diff --git a/docs/intro.ipynb b/docs/intro.ipynb
index 98303a8..53b4dc7 100644
--- a/docs/intro.ipynb
+++ b/docs/intro.ipynb
@@ -17,7 +17,7 @@
     "\n",
     "A complete list of stations and metadata can be found in the [station catalog](../station_catalog).\n",
     "\n",
-    "For more details on the SolarStations.org catalog, the reader is referred to the data article published in Solar Energy, DOI: [10.1016/j.solener.2025.113457](https://doi.org/10.1016/j.solener.2025.113457). We hope that you will cite the article if you use the catalog in published works.\n",
+    "For more details on the SolarStations.org catalog, the reader is referred to the data article published in Solar Energy [10.1016/j.solener.2025.113457](https://doi.org/10.1016/j.solener.2025.113457). We hope that you will cite the article if you use the catalog in published works.\n",
     "\n",
     "To find the nearest station to a point of interest, check out the interactive map below. Note that it is possible to click on a station icon to get the station name and country."
    ]
diff --git a/docs/myst.yml b/docs/myst.yml
index 670c15a..f0eb797 100644
--- a/docs/myst.yml
+++ b/docs/myst.yml
@@ -49,7 +49,7 @@ project:
         - file: station_network_pvlive.ipynb
         - file: station_network_eye2sky.ipynb
           title: Eye2sky
-        - file: geba_network.md
+        - file: station_network_geba.md
         - file: other_data_sources.md
           title: Miscellaneous
 
diff --git a/docs/station_catalog.ipynb b/docs/station_catalog.ipynb
index 7153ffb..e0f8e91 100644
--- a/docs/station_catalog.ipynb
+++ b/docs/station_catalog.ipynb
@@ -5,7 +5,14 @@
    "metadata": {
     "tags": []
    },
-   "source": "# Station catalog\n\nThis site provides an overview of available solar irradiance monitoring stations worldwide and metadata of the stations.\nThe metadata fields are further described in the [metadata](station_metadata) section.\n\nThe table is also available to download in CSV form: {download}`SolarStationsOrg-station-catalog.csv <SolarStationsOrg-station-catalog.csv>`"
+   "source": [
+    "# Station catalog\n",
+    "\n",
+    "This site provides an overview of available solar irradiance monitoring stations worldwide and metadata of the stations.\n",
+    "The metadata fields are further described in the section on [metadata](station_metadata).\n",
+    "\n",
+    "The table is also available to download as a CSV file: {download}`SolarStationsOrg-station-catalog.csv <SolarStationsOrg-station-catalog.csv>`"
+   ]
   },
   {
    "cell_type": "code",
@@ -429,4 +436,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/docs/station_metadata.md b/docs/station_metadata.md
index 3d525aa..efb03b3 100644
--- a/docs/station_metadata.md
+++ b/docs/station_metadata.md
@@ -12,7 +12,7 @@ The central part of the catalog is the list of stations and their metadata which
 * Data availability ("Freely", "Upon request", "Not available", or blank if unknown)
 * Station tier (see [station requirements](station_requirements)).
 * Instruments and components (see below)
-* Long-term annual irradiance (climatology) from NASA POWER (GHI, DNI, and DHI)
+* Long-term annual irradiance (climatology) of GHI, DNI, and DHI obtained from NASA POWER
 
 Additional information of some of the metadata fields is provided below.
