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   "source": [
    "# Normalized Mean Square Error\n",
    "\n",
    "This notebook computes the normalized mean square error of atmospheric surface pressure.\n",
    "It is compared to ERA5 observations, as well as the CESM2 large ensemble and CMIP6 model output."
   ]
  },
  {
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   "source": [
    "import glob\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "\n",
    "from nmse_utils import nmse\n",
    "from averaging_utils import seasonal_climatology_weighted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d67416c-a2d4-403b-85f4-647aa0a816eb",
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    },
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   },
   "source": [
    "## Parameters\n",
    "\n",
    "These variables are set in `config.yml`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b7486e94-e493-4369-9767-90eb15c0ac3a",
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     "parameters",
     "hide-input"
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   "source": [
    "CESM_output_dir = \"\"\n",
    "case_name = \"\"\n",
    "start_date = \"\"\n",
    "end_date = \"\"\n",
    "base_case_output_dir = None\n",
    "ts_dir = None\n",
    "base_case_name = None\n",
    "base_start_date = None\n",
    "base_end_date = None\n",
    "obs_data_dir = \"\"\n",
    "validation_path = \"\"\n",
    "regridded_output = False\n",
    "base_regridded_output = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f4fc1986",
   "metadata": {
    "execution": {
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    "tags": [
     "injected-parameters"
    ]
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   "source": [
    "# Parameters\n",
    "case_name = \"b.e30_beta02.BLT1850.ne30_t232.104\"\n",
    "base_case_name = \"b.e23_alpha17f.BLT1850.ne30_t232.092\"\n",
    "CESM_output_dir = \"/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CESM_output_for_testing\"\n",
    "start_date = \"0001-01-01\"\n",
    "end_date = \"0101-01-01\"\n",
    "base_start_date = \"0001-01-01\"\n",
    "base_end_date = \"0101-01-01\"\n",
    "obs_data_dir = (\n",
    "    \"/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CUPiD_obs_data\"\n",
    ")\n",
    "ts_dir = None\n",
    "lc_kwargs = {\"threads_per_worker\": 1}\n",
    "serial = False\n",
    "regridded_output = False\n",
    "base_regridded_output = True\n",
    "validation_path = (\n",
    "    \"atm/analysis_datasets/fv0.9x1.25/seasonal_climatology/nmse_validation/PSL/\"\n",
    ")\n",
    "subset_kwargs = {}\n",
    "product = \"/glade/work/richling/CUPid_pr_test/CUPiD/examples/key_metrics/computed_notebooks//atm/Global_PSL_NMSE_compare_obs_lens.ipynb\"\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9dfe1566-abe3-4b23-a59c-113334a0458f",
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    "editable": true,
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     "hide-input"
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   "source": [
    "# Want some base case parameter defaults to equal control case values\n",
    "if base_case_name is not None:\n",
    "    if base_case_output_dir is None:\n",
    "        base_case_output_dir = CESM_output_dir\n",
    "\n",
    "    if base_start_date is None:\n",
    "        base_start_date = start_date\n",
    "\n",
    "    if base_end_date is None:\n",
    "        base_end_date = end_date\n",
    "\n",
    "    if base_regridded_output is None:\n",
    "        base_regridded_output = regridded_output\n",
    "if ts_dir is None:\n",
    "    ts_dir = CESM_output_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74c7803f-a8c5-445d-9233-0aa2663c58bd",
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    "editable": true,
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     "status": "completed"
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    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Read in the current case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7f4132b5-db1f-4ae8-92df-07dd531b650e",
