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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"
   ]
  },
  {
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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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     "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": "4eaf6efe",
   "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 = \"Obs\"\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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   "execution_count": 4,
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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 base_case_name == \"Obs\":\n",
    "    base_case_name = None\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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    },
    "tags": []
   },
   "source": [
    "## Read in the current case"
   ]
  },
  {
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   "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",
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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,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WHATS HAPPENIGN HERE None\n"
     ]
    }
   ],
   "source": [
    "print(\"WHATS HAPPENIGN HERE\",base_case_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "318b8c9a-344f-41d5-87be-593847e4b6f1",
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   "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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   "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": 10,
   "id": "073a2ad0-81e6-4817-9024-4b9b718fabb4",
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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",
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    "editable": true,
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    },
    "tags": []
   },
   "source": [
    "## Read in validation data and other CMIP models for comparison (precomputed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
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    },
    "tags": []
   },
   "source": [
    "## Compute the NMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": {
    "editable": true,
    "papermill": {
     "duration": 0.002307,
     "end_time": "2025-10-13T17:49:33.448904",
     "exception": false,
     "start_time": "2025-10-13T17:49:33.446597",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "### Set up the plot panel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "53494900-0145-4ab2-85b8-5ed6ae347892",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-10-13T17:49:33.455055Z",
     "iopub.status.busy": "2025-10-13T17:49:33.454888Z",
     "iopub.status.idle": "2025-10-13T17:49:33.459631Z",
     "shell.execute_reply": "2025-10-13T17:49:33.459037Z"
    },
    "papermill": {
     "duration": 0.00911,
     "end_time": "2025-10-13T17:49:33.460206",
     "exception": false,
     "start_time": "2025-10-13T17:49:33.451096",
     "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": 14,
   "id": "56b4cd99-a27e-4f28-86c2-8013e7c7bc78",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-10-13T17:49:33.465854Z",
     "iopub.status.busy": "2025-10-13T17:49:33.465565Z",
     "iopub.status.idle": "2025-10-13T17:49:33.978056Z",
     "shell.execute_reply": "2025-10-13T17:49:33.977366Z"
    },
    "papermill": {
     "duration": 0.516497,
     "end_time": "2025-10-13T17:49:33.979272",
     "exception": false,
     "start_time": "2025-10-13T17:49:33.462775",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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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": 27.982836,
   "end_time": "2025-10-13T17:49:34.704504",
   "exception": null,
   "input_path": "/glade/derecho/scratch/richling/tmp/tmpljh8iqc0.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": "Obs",
    "base_end_date": "0101-01-01",
    "base_regridded_output": true,
    "base_start_date": "0001-01-01",
    "case_name": "b.e30_beta02.BLT1850.ne30_t232.104",
    "end_date": "0101-01-01",
    "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": "2025-10-13T17:49:06.721668"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}