 
diff --git a/docs/station_network_bsrn.ipynb b/docs/station_network_bsrn.ipynb
index bcd3ae7..ae599a8 100644
--- a/docs/station_network_bsrn.ipynb
+++ b/docs/station_network_bsrn.ipynb
@@ -7,7 +7,7 @@
     "# Baseline Surface Radiation Network (BSRN)\n",
     "\n",
     "The [Baseline Surface Radiation Network (BSRN)](https://bsrn.awi.de/) is a global network of high-quality solar irradiance monitoring stations under the [World Climate Research Programme (WCRP)](https://www.wcrp-climate.org/) (Driemel et al., 2018). The procedures of the BSRN are described in great detail in the [BSRN Operations Manual\n",
-    "Version 2.1](https://bsrn.awi.de/fileadmin/user_upload/bsrn.awi.de/Publications/McArthur.pdf).\n",
+    "Version 2.1](https://bsrn.awi.de/fileadmin/user_upload/bsrn.awi.de/Publications/McArthur.pdf). The BSRN is also described in https://doi.org/10.5194/essd-10-1491-2018.\n",
     "\n",
     "According to the [World Radiation Monitoring Center (WRMC)](https://bsrn.awi.de/project/objectives/):\n",
     "> The data [from the BSRN stations] are of primary importance in supporting the validation and confirmation of satellite and computer model estimates of these quantities. At a small number of stations (currently 74 in total, 58 active) in contrasting climatic zones, covering a latitude range from 80°N to 90°S, solar and atmospheric radiation is measured with instruments of the highest available accuracy and with high time resolution.\n",
@@ -2867,7 +2867,7 @@
     "Please read the [BSRN data release guidelines](https://bsrn.awi.de/data/conditions-of-data-release/) before using any data and make sure to properly cite the BSRN.\n",
     "```\n",
     "\n",
-    "The station-to-archive files can be parsed and downloaded using the [pvlib-python](https://pvlib-python.readthedocs.io) library, specifically the [`pvlib.iotools.get_bsrn`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_bsrn.html) function. If you use the pvlib iotools for published work, please cite Jensen et al. (2023) which provides additional background information. An example of how to use pvlib to download two months of data from the Cabauw (CAB) station is shown below:"
+    "The station-to-archive files can be parsed and downloaded using the [pvlib-python](https://pvlib-python.readthedocs.io) library, specifically the [`pvlib.iotools.get_bsrn`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_bsrn.html) function (described in https://doi.org/10.1016/j.solener.2023.112092). If you use the pvlib iotools for published work, please cite Jensen et al. (2023) which provides additional background information. An example of how to use pvlib to download two months of data from the Cabauw (CAB) station is shown below:"
    ]
   },
   {
@@ -2991,17 +2991,6 @@
     "Notice how there are multiple periods where there is gaps in the irradiance data."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## References\n",
-    "\n",
-    "* A. Driemel, J. Augustine, K. Behrens, S. Colle, C. Cox, E. Cuevas-Agulló, F. M. Denn, T. Duprat, M. Fukuda, H. Grobe, M. Haeffelin, G. Hodges, N. Hyett, O. Ijima, A. Kallis, W. Knap, V. Kustov, C. N. Long, D. Longenecker, A. Lupi, M. Maturilli, M. Mimouni, L. Ntsangwane, H. Ogihara, X. Olano, M. Olefs, M. Omori, L. Passamani, E. B. Pereira, H. Schmithüsen, S. Schumacher, R. Sieger, J. Tamlyn, R. Vogt, L. Vuilleumier, X. Xia, A. Ohmura, and G. König-Langlo. Baseline surface radiation network (BSRN): structure and data description (1992–2017). Earth System Science Data, 10(3):1491–1501, 2018. doi: [10.5194/essd-10-1491-2018](https://doi.org/10.5194/essd-10-1491-2018).\n",
-    "\n",
-    "* Adam R. Jensen, Kevin S. Anderson, William F. Holmgren, Mark A. Mikofski, Clifford W. Hansen, Leland J. Boeman, and Roel Loonen. Pvlib iotools—open-source python functions for seamless access to solar irradiance data. Solar Energy, 266:112092, 2023. doi: [10.1016/j.solener.2023.112092](https://doi.org/10.1016/j.solener.2023.112092).."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/docs/station_network_eye2sky.ipynb b/docs/station_network_eye2sky.ipynb
index 4c1fd57..829470e 100644
--- a/docs/station_network_eye2sky.ipynb
+++ b/docs/station_network_eye2sky.ipynb
@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "# Eye2sky network\n",
-    "This page presents an overview of the [Eye2Sky cloud camera network](https://www.dlr.de/en/ve/research-and-transfer/research-infrastructure/laboratories-infrastructures/eye2sky) in north-west Germany. Part of the dataset has been made [public on Zenodo](https://zenodo.org/records/12804613), and an example of how to retrieve data using Python is provided. See [Schmidt et al. (2022)](https://doi.org/10.1049/icp.2022.2778) for more information.\n",
