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   "source": [
    "# SLP (NMSE)\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"
   ]
  },
  {
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   "id": "9d67416c-a2d4-403b-85f4-647aa0a816eb",
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   },
   "source": [
    "## Parameters\n",
    "\n",
    "These variables are set in `config.yml`"
   ]
  },
  {
   "cell_type": "code",
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   "id": "b7486e94-e493-4369-9767-90eb15c0ac3a",
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     "parameters",
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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": "68a16d3a",
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    "tags": [
     "injected-parameters"
    ]
   },
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   "source": [
    "# Parameters\n",
    "case_name = \"b.e30_alpha07c_cesm.B1850C_LTso.ne30_t232_wgx3.232\"\n",
    "base_case_name = \"b.e30_alpha07c_cesm.B1850C_LTso.ne30_t232_wgx3.228\"\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 = \"0021-01-01\"\n",
    "base_start_date = \"0001-01-01\"\n",
    "base_end_date = \"0045-01-01\"\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 = False\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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   "execution_count": 4,
   "id": "9dfe1566-abe3-4b23-a59c-113334a0458f",
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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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     "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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     "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_alpha07c_cesm.B1850C_LTso.ne30_t232_wgx3.232/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",
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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_alpha07c_cesm.B1850C_LTso.ne30_t232_wgx3.228/atm/proc/tseries\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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     "slide_type": ""