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     "status": "completed"
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     "hide-input"
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   "source": [
    "def fix_time_dim(dat):\n",
    "    \"\"\"CESM2 output sets time as the end of the averaging interval (e.g. January average is midnight on February 1st);\n",
    "    This function sets the time dimension to the midpoint of the averaging interval.\n",
    "    Note that CESM3 output sets time to the midpoint already, so this function should not change CESM3 data.\"\"\"\n",
    "    if \"time\" not in dat.dims:\n",
    "        return dat\n",
    "    if \"bounds\" not in dat.time.attrs:\n",
    "        return dat\n",
    "    time_bounds_avg = dat[dat.time.attrs[\"bounds\"]].mean(\"nbnd\")\n",
    "    time_bounds_avg.attrs = dat.time.attrs\n",
    "    dat = dat.assign_coords({\"time\": time_bounds_avg})\n",
    "    return xr.decode_cf(dat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "caf05d8b-a711-40fb-b88a-c10472a49d30",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CESM_output_for_testing/b.e30_beta02.BLT1850.ne30_t232.104/atm/proc/tseries\n"
     ]
    }
   ],
   "source": [
    "if regridded_output:\n",
    "    file_path = f\"{ts_dir}/{case_name}/atm/proc/tseries/regrid\"\n",
    "else:\n",
    "    file_path = f\"{ts_dir}/{case_name}/atm/proc/tseries\"\n",
    "print(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "318b8c9a-344f-41d5-87be-593847e4b6f1",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CESM_output_for_testing/b.e23_alpha17f.BLT1850.ne30_t232.092/atm/proc/tseries/regrid\n"
     ]
    }
   ],
   "source": [
    "if base_case_name is not None:\n",
    "    if base_regridded_output:\n",
    "        base_file_path = f\"{ts_dir}/{base_case_name}/atm/proc/tseries/regrid\"\n",
    "    else:\n",
    "        base_file_path = f\"{ts_dir}/{base_case_name}/atm/proc/tseries\"\n",
    "    print(base_file_path)"
   ]
  },
  {
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   "execution_count": 8,
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   "source": [
    "dat = (\n",
    "    fix_time_dim(xr.open_mfdataset(f\"{file_path}/*PSL*.nc\", decode_times=False))\n",
    "    .sel(time=slice(start_date, end_date))\n",
    "    .PSL\n",
    "    / 100.0\n",
    ")\n",
    "\n",
    "# Ensure all datasets have the same coordinates as the output data\n",
    "# (Avoid round-off level differences since all data should be on the same grid)\n",
    "lon = dat.lon.data\n",
    "lat = dat.lat.data\n",
    "\n",
    "if base_case_name is not None:\n",
    "    base_dat = (\n",
    "        fix_time_dim(\n",
    "            xr.open_mfdataset(f\"{base_file_path}/*PSL*.nc\", decode_times=False)\n",
    "        )\n",
    "        .sel(time=slice(base_start_date, base_end_date))\n",
    "        .assign_coords({\"lon\": lon, \"lat\": lat})\n",
    "        .PSL\n",
    "        / 100.0\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "073a2ad0-81e6-4817-9024-4b9b718fabb4",
   "metadata": {
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    "execution": {
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     "slide_type": ""
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     "hide-input"
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   "outputs": [],
   "source": [
    "# --Compute seasonal and annual means\n",
    "dat = seasonal_climatology_weighted(dat).load()\n",
    "\n",
    "if base_case_name is not None:\n",
    "    base_dat = seasonal_climatology_weighted(base_dat).load()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0527e3e-cd26-46b5-8c1e-08882109e12e",
   "metadata": {
    "editable": true,
    "papermill": {
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    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Read in validation data and other CMIP models for comparison (precomputed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "126e65b3-2b8c-400c-af02-2ad0b0f82e6e",