+    "This page presents an overview of the [Eye2Sky cloud camera network](https://www.dlr.de/en/ve/research-and-transfer/research-infrastructure/laboratories-infrastructures/eye2sky) in north-west Germany. Part of the dataset has been made [public on Zenodo](https://zenodo.org/records/12804613), and an example of how to retrieve data using Python is provided. See https://doi.org/10.1049/icp.2022.2778 for more information.\n",
     "\n",
     "> \"In north-west Germany between Oldenburg, the North Sea coast and the Dutch border, the Institute of Networked Energy Systems operates the unique Eye2Sky cloud camera network as a research infrastructure. The network is growing dynamically and currently consists of around 30 stations. The heart of each station is a cloud camera, also known as an all-sky imager (ASI). This is a commercially available webcam with a fisheye lens supplemented by a ventilation and heating system that ensures optimum image quality even in bad weather. A third of the stations are equipped with a rotating shadowband irradiometer (RSI), other radiation sensors and meteorological sensors for temperature and humidity, for example, to validate and calibrate the algorithms.\"\n"
    ]
@@ -2522,15 +2522,6 @@
     "            plt.show()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## References\n",
-    "\n",
-    "* T. Schmidt, J. Stührenberg, N. Blum, J. Lezaca, A. Hammer and T. Vogt, \"A network of all sky imagers (ASI) enabling accurate and high-resolution very short-term forecasts of solar irradiance,\" 21st Wind & Solar Integration Workshop (WIW 2022), Hybrid Conference, The Hague, Netherlands, 2022, pp. 372-378, doi: [10.1049/icp.2022.2778](https://doi.org/10.1049/icp.2022.2778)."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/docs/geba_network.md b/docs/station_network_geba.md
similarity index 100%
rename from docs/geba_network.md
rename to docs/station_network_geba.md
diff --git a/docs/station_network_midc.ipynb b/docs/station_network_midc.ipynb
index 39944b8..53dbf30 100644
--- a/docs/station_network_midc.ipynb
+++ b/docs/station_network_midc.ipynb
@@ -13,7 +13,7 @@
    "source": [
     "# NREL MIDC station network\n",
     "\n",
-    "The [Measurement and Instrumentation Data Center (MIDC)](https://midcdmz.nlr.gov/) is operated by NREL and provides irradiance and meteorological data from a number of ground stations in the U.S. The stations vary in quality, with some stations measuring all three components with high-quality instruments and other stations featuring a rotating shadow band pyranometer.\n",
+    "The [Measurement and Instrumentation Data Center (MIDC)](https://midcdmz.nlr.gov/) is operated by National Laboratory of the Rockies (NLR formerly NREL) and provides irradiance and meteorological data from a number of ground stations in the U.S. The stations vary in quality, with some stations measuring all three components with high-quality instruments and other stations featuring a rotating shadow band pyranometer.\n",
     "\n",
     "Note, the MIDC includes several inactive stations. Also, several of the active stations are no longer cleaned or calibrated frequently. For these reasons, the SolarStations.org catalog only includes the SRRL BMS, SOLARTAC, and Flatirons M2 sites, as these measures all three irradiance components and are active. See the map below for the locations of the stations."
    ]
@@ -2620,14 +2620,14 @@
    "source": [
     "## Baseline Measurement System (BMS)\n",
     "\n",
-    "The most notable station is the [Baseline Measurement System (BMS)](https://midcdmz.nlr.gov/apps/sitehome.pl?site=BMS) at NREL's [Solar Radiation Research Laboratory (SRRL)](https://www.nlr.gov/grid/solar-radiation-research-laboratory) outside of Denver, Colorado. The BMS features the world's largest collection of operating pyranometers and pyrheliometers. A number of sky imagers, PV reference cells, and spectral radiometers are also located at the site. Instruments at the BMS are cleaned each weekday and frequently calibrated. Due to the large collection of co-located and well maintained instruments, the BMS data is ideal for comparing different types of instruments.\n",
+    "The most notable station is the [Baseline Measurement System (BMS)](https://midcdmz.nlr.gov/apps/sitehome.pl?site=BMS) at NLR's [Solar Radiation Research Laboratory (SRRL)](https://www.nlr.gov/grid/solar-radiation-research-laboratory) outside of Denver, Colorado. The BMS features the world's largest collection of operating pyranometers and pyrheliometers. A number of sky imagers, PV reference cells, and spectral radiometers are also located at the site. Instruments at the BMS are cleaned each weekday and frequently calibrated. Due to the large collection of co-located and well maintained instruments, the BMS data is ideal for comparing different types of instruments.\n",
