diff --git a/docs/sphinx/source/reference/effects_on_pv_system_output.rst b/docs/sphinx/source/reference/effects_on_pv_system_output.rst
index 62c1035860..6144df2802 100644
--- a/docs/sphinx/source/reference/effects_on_pv_system_output.rst
+++ b/docs/sphinx/source/reference/effects_on_pv_system_output.rst
@@ -49,3 +49,6 @@ Spectrum
    :toctree: generated/
 
    spectrum.spectrl2
+   spectrum.get_example_spectral_response
+   spectrum.get_am15g
+   spectrum.calc_spectral_mismatch_field
diff --git a/docs/sphinx/source/whatsnew/v0.9.3.rst b/docs/sphinx/source/whatsnew/v0.9.3.rst
index 3bd6c45e4f..568ebdfd78 100644
--- a/docs/sphinx/source/whatsnew/v0.9.3.rst
+++ b/docs/sphinx/source/whatsnew/v0.9.3.rst
@@ -12,6 +12,11 @@ Enhancements
 * Low resolution altitude lookup map
   :py:func:`~pvlib.location.lookup_altitude`
   (:issue:`1516`, :pull:`1518`)
+* New module to calculate spectral mismatch from field spectral measurements
+  :py:func:`~pvlib.spectrum.mismatch.get_example_spectral_response`
+  :py:func:`~pvlib.spectrum.mismatch.get_am15g`
+  :py:func:`~pvlib.spectrum.mismatch.calc_spectral_mismatch_field`
+  (:issue:`1523`, :pull:`1524`)
 * Added Townsend-Powers monthly snow loss model:
   :py:func:`pvlib.snow.loss_townsend`
   (:issue:`1246`, :pull:`1251`, :pull:`1468`)
@@ -40,5 +45,10 @@ Contributors
 ~~~~~~~~~~~~
 * João Guilherme (:ghuser:`joaoguilhermeS`)
 * Nicolas Martinez (:ghuser:`nicomt`)
+* Anton Driesse (:ghuser:`adriesse`)
+* Cliff Hansen (:ghuser:`cwhanse`)
+* Kevin Anderson (:ghuser:`kanderso-nrel`)
+* Mark Campanelli (:ghuser:`markcampanelli`)
+* Adam R. Jensen (:ghuser:`AdamRJensen`)
 * Abhishek Parikh (:ghuser:`abhisheksparikh`)
 * Taos Transue (:ghuser:`reepoi`)
diff --git a/pvlib/data/astm_g173_am15g.csv b/pvlib/data/astm_g173_am15g.csv
new file mode 100644
index 0000000000..893c04dd30
--- /dev/null
+++ b/pvlib/data/astm_g173_am15g.csv
@@ -0,0 +1,2003 @@
+wavelength [nm],am15g [(W/m^2)/nm]
+280.0,4.7309e-23
+280.5,1.2307e-21
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+3895.0,0.007476
+3900.0,0.0079254
+3905.0,0.0079269
+3910.0,0.0071353
+3915.0,0.0069868
+3920.0,0.0069466
+3925.0,0.006852
+3930.0,0.0070502
+3935.0,0.0073541
+3940.0,0.0074027
+3945.0,0.0075412
+3950.0,0.0076277
+3955.0,0.0077199
+3960.0,0.0077482
+3965.0,0.0078057
+3970.0,0.0076806
+3975.0,0.0075097
+3980.0,0.0073872
+3985.0,0.0074327
+3990.0,0.0073723
+3995.0,0.00721
+4000.0,0.0071043
diff --git a/pvlib/spectrum/__init__.py b/pvlib/spectrum/__init__.py
index 36b3503d13..b3d838acfe 100644
--- a/pvlib/spectrum/__init__.py
+++ b/pvlib/spectrum/__init__.py
@@ -1 +1,3 @@
 from pvlib.spectrum.spectrl2 import spectrl2  # noqa: F401
+from pvlib.spectrum.mismatch import (get_example_spectral_response, get_am15g,
+                                     calc_spectral_mismatch_field)
diff --git a/pvlib/spectrum/mismatch.py b/pvlib/spectrum/mismatch.py
new file mode 100644
index 0000000000..5db4649ddd
--- /dev/null
+++ b/pvlib/spectrum/mismatch.py
@@ -0,0 +1,237 @@
+"""
+The ``mismatch`` module provides functions for spectral mismatch calculations.
+"""
+
+import pvlib
+import numpy as np
+import pandas as pd
+from scipy.interpolate import interp1d
+import os
+
+
+def get_example_spectral_response(wavelength=None):
+    '''
+    Generate a generic smooth spectral response (SR) for tests and experiments.
