diff --git a/Jupyter_Notebooks/conditional_quantile_plot.ipynb b/Jupyter_Notebooks/conditional_quantile_plot.ipynb
index 01922a48122017c03e1121f4a6bcc4d72884cbdc..11cb04be0ce3fee8d21c5226405cb91a9a0c8cf9 100644
--- a/Jupyter_Notebooks/conditional_quantile_plot.ipynb
+++ b/Jupyter_Notebooks/conditional_quantile_plot.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "under-cooler",
+   "id": "downtown-archive",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,7 +19,7 @@
   {
    "cell_type": "code",
    "execution_count": 79,
-   "id": "becoming-dover",
+   "id": "perfect-julian",
    "metadata": {},
    "outputs": [
     {
@@ -44,12 +44,13 @@
     }
    ],
    "source": [
+    "# exemplary model to evaluate\n",
     "forecast_path = \"/p/home/jusers/langguth1/juwels/video_prediction_shared_folder/results/era5-Y2007-2019M01to12-80x48-3960N0180E-2t_tcc_t_850_langguth1/savp/20210505T131220_mache1_karim_savp_smreg_cv3_3\"\n",
     "fnames= os.path.join(forecast_path, \"vfp_date_*sample_ind_*.nc\" )\n",
-    "\n",
+    "# get a list of all forecast files\n",
     "fnames = glob.glob(fnames)\n",
-    "print(len(fnames))\n",
     "\n",
+    "# randomly open one file to take a look at its content\n",
     "dfile = xr.open_dataset(fnames[99])\n",
     "\n",
     "print(dfile.data_vars)\n",
@@ -60,10 +61,11 @@
   {
    "cell_type": "code",
    "execution_count": 80,
-   "id": "editorial-bunny",
+   "id": "strong-ghana",
    "metadata": {},
    "outputs": [],
    "source": [
+    "# some auxiliary functions to enhance data query with open_mfdataset\n",
     "def non_interst_vars(ds):\n",
     "    \"\"\"\n",
     "    Creates list of variables that are not of interest. For this, vars2proc must be defined at global scope\n",
@@ -89,7 +91,7 @@
   {
    "cell_type": "code",
    "execution_count": 81,
-   "id": "great-metro",
+   "id": "simplified-cinema",
    "metadata": {},
    "outputs": [
     {
@@ -101,19 +103,19 @@
     }
    ],
    "source": [
+    "# choose variable of interest and load data into memory (i.e. the dataset is not a dask-array anymore!!!)\n",
     "vars2proc = [\"2t_savp_fcst\", \"2t_ref\"]\n",
     "\n",
     "time0 = time.time()\n",
     "with xr.open_mfdataset(fnames, decode_cf=True, combine=\"nested\", concat_dim=[\"init_time\"], compat=\"broadcast_equals\", preprocess=get_relevant_vars) as dfiles:\n",
     "    data = dfiles.load()\n",
-    "    #times0 = dfiles[\"time_forecast\"]\n",
     "    print(\"Registering and loading data took {0:.2f} seconds\".format(time.time()- time0))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 82,
-   "id": "hindu-wesley",
+   "id": "appropriate-springer",
    "metadata": {},
    "outputs": [
     {
@@ -134,14 +136,14 @@
     }
    ],
    "source": [
-    "data_correct = xr.Dataset({\"2t_savp_fcst\": data[\"2t_savp_fcst\"], \"2t_ref\": data[\"2t_ref\"]})\n",
-    "print(data_correct)"
+    "# Take a look at the data\n",
+    "print(data)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 83,
-   "id": "sweet-happening",
+   "id": "governmental-astrology",
    "metadata": {},
    "outputs": [
     {
@@ -153,59 +155,65 @@
     }
    ],
    "source": [
-    "data_fcst, data_ref = data_correct[\"2t_savp_fcst\"], data_correct[\"2t_ref\"]\n",
+    "# get the vaiables of interest as data arrays\n",
+    "data_fcst, data_ref = data[\"2t_savp_fcst\"], data[\"2t_ref\"]\n",
     "\n",
+    "# create the bins for which quantiles are plotted based on forecasts (=conditioning variable)\n",
     "fcst_min, fcst_max = np.floor(np.min(data_fcst)), np.ceil(np.max(data_fcst))\n",
     "x_bins = list(np.arange(int(fcst_min), int(fcst_max) + 1))\n",
+    "# center point of bins\n",
     "x_bins_c = 0.5*(np.asarray(x_bins[0:-1]) + np.asarray(x_bins[1:]))\n",
-    "nbins = len(x_bins) - 1\n",
-    "\n",
-    "print(x_bins)"
+    "nbins = len(x_bins) - 1"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 84,
-   "id": "incorporate-flooring",
+   "id": "wound-maria",
    "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
+    "# set the quantiles and initialize data array\n",
     "quantiles = [0.05, 0.5, 0.95]\n",
     "nquantiles = len(quantiles)\n",
     "quantile_panel = xr.DataArray(np.full((nbins, nquantiles), np.nan), coords={\"bin_center\": x_bins_c, \"quantile\": quantiles},\n",
     "                              dims=[\"bin_center\", \"quantile\"])\n",
+    "# populate the quantile data array\n",
     "for i in np.arange(nbins):\n",
+    "    # conditioning of ground truth based on forecast\n",
     "    data_cropped = data_correct[\"2t_ref\"].where(np.logical_and(data_correct[\"2t_savp_fcst\"] >= x_bins[i],\n",
     "                                                               data_correct[\"2t_savp_fcst\"] < x_bins[i+1]))\n",
+    "    # quantile-calculation\n",
     "    quantile_panel.loc[dict(bin_center=x_bins_c[i])] = data_cropped.quantile([0.05, 0.5, 0.95])\n",
-    "    \n",
+    "  \n",
+    "# transform \n",
     "x_bins_c = x_bins_c - 273.15\n",
     "quantile_panel = quantile_panel - 273.15"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
-   "id": "brilliant-aberdeen",
+   "execution_count": 1,
+   "id": "lightweight-settlement",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 648x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "ename": "NameError",
+     "evalue": "name 'plt' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-94561a8add4c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# create plot of conditional forecast\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mls_all\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"--\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"-\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"--\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mlw_all\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
+     ]
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(figsize=(9,6))\n",
+    "# create plot of conditional forecast\n",
+    "fig, ax = plt.subplots(figsize=(12,6))\n",
     "\n",
     "ls_all = [\"--\", \"-\", \"--\"]\n",
     "lw_all = [2., 1.5, 2.]\n",
@@ -225,7 +233,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "relevant-freight",
+   "id": "selective-dubai",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -235,7 +243,7 @@
   {
    "cell_type": "code",
    "execution_count": 148,
-   "id": "ordered-cambridge",
+   "id": "pending-formula",
    "metadata": {},
    "outputs": [
     {
@@ -303,7 +311,7 @@
   {
    "cell_type": "code",
    "execution_count": 149,
-   "id": "furnished-customer",
+   "id": "congressional-closure",
    "metadata": {},
    "outputs": [
     {
@@ -359,7 +367,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "electrical-evening",
+   "id": "mathematical-alfred",
    "metadata": {},
    "outputs": [],
    "source": []