diff --git a/examples/example-2d.ipynb b/examples/example-2d.ipynb
index 566d690e24922af4f83b4c7fd721e854bfe56aec..ce62194f97e51a3029f091e11c8fb50db034a876 100644
--- a/examples/example-2d.ipynb
+++ b/examples/example-2d.ipynb
@@ -66,9 +66,7 @@
     "\n",
     "Note that we also need to specify the starting triangle and the search radius. For limited-area grids, the starting triangle should usually be in the middle of the grid. The radius define the number of steps taken to enlarge the cubulated triangles \"outward\".\n",
     "\n",
-    "For the grid used here, start_triangle = 5738 is a good choice. As for the radius, set:\n",
-    "* radius = 102 to obtain the whole grid\n",
-    "* radius = 56 for maximal area not touching the boundaries"
+    "For the grid used here, start_triangle = 5570 is a good choice. As for the radius, use radius = 104 to cover the whole grid."
    ]
   },
   {
@@ -77,7 +75,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cubulation = tricco.compute_cubulation(start_triangle=5738, radius=102, print_progress=False)"
+    "cubulation = tricco.compute_cubulation(start_triangle=5570, radius=104, print_progress=False)"
    ]
   },
   {
@@ -95,16 +93,16 @@
     {
      "data": {
       "text/plain": [
-       "[array([5738, array([51, 51, 51])], dtype=object),\n",
-       " array([5734, array([52, 51, 51])], dtype=object),\n",
-       " array([5741, array([51, 52, 51])], dtype=object),\n",
-       " array([5735, array([51, 51, 52])], dtype=object),\n",
-       " array([5904, array([52, 51, 50])], dtype=object),\n",
-       " array([5731, array([52, 50, 51])], dtype=object),\n",
-       " array([5745, array([51, 52, 50])], dtype=object),\n",
-       " array([5739, array([50, 52, 51])], dtype=object),\n",
-       " array([5736, array([50, 51, 52])], dtype=object),\n",
-       " array([5737, array([51, 50, 52])], dtype=object)]"
+       "[array([5570, array([52, 52, 52])], dtype=object),\n",
+       " array([5566, array([53, 52, 52])], dtype=object),\n",
+       " array([5573, array([52, 53, 52])], dtype=object),\n",
+       " array([5567, array([52, 52, 53])], dtype=object),\n",
+       " array([5736, array([53, 52, 51])], dtype=object),\n",
+       " array([5563, array([53, 51, 52])], dtype=object),\n",
+       " array([5577, array([52, 53, 51])], dtype=object),\n",
+       " array([5571, array([51, 53, 52])], dtype=object),\n",
+       " array([5568, array([51, 52, 53])], dtype=object),\n",
+       " array([5569, array([52, 51, 53])], dtype=object)]"
       ]
      },
      "execution_count": 4,
@@ -130,7 +128,7 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "np.save('icon-grid_nawdex_78w40e23n80n_R80000m_cubulation_radius102_start5738', cubulation)"
+    "np.save('icon-grid_nawdex_78w40e23n80n_R80000m_cubulation_radius104_start5570', cubulation)"
    ]
   },
   {
@@ -330,22 +328,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x1008 with 8 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import matplotlib.tri as tri\n",
@@ -373,32 +358,36 @@
     "    plt.ylabel('latitude', fontsize=8)\n",
     "    \n",
     "# plotting\n",
-    "plt.figure(figsize=(6,14))\n",
+    "plt.figure(figsize=(12,7))\n",
     "\n",
-    "ax=plt.subplot(4,1,1); make_niceplot(ax)\n",
+    "ax=plt.subplot(2,2,1); make_niceplot(ax)\n",
     "plt.title('Total cloud cover (%)', fontsize=10)\n",
     "c=plt.tripcolor(vlon, vlat, vertex_of_cell.transpose(), facecolors=field_orig,\n",
     "                vmin=0, vmax=100, edgecolors='none', cmap=plt.get_cmap('Blues_r'))\n",
     "plt.colorbar(c, ticks=[0,100]);\n",
+    "plt.text(-90,88, 'a)', color='k', size=14, ha='left', va='top')\n",
     "\n",
-    "ax=plt.subplot(4,1,2); make_niceplot(ax)\n",
+    "ax=plt.subplot(2,2,3); make_niceplot(ax)\n",
     "plt.title('Total cloud cover (thresholded at 85%)', fontsize=10)\n",
     "c=plt.tripcolor(vlon, vlat, vertex_of_cell.transpose(), facecolors=field, \n",
     "                vmin=0, vmax=1, edgecolors='none', cmap=plt.get_cmap('Blues_r'))\n",
     "plt.colorbar(c, ticks=[0,1]);\n",
