{ "cells": [ { "cell_type": "markdown", "id": "17ce006e-6c8d-4daf-a006-7524a1ca459a", "metadata": {}, "source": [ "# NREL MIDC station network\n", "\n", "The [Measurement and Instrumentation Data Center (MIDC)](https://midcdmz.nrel.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", "\n", "The most notable station is the [Baseline Measurement System (BMS)](https://midcdmz.nrel.gov/apps/sitehome.pl?site=BMS) at NREL's [Solar Radiation Research Laboratory (SRRL)](https://www.nrel.gov/esif/solar-radiation-research-laboratory.html) 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. Thus, 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", "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 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." ] }, { "cell_type": "code", "execution_count": 1, "id": "8767189d-cb61-43cc-b473-eaf4b54f2f1a", "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "
Station IdentifierStation full nameStation AbbreviationLatitudeLongitudeElevationActive
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\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "from itables import init_notebook_mode, show\n", "init_notebook_mode(all_interactive=True)\n", "\n", "stations_midc_url = 'https://midcdmz.nrel.gov/apps/data_api_doc.pl?_idtextlist_'\n", "stations = pd.read_csv(stations_midc_url)\n", "stations = stations.rename(columns={\n", " 'STATION_ID': 'Station Identifier',\n", " 'STATION_FULLNAME': 'Station full name',\n", " 'STATION_SHORTNAME': 'Station Abbreviation',\n", " 'LATITUDE_DEG': 'Latitude',\n", " 'LONGITUDE_DEG': 'Longitude',\n", " 'ELEVATION_M': 'Elevation',\n", " 'ACTIVE': 'Active'})\n", "\n", "del stations['RESERVED']\n", "\n", "show(stations, scrollY=\"500px\", scrollX=True, scrollCollapse=True, paging=False, classes=\"display\", order=[[0, \"asc\"]],\n", " showIndex=False, columnDefs=[{\"className\": \"dt-left\", \"targets\": \"_all\"}])" ] }, { "cell_type": "code", "execution_count": 5, "id": "5bf99ba6-c344-4d6e-bb51-9c17d724df54", "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import folium\n", "from folium import plugins\n", "import folium_legend\n", "\n", "EsriImagery = \"https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}\"\n", "EsriAttribution = \"Tiles © Esri — Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community\"\n", "\n", "# Create Folium map\n", "m = folium.Map(\n", " location=[35, -107],\n", " zoom_start=3, min_zoom=1, max_bounds=True,\n", " control_scale=True, # Adds distance scale in lower left corner\n", " tiles='openstreetmap',\n", ")\n", "\n", "# Function for determining station color\n", "def marker_color(row):\n", " if row['Active'] == 1: # active station\n", " color = '#008000' # green\n", " else: # inactive/closed station\n", " color = '#ff422b' # red\n", " return color\n", "\n", "# SRRL, STAC, and UoE has multiple stations with same latitude/longitude\n", "# append the main stations to the end, so they plot ontop\n", "stations_to_plot = pd.concat([\n", " stations,\n", " stations[stations['Station Identifier'] == 'UOSMRL'],\n", " stations[stations['Station Identifier'] == 'BMS'],\n", " stations[stations['Station Identifier'] == 'STAC']])\n", "\n", "# Add each station to the map\n", "for index, row in stations_to_plot.iterrows():\n", " color = marker_color(row)\n", " folium.CircleMarker(\n", " location=[row['Latitude'], row['Longitude']],\n", " popup=f\"{row['Station full name']} ({row['Station Identifier']})\",\n", " tooltip=f\"{row['Station full name']} ({row['Station Identifier']})\",\n", " radius=5, color=color,\n", " fill_color=color, fill=True).add_to(m)\n", "\n", "folium.raster_layers.TileLayer(EsriImagery, name='World imagery', attr=EsriAttribution, show=False).add_to(m)\n", "folium.LayerControl(position='topright').add_to(m)\n", "\n", "# Additional options and plugins\n", "plugins.Fullscreen(position='bottomright').add_to(m) # Add full screen button to map\n", "folium.LatLngPopup().add_to(m) # Show latitude/longitude when clicking on the map\n", "\n", "# Create legend\n", "labels = ['Active', 'Inactive']\n", "colors = ['#008000', '#ff422b'] # copied from above\n", "legend = folium_legend.make_legend(labels, colors, title=\"Station status\")\n", "m.get_root().html.add_child(legend) # Add Legend to map\n", "\n", "# Show the map\n", "m" ] }, { "cell_type": "markdown", "id": "d20c658c-8978-49ba-a05f-216dc5bb2cc7", "metadata": { "tags": [] }, "source": [ "## Data retrieval\n", "Data from the MIDC can be retrieved from the MIDC website or using the [MIDC raw data API](https://midcdmz.nrel.gov/apps/data_api_doc.pl).\n", "\n", "```{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", "```\n", "\n", "Conveniently, the [pvlib-python](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.read_midc_raw_data_from_nrel.html) library features a wrapper around the MIDC API making retrieving data seamless. The use of the function is shown below, demonstrating how to retrieve five days of data from the BMS:" ] }, { "cell_type": "code", "execution_count": 3, "id": "f8f92029-cbd6-4aab-b235-c018f1bfdeac", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\arajen\\Anaconda3\\envs\\solarstations\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.3\n", " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" ] }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "
CR3000 Zen Angle [degrees]Global LI-200 [W/m^2]Global CMP22 (vent/cor) [W/m^2]Global RG780 PSP [W/m^2]Global CM3 (cor) [W/m^2]
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\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pvlib\n", "\n", "data = pvlib.iotools.read_midc_raw_data_from_nrel(\n", " site='BMS', # station identifier\n", " start=pd.Timestamp(2020,6,1),\n", " end=pd.Timestamp(2020,6,5))\n", "\n", "# show a subset of the data\n", "show(data.iloc[:5, 5:10], dom=\"tpr\")" ] }, { "cell_type": "markdown", "id": "e1b94326-6642-40b5-bc46-9ca56c623faa", "metadata": {}, "source": [ "
\n", "\n", "The retrieved BMS dataset contains numerous instruments measuring the same irradiance component. Let's, for example, compare the global horizontal irradiance (GHI) measured by a high-quality CMP22 pyranometer with that of a low-cost CM3 pyranometer:" ] }, { "cell_type": "code", "execution_count": 4, "id": "96626626-3a55-499f-a214-aa7859f38dd0", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "fig, axes = plt.subplots(ncols=2, figsize=(10,4))\n", "# plot both measurement as a time-series\n", "data[['Global CMP22 (vent/cor) [W/m^2]', 'Global CM3 (cor) [W/m^2]']].plot(\n", " ax=axes[0], alpha=0.8, ylim=[-20, 1500])\n", "# compare measurements with a scatter plot\n", "data.plot.scatter(ax=axes[1], s=1, grid=True,\n", " x='Global CMP22 (vent/cor) [W/m^2]',\n", " y='Global CM3 (cor) [W/m^2]',\n", " xlim=[-20, 1300], ylim=[-20, 1300])\n", "fig.tight_layout()" ] }, { "cell_type": "code", "execution_count": null, "id": "0bed1fef-f3bf-4063-b64c-8142d6b821db", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.4" } }, "nbformat": 4, "nbformat_minor": 5 }