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     "hide-input"
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   "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,
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    },
    "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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   "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",
   "metadata": {
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    "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": "2025-10-28T23:29:18.703693Z",
     "iopub.status.busy": "2025-10-28T23:29:18.703410Z",
     "iopub.status.idle": "2025-10-28T23:29:18.711152Z",
     "shell.execute_reply": "2025-10-28T23:29:18.710647Z"
    },
    "papermill": {
     "duration": 0.012793,
     "end_time": "2025-10-28T23:29:18.711708",
     "exception": false,
     "start_time": "2025-10-28T23:29:18.698915",
     "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": "2025-10-28T23:29:18.716665Z",
     "iopub.status.busy": "2025-10-28T23:29:18.716557Z",
     "iopub.status.idle": "2025-10-28T23:29:19.226458Z",
     "shell.execute_reply": "2025-10-28T23:29:19.225729Z"
    },
    "papermill": {
     "duration": 0.513774,
     "end_time": "2025-10-28T23:29:19.227799",
     "exception": false,
     "start_time": "2025-10-28T23:29:18.714025",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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baWmpzp8/39BpwEpeXl5yc3O6h1cBAADQyFB/uAZb1R9O19T38vJSaGio0tLSdNttt5n3p6WlaeTIkTWOs3v3brVr184eKQIAABdmMpmUk5OjkydPNnQqsAE3Nzd17txZXl5eDZ0KAAAAUAn1h2uxVf3hdE19SYqLi1N0dLT69eunsLAwrVixQpmZmZoyZYqk8lfnHDt2TKtXr5YkJSYmKjg4WCEhISouLtaaNWu0YcMGbdiwoSEvAwAAOKGKG+o2bdqoSZMmLKLqxMrKypSVlaXs7GwFBgby7xIAAAAOh/rDddiy/nDKpv6YMWN04sQJJSQkKDs7W7169dLmzZsVFBQkScrOzlZmZqZ5fHFxsR5++GEdO3ZMvr6+CgkJ0XvvvacRI0Y01CUAAAAnVFpaar6hbtWqVUOnAxto3bq1srKyVFJSIk9Pz4ZOBwAAADCj/nA9tqo/DCaTyWTDvFxWQUGB/P39lZ+fLz8/v4ZOBwAANIBz587p0KFDCg4Olq+vb0OnAxs4e/asDh8+rM6dO8vHx8fiO+7/0JD47w8AAFB/uB5b1R9OOVO/IWVkZKhZs2ZWxTAajQoMDLRRRgAAoL7xyKvr4N8lHB31BwAA4J7Vddjq3yVN/Vq6/vrrrY7h6+urffv2cWMNAAAAOICkpCQ999xzys7OVkhIiBITExUREVHl2O3bt+uxxx7Tvn37dObMGQUFBWny5MmaPn26eUxycrL++te/Vjr27NmzlWZkXQr1BwAAAC5EU7+WbrnlFrVr167Ox+fl5Sk1NVV5eXncVAMAAAANLCUlRbGxsUpKSlJ4eLiWL1+u4cOHa8+ePVXerzdt2lR/+9vf1Lt3bzVt2lTbt2/X5MmT1bRpUz3wwAPmcX5+fvrxxx8tjq1tQ1+i/gAAAEBlNPVrqVWrVmrfvn1DpwEAAJxYaamUni5lZ0vt2kkREZK7e0NnBTROixYt0oQJEzRx4kRJUmJiorZs2aJly5ZpwYIFlcb37dtXffv2NX8ODg5Wamqq0tPTLZr6BoNBbdu2tTo/6g8AAGAt6g/X49bQCQAAADQmqalScLB0ww3SPfeU/29wcPl+ezAYDBfdxo0bZx731ltvVTp+3LhxuvXWW6v9nJubq8mTJyswMFDe3t5q27atoqKitHPnzmpzmjNnjvn8bm5uat++ve69914dPXrUYlxwcLASExMrHZ+YmKjg4OBK8aZMmWIxLiMjQwaDQYcPH642FzRuxcXF2rVrlyIjIy32R0ZGaseOHTWKsXv3bu3YsaPSa3IKCwsVFBSkjh076s9//rN279590ThFRUUqKCiw2AAAAKxF/eGa9QdNfQAAgHqSmirdcYf0yy+W+48dK99vjxvr7Oxs85aYmCg/Pz+LfS+88IJV8W+//XZ98803evXVV/XTTz/pnXfe0ZAhQ/Trr79e9LiQkBBlZ2frl19+UUpKir777juNHj26znn4+PjolVde0U8//VTnGGh88vLyVFpaqoCAAIv9AQEBysnJueixHTt2lLe3t/r166epU6eaZ/pLUvfu3ZWcnKx33nlHa9eulY+Pj8LDw7V///5q4y1YsED+/v7mrVOnTtZdHAAAaPSoP37navUHr98BAACoB6Wl0kMPSSZT5e9MJslgkGJjpZEjbfso7B9f/+Hv72+zV4JI0smTJ7V9+3Zt3brVPEs5KChI/fv3v+SxHh4e5jzat2+vSZMm6cEHH1RBQYH8/PxqncuVV16pNm3a6PHHH9e6detqfTwaN4PBYPHZZDJV2neh9PR0FRYW6vPPP9eMGTPUpUsX3X333ZKkgQMHauDAgeax4eHhuuaaa/TPf/5TS5YsqTJefHy84uLizJ8LCgpo7AMAgDqj/rDkavUHM/UBAADqQXp65Rkyf2QySUePlo9zFs2aNVOzZs301ltvqaioqM5xcnJylJqaKnd3d7lbUVE8/fTT2rBhg7788ss6x0DjYjQa5e7uXmlWfm5ubqXZ+xfq3LmzrrrqKk2aNEnTp0/XnDlzqh3r5uama6+99qIz9b29veXn52exAQAA1BX1R/Vcof6gqQ8AAFAPsrNtO84e7r77bvONcsX22muvVTvew8NDycnJevXVV9WiRQuFh4frH//4h7799ttLnuu7775Ts2bN1KRJE7Vr105bt27V1KlT1bRp0zrnf80112j06NGaMWNGnWOgcfHy8lJoaKjS0tIs9qelpWnQoEE1jmMymS5aWJpMJmVkZKhdu3Z1zhUAAKA2qD8suVr9QVMfAACgHtS0l9eQPb/FixcrIyPDYvvLX/5y0WNuv/12ZWVl6Z133lFUVJS2bt2qa665RsnJyRc97sorr1RGRoa+/PJLzZ8/X3369NH8+fOtvoYnn3xS6enp+vDDD62OhcYhLi5OL7/8slauXKm9e/dq+vTpyszMNC98Fh8fr/vuu888funSpdq0aZP279+v/fv3a9WqVVq4cKHGjh1rHjN37lxt2bJFBw8eVEZGhiZMmKCMjIxKi6kBAADYC/WHJVerP3inPgAAQD2IiJA6dixflKqq91oaDOXfR0TUf24V2rZtqy5duljsa968uU6ePHnR43x8fDRs2DANGzZMs2fP1sSJE/XEE09o3Lhx1R7j5eVlPldISIj279+v//u//9O///1v8xg/Pz/l5+dXOvbkyZPy9/evMu4VV1yhSZMmacaMGXrllVcumjcgSWPGjNGJEyeUkJCg7Oxs9erVS5s3b1ZQUJCk8sXeMjMzzePLysoUHx+vQ4cOycPDQ1dccYWefvppTZ482Tzm5MmTeuCBB5STkyN/f3/17dtX27Ztq9H7XgEAAGyB+sOSq9UfzNQHAACoB+7u0gsvlP/zhetvVnxOTLTtIlUNpWfPnjp9+nStjpk