Nr, total_points))\n", "\n", "# loop through the tiles and fill in the XC, YC, and mask points for interpolation\n", "points_counted = 0\n", "for tile_number in tile_list:\n", " tile_N = np.size(ecco_XC_tiles[tile_number])\n", " \n", " ecco_XC_points[points_counted:points_counted+tile_N] = ecco_XC_tiles[tile_number].ravel()\n", " ecco_YC_points[points_counted:points_counted+tile_N] = ecco_YC_tiles[tile_number].ravel()\n", "\n", " ecco_AngleCS_points[points_counted:points_counted+tile_N] = ecco_AngleCS_tiles[tile_number].ravel()\n", " ecco_AngleSN_points[points_counted:points_counted+tile_N] = ecco_AngleSN_tiles[tile_number].ravel()\n", " \n", " for k in range(ecco_Nr):\n", " level_hFacC = ecco_hFacC_tiles[tile_number][k, :, :]\n", " if tile_number<7:\n", " level_hFacW = ecco_hFacW_tiles[tile_number][k, :, :]\n", " level_hFacS = ecco_hFacS_tiles[tile_number][k, :, :]\n", " else:\n", " level_hFacS = ecco_hFacW_tiles[tile_number][k, :, :] # these are switched due to the \n", " level_hFacW = ecco_hFacS_tiles[tile_number][k, :, :] # assumptions about velocity - see note below\n", " ecco_hFacW_points[k, points_counted:points_counted+tile_N] = level_hFacW.ravel()\n", " ecco_hFacS_points[k, points_counted:points_counted+tile_N] = level_hFacS.ravel()\n", " level_maskC = np.copy(level_hFacC)\n", " level_maskC[level_maskC>0] = 1\n", " level_maskW = np.copy(level_hFacW)\n", " level_maskW[level_maskW>0] = 1\n", " level_maskS = np.copy(level_hFacS)\n", " level_maskS[level_maskS>0] = 1\n", " ecco_maskC_points[k, points_counted:points_counted+tile_N] = level_maskC.ravel()\n", " ecco_maskW_points[k, points_counted:points_counted+tile_N] = level_maskW.ravel()\n", " ecco_maskS_points[k, points_counted:points_counted+tile_N] = level_maskS.ravel()\n", " \n", " points_counted += tile_N\n", "\n", "# remove the points outside the immediate area\n", "local_indices = (ecco_XC_points<-140) & (ecco_XC_points>-160) & (ecco_YC_points>65) & (ecco_YC_points<80)\n", "ecco_maskC_points = ecco_maskC_points[:, local_indices]\n", "ecco_maskW_points = ecco_maskW_points[:, local_indices]\n", "ecco_maskS_points = ecco_maskS_points[:, local_indices]\n", "ecco_hFacW_points = ecco_hFacW_points[:, local_indices]\n", "ecco_hFacS_points = ecco_hFacS_points[:, local_indices]\n", "ecco_YC_points = ecco_YC_points[local_indices]\n", "ecco_XC_points = ecco_XC_points[local_indices]" ] }, { "cell_type": "markdown", "id": "1b93a75f-cbc4-4c16-ba08-e120da5d350b", "metadata": {}, "source": [ "We can double check we have selected the right points by recreating the same plot above:" ] }, { "cell_type": "code", "execution_count": 11, "id": "a7c22076-6a16-4b13-abc6-b8ad38bbdf4d", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot the ECCO tile points\n", "plt.plot(ecco_XC_points,ecco_YC_points,'k.')\n", "\n", "# plot the boundary of the regional model\n", "plt.plot(XC[:,0],YC[:,0], 'g-')\n", "plt.plot(XC[:,-1],YC[:,-1], 'g-')\n", "plt.plot(XC[0,:],YC[0,:], 'g-')\n", "plt.plot(XC[-1,:],YC[-1,:], 'g-')\n", "\n", "plt.xlabel('Longitude')\n", "plt.ylabel('Latitude')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "3a5e1fb6-fcd6-463d-9395-07837dad6a76", "metadata": {}, "source": [ "### Step 5: Interpolate the ECCO mask vertically" ] }, { "cell_type": "markdown", "id": "81a81309-05dc-4253-91f5-1d999c0d87a4", "metadata": {}, "source": [ "Next, we