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    "editable": true,
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   "source": [
    "# ---ERA5\n",
    "era5 = xr.open_dataset(\n",
    "    os.path.join(obs_data_dir, validation_path, \"PSL_ERA5.nc\")\n",
    ").assign_coords({\"lon\": lon, \"lat\": lat})\n",
    "era5 = era5 / 100.0  # convert to hPa\n",
    "\n",
    "# ---CESM2\n",
    "lens2 = xr.open_dataset(\n",
    "    os.path.join(obs_data_dir, validation_path, \"PSL_LENS2.nc\")\n",
    ").assign_coords({\"lon\": lon, \"lat\": lat})\n",
    "lens2 = lens2 / 100.0  # convert to hPa\n",
    "\n",
    "# ---CMIP6\n",
    "modelfiles = sorted(\n",
    "    glob.glob(f\"{os.path.join(obs_data_dir,validation_path)}/CMIP6/*.nc\")\n",
    ")\n",
    "datcmip6 = [\n",
    "    xr.open_dataset(ifile).assign_coords({\"lon\": lon, \"lat\": lat}).mean(\"M\")\n",
    "    for ifile in modelfiles\n",
    "]\n",
    "datcmip6 = xr.concat(datcmip6, dim=\"model\")\n",
    "datcmip6 = datcmip6 / 100.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22cc331d-413c-4a87-bd89-812ad118cf8c",
   "metadata": {
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    },
    "tags": []
   },
   "source": [
    "## Compute the NMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6857717d-7514-45b5-ba33-a774f38b7c3e",
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   "source": [
    "nmse_dat = []\n",
    "nmse_cesm2 = []\n",
    "nmse_cmip6 = []\n",
    "if base_case_name is not None:\n",
    "    nmse_base_dat = []\n",
    "else:\n",
    "    nmse_base_dat = {key: None for key in [\"AM\", \"DJF\", \"MAM\", \"JJA\", \"SON\"]}\n",
    "for ivar in era5.data_vars:\n",
    "    nmse_dat.append(nmse(era5[ivar], dat[ivar]))\n",
    "    nmse_cesm2.append(nmse(era5[ivar], lens2[ivar]))\n",
    "    nmse_cmip6.append(nmse(era5[ivar], datcmip6[ivar]))\n",
    "    if base_case_name is not None:\n",
    "        nmse_base_dat.append(nmse(era5[ivar], base_dat[ivar]))\n",
    "nmse_dat = xr.merge(nmse_dat)\n",
    "nmse_cesm2 = xr.merge(nmse_cesm2)\n",
    "nmse_cmip6 = xr.merge(nmse_cmip6)\n",
    "if base_case_name is not None:\n",
    "    nmse_base_dat = xr.merge(nmse_base_dat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1014f119-fc3f-428b-99ca-ab9de700148d",
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     "status": "completed"
    },
    "slideshow": {
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    },
    "tags": []
   },
   "source": [
    "### Set up the plot panel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "53494900-0145-4ab2-85b8-5ed6ae347892",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-10-10T22:54:25.576414Z",
     "iopub.status.busy": "2025-10-10T22:54:25.576169Z",
     "iopub.status.idle": "2025-10-10T22:54:25.582386Z",
     "shell.execute_reply": "2025-10-10T22:54:25.581665Z"
    },
    "papermill": {
     "duration": 0.011222,
     "end_time": "2025-10-10T22:54:25.583304",
     "exception": false,
     "start_time": "2025-10-10T22:54:25.572082",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "def plotnmse(fig, cmip6, cesm2, cesm3, cesm_baseline, x1, x2, y1, y2, titlestr):\n",
    "    ax = fig.add_axes([x1, y1, x2 - x1, y2 - y1])\n",
    "\n",
    "    cmip6 = cmip6.sortby(cmip6, ascending=False)\n",
    "    binedges = np.arange(0, cmip6.size, 1)\n",
    "    ax.bar(\n",
    "        binedges,\n",
    "        cmip6,\n",
    "        width=1,\n",
    "        bottom=0,\n",
    "        edgecolor=\"black\",\n",
    "        color=\"gray\",\n",
    "        label=\"CMIP6\",\n",
    "    )\n",
    "\n",
    "    ax.plot(cmip6.size + 1, cesm3, \"o\", color=\"blue\", label=\"THIS RUN\")\n",
    "    if cesm_baseline is not None:\n",
    "        ax.plot(cmip6.size + 1, cesm_baseline, \"x\", color=\"red\", label=\"BASELINE\")\n",
    "\n",
    "    ax.fill_between(\n",
    "        np.arange(0, cmip6.size + 3, 1) - 0.5,\n",