1a5bWrl2rr7/+2ryve/fuVb6f8ssvv9SVV15ZbazZs2frp59+0htvvFGrHNB4xcTE6PDhwyoqKtKuXbt03XXXmb9LTk7W1q1bzZ+nTZum77//XqdPn1Z+fr6+/vpr/d///Z/c3H4vrRYvXqwjR46oqKhIubm52rJli8LCwurzkgAAQCNH/XFxzl5/0NQHAACoJ6NGSW++KXXoYLm/Y8fy/aNGNUxedXXixAn96U9/0po1a/Ttt9/q0KFDWr9+vZ599lmNHDmyVrEuv/xyjRw5UrNnzzbvi4uL0/vvv6+EhATt2bNHe/bs0bx58/TBBx/o73//e7WxAgICFBcXpyVLltT52gAAAABnR/1RPWevP3j9DgAAQD0aNUoaOVJKTy9flKpdu/JHXp1xhkyzZs00YMAALV68WAcOHND58+fVqVMnTZo0Sf/4xz9qHe/vf/+7wsPD9cUXX2jAgAEaOHCgtmzZooSEBCUmJkoqf1R2y5YtGjBgwEVjPfLII1q2bJnOnTtXl0sDAAAAXAL1R/Wcuf4wmExVvVUJFyooKJC/v7/GjRun4ODgOsfJysrSihUrtGvXLl1zzTW2SxAAANjduXPndOjQIXXu3Fk+Pj4NnQ5s4GL/Tivu//Lz8+Xn59dAGaKxov4AAADUH67HVvUHr98BAAAAAAAAAMBJ0NQHAAAAAAAAAMBJ0NQHAAAAAAAAAMBJ0NQHAAAAAAAAAMBJ0NQHAAAAAAAAAMBJ0NQHAAAAAAAAAMBJ0NQHAAAAAAAAAMBJ0NQHAAAAAAAAAMBJ0NQHAAAAAAAAAMBJ0NQHAABoBMaNGyeDwWDeWrVqpZtuuknffvttpbEPPPCA3N3d9cYbb1T67vTp03rsscd0+eWXy8fHR61bt9aQIUP07rvvmscMGTLE4lwV25QpU8xjDAaD3nrrrSpz3bp1qwwGg06ePGnxuVevXiotLbUY26JFCyUnJ5s/BwcHV3nup59+uhZ/LQAAAADWoP6wb/1BUx8AAKC+zJkjzZtX9Xfz5pV/b0c33XSTsrOzlZ2drY8//lgeHh7685//bDHmzJkzSklJ0SOPPKJXXnmlUowpU6borbfe0osvvqh9+/bpgw8+0O23364TJ05YjJs0aZL5XBXbs88+a1X+Bw4c0OrVqy85LiEhodK5p02bZtW5AQAAAKdD/WFV/o5cf3jYNToAAAB+5+4uzZ5d/s+zZv2+f9688v0JCXY9vbe3t9q2bStJatu2rR577DFdd911On78uFq3bi1JWr9+vXr27Kn4+Hi1a9dOhw8fVnBwsDnGpk2b9MILL2jEiBGSymemhIaGVjpXkyZNzOeylWnTpumJJ57Q3XffLR8fn2rHNW/e3ObnBgAAAJwO9YdVHLn+YKY+AABAfZk1q/zGefbs32fM/PGG+o832nZWWFio1157TV26dFGrVq3M+1955RWNHTtW/v7+GjFihFatWmVxXNu2bbV582adOnWq3nKtEBsbq5KSEr344ov1fm4AAADA6VB/WMWR6w+a+gAAAPXpjzfW3t71ekP97rvvqlmzZmrWrJmaN2+ud955RykpKXJzK78l3L9/vz7//HONGTNGkjR27FitWrVKZWVl5hgrVqzQjh071KpVK1177bWaPn26Pvvss0rnSkpKMp+rYnv11Vetyr9JkyZ64okntGDBAuXn51c77rHHHqt07q1bt1p1bgAAAMApUX/UmSPXHzT1AQAA6tusWZKXl1RcXP6/9TRD5oYbblBGRoYyMjL0xRdfKDIyUsOHD9eRI0cklc+SiYqKktFolCSNGDFCp0+f1kcffWSOcd111+ngwYP6+OOPdfvtt+uHH35QRESE5l3wrs57773XfK6K7bbbbrP6GiZMmCCj0ahnnnmm2jGPPPJIpXMPGDDA6nMDAAAATon6o84ctf6gqQ8AAFDf5s37/Ya6uLj6xatsrGnTpurSpYu6dOmi/v3765VXXtHp06f1r3/9S6WlpVq9erXee+89eXh4yMPDQ02aNNGvv/5aacEqT09PRUREaMaMGfrwww+VkJCgefPmqbi42DzG39/ffK6Kzc/Pz+pr8PDw0JNPPqkXXnhBWVlZVY4xGo2Vzu3r62v1uQEAAACnRP1RZ45af9DUBwAAqE9/fIdlUVHld1zWI4PBIDc3N509e9b8nsrdu3dbzDBZv3693nrrLZ04caLaOD179lRJSYnOnTtXL3nfeeedCgkJ0dy5c+vlfAAAAIDTov6wmiPWHx4NnQAAAECjUdWiVBX/O3u25Wc7KCoqUk5OjiTpt99+04svvqjCwkLdcsstSkxM1M0336yrr77a4piQkBDFxsZqzZo1euihhzRkyBDdfffd6tevn1q1aqU9e/boH//4h2644QaLmTBnzpwxn6uCt7e3LrvsMvPnQ4cOKSMjw2JMly5danQtTz/9tKKioqr87tSpU5XO3aRJE5vM1AEAAACcBvWHy9YfzNQHAACoL6WlVS9KVbF4VWmpXU//wQcfqF27dmrXrp0GDBigL7/8UuvXr1ePHj303nvv6fbbb690jMFg0KhRo8yPwEZFRenVV19VZGSkevTooWnTpikqKkrr1q2zOO5f//qX+VwV2913320xJi4uTn379rXYvvrqqxpdy5/+9Cf96U9/UklJSaXvZs+eXencjz76aE3/TGiEkpKS1LlzZ/n4+Cg0NFTp6enVjt2+fbvCw8PVqlUr+fr6qnv37lq8eHGlcRs2bFDPnj3l7e2tnj17auPGjfa8BAAAgMqoPyzGuFL9YTCZTCa7nsFFFBQUyN/fX+PGjVNwcHCd42RlZWnFihXatWuXrrnmGtslCAAA7O7cuXM6dOiQufkH53exf6cV93/5+fnM8ndhKSkpio6OVlJSksLDw7V8+XK9/PLL2rNnjwIDAyuN3717t/bt26fevXuradOm2r59uyZPnqzFixfrgQcekCTt3LnTvIDbbbfdpo0bN2r27Nnavn17jRdNo/4AAADUH67HVvUHM/UBAAAANFqLFi3ShAkTNHHiRPXo0UOJiYnq1KmTli1bVuX4vn376u6771ZISIiCg4M1duxYRUVFWczuT0xM1LBhwxQfH6/u3bsrPj5eQ4cOVWJiYj1dFQAAAFyZ0zb1a/OI7B999tln8vDwUJ8+feybIAAAAACHVlxcrF27dikyMtJif2RkpHbs2FGjGLt379aOHTt0/fXXm/ft3LmzUsyoqKiLxiwqKlJBQYHFBgAAAFTFKZv6KSkpio2N1cyZM7V7925FRERo+PDhyszMvOhx+fn5uu+++zR06NB6yhQAAACAo8rLy1NpaakCAgIs9gcEBFRa7OxCHTt2lLe3t/r166epU6dq4sSJ5u9ycnJqHXPBggXy9/c3b506darDFQEAAKAxcMqmfm0fka0wefJk3XPPPQoLC6unTAEAAAA4OoPBYPHZZDJV2neh9PR0ffXVV3rppZeUmJiotWvXWhUzPj5e+fn55u3o0aO1vAoAAAA0Fh4NnUBtVTwiO2PGDIv9l3pEdtWqVTpw4IDWrFmjJ5988pLnKSoqUlFRkfkzj78CAAAArsVoNMrd3b3SDPrc3NxKM+0v1LlzZ0nSVVddpf/973+aM2eO7r77bklS27Ztax3T29tb3t7edbkMAAAANDJON1O/Lo/I7t+/XzNmzNBrr70mD4+a/Y7B468AAACAa/Py8lJoaKjS0tIs9qelpWnQoEE1jmMymSwmBIWFhVWK+eGHH9YqJgAAAFAdp5upX6Gmj7OWlpbqnnvu0dy5c9WtW7cax4+Pj1dcXJz5c0FBAY19AAAAwMXExcUpOjpa/fr1U1hYmFasWKHMzExNmTJFUnldcOzYMa1evVqStHTpUgUGBqp79+6SpO3bt2vhwoWaNm2aOeZDDz2k6667Ts8884xGjhypt99+Wx999JG2b99e/xcIAAAAl+N0Tf3aPiJ76tQpffXVV9q9e7f+9re/SZLKyspkMpnk4eGhDz/8UH/6058qHcfjrwAAAIDrGzNmjE6cOKGEhARlZ2erV69e2rx5s4KCgiRJ2dnZyszMNI8vKytTfHy8Dh06JA8PD11xxRV6+umnNXnyZPOYQYMG6Y033tDjjz+uWbNm6YorrlBKSooGDBhQ79cHAAAA1+N0Tf0/PiJ72223mfenpaVp5MiRlcb7+fnpu+++s9iXlJSk//znP3rzzTfN78IEAAAA0DjFxMQoJiamyu+Sk5MtPk+bNs1iVn517rjjDt1xxx22SA8AAACw4HRNfal2j8i6ubmpV69eFse3adNGPj4+lfYDAADUhencWen8+fo7oaenDD6+9Xc+AAAAAA6D+gNO2dSv7SOyAAAA9mI6d1Zl29JkOnO63s5paNJUbtcNq9WN9bhx