+
+    Parameters
+    ----------
+    wavelength: 1-D sequence of numeric, optional
+        Wavelengths at which spectral response values are generated.
+        By default ``wavelength`` is from 280 to 1200 in 5 nm intervals. [nm]
+
+    Returns
+    -------
+    spectral_response : pandas.Series
+        The relative spectral response indexed by ``wavelength`` in nm. [-]
+
+    Notes
+    -----
+    This spectral response is based on measurements taken on a c-Si cell.
+    A small number of points near the measured curve are used to define
+    a cubic spline having no undue oscillations, as shown in [1]_.  The spline
+    can be interpolated at arbitrary wavelengths to produce a continuous,
+    smooth curve , which makes it suitable for experimenting with spectral
+    data of different resolutions.
+
+    References
+    ----------
+    .. [1] Driesse, Anton, and Stein, Joshua. "Global Normal Spectral
+       Irradiance in Albuquerque: a One-Year Open Dataset for PV Research".
+       United States 2020. :doi:`10.2172/1814068`.
+    '''
+    # Contributed by Anton Driesse (@adriesse), PV Performance Labs. Aug. 2022
+
+    SR_DATA = np.array([[ 290, 0.00],
+                        [ 350, 0.27],
+                        [ 400, 0.37],
+                        [ 500, 0.52],
+                        [ 650, 0.71],
+                        [ 800, 0.88],
+                        [ 900, 0.97],
+                        [ 950, 1.00],
+                        [1000, 0.93],
+                        [1050, 0.58],
+                        [1100, 0.21],
+                        [1150, 0.05],
+                        [1190, 0.00]]).transpose()
+
+    if wavelength is None:
+        resolution = 5.0
+        wavelength = np.arange(280, 1200 + resolution, resolution)
+
+    interpolator = interp1d(SR_DATA[0], SR_DATA[1],
+                            kind='cubic',
+                            bounds_error=False,
+                            fill_value=0.0,
+                            copy=False,
+                            assume_sorted=True)
+
+    sr = pd.Series(data=interpolator(wavelength), index=wavelength)
+
+    sr.index.name = 'wavelength'
+    sr.name = 'spectral_response'
+
+    return sr
+
+
+def get_am15g(wavelength=None):
+    '''
+    Read the ASTM G173-03 AM1.5 global spectrum on a 37-degree tilted surface,
+    optionally interpolated to the specified wavelength(s).
+
+    Global (tilted) irradiance includes direct and diffuse irradiance from sky
+    and ground reflections, and is more formally called hemispherical
+    irradiance (on a tilted surface).  In the context of photovoltaic systems
+    the irradiance on a flat receiver is frequently called plane-of-array (POA)
+    irradiance.
+
+    Parameters
+    ----------
+    wavelength: 1-D sequence of numeric, optional
+        Wavelengths at which the spectrum is interpolated.
+        By default the 2002 wavelengths of the standard are returned. [nm]
+
+    Returns
+    -------
+    am15g: pandas.Series
+        The AM1.5g standard spectrum indexed by ``wavelength``. [(W/m^2)/nm]
+
+    Notes
+    -----
+    If ``wavelength`` is specified this function uses linear interpolation.
+
+    If the values in ``wavelength`` are too widely spaced, the integral of the
+    spectrum may deviate from the standard value of 1000.37 W/m^2.
+
+    The values in the data file provided with pvlib-python are copied from an
+    Excel file distributed by NREL, which is found here:
+    https://www.nrel.gov/grid/solar-resource/assets/data/astmg173.xls
+
+    More information about reference spectra is found here:
+    https://www.nrel.gov/grid/solar-resource/spectra-am1.5.html
+
+    References
+    ----------
+    .. [1] ASTM "G173-03 Standard Tables for Reference Solar Spectral
+        Irradiances: Direct Normal and Hemispherical on 37° Tilted Surface."
+    '''
+    # Contributed by Anton Driesse (@adriesse), PV Performance Labs. Aug. 2022
+
+    pvlib_path = pvlib.__path__[0]
+    filepath = os.path.join(pvlib_path, 'data', 'astm_g173_am15g.csv')
+
+    am15g = pd.read_csv(filepath, index_col=0).squeeze()
+
+    if wavelength is not None:
+        interpolator = interp1d(am15g.index, am15g,
+                                kind='linear',
+                                bounds_error=False,
+                                fill_value=0.0,
+                                copy=False,
+                                assume_sorted=True)
+
+        am15g = pd.Series(data=interpolator(wavelength), index=wavelength)
+
+    am15g.index.name = 'wavelength'
+    am15g.name = 'am15g'
+
+    return am15g
+
+
+def calc_spectral_mismatch_field(sr, e_sun, e_ref=None):
+    """
+    Calculate spectral mismatch between a test device and broadband reference
+    device under specified solar spectral irradiance conditions.