     "\n",
     "For the three standard components, the main instruments are:\n",
     "- Global CMP22 (vent/cor) [W/m^2]\n",
     "- Diffuse CM22-1 (vent/cor) [W/m^2]\n",
     "- Diffuse CM22\\-2 (vent/cor) [W/m^2] (main backup)\n",
     "- Direct CHP1-1 [W/m^2]\n",
-    "- Direct CHP1-2 [W/m^2] (main backup)p)\r\n",
+    "- Direct CHP1-2 [W/m^2] (main backup)\n",
     "\n"
    ]
   },
@@ -2644,12 +2644,10 @@
     "```{admonition} Note\n",
     "If you use data from the MIDC in any publication, make sure to cite it. As an example, the citation for the BMS site is:\n",
     "\n",
-    "Andreas, A.; Stoffel, T.; (1981). NREL Solar Radiation Research Laboratory (SRRL): Baseline\n",
-    "Measurement System (BMS); Golden, Colorado (Data); NREL Report No. DA-5500-56488.\n",
-    "http://dx.doi.org/10.5439/1052221\n",
+    "A. Andreas and T. Stoffel, \"NREL Solar Radiation Research Laboratory (SRRL): Baseline Measurement System (BMS),\" Golden, CO, USA, NREL Report No. DA-5500-56488, 1981. [Online]. http://doi.org/10.5439/1052221\n",
     "```\n",
     "\n",
-    "Data can be downloaded and parsed conveniently using the pvlib-python iotools module using the function [`pvlib.iotools.read_midc_raw_data_from_nrel`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.read_midc_raw_data_from_nrel.html). If you use pvlib iotools for published work, please Jensen et al. (2023) which provides additional background information. The use of the function is shown below, demonstrating how to retrieve five days of data:"
+    "Data can be downloaded and parsed conveniently using the pvlib-python iotools module using the function [`pvlib.iotools.read_midc_raw_data_from_nrel`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.read_midc_raw_data_from_nrel.html). If you use pvlib iotools for published work, please cite https://doi.org/10.1016/j.solener.2023.112092 which provides additional background information. The use of the function is shown below, demonstrating how to retrieve five days of data:"
    ]
   },
   {
@@ -2744,7 +2742,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 720x288 with 2 Axes>"
       ]
@@ -2770,16 +2768,6 @@
     "fig.tight_layout()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "0f53a4d4-df37-4cc7-aceb-19fdea09a067",
-   "metadata": {},
-   "source": [
-    "## References\n",
-    "\n",
-    "* Adam R. Jensen, Kevin S. Anderson, William F. Holmgren, Mark A. Mikofski, Clifford W. Hansen, Leland J. Boeman, and Roel Loonen. Pvlib iotools—open-source python functions for seamless access to solar irradiance data. Solar Energy, 266:112092, 2023. doi: [10.1016/j.solener.2023.112092](https://doi.org/10.1016/j.solener.2023.112092)."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/docs/station_network_solrad.ipynb b/docs/station_network_solrad.ipynb
index 805404f..e8d912d 100644
--- a/docs/station_network_solrad.ipynb
+++ b/docs/station_network_solrad.ipynb
@@ -14,7 +14,7 @@
     "\n",
     "The [SOLRAD](https://gml.noaa.gov/grad/solrad/) network consists of 9 surface radiation monitoring stations and is operated by the [National Oceanic and Atmospheric Administration (NOAA)](https://www.noaa.gov/).\n",
     "\n",
-    "The reader is referred to Hicks et al. (1996) for background information on the SOLRAD network.\n",
+    "The reader is referred to https://doi.org/10.1175/1520-0477(1996)077%3C2857:TNISIS%3E2.0.CO;2 for background information on the SOLRAD network.\n",
     "\n",
     "```{admonition} Instrument calibrations\n",
     ":class: dropdown\n",
@@ -809,7 +809,7 @@
     "\n",
     "Measurements from the SOLRAD stations are stored in daily ASCII text files and can be freely downloaded from the [SOLRAD FTP server](https://gml.noaa.gov/aftp/data/radiation/solrad/). Since January 1st 2015, data has been logged as 1-minute averages of 1-second samples. Prior to this, the data was stored as 3-minute averages.\n",
     "\n",
-    "Data can be downloaded and parsed conveniently using the pvlib-python iotools module using the function [`pvlib.iotools.get_solrad`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_solrad.html). If you use pvlib iotools for published work, please cite Jensen et al. (2023) which provides additional background information. The use of the function is shown below, demonstrating how to retrieve five days of data:"
+    "Data can be downloaded and parsed conveniently using the pvlib-python iotools module using the function [`pvlib.iotools.get_solrad`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_solrad.html). If you use pvlib iotools for published work, please cite https://doi.org/10.1016/j.solener.2023.112092 which provides additional background information. The use of the function is shown below, demonstrating how to retrieve five days of data:"