+    "plt.text(-90,88, 'b)', color='k', size=14, ha='left', va='top')\n",
     "\n",
-    "ax=plt.subplot(4,1,3); make_niceplot(ax)\n",
+    "ax=plt.subplot(2,2,2); make_niceplot(ax)\n",
     "plt.title('Vertex connectivity', fontsize=10); make_niceplot(ax)\n",
     "c=plt.tripcolor(vlon, vlat, vertex_of_cell.transpose(), facecolors=field_cc_vertex, \n",
     "                vmin=-0.5, vmax=len(components_vertex)+0.5, edgecolors='none', cmap=make_colormap(ncolors=len(components_vertex)+1))\n",
     "plt.colorbar(c, ticks=[i for i in range(0,len(components_vertex)+1,10)]);\n",
+    "plt.text(-90,88, 'c)', color='k', size=14, ha='left', va='top')\n",
     "\n",
-    "ax=plt.subplot(4,1,4); make_niceplot(ax)\n",
+    "ax=plt.subplot(2,2,4); make_niceplot(ax)\n",
     "plt.title('Edge connectivity', fontsize=10); make_niceplot(ax)\n",
     "c=plt.tripcolor(vlon, vlat, vertex_of_cell.transpose(), facecolors=field_cc_edge, \n",
     "                vmin=-0.5, vmax=len(components_edge)+0.5, edgecolors='none', cmap=make_colormap(ncolors=len(components_edge)+1))\n",
     "plt.colorbar(c, ticks=[i for i in range(0,len(components_edge)+1,10)]);\n",
     "plt.xlabel('longitude', fontsize=8);\n",
+    "plt.text(-90,88, 'd)', color='k', size=14, ha='left', va='top')\n",
     "\n",
     "plt.savefig('example-2d_plot.pdf')"
    ]
diff --git a/examples/example-2d_plot.pdf b/examples/example-2d_plot.pdf
index aef6eab0abd34c455de840ce7c43cdbf7489ff5c..44021b23fea0157907d73975c19f4e8b6ab1f859 100644
Binary files a/examples/example-2d_plot.pdf and b/examples/example-2d_plot.pdf differ
diff --git a/examples/example-3d-edge_plot.pdf b/examples/example-3d-edge_plot.pdf
index 0ce608d2c996dab9dd10ef8113cad172ad8167d9..2674e63bf671ef41a68972acedcea44a30b0f7e5 100644
Binary files a/examples/example-3d-edge_plot.pdf and b/examples/example-3d-edge_plot.pdf differ
diff --git a/examples/example-3d-vertex_plot.pdf b/examples/example-3d-vertex_plot.pdf
index c4d7436bf872c165ae62e2ee50eda5043dc51fb3..bfd87e8d7bf9262a419616c2dbfca1a5601a9df2 100644
Binary files a/examples/example-3d-vertex_plot.pdf and b/examples/example-3d-vertex_plot.pdf differ
diff --git a/examples/example-3d.ipynb b/examples/example-3d.ipynb
index e4966fbdeaba6e20e67bee347aa1da4e9bf0e332..55f755fb7d780320a1dbac573f6a4eadc0df417c 100644
--- a/examples/example-3d.ipynb
+++ b/examples/example-3d.ipynb
@@ -59,7 +59,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Perform cubulation of the horizontal grid. The choice of start_triangle=5738 and radius=102 makes sure that we cover the entire triangular grid."
+    "Perform cubulation of the horizontal grid. The choice of start_triangle=5570 and radius=104 makes sure that we cover the entire triangular grid."
    ]
   },
   {
@@ -68,7 +68,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cubulation = tricco.compute_cubulation(start_triangle=5738, radius=102, print_progress=False)"
+    "cubulation = tricco.compute_cubulation(start_triangle=5570, radius=104, print_progress=False)"
    ]
   },
   {
@@ -350,7 +350,7 @@
     "index=5\n",
     "ax = plot_single_component(components_vertex[index], viewangle=[30,150])\n",
     "ax.set_xlim3d(-45,0); ax.set_ylim3d(35,60); ax.set_zlim3d(5,55)\n",
-    "plt.title('Connected component using vertex connectivity', fontsize=18);\n",
+    "plt.title('Vertex connectivity', fontsize=18);\n",
     "plt.savefig('example-3d-vertex_plot.pdf')"
    ]
   },
@@ -376,7 +376,7 @@
     "index=6\n",
     "plot_single_component(components_edge[index], viewangle=[30,150])\n",
     "ax.set_xlim3d(-45,0); ax.set_ylim3d(35,60); ax.set_zlim3d(5,55)\n",
-    "plt.title('Connected component using edge connectivity', fontsize=18);\n",
+    "plt.title('Edge connectivity', fontsize=18);\n",
     "plt.savefig('example-3d-edge_plot.pdf')"
    ]
   },
diff --git a/examples/find_radius.py b/examples/find_radius.py
index 08bd7c6c3524cd582ff4e0cb1f5b3282f2d187ba..f0ce6368f724276d6bad16a12bc56422af4b820f 100644
--- a/examples/find_radius.py
+++ b/examples/find_radius.py
@@ -3,11 +3,13 @@
 
