Solar Radiation Monitoring Laboratory (SRML)#

The Solar Radiation Monitoring Laboratory (SRML) at the University of Oregon has been providing solar radiation data for the Northeastern United States since 1975. The SRML monitoring station network consists of both high-quality stations (Tier 1) that measure all three irradiance components at a 1-minute resolution, as well as stations with low-quality instruments (Tier 2) 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’ station listing; however, all active stations are shown below.

Station full name Abbreviation State Country Latitude Longitude Elevation Time period Network Owner Comment Data availability Tier Instrument Components
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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 read_srml_month_from_solardat function. A list of the station acronyms can be found here.

Help support the SRML

If you find the data useful, please consider donating to support the SRML.

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

import pvlib

df = pvlib.iotools.read_srml_month_from_solardat(

# print the first 12 rows of data
show(df.head(), scrollX=True, scrollCollapse=True, paging=False, maxColumns=100, dom="tpr")
ghi_0 ghi_0_flag dni_0 dni_0_flag dhi_3 dhi_3_flag ghi_2 ghi_2_flag dni_2 dni_2_flag dhi_2 dhi_2_flag temp_air_0 temp_air_0_flag ghi_1 ghi_1_flag dhi_1 dhi_1_flag temp_air_1 temp_air_1_flag relative_humidity_1 relative_humidity_1_flag 9151 9151_flag wind_dir_1 wind_dir_1_flag wind_speed_1 wind_speed_1_flag dhi_0 dhi_0_flag
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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 = df[['ghi_0','dni_0','dhi_3','temp_air_1','wind_speed_1']].plot(
    subplots=True, legend=False, rot=0, figsize=(8,8), 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)