will address the differences in the vertical grid layers. We can see that they are different by checking their length:" ] }, { "cell_type": "code", "execution_count": 12, "id": "b5074d36-8695-4ab2-8554-59830cbc8ce2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Depth levels in the ECCO grid: 50\n", "Depth levels in the regional model grid: 81\n" ] } ], "source": [ "print('Depth levels in the ECCO grid:',str(np.shape(ecco_DRF)[0]))\n", "print('Depth levels in the regional model grid:',str(np.shape(delR)[0]))" ] }, { "cell_type": "markdown", "id": "cd412966-17bf-42ca-aa54-4e9c1efce5ab", "metadata": {}, "source": [ "And also by plotting these fields vertically:" ] }, { "cell_type": "code", "execution_count": 13, "id": "a90aeb0b-6512-48e3-a18d-f71c8946b552", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(9,4))\n", "\n", "plt.subplot(1,2,1)\n", "plt.plot(ecco_DRF, -1*ecco_RF[:-1], 'k.')\n", "plt.gca().set_ylim([np.max(-1*ecco_RF[:-1]), 0])\n", "plt.title('ECCO Vertical Grid')\n", "plt.ylabel('Depth (m)')\n", "plt.xlabel('Grid Spacing')\n", "\n", "plt.subplot(1,2,2)\n", "plt.plot(delR, Z, 'k.') \n", "plt.gca().set_ylim([np.max(-1*ecco_RF[:-1]), 0])\n", "plt.title('Regional Model Vertical Grid')\n", "plt.xlabel('Grid Spacing (m)')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "248f9f93-8aa3-4d70-b032-8ebc68159302", "metadata": {}, "source": [ "To address these differences, we can use the `vertical` module of the **eccoseas** package to interpolate the ECCO mask onto the same vertical grid levels as the regional mask:" ] }, { "cell_type": "code", "execution_count": 14, "id": "ed2c9db0-a439-4171-a896-08f6aa55fcb3", "metadata": {}, "outputs": [], "source": [ "_, ecco_maskC_points_extended = \\\n", " vertical.interpolate_var_grid_faces_to_new_depth_levels(ecco_maskC_points, ecco_maskC_points,\n", " np.array(ecco_DRF), np.array(delR))" ] }, { "cell_type": "markdown", "id": "e48c2d06-db64-42c2-af27-0d551a3bc8d7", "metadata": {}, "source": [ "Let's take a look at the new mask relative to the old one:" ] }, { "cell_type": "code", "execution_count": 15, "id": "8094ab25-3b62-42d4-be4b-517051ac320a", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(9,4))\n", "random_plot_index = 117\n", "\n", "plt.subplot(1,2,1)\n", "plt.plot(ecco_maskC_points[:,random_plot_index], -1*ecco_RF[:-1], 'k.')\n", "plt.gca().set_ylim([np.max(-1*ecco_RF[:-1]), 0])\n", "plt.title('Mask on ECCO Vertical Grid\\n(length: '+str(np.size(ecco_maskC_points[:,random_plot_index]))+')')\n", "plt.ylabel('Depth (m)')\n", "plt.xlabel('Wet Mask (1=ocean, 0=land)')\n", "\n", "plt.subplot(1,2,2)\n", "plt.plot(ecco_maskC_points_extended[:,random_plot_index], Z, 'k.') \n", "plt.gca().set_ylim([np.max(-1*ecco_RF[:-1]), 0])\n", "plt.title('Mask on Regional Model Vertical Grid\\n(length: '+str(np.size(ecco_maskC_points_extended[:,random_plot_index]))+')')\n", "plt.xlabel('Wet Mask (1=ocean, 0=land)')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "c4462677-8079-45e5-9faf-d39452350218", "metadata": {}, "source": [ "Now that our masks are on the same grid levels, we are almost ready to make our interpolation grid." ] }, { "cell_type": "markdown", "id": "8b21a809", "metadata": {}, "source": [ "### Step 6: Reprojecting the Model Grid" ] }, { "cell_type": "markdown", "id": "d8817d36-f64d-40a5-bd69-b2895b71edd5", "metadata": {}, "source": [ "Given the location of this model at high latitude, we are going to run into some curious interpolation artefacts if bilinear interpolation is performed using longitude and latitude as coordinates. To demonstrate this issue, let's read in the `Theta` grid from the pickup file and try to interpolate it without reprojecting:" ] }, { "cell_type": "code", "execution_count": 16, "id": "77aaafe6-2383-4017-a200-d36854df70b6", "metadata": {}, "outputs": [], "source": [ "# read in the global pickup file\n", "global_pickup_faces, global_pickup_metadata = \\\n", " pickup.read_ecco_pickup_file_to_faces(os.path.join(data_folder,'pickup.0000000001'), llc=270)\n", "\n", "# convert to tiles\n", "theta_faces = global_pickup_faces['Theta']\n", "theta_tiles = grid.ecco_faces_to_tiles(theta_faces, llc=270, dim=3)\n", "\n", "# preprocess like the grid fields\n", "N = np.shape(theta_tiles[1])[-1]*np.shape(theta_tiles[1])[-2]\n", "theta_grid = np.zeros((ecco_Nr, N*len(tile_list)))\n", "points_counted = 0\n", "for tile_number in tile_list:\n", " for k in range(ecco_Nr):\n", " theta_grid[k,points_counted:points_counted+N] = \\\n", " theta_tiles[tile_number][k, :, :].ravel()\n", " points_counted += N\n", "theta_grid = theta_grid[:,local_indices]" ] }, { "cell_type": "code", "execution_count": 17, "id": "c4d95ec2-1dd2-4416-829d-a5ec855540f8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Working on level 0 of 50 (338562 nonzero points found)\n" ] } ], "source": [ "interpolated_theta = horizontal.downscale_3D_points(np.column_stack([ecco_XC_points, ecco_YC_points]),\n", " theta_grid, ecco_maskC_points_extended, \n", " XC, YC, maskC, testing=True, printing=True,\n", " use_legacy=True, verbose=True)" ] }, { "cell_type": "code", "execution_count": 18, "id": "7cdabd0f-2c21-4aea-9f66-5d0e03d297d8", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_grid = np.ma.masked_where(interpolated_theta[0,:,:]==0, interpolated_theta[0,:,:])\n", "C = plt.pcolormesh(plot_grid,vmin=-1.97, vmax=-1.85, cmap='turbo')\n", "plt.colorbar(C, label='Potential Temperature ($^{\\circ}$C)')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "feab010a-f42e-4009-bfff-f9f2f9a61a69", "metadata": {}, "source": [ "Here, we see two problems - first, the interpolation has produced a lot of triangular artefacts resulting from using longitude and latitude as coordinates. We also see some issue with values near the coastline resulting from differing coastlines in the models.\n", "\n", "First, let's try reprojecting coordinates to that distances are computed more appropriately for the location. We can do this with the `reprojection` model provided with **eccoseas**:" ] }, { "cell_type": "code", "execution_count": 19, "id": "5b0ba803-c921-427c-80e9-2f556891367d", "metadata": {}, "outputs": [], "source": [ "utm_zone_epsg = 32606\n", "utm_coordinates = reprojection.reproject_polygon(np.column_stack([ecco_XC_points, ecco_YC_points]), 4326, utm_zone_epsg)\n", "\n", "regional_coordinates = reprojection.reproject_polygon(np.column_stack([XC.ravel(), YC.ravel()]), 4326, utm_zone_epsg)\n", "X = regional_coordinates[:,0].reshape(np.shape(XC))\n", "Y = regional_coordinates[:,1].reshape(np.shape(XC))" ] }, { "cell_type": "markdown", "id": "13d2c6a9-b9e3-4191-a861-4bb5fda36c46", "metadata": {}, "source": [ "When using these coordinates, the triangular