diff --git a/.gitignore b/.gitignore
index c0fc44dc4f334af3ea7f4d6f6b00327be4938fa0..07852fd8c5cf68c5dc3d90c569ac7f7c6ead14b1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,2 @@
+.ipynb_checkpoints/
 */.ipynb_checkpoints/
diff --git a/README.md b/README.md
index 35363f691798ad9c4905312f4120dacbc0d28758..9451f2d1a617eef58335f6257869529a0a8f8bc8 100644
--- a/README.md
+++ b/README.md
@@ -2,5 +2,14 @@
 
 Git repo for the exercises.
 
-The directory `organisational` contains information on the content of the exercises and to get started with the data analysis.
+Directory structure:
+ 
+ * `organisational`: information on the content of the exercises and to get started with the data analysis
+ * `python-kernel`: create python kernel on IMG Teachinghub via the Masterhub access, using the account avoigt
+ * `era5-download`: scripts to retrieve ERA5 and ERA5-Land data from the Copernicus Data Store
+ * `analysis`: scripts to calculate PV potential etc.
 
+Notes for Aiko Voigt:
+
+ * The ERA5 and ERA5-Land download scripts need to be executed logged in as avoigt to the Masterhub via wolke.img.univie.ac.at. The data will be put in a directory only accessible to avoigt.
+ * Then, the data can be copied to `/stufs/lehre/msc-intro-comp-met-ex-w2024/data/`. There, it is accessible to everybody logged in via the Moodle Teachinghub under `LEHRE/msc-intro-comp-met-ex-w2024/data/`.
diff --git a/analysis/exercise_20241022.ipynb b/analysis/exercise_20241022.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..23791325257cf39879a201b382a7c78e89b51477
--- /dev/null
+++ b/analysis/exercise_20241022.ipynb
@@ -0,0 +1,185 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7d41920d-ccc6-4657-bacc-19006b87d7b8",
+   "metadata": {},
+   "source": [
+    "# Example calculation of PV potential for ERA5 data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "99875287-2b84-460f-9c90-2e2542ff4e9b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1f4067af-2ee7-43f5-b43d-94739e229f5c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# location of era5 data on teachinghub\n",
+    "path=\"/home/voigta80/LEHRE/msc-intro-comp-met-ex-w2024/data/era5/\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2c87dd8-340b-4487-a4a0-e3d4c6dbc9aa",
+   "metadata": {},
+   "source": [
+    "Load data for Jan and Feb 1979."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "daf3daff-d39d-4a49-8c13-3ffba150a8b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds=xr.open_mfdataset([path+\"era5-1979-01.nc\", path+\"era5-1979-02.nc\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8af1d101-3f38-42b9-9f9d-f47c37b660f3",
+   "metadata": {},
+   "source": [
+    "Calculate wind speed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "07374d0b-6a20-4027-9b78-65e59b26b959",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds[\"wspd\"] = np.sqrt(np.power(ds[\"u10\"],2)+np.power(ds[\"v10\"],2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c484d2f8-130c-45b8-a741-e118f58726c1",
+   "metadata": {},
+   "source": [
+    "Calculate PV potential following Jerez et al. 2015, https://www.nature.com/articles/ncomms10014.\n",
+    "\n",
+    "**Note:** radiative fluxes are accumulated over 1 hour, so we need to divided by seconds per hour to obtain fluxes in Wm-2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "447d7b3b-957c-44a2-abae-72e6ef3a1ec7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sechour=3600 # secondd per hour\n",
+    "\n",
+    "c1 = 4.3\n",
+    "c2 = 0.943\n",
+    "c3 = 0.028\n",
+    "c4 = -1.528\n",
+    "\n",
+    "# cell temperature\n",
+    "T_cell = c1 + c2 * (ds.t2m - 273.15) + c3 * ds.ssrd/sechour + c4 * ds.wspd\n",
+    "\n",
+    "# performance ratio\n",
+    "beta = -0.005\n",
+    "p_r = 1 + beta*(T_cell-25)\n",
+    "\n",
+    "# pv potential\n",
+    "pv_pot = p_r * ds.ssrd/(sechour) * 1/1000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "036de419-2716-4ead-bd94-1d551b03b29c",
+   "metadata": {},
+   "source": [
+    "Calculate time-mean PV potential and plot as a map."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3bc58d12-7d2d-45ae-996b-e18764af4889",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pv_pot_tmean = pv_pot.mean(\"valid_time\").compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "65dc280c-78e0-483f-833e-9b32df81c728",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x7f841d6a3130>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.contourf(ds.longitude, ds.latitude, pv_pot_tmean)\n",
+    "plt.colorbar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70cc5656-f511-437c-bddf-73ea620d37ca",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "MagicPy",
+   "language": "python",
+   "name": "magicpy"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}