+
+    Parameters
+    ----------
+    sr: pandas.Series
+        The relative spectral response of one (photovoltaic) test device.
+        The index of the Series must contain wavelength values in nm. [-]
+
+    e_sun: pandas.DataFrame or pandas.Series
+        One or more measured solar irradiance spectra in a pandas.DataFrame
+        having wavelength in nm as column index.  A single spectrum may be
+        be given as a pandas.Series having wavelength in nm as index.
+        [(W/m^2)/nm]
+
+    e_ref: pandas.Series, optional
+        The reference spectrum to use for the mismatch calculation.
+        The index of the Series must contain wavelength values in nm.
+        The default is the ASTM G173-03 global tilted spectrum. [(W/m^2)/nm]
+
+    Returns
+    -------
+    smm: pandas.Series or float if a single measured spectrum is provided. [-]
+
+    Notes
+    -----
+    Measured solar spectral irradiance usually covers a wavelength range
+    that is smaller than the range considered as broadband irradiance.
+    The infrared limit for the former typically lies around 1100 or 1600 nm,
+    whereas the latter extends to around 2800 or 4000 nm.  To avoid imbalance
+    between the magnitudes of the integrated spectra (the broadband values)
+    this function truncates the reference spectrum to the same range as the
+    measured (or simulated) field spectra. The assumption implicit in this
+    truncation is that the energy in the unmeasured wavelength range
+    is the same fraction of the broadband energy for both the measured
+    spectra and the reference spectrum.
+
+    If the default reference spectrum is used it is linearly interpolated
+    to the wavelengths of the measured spectrum, but if a reference spectrum
+    is provided via the parameter ``e_ref`` it is used without change. This
+    makes it possible to avoid interpolation, or to use a different method of
+    interpolation, or to avoid truncation.
+
+    The spectral response is linearly interpolated to the wavelengths of each
+    spectrum with which is it multiplied internally (``e_sun`` and ``e_ref``).
+    If the wavelengths of the spectral response already match one or both
+    of these spectra interpolation has no effect; therefore, another type of
+    interpolation could be used to process ``sr`` before calling this function.
+
+    The standards describing mismatch calculations focus on indoor laboratory
+    applications, but are applicable to outdoor performance as well.
+    The 2016 version of ASTM E973 [1]_ is somewhat more difficult to
+    read than the 2010 version [2]_ because it includes adjustments for
+    the temperature dependency of spectral response, which led to a
+    formulation using quantum efficiency (QE).
+    IEC 60904-7 is clearer and also discusses the use of a broadband
+    reference device. [3]_
+
+    References
+    ----------
+    .. [1] ASTM "E973-16 Standard Test Method for Determination of the
+       Spectral Mismatch Parameter Between a Photovoltaic Device and a
+       Photovoltaic Reference Cell" :doi:`10.1520/E0973-16R20`
+    .. [2] ASTM "E973-10 Standard Test Method for Determination of the
+       Spectral Mismatch Parameter Between a Photovoltaic Device and a
+       Photovoltaic Reference Cell" :doi:`10.1520/E0973-10`
+    .. [3] IEC 60904-7 "Computation of the spectral mismatch correction
+       for measurements of photovoltaic devices"
+    """
+    # Contributed by Anton Driesse (@adriesse), PV Performance Labs. Aug. 2022
+
+    # get the reference spectrum at wavelengths matching the measured spectra
+    if e_ref is None:
+        e_ref = get_am15g(wavelength=e_sun.T.index)
+
+    # interpolate the sr at the wavelengths of the spectra
+    # reference spectrum wavelengths may differ if e_ref is from caller
+    sr_sun = np.interp(e_sun.T.index, sr.index, sr, left=0.0, right=0.0)
+    sr_ref = np.interp(e_ref.T.index, sr.index, sr, left=0.0, right=0.0)
+