    "        np.arange(0, cmip6.size + 3, 1) * 0 + np.array(cesm2.min()),\n",
    "        np.arange(0, cmip6.size + 3, 1) * 0 + np.array(cesm2.max()),\n",
    "        color=\"salmon\",\n",
    "        alpha=0.5,\n",
    "        label=\"LENS2\",\n",
    "    )\n",
    "\n",
    "    ax.set_xlim(-0.5, cmip6.size + 2 - 0.5)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_ylabel(\"NMSE\", fontsize=14)\n",
    "    ax.set_title(titlestr, fontsize=16)\n",
    "\n",
    "    ax.legend()\n",
    "\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "56b4cd99-a27e-4f28-86c2-8013e7c7bc78",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-10-10T22:54:25.590465Z",
     "iopub.status.busy": "2025-10-10T22:54:25.590281Z",
     "iopub.status.idle": "2025-10-10T22:54:27.518304Z",
     "shell.execute_reply": "2025-10-10T22:54:27.517501Z"
    },
    "papermill": {
     "duration": 1.933984,
     "end_time": "2025-10-10T22:54:27.520553",
     "exception": false,
     "start_time": "2025-10-10T22:54:25.586569",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1600 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 16))\n",
    "\n",
    "vert_coord = 0.99\n",
    "fig.text(\n",
    "    0.5,\n",
    "    vert_coord,\n",
    "    \"THIS RUN = \" + case_name + \" \" + start_date + \" to \" + end_date,\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    "    fontsize=14,\n",
    "    color=\"royalblue\",\n",
    ")\n",
    "vert_coord = vert_coord - 0.015\n",
    "if base_case_name is not None:\n",
    "    fig.text(\n",
    "        0.5,\n",
    "        vert_coord,\n",
    "        \"BASELINE RUN = \"\n",
    "        + base_case_name\n",
    "        + \" \"\n",
    "        + base_start_date\n",
    "        + \" to \"\n",
    "        + base_end_date,\n",
    "        ha=\"center\",\n",
    "        va=\"center\",\n",
    "        fontsize=14,\n",
    "        color=\"red\",\n",
    "    )\n",
    "    vert_coord = vert_coord - 0.015\n",
    "\n",
    "fig.text(\n",
    "    0.5,\n",
    "    vert_coord,\n",
    "    \"Other runs = 1979-01-01 to 2023-12-31\",\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    "    fontsize=14,\n",
    ")\n",
    "vert_coord = vert_coord - 0.015\n",
    "\n",
    "fig.text(\n",
    "    0.5,\n",
    "    vert_coord,\n",
    "    \"Validation data = ERA5 1979-01-01 to 2023-12-31\",\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    "    fontsize=14,\n",
    ")\n",
    "vert_coord = vert_coord - 0.03\n",
    "\n",
    "ax = plotnmse(\n",
    "    fig,\n",
    "    nmse_cmip6[\"AM\"],\n",
    "    nmse_cesm2[\"AM\"],\n",
    "    nmse_dat[\"AM\"],\n",
    "    nmse_base_dat[\"AM\"],\n",
    "    0.3,\n",
    "    0.7,\n",
    "    vert_coord - 0.16,\n",
    "    vert_coord,\n",
    "    \"NMSE, SLP, AM\",\n",
    ")\n",
    "ax = plotnmse(\n",
    "    fig,\n",
    "    nmse_cmip6[\"DJF\"],\n",
    "    nmse_cesm2[\"DJF\"],\n",
    "    nmse_dat[\"DJF\"],\n",
    "    nmse_base_dat[\"DJF\"],\n",
    "    0.05,\n",
    "    0.45,\n",
    "    0.57,\n",
    "    0.72,\n",
    "    \"NMSE, SLP, DJF\",\n",
    ")\n",
    "ax = plotnmse(\n",
    "    fig,\n",
    "    nmse_cmip6[\"MAM\"],\n",
    "    nmse_cesm2[\"MAM\"],\n",
    "    nmse_dat[\"MAM\"],\n",
    "    nmse_base_dat[\"MAM\"],\n",
    "    0.55,\n",
    "    0.95,\n",
    "    0.57,\n",
    "    0.72,\n",
    "    \"NMSE, SLP, MAM\",\n",
    ")\n",
    "ax = plotnmse(\n",
    "    fig,\n",
    "    nmse_cmip6[\"JJA\"],\n",
    "    nmse_cesm2[\"JJA\"],\n",
    "    nmse_dat[\"JJA\"],\n",
    "    nmse_base_dat[\"JJA\"],\n",
    "    0.05,\n",
    "    0.45,\n",
    "    0.37,\n",
    "    0.52,\n",
    "    \"NMSE, SLP, JJA\",\n",
    ")\n",
    "ax = plotnmse(\n",
    "    fig,\n",
    "    nmse_cmip6[\"SON\"],\n",
    "    nmse_cesm2[\"SON\"],\n",
    "    nmse_dat[\"SON\"],\n",
    "    nmse_base_dat[\"SON\"],\n",
    "    0.55,\n",
    "    0.95,\n",
    "    0.37,\n",
    "    0.52,\n",
    "    \"NMSE, SLP, SON\",\n",
    ")"
   ]
  }
 ],
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