diff --git a/doc/source/notebooks/retrieve_meteoswiss_data_from_cscs.ipynb b/doc/source/notebooks/retrieve_meteoswiss_data_from_cscs.ipynb
index e69de29b..4c9406a3 100644
--- a/doc/source/notebooks/retrieve_meteoswiss_data_from_cscs.ipynb
+++ b/doc/source/notebooks/retrieve_meteoswiss_data_from_cscs.ipynb
@@ -0,0 +1,266 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Retrieving MeteoSwiss products from CSCS with pyrad"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Most MeteoSwiss products can be retrieved from the CSCS or MeteoSwiss servers using the functions documented [here](https://meteoswiss.github.io/pyrad/API/generated/pyrad.util.html#data-retrieval-utilities).\n",
+    "\n",
+    "The reading/writing functions for these files are available in the [MeteoSwiss Py-ART fork](https://github.com/MeteoSwiss/pyart). Please also check the following [notebook](https://meteoswiss.github.io/pyart/notebooks/read_mch_metranet_data.html) for an example of reading/writing such files.\n",
+    "\n",
+    "Please also check the [internal MeteoSwiss confluence page]()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Most products can be fetched with the *retrieve_mch_prod* function. You need to specify an output directory, make sure there is sufficient space in the output directory before you run the command!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example 1: reading polar data from Albis at low-resolution (500m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<frozen importlib._bootstrap>:530: DeprecationWarning: the load_module() method is deprecated and slated for removal in Python 3.12; use exec_module() instead\n",
+      "/users/wolfensb/pyrad/src/pyart/pyart/io/nexrad_level3.py:11: DeprecationWarning: 'xdrlib' is deprecated and slated for removal in Python 3.13\n",
+      "  from xdrlib import Unpacker\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n",
+      "## library for working with weather radar data. Py-ART is partly\n",
+      "## supported by the U.S. Department of Energy as part of the Atmospheric\n",
+      "## Radiation Measurement (ARM) Climate Research Facility, an Office of\n",
+      "## Science user facility.\n",
+      "##\n",
+      "## If you use this software to prepare a publication, please cite:\n",
+      "##\n",
+      "##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n",
+      "\n",
+      "Welcome to PyDDA 2.0.0\n",
+      "If you are using PyDDA in your publications, please cite:\n",
+      "Jackson et al. (2020) Journal of Open Research Science\n",
+      "Detecting Jax...\n",
+      "Jax/JaxOpt are not installed on your system, unable to use Jax engine.\n",
+      "Detecting TensorFlow...\n",
+      "Unable to load both TensorFlow and tensorflow-probability. TensorFlow engine disabled.\n",
+      "No module named 'tensorflow'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/wolfensb/pyrad/src/pyrad_proc/pyrad/flow/flow_aux.py:54: UserWarning: Memory profiler not available\n",
+      "  warn(\"Memory profiler not available\")\n",
+      "/users/wolfensb/pyrad/src/pyrad_proc/pyrad/flow/flow_control.py:56: UserWarning: dask not available: The processing will not be parallelized\n",
+      "  warn(\"dask not available: The processing will not be parallelized\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "60\n",
+      "/scratch/wolfensb//MLA2413110100U.001\n",
+      "dict_keys(['reflectivity', 'signal_to_noise_ratio', 'reflectivity_vv', 'differential_reflectivity', 'uncorrected_cross_correlation_ratio', 'uncorrected_differential_phase', 'velocity', 'spectrum_width', 'reflectivity_hh_clut'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pyrad.util import retrieve_mch_prod\n",
+    "import pyart\n",
+    "import datetime\n",
+    "\n",
+    "OUTPUT_DIRECTORY = '/scratch/wolfensb/temp/' # Adjust to your needs\n",
+    "T0 = datetime.datetime(2024,5,10,10,10)\n",
+    "T1 = datetime.datetime(2024,5,10,10,20)\n",
+    "\n",
+    "files = retrieve_mch_prod('/scratch/wolfensb/',T0, T1,  product_name='MLA')\n",
+    "\n",
+    "print(len(files))\n",
+    "print(files[0])\n",
+    "\n",
+    "radar = pyart.aux_io.read_metranet(files[0])\n",