    ]
   },
   {
@@ -1023,15 +1023,6 @@
     "    ax.set_ylim(-10, 1400)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## References\n",
-    "* Adam R. Jensen, Kevin S. Anderson, William F. Holmgren, Mark A. Mikofski, Clifford W. Hansen, Leland J. Boeman, and Roel Loonen. Pvlib iotools—open-source python functions for seamless access to solar irradiance data. Solar Energy, 266:112092, 2023. doi: [10.1016/j.solener.2023.112092](https://doi.org/10.1016/j.solener.2023.112092).\n",
-    "* B. B. Hicks, J. J. DeLuisi, and D. R. Matt. The NOAA integrated surface irradiance study (ISIS) - a new surface radiation monitoring program. Bulletin of the American Meteorological Society, 77(12):2857–2864, 1996. doi: [10.1175/1520-0477(1996)077<2857:TNISIS>2.0.CO;2](https://doi.org/10.1175/1520-0477(1996)077<2857:TNISIS>2.0.CO;2)."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/docs/station_network_srml.ipynb b/docs/station_network_srml.ipynb
index 485386f..5301bad 100644
--- a/docs/station_network_srml.ipynb
+++ b/docs/station_network_srml.ipynb
@@ -737,7 +737,7 @@
    "source": [
     "## Data retrieval\n",
     "\n",
-    "Data from the SRML stations are stored in monthly files for each station and can be freely [downloaded](http://solardata.uoregon.edu/SelectArchivalUpdatedFormat.html) from their website. The data can also be downloaded programmatically using the [pvlib-python](https://pvlib-python.readthedocs.io) library, specifically the [`pvlib.iotools.get_srml`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_srml.html) function. If you use pvlib iotools for published work, please cite Jensen et al. (2023) which provides additional background information. A list of the station acronyms can be found [here](http://solardata.uoregon.edu/StationIDCodes.html).\n",
+    "Data from the SRML stations are stored in monthly files for each station and can be freely [downloaded](http://solardata.uoregon.edu/SelectArchivalUpdatedFormat.html) from their website. The data can also be downloaded programmatically using the [pvlib-python](https://pvlib-python.readthedocs.io) library, specifically the [`pvlib.iotools.get_srml`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_srml.html) function. If you use pvlib iotools for published work, please cite https://doi.org/10.1016/j.solener.2023.112092 which provides additional background information. A list of the station acronyms can be found [here](http://solardata.uoregon.edu/StationIDCodes.html).\n",
     "\n",
     "An example of how to use pvlib to download data from the [Hermiston station](http://solardata.uoregon.edu/Hermiston.html) for June 2020 is shown here:"
    ]
@@ -894,14 +894,6 @@
     "_ = axes[4].set_ylabel('Wind\\nspeed [m/s]'), axes[4].set_ylim(0,15)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## References\n",
-    "* Adam R. Jensen, Kevin S. Anderson, William F. Holmgren, Mark A. Mikofski, Clifford W. Hansen, Leland J. Boeman, and Roel Loonen. Pvlib iotools—open-source python functions for seamless access to solar irradiance data. Solar Energy, 266:112092, 2023. doi: [10.1016/j.solener.2023.112092](https://doi.org/10.1016/j.solener.2023.112092)."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/docs/station_network_surfrad.ipynb b/docs/station_network_surfrad.ipynb
index 545b295..0bf71db 100644
--- a/docs/station_network_surfrad.ipynb
+++ b/docs/station_network_surfrad.ipynb
@@ -14,7 +14,7 @@
     "\n",
     "The [SURFRAD](https://gml.noaa.gov/grad/surfrad/sitepage.html) network was established in 1993 and is operated by the [National Oceanic and Atmospheric Administration (NOAA)](https://www.noaa.gov/). The network features six active and two inactive stations in the contiguous United States. The stations are all Tier 1 stations and measurements are generally of a very high quality due to rigorous maintenance procedures and frequent inspection of data.\n",
     "\n",
-    "The reader is referred to Augustine et al. (2000) and Augustine et al. (2005) for background information on the SURFRAD network.\n",
+    "The reader is referred to https://doi.org/10.1175/1520-0477(2000)081%3C2341:SANSRB%3E2.3.CO;2 and https://doi.org/10.1175/JTECH1806.1 for information on the SURFRAD network.\n",
     "\n",
     "```{admonition} Instrument calibrations\n",
     ":class: dropdown\n",
@@ -838,7 +838,7 @@
     "\n",
