Solar Radiation Monitoring Laboratory (SRML)

The Solar Radiation Monitoring Laboratory (SRML) at the University of Oregon has been providing solar irradiance data for the Northeastern United States since 1975. The SRML monitoring station network consists of both high-quality stations that measure all three irradiance components at a 1-minute resolution, as well as stations with low-quality instruments that only log measurements hourly. A full list of the 42 stations (including discontinued stations) can be found at the SRML website.

Only the high-quality SRML stations are included in the SolarStations.org’ station catalog; however, all active stations are shown below.

import pandas as pd
from itables import init_notebook_mode, show
init_notebook_mode(all_interactive=True)

stations = pd.read_csv('../data/station_network_srml.csv').fillna('')
stations = stations[stations['Network'].str.contains('SRML')]
del stations['URL']  # Remove the URL column to avoid cluttering the site

show(stations, scrollY="500px", scrollX=True, scrollCollapse=True, paging=False, classes="display", order=[[0, "asc"]],
     showIndex=False, columnDefs=[{"className": "dt-left", "targets": "_all"}])

import folium
from folium import plugins
import folium_legend

EsriImagery = "https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}"
EsriAttribution = "Tiles &copy; Esri &mdash; Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community"

# Create Folium map
m = folium.Map(
    location=[45, -118],
    zoom_start=5, min_zoom=3, max_bounds=True,
    control_scale=True,  # Adds distance scale in lower left corner
    tiles='openstreetmap',
)

# Add each station to the map
# Consider using apply instead of for loop to add stations in case of many stations
for index, row in stations.iterrows():
    if row['Tier'] == 1:  # Tier 1 stations
        color = 'purple'
    else:
        color = 'orange'  # Tier 2 stations
    folium.CircleMarker(
        location=[row['Latitude'], row['Longitude']],
        popup=row['Station name'] + ' - ' + str(row['State']) + ' ' + row['Country'],
        tooltip=row['Abbreviation'],
        radius=9, color=color,
        fill_color=color, fill=True).add_to(m)

folium.raster_layers.TileLayer(EsriImagery, name='World imagery', attr=EsriAttribution, show=False).add_to(m)
folium.LayerControl(position='topleft').add_to(m)

# Additional options and plugins
# Note it's not possible to change the position of the scale
plugins.MiniMap(toggle_display=True, zoom_level_fixed=2, minimized=True, position='bottomright').add_to(m)  # Add minimap to the map
plugins.Fullscreen(position='topright').add_to(m)  # Add full screen button to map
folium.LatLngPopup().add_to(m)  # Show latitude/longitude when clicking on the map
# plugins.LocateControl(position='topright').add_to(m)  # Add button for your position
# plugins.MeasureControl(position='topleft').add_to(m)  # Add distance length measurement tool

# Create legend
labels = ['Tier 1', 'Tier 2']
colors = ['purple', 'orange']  # copied from above
legend = folium_legend.make_legend(labels, colors, title="Station quality")
m.get_root().html.add_child(legend)  # Add Legend to map

# Show the map
m

Data retrieval¶

Data from the SRML stations are stored in monthly files for each station and can be freely downloaded from their website. The data can also be downloaded programmatically using the pvlib-python library, specifically the pvlib.iotools.get_srml 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.

An example of how to use pvlib to download data from the Hermiston station for June 2020 is shown here:

import pvlib

data, meta = pvlib.iotools.get_srml(
    station='HE',
    start='2020-06-01',
    end='2020-06-30',
    url='http://solardata.uoregon.edu/download/Archive/')

show(data.head(), scrollX=True, scrollCollapse=True, paging=False, maxColumns=100, dom="tpr")

The data retrieved from the Hermiston station include measurements of the three irradiance components and additional weather parameters including temperature and humidity. A few of the parameters in the downloaded datasets are visualized below.

axes = data[['ghi_0','dni_0','dhi_3','temp_air_1','wind_speed_1']].plot(
    subplots=True, legend=False, rot=0, figsize=(6, 6), sharex=True)

# Set y-labels and y-limits
axes[0].set_ylabel('GHI [W/m$^2$]'), axes[0].set_ylim(-10,1400)
axes[1].set_ylabel('DNI [W/m$^2$]'), axes[1].set_ylim(-10,1400)
axes[2].set_ylabel('DHI [W/m$^2$]'), axes[2].set_ylim(-10,1400)
axes[3].set_ylabel('Temperature [°]'), axes[3].set_ylim(0,40)
_ = axes[4].set_ylabel('Wind\nspeed [m/s]'), axes[4].set_ylim(0,15)

References¶

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: Jensen et al. (2023).

References¶

Jensen, A. R., Anderson, K. S., Holmgren, W. F., Mikofski, M. A., Hansen, C. W., Boeman, L. J., & Loonen, R. (2023). pvlib iotools—Open-source Python functions for seamless access to solar irradiance data. Solar Energy, 266, 112092. 10.1016/j.solener.2023.112092