artefacts are alleviated:" ] }, { "cell_type": "code", "execution_count": 20, "id": "e7fff730-55b8-478f-a790-c1f7569f94d3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Working on level 0 of 50 (338562 nonzero points found)\n" ] } ], "source": [ "interpolated_theta = horizontal.downscale_3D_points(utm_coordinates,\n", " theta_grid, ecco_maskC_points, \n", " X, Y, maskC, \n", " testing=True, printing=True)" ] }, { "cell_type": "code", "execution_count": 21, "id": "bec72c8d-5944-45b2-ac97-cb009e9c1f05", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_grid = np.ma.masked_where(interpolated_theta[0,:,:]==0, interpolated_theta[0,:,:])\n", "C = plt.pcolormesh(plot_grid,vmin=-1.97, vmax=-1.85, cmap='turbo')\n", "plt.colorbar(C, label='Potential Temperature ($^{\\circ}$C)')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "1dfb6633-502b-46e1-b116-27d9b0284d6d", "metadata": {}, "source": [ "Next, for the coastline issue, let's make a land threshold mask to avoid areas which would be wet in the original ECCO grid:" ] }, { "cell_type": "code", "execution_count": 22, "id": "30f454eb-828a-41c9-98b7-a9d56a148e09", "metadata": {}, "outputs": [], "source": [ "ecco_maskC_on_regional_domain = np.zeros_like(maskC)\n", "for k in range(1):\n", " ecco_maskC_on_regional_domain[k,:,:] = griddata(utm_coordinates, ecco_maskC_points[k,:], (X, Y), method='nearest')" ] }, { "cell_type": "markdown", "id": "6576329b-2111-4e96-83aa-cb07f675fef9", "metadata": {}, "source": [ "Let's take a look:" ] }, { "cell_type": "code", "execution_count": 23, "id": "4fb61074-f2d2-4cca-b0fb-e347df46c4a5", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,5))\n", "plt.subplot(1,2,1)\n", "plt.pcolormesh(ecco_maskC_on_regional_domain[0,:,:], cmap='Blues')\n", "plt.title('ECCO Wet Grid on Regional Domain\\n(surface)')\n", "plt.xlabel('Model Columns')\n", "plt.ylabel('Model Rows')\n", "\n", "plt.subplot(1,2,2)\n", "plt.pcolormesh(maskC[0,:,:], cmap='Blues')\n", "plt.title('Regional Domain Wet Grid\\n(surface)')\n", "plt.xlabel('Model Columns')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "3b1d8ace-ebb5-4d34-9102-87c129419ce2", "metadata": {}, "source": [ "As we can see, the coastlines are much different. In addition, if any dry cell from ECCO was counted as a wet cell in the interpolation, this 0 value would cause some interpolation artefacts. To alleviate this potential problem, I will apply one additional buffer from the ECCO coastline equal to half the grid cell size in this area:" ] }, { "cell_type": "code", "execution_count": 24, "id": "e34e0017-f9bc-426e-aabf-e684a0152980", "metadata": {}, "outputs": [], "source": [ "threshold = 20e3 # approximate ECCO resolution in this region\n", "for k in range(1):\n", " level = np.ones_like(ecco_maskC_on_regional_domain[k,:,:])\n", " for p in range(np.shape(utm_coordinates)[0]):\n", " if ecco_maskC_points[k,p]==0:\n", " dist = ((utm_coordinates[p,0]-X)**2 + (utm_coordinates[p,1]-Y)**2)**0.5\n", " level[dist" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,5))\n", "plt.subplot(1,2,1)\n", "plt.pcolormesh(ecco_maskC_on_regional_domain[0,:,:], cmap='Blues')\n", "plt.title('ECCO Wet Grid on Regional Domain\\n(surface)')\n", "plt.xlabel('Model Columns')\n", "plt.ylabel('Model Rows')\n", "\n", "plt.subplot(1,2,2)\n", "plt.pcolormesh(maskC[0,:,:], cmap='Blues')\n", "plt.title('Regional