     "Measurements from the SURFRAD stations are stored in daily ASCII text files and can be freely downloaded from the [SURFRAD FTP server](https://gml.noaa.gov/aftp/data/radiation/surfrad/). Since January 1st, 2009, data has been logged as 1-minute averages. Prior to this, the data was stored as 3-minute averages.\n",
     "\n",
-    "Individual data files can be downloaded and parsed conveniently using the pvlib-python iotools module using the function [`pvlib.iotools.read_surfrad`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.read_surfrad.html). Note, there is not yet a function to retrieve multiple SURFRAD files given a station and a date range. If you use pvlib iotools for published work, please cite Jensen et al. (2023) which provides additional background information. The use of the function is shown below, demonstrating how to retrieve five days of data:"
+    "Individual data files can be downloaded and parsed conveniently using the pvlib-python iotools module using the function [`pvlib.iotools.read_surfrad`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.read_surfrad.html). Note, there is not yet a function to retrieve multiple SURFRAD files given a station and a date range. If you use pvlib iotools for published work, please cite https://doi.org/10.1016/j.solener.2023.112092 which provides additional background information. The use of the function is shown below, demonstrating how to retrieve five days of data:"
    ]
   },
   {
@@ -1301,16 +1301,6 @@
     "    ax.set_ylim(-10, 1400)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## References\n",
-    "* Adam R. Jensen, Kevin S. Anderson, William F. Holmgren, Mark A. Mikofski, Clifford W. Hansen, Leland J. Boeman, and Roel Loonen. Pvlib iotools—open-source python functions for seamless access to solar irradiance data. Solar Energy, 266:112092, 2023. doi: [10.1016/j.solener.2023.112092](https://doi.org/10.1016/j.solener.2023.112092).\n",
-    "* J. A. Augustine, J. J. DeLuisi, and C. N. Long. SURFRAD — A national surface radiation budget network for atmospheric research. Bulletin of the American Meteorological Society, 81:2341–2357, 2000. doi: [10.1175/1520-0477(2000)081<2341:SANSRB>2.3.CO;2](https://doi.org/10.1175/1520-0477(2000)081<2341:SANSRB>2.3.CO;2).\n",
-    "* J. A. Augustine, G. B. Hodges, C. R. Cornwall, J. J. Michalsky, and C. I. Medina. An update on SURFRAD — the GCOS surface radiation budget network for the continental United States. Journal of Atmospheric and Oceanic Technology, 22:1460–1472, 2005. doi: [10.1175/JTECH1806.1](https://doi.org/10.1175/JTECH1806.1)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/docs/station_requirements.md b/docs/station_requirements.md
index 6a4af6d..64c665c 100644
--- a/docs/station_requirements.md
+++ b/docs/station_requirements.md
@@ -3,7 +3,7 @@
 This section defines the minimum requirements for a station to be included in the list of multi-component solar irradiance monitoring stations. The most restricting criteria is that stations are required to measure at least two of the three irradiance components, such that the remaining component can be calculated.
 
 ## Station categorization
-Accepted stations are classified into two different categories: Tier 1 and Tier 2 stations, which are defined below.
+Accepted stations are classified into two different categories: Tier 1 and Tier 2 stations, which are defined below (see https://doi.org/10.1016/j.solener.2023.112092).
 
 ### Tier 1 stations
 Tier 1 stations are defined as those that meet all of the following requirements (classification is according to ISO 9060):
@@ -18,5 +18,9 @@ Tier 2 stations are defined as those that do not meet the Tier 1 requirements bu
 * Meets two of the three requirements of Tier 1 stations
 * Measures GHI and DHI using a rotating shadowband pyranometer or SPN1
 
-### Non-qualifying stations
-Stations that only measure GHI are not considered, which is in part because there are thousands of such stations worldwide and there are limited methods for assessing the quality of the measurements. Also, stations that measure DHI using a manually adjusted shadow band are generally not considered, as such measurements are notoriously unreliable due to the shadow band having to be adjusted every few days.
+Note, stations that measure DHI using a manually adjusted shadow band are not included in the catalog, as such measurements are notoriously unreliable due to the shadow band having to be adjusted every few days.
+
+### Tier 3 stations (not included)
+Tier 3 stations are defines as those that only measure GHI. Automatic weather stations featuring a single pyranometer are examples of Tier 3 stations.
+
+Tier 3 stations are not included in this catalog, which is in part because there are thousands of such stations worldwide and there are limited methods for assessing the quality of the measurements.