 # Written for the limited-area grids of ICON used in the Tricco introduction paper.
 
+# Use example on mistral: /pf/b/b380459/conda-envs/Nawdex-Hackathon/bin/python3 find_radius.py R80000m 5570 200
+
 # parse command line parameters
 import sys
-resol  = sys.argv[1]
-start  = int(sys.argv[2])
-radius = int(sys.argv[3])
+resol  = sys.argv[1]        # grid resolution, e.g., R80000m
+start  = int(sys.argv[2])   # index of start cell
+radius = int(sys.argv[3])   # maximum search radius
 
 print('-----------------------------------------')
 print('Working on ICON grid with resolution', resol)
@@ -19,7 +21,9 @@ sys.path.append('/pf/b/b380459/connected-components-3d/')
 sys.path.append('/pf/b/b380459/BigDataClouds/tricco/')
 import tricco
 
-tricco.grid_functions.grid = tricco.prepare_grid(model='ICON', path='./data/', 
-                                                 file='icon-grid_nawdex_78w40e23n80n_'+resol+'.nc')
+# gridfile including path
+gridfile = '/work/bb1018/b380459/NAWDEX/grids/icon-grid_nawdex_78w40e23n80n_'+resol+'.nc'
+
+tricco.grid_functions.grid = tricco.prepare_grid(model='ICON', path='/', file=gridfile)
 
 cubulation = tricco.compute_cubulation(start_triangle=start, radius=radius, print_progress=True)
diff --git a/examples/find_startcell.py b/examples/find_startcell.py
index f27787d3971b7402e97541c6e8b5bc01ce5198b7..872c310acbcb8136392d5ca9ce9d67e67ca0cbd7 100644
--- a/examples/find_startcell.py
+++ b/examples/find_startcell.py
@@ -3,33 +3,42 @@
 
 # Written for the limited-area grids of ICON used in the Tricco introduction paper.
 
+# Use example on mistral: /pf/b/b380459/conda-envs/Nawdex-Hackathon/bin/python3 find_startcell.py R80000m 51.5 19
+
 # convert rad to deg
 import numpy as np
 rad2deg=180.0/np.pi
+deg2rad=np.pi/180.0
 
-# parse command line parameters
+# parse command line parameters, convert to radians
 import sys
-resol      = sys.argv[1]
-lat        = float(sys.argv[2])
-lon        = float(sys.argv[3])
+resol      = sys.argv[1]                # resolution, e.g., R80000m
+lat        = deg2rad*float(sys.argv[2]) # latitude position
+lon        = deg2rad*float(sys.argv[3]) # longitude position
 
 print('-----------------------------------------')
 print('Working on ICON grid with resolution', resol)
-print('Searching for cell closest to lat', lat, 'and lon', lon)
+print('Searching for cell closest to lat', lat*rad2deg, 'and lon', lon*rad2deg, 'using the Haversine formulae')
 
 # gridfile including path
 gridfile = '/work/bb1018/b380459/NAWDEX/grids/icon-grid_nawdex_78w40e23n80n_'+resol+'.nc'
 
-# load lat-lon info of grid and convert to deg
+# load lat-lon info of grid in radians
 import xarray as xr
-ds_grid  = xr.load_dataset(gridfile)
-clat = rad2deg*ds_grid['clat'].values
-clon = rad2deg*ds_grid['clon'].values
+ds_grid = xr.load_dataset(gridfile)
+clat = ds_grid['clat'].values
+clon = ds_grid['clon'].values
+
+dlat = np.abs(clat-lat)
+dlon = np.abs(clon-lon)
 
-dist = np.power(clat-lat,2) + np.power(clon-lon,2)
+# Use Haversine formulae for the distance on a sphere
+# https://en.wikipedia.org/wiki/Haversine_formula
+havs = np.power(np.sin(dlat/2.0),2) + np.cos(clat)*np.cos(lat)*np.power(np.sin(dlon/2.0),2)
+dist = 2*np.arcsin(np.sqrt(havs)) 
 
 print('Closest cell has index', np.argmin(dist))
-print('Note: The startcell for tricco is the cell index - 1.')
-print('      This is because the found cell index is on the ICON grid')
-print('      and the ICON indexing starts with 1.')
+print('Note: The index found here is shited by -1 relative to the ICON grid')
+print('      indexing, as the latter starts with 1. Therefore, the')
+print('      index found here is the value that should be used in Tricco.')
 print('-----------------------------------------')