Domain Wet Grid\\n(surface)')\n", "plt.xlabel('Model Columns')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "2b02232e-458f-4822-ad7d-3d0d51e5eb07", "metadata": {}, "source": [ "With this mask, the coastline values will be more sensibly filled:" ] }, { "cell_type": "code", "execution_count": 26, "id": "3b1653cf-0ec7-42ec-a25d-7aa216d6ac65", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Working on level 0 of 50 (338562 nonzero points found)\n" ] } ], "source": [ "interpolated_theta = horizontal.downscale_3D_points(utm_coordinates,\n", " theta_grid, ecco_maskC_points, \n", " X, Y, maskC, ecco_maskC_on_regional_domain,\n", " testing=True, printing=True)" ] }, { "cell_type": "code", "execution_count": 27, "id": "ed9e7c59-2985-4201-925e-1c7755a2a042", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_grid = np.ma.masked_where(interpolated_theta[0,:,:]==0, interpolated_theta[0,:,:])\n", "C = plt.pcolormesh(plot_grid,vmin=-1.97, vmax=-1.85, cmap='turbo')\n", "plt.colorbar(C, label='Potential Temperature ($^{\\circ}$C)')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "5c7342d6-bfaf-4d75-9f94-46830c5f1cd5", "metadata": {}, "source": [ "### Step 7: Creating the Interpolation Grid " ] }, { "cell_type": "markdown", "id": "83c32aaf-b8c4-400c-b156-8d4b6daaf852", "metadata": {}, "source": [ "Finally, with the grid read in, interpolated to the same vertical levels, and reprojected to a sensible coordinate system, we can create our interpolation grid:" ] }, { "cell_type": "code", "execution_count": 28, "id": "dad6a9bb-e515-45bd-b58e-d167dfdb476a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Working on level 0 of 81 (338562 nonzero points found)\n", " - Remaining points before horizontal spread: 32538\n", " - Remaining points before downward spread: 0\n", " - Working on level 0 of 81 (337553 nonzero points found)\n", " - Remaining points before horizontal spread: 31532\n", " - Remaining points before downward spread: 0\n", " - Working on level 0 of 81 (338039 nonzero points found)\n", " - Remaining points before horizontal spread: 32018\n", " - Remaining points before downward spread: 0\n" ] } ], "source": [ "gridC, gridS, gridW = \\\n", "ecco_interp.create_interpolation_grids(utm_coordinates[:,0], utm_coordinates[:,1],\n", " ecco_maskC_points, ecco_maskC_on_regional_domain,\n", " X, Y, maskC, maskS, maskW,\n", " testing=True, printing=True)" ] }, { "cell_type": "markdown", "id": "23bc3662-696e-4d09-9b09-b65be15833de", "metadata": {}, "source": [ "With this grid computed, we can now store it as a netCDF file which can be used later for all of our initial conditions:" ] }, { "cell_type": "code", "execution_count": 29, "id": "0c9cc204", "metadata": {}, "outputs": [], "source": [ "interpolation_type_grid_C, source_row_grid_C, source_col_grid_C, source_level_grid_C = gridC\n", "interpolation_type_grid_S, source_row_grid_S, source_col_grid_S, source_level_grid_S = gridS\n", "interpolation_type_grid_W, source_row_grid_W, source_col_grid_W, source_level_grid_W = gridW" ] }, { "cell_type": "code", "execution_count": 30, "id": "9458adc0-d0bd-420a-a726-c692ab243de7", "metadata": {}, "outputs": [], "source": [ "output_file = os.path.join(data_folder,'NorthSlope_interpolation_grid.nc')\n", "ecco_interp.write_interpolation_grid_to_nc(output_file,\n", " interpolation_type_grid_C, source_row_grid_C, source_col_grid_C, source_level_grid_C,\n", " interpolation_type_grid_S, source_row_grid_S, source_col_grid_S, source_level_grid_S,\n", " interpolation_type_grid_W, source_row_grid_W, source_col_grid_W, source_level_grid_W)" ] }, { "cell_type": "markdown", "id": "2d835a0e-9c7b-4f1a-8e1a-25604803ac5b", "metadata": {}, "source": [ "### Step 8: Visualizing the Interpolation Grid" ] }, { "cell_type": "markdown", "id": "395c2ed4-8ae0-42dc-88f9-075eae874f22", "metadata": {}, "source": [ "Now that we've computed our interpolation grid, let's have a peek at the results:" ] }, { "cell_type": "code", "execution_count": 31, "id": "d2a43d74-eec5-4eb6-a6e9-8d6649c6c9d2", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,10))\n", "\n", "plt.subplot(2,2,1)\n", "C = plt.pcolormesh(interpolation_type_grid_C[0,:,:])\n", "cbar = plt.colorbar(C)\n", "cbar.set_ticks([0,1,2])\n", "cbar.set_ticklabels(['Not\\nInterpolated','Bi-linear\\nInterpolation','Hydrostatically\\n\"Spread\"'])\n", "plt.ylabel('Model Rows')\n", "plt.title('Interpolation Type')\n", "\n", "plt.subplot(2,2,2)\n", "C = plt.pcolormesh(source_row_grid_C[0,:,:])\n", "plt.colorbar(C)\n", "plt.title('Source Row\\n(used for spreading routine)')\n", "\n", "plt.subplot(2,2,3)\n", "C = plt.pcolormesh(source_col_grid_C[0,:,:])\n", "plt.colorbar(C)\n", "plt.xlabel('Model Columns')\n", "plt.ylabel('Model Rows')\n", "plt.title('Source Column\\n(used for spreading routine)')\n", "\n", "plt.subplot(2,2,4)\n", "C = plt.pcolormesh(source_level_grid_C[0,:,:],vmin=0,vmax=81)\n", "plt.colorbar(C)\n", "plt.xlabel('Model Columns')\n", "plt.title('Source Level\\n(used for spreading routine)')\n", "\n", "plt.tight_layout()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "6d118b2d-100a-469c-99e9-b4cf2fad69b8", "metadata": {}, "source": [ "As we can see above, most of the cells in the regional domain will be filled by linear interpolation from the ECCO grid. However, in the areas near the coastline that were unresolved in the ECCO model, we fill these regions somehow. The approach taken here is to assume that the grid will be filled hydrostatically. In other words, we assume that the depth levels in the model will have roughly equal density and adjacent grid cells should have the same values. If spreading cannot occur horizontally, such as in the case of local minima in the bathymetry, the spreading is conducted vertically using the grid cell above (hence the source level grid is also stored). In the plot above, the source grid is all 0 since we are at the surface, but this is not true in general.\n", "\n", "This spreading routine is fairly expensive (as you will find if you run the cells above) so it useful to only do this one time - and this is the motivation for creating and saving this interpolation grid. Here, the source row, column, and level is stored so that, when spreading is necessary, the source locations are already identified and can be used readily. " ] }, { "cell_type": "markdown", "id": "c09ea7f2-2254-47f8-989b-04990088b89a", "metadata": {}, "source": [ "With this interpolation grid in hand, we are ready to make our initial conditions." ] } ], "metadata": { "kernelspec": { "display_name": "mitgcm", "language": "python", "name": "mitgcm" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.10" } }, "nbformat": 4, "nbformat_minor": 5 }