+    "print(radar.fields.keys())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example 2: same as example 1 but getting only certain sweeps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12\n",
+      "[np.str_('/scratch/wolfensb//MLA2413110100U.001'), np.str_('/scratch/wolfensb//MLA2413110100U.002'), np.str_('/scratch/wolfensb//MLA2413110100U.003'), np.str_('/scratch/wolfensb//MLA2413110100U.004')]\n"
+     ]
+    }
+   ],
+   "source": [
+    "files = retrieve_mch_prod('/scratch/wolfensb/',T0, T1, product_name='MLA', sweeps = [1,2,3,4])\n",
+    "print(len(files))\n",
+    "print(files[0:4])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example 3: getting a Cartesian product, RZC = radar QPE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n",
+      "[np.str_('/scratch/wolfensb//RZC241311010VL.001'), np.str_('/scratch/wolfensb//RZC241311012VL.001'), np.str_('/scratch/wolfensb//RZC241311015VL.001'), np.str_('/scratch/wolfensb//RZC241311017VL.001')]\n"
+     ]
+    }
+   ],
+   "source": [
+    "files = retrieve_mch_prod('/scratch/wolfensb/',T0, T1,  product_name='RZC')\n",
+    "print(len(files))\n",
+    "print(files)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example 3: getting a Cartesian product, CPC = radar-gauge QPE, but only at hourly accumulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n",
+      "[np.str_('/scratch/wolfensb//CPC/CPC2413110100_00060.801.gif'), np.str_('/scratch/wolfensb//CPC/CPC2413110200_00060.801.gif')]\n"
+     ]
+    }
+   ],
+   "source": [
+    "files = retrieve_mch_prod('/scratch/wolfensb/',T0, T1,  product_name='CPC', pattern='*00060*')\n",
+    "print(len(files))\n",
+    "print(files)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example 4: more complex filtering of retrieved files with regex"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Extracting all CPC files except those at 5 minute resolution (CPC*_00005.801.gif)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<>:4: SyntaxWarning: invalid escape sequence '\\.'\n",
+      "<>:4: SyntaxWarning: invalid escape sequence '\\.'\n",
+      "/tmp/ipykernel_136299/2246465886.py:4: SyntaxWarning: invalid escape sequence '\\.'\n",
+      "  files = retrieve_mch_prod('/scratch/wolfensb/',T0, T1,  product_name='CPC', pattern='^(?!.*00005\\.801\\.gif$).*\\.gif$', pattern_type='regex')\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "74\n",
+      "[np.str_('/scratch/wolfensb//CPC/CPC2413108100_00060.801.gif'), np.str_('/scratch/wolfensb//CPC/CPC2413108100_00180.801.gif'), np.str_('/scratch/wolfensb//CPC/CPC2413108100_00360.801.gif'), np.str_('/scratch/wolfensb//CPC/CPC2413108100_00720.801.gif')]\n"
+     ]
+    }
+   ],
+   "source": [
+    "T0 = datetime.datetime(2024,5,10,8,10)\n",
+    "T1 = datetime.datetime(2024,5,10,10,20)\n",
+    "files = retrieve_mch_prod('/scratch/wolfensb/',T0, T1,  product_name='CPC', pattern='^(?!.*00005\\.801\\.gif$).*\\.gif$', pattern_type='regex')\n",
+    "\n",
+    "print(files)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pyart_new",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/pyrad_proc/pyrad/util/data_retrieval_utils.py b/src/pyrad_proc/pyrad/util/data_retrieval_utils.py
index 524ec36a..5afaf1db 100644
--- a/src/pyrad_proc/pyrad/util/data_retrieval_utils.py
+++ b/src/pyrad_proc/pyrad/util/data_retrieval_utils.py
@@ -465,7 +465,7 @@ def retrieve_mch_prod_RT(
             content_zip = [
                 c
                 for c in content_zip
-                if re.match(os.path.basename(c), pattern) is not None
+                if re.match(pattern, os.path.basename(c)) is not None
             ]
         else:
             raise ValueError('Unknown pattern_type, must be either "shell" or "regex".')
@@ -540,7 +540,7 @@ def _retrieve_prod_daily(
             content_zip = [
                 c
                 for c in content_zip
-                if re.match(os.path.basename(c), pattern) is not None
+                if re.match(pattern, os.path.basename(c)) is not None
             ]
         else:
             raise ValueError('Unknown pattern_type, must be either "shell" or "regex".')
@@ -590,7 +590,7 @@ def _retrieve_prod_daily(
         subprocess.call(cmd, shell=True)
 
     files = sorted(np.array([folder_out + c for c in content_zip[conditions]]))
-
+    
     return files