43Ty5Em99dZblb4LDg7WkSNHKu1fsGCBZsyYocOHD6tz585q3bq1Dhw4oObNm5vH9OnTR7feeqvmzJkjSTp48KBmzpypTz/9VL/++quMRqNCQ0P13HPPqVu3bjp8+LDmzZun//znP8rJyVH79u01duxYzZw5U15eXrX+WwAAAACNCfUH9YfkpE19qXaPyF5ozpw55n/xAAAAVjl/vvyG2sNT8qyHm8LzxeXnO39esuFsmYSEBE2aNMli3x9vnqXytYoWLlyouXPnVhmjuLhYw4YNU/fu3ZWamqp27drpl19+0ebNm5Wfny9J2rdvn8rKyrR8+XJ16dJF33//vSZNmqTTp09r4cKFNrseAAAAwCVRf5g15vrDaZv6AAAADsXTSwZvb7ufxiRJJbZ/1LZ58+Zq27btRcdMmzZNixYt0tSpU9WmTZtK3+/Zs0cHDx7Uf/7zH/MTlEFBQQoPDzePuemmm3TTTTeZP19++eX68ccftWzZMqe+qQYAAADqFfVHo64/3Bo6AQAAADiHu+++W126dFFCQkKV37du3Vpubm568803VVpaWuO4+fn5atmypa3SBAAAAOACqD+qR1MfAAAAeuyxx9SsWTOLbevWrRZjDAaDnn76aa1YsUIHDhyoFKNDhw5asmSJZs+ercsuu0x/+tOfNG/ePB08eLDa8x44cED//Oc/NWXKFFtfEgAAAAAHRf1hHZr6AAAA0COPPKKMjAyLbcCAAZXGRUVFafDgwZo1a1aVcaZOnaqcnBytWbNGYWFhWr9+vUJCQpSWllZpbFZWlm666Sbdeeedmjhxos2vCQAAAIBjov6wDk19AAAAyGg0qkuXLhabr2/VC2E9/fTTSklJ0e7du6v8vnnz5vrLX/6i+fPn65tvvlFERISefPJJizFZWVm64YYbFBYWphUrVtj8egAAAAA4LuoP69DUBwAAQK30799fo0aN0owZMy451mAwqHv37jp9+rR537FjxzRkyBBdc801WrVqldzcuCUFAAAAUDXqj8o8GjoBAAAA1I/8/HxlZGRY7KtYIOrUqVPKycmx+K5Jkyby8/OrMtb8+fMVEhIiD4/fbyczMjL0xBNPKDo6Wj179pSXl5c+/fRTrVy5Uo899pik8hkyQ4YMUWBgoBYuXKjjx4+bj2/btq0tLhMAAACAA6D+sB+a+gAAALZwvlimejpPXW3dulV9+/a12Hf//fdLkmbPnq3Zs2dbfDd58mS99NJLVcbq1q2bxo8fb/HoaseOHRUcHKy5c+fq8OHDMhgM5s/Tp0+XJH344Yf6+eef9fPPP6tjx44WMU2mevkLAgAAAM6P+qNR1x8GkzNnX48KCgrk7++vcePGKTg4uM5xsrKytGLFCu3atUvXXHON7RIEAAB2d+7cOR06dEidO3eWj4+PJMl07qzKtqXJdOb0JY62HUOTpnK7bpgMPlW/cxI1V9W/0woV93/5+fnVzhgC7IX6AwAAUH+4HlvVH8zUBwAAsILBx1du1w2Tzp+vv5N6enJDDQAAADRC1B+QaOoDAABYzeDjK3GTCwAAAKAeUH/A+Zf6BQAAAAAAAACgkaCpDwAAAAAAAACAk6CpDwAAAAAAAACAk6CpDwAAAAAAAACAk6CpDwAAAAAAAACAk6CpDwAAAAAAAACAk6CpDwAAAAAAAACAk/Bo6AQAAACcXWZmpvLy8urtfEajUYGBgfV2PsDVJSUl6bnnnlN2drZCQkKUmJioiIiIKsempqZq2bJlysjIUFFRkUJCQjRnzhxFRUWZxyQnJ+uvf/1rpWPPnj0rHx8fu10HAABoHKg/QFMfAADACpmZmerevbvOnj1bb+f09fXVvn37an1jnZOTo/nz5+u9997TsWPH1KZNG/Xp00exsbEaOnSogoODdeTIEa1du1Z33XWXxbEhISHas2ePVq1apXHjxkmSgoODFRsbq9jYWPPnI0eOmHO8/PLLNW3aNE2ePNkcp6ioSAkJCVqzZo1ycnLUsWNHzZw5U+PHj6/7HwSwQkpKimJjY5WUlKTw8HAtX75cw4cP1549e6r8v7Ft27Zp2LBheuqpp9SiRQutWrVKt9xyi7744gv17dvXPM7Pz08//vijxbE09AEAgLWoP6g/JJr6AAAAVsnLy9PZs2c1atQoGY3Gejlfamqq8vLyanVTffjwYYWHh6tFixZ69tln1bt3b50/f15btmzR1KlTtW/fPklSp06dtGrVKoub6s8//1w5OTlq2rTpJc+TkJCgSZMmqbCwUMnJyZoyZYpatGihMWPGSJJGjx6t//3vf3rllVfUpUsX5ebmqqSkpJZ/BcB2Fi1apAkTJmjixImSpMTERG3ZskXLli3TggULKo1PTEy0+PzUU0/p7bff1qZNmyya+gaDQW3btrVr7gAAoPGh/rDUWOsPmvoAAAA2YDQa1b59+4ZOo1oxMTEyGAz673//a3FzHBISYjFL5d5779XixYt19OhRderUSZK0cuVK3XvvvVq9evUlz9O8eXNzI/PJJ5/UunXr9NZbb2nMmDH64IMP9Omnn+rgwYNq2bKlpPLZNUBDKS4u1q5duzRjxgyL/ZGRkdqxY0eNYpSVlenUqVPm/6YrFBYWKigoSKWlperTp4/mzZtn0fS/UFFRkYqKisyfCwoKanElAACgsaH+KNdY6w8WygUAAHBxv/76qz744ANNnTq1ytkuLVq0MP9zQECAoqKi9Oqrr0qSzpw5o5SUlDo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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",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cupid-analysis",
   "language": "python",
   "name": "cupid-analysis"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  },
  "papermill": {
   "duration": 16.180359,
   "end_time": "2025-10-28T23:29:19.855799",
   "exception": null,
   "input_path": "/glade/derecho/scratch/richling/tmp/tmpd50sy73v.ipynb",
   "output_path": "/glade/work/richling/CUPid_pr_test/CUPiD/examples/key_metrics/computed_notebooks/atm/Global_PSL_NMSE_compare_obs_lens.ipynb",
   "parameters": {
    "CESM_output_dir": "/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CESM_output_for_testing",
    "base_case_name": "b.e30_alpha07c_cesm.B1850C_LTso.ne30_t232_wgx3.228",
    "base_end_date": "0045-01-01",
    "base_regridded_output": false,
    "base_start_date": "0001-01-01",
    "case_name": "b.e30_alpha07c_cesm.B1850C_LTso.ne30_t232_wgx3.232",
    "end_date": "0021-01-01",
    "lc_kwargs": {
     "threads_per_worker": 1
    },
    "obs_data_dir": "/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CUPiD_obs_data",
    "product": "/glade/work/richling/CUPid_pr_test/CUPiD/examples/key_metrics/computed_notebooks//atm/Global_PSL_NMSE_compare_obs_lens.ipynb",
    "regridded_output": false,
    "serial": false,
    "start_date": "0001-01-01",
    "subset_kwargs": {},
    "ts_dir": null,
    "validation_path": "atm/analysis_datasets/fv0.9x1.25/seasonal_climatology/nmse_validation/PSL/"
   },
   "start_time": "2025-10-28T23:29:03.675440"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}