+    # a helper function to make usable fraction calculations more readable
+    def integrate(e):
+        return np.trapz(e, x=e.T.index, axis=-1)
+
+    # calculate usable fractions
+    uf_sun = integrate(e_sun * sr_sun) / integrate(e_sun)
+    uf_ref = integrate(e_ref * sr_ref) / integrate(e_ref)
+
+    # mismatch is the ratio or quotient of the usable fractions
+    smm = uf_sun / uf_ref
+
+    if isinstance(e_sun, pd.DataFrame):
+        smm = pd.Series(smm, index=e_sun.index)
+
+    return smm
diff --git a/pvlib/tests/test_spectrum.py b/pvlib/tests/test_spectrum.py
index cfcd50d1fe..80b53d2af3 100644
--- a/pvlib/tests/test_spectrum.py
+++ b/pvlib/tests/test_spectrum.py
@@ -1,13 +1,13 @@
 import pytest
-from numpy.testing import assert_allclose
+from numpy.testing import assert_allclose, assert_approx_equal, assert_equal
 import pandas as pd
 import numpy as np
 from pvlib import spectrum
+
 from .conftest import DATA_DIR
 
 SPECTRL2_TEST_DATA = DATA_DIR / 'spectrl2_example_spectra.csv'
 
-
 @pytest.fixture
 def spectrl2_data():
     # reference spectra generated with solar_utils==0.3
@@ -34,9 +34,9 @@ def spectrl2_data():
         'aerosol_turbidity_500nm': 0.1,
         'dayofyear': 75
     }
-    df = pd.read_csv(SPECTRL2_TEST_DATA)
+    df = pd.read_csv(SPECTRL2_TEST_DATA, index_col=0)
     # convert um to nm
-    df['wavelength'] *= 1000
+    df['wavelength'] = np.round(df['wavelength'] * 1000, 1)
     df[['specdif', 'specdir', 'specetr', 'specglo']] /= 1000
     return kwargs, df
 
@@ -106,3 +106,68 @@ def test_aoi_gt_90(spectrl2_data):
     for key in ['poa_direct', 'poa_global']:
         message = f'{key} contains negative values for aoi>90'
         assert np.all(spectra[key] >= 0), message
+
+
+def test_get_example_spectral_response():
+    # test that the sample sr is read and interpolated correctly
+    sr = spectrum.get_example_spectral_response()
+    assert_equal(len(sr), 185)
+    assert_equal(np.sum(sr.index), 136900)
+    assert_approx_equal(np.sum(sr), 107.6116)
+
+    wavelength = [270, 850, 950, 1200, 4001]
+    expected = [0.0, 0.92778, 1.0, 0.0, 0.0]
+
+    sr = spectrum.get_example_spectral_response(wavelength)
+    assert_equal(len(sr), len(wavelength))
+    assert_allclose(sr, expected, rtol=1e-5)
+
+
+def test_get_am15g():
+    # test that the reference spectrum is read and interpolated correctly
+    e = spectrum.get_am15g()
+    assert_equal(len(e), 2002)
+    assert_equal(np.sum(e.index), 2761442)
+    assert_approx_equal(np.sum(e), 1002.88, significant=6)
+
+    wavelength = [270, 850, 950, 1200, 4001]
+    expected = [0.0, 0.893720, 0.147260, 0.448250, 0.0]
+
+    e = spectrum.get_am15g(wavelength)
+    assert_equal(len(e), len(wavelength))
+    assert_allclose(e, expected, rtol=1e-6)
+
+
+def test_calc_spectral_mismatch_field(spectrl2_data):
+    # test that the mismatch is calculated correctly with
+    # - default and custom reference sepctrum
+    # - single or multiple sun spectra
+
+    # sample data
+    _, e_sun = spectrl2_data
+    e_sun = e_sun.set_index('wavelength')
+    e_sun = e_sun.transpose()
+
+    e_ref = spectrum.get_am15g()
+    sr = spectrum.get_example_spectral_response()
+
+    # test with single sun spectrum, same as ref spectrum
+    mm = spectrum.calc_spectral_mismatch_field(sr, e_sun=e_ref)
+    assert_approx_equal(mm, 1.0, significant=6)
+
+    # test with single sun spectrum
+    mm = spectrum.calc_spectral_mismatch_field(sr, e_sun=e_sun.loc['specglo'])
+    assert_approx_equal(mm, 0.992397, significant=6)
+
+    # test with single sun spectrum, also used as reference spectrum
+    mm = spectrum.calc_spectral_mismatch_field(sr,
+                                               e_sun=e_sun.loc['specglo'],
+                                               e_ref=e_sun.loc['specglo'])
+    assert_approx_equal(mm, 1.0, significant=6)
+
+    # test with multiple sun spectra
+    expected = [0.972982, 0.995581, 0.899782, 0.992397]
+
+    mm = spectrum.calc_spectral_mismatch_field(sr, e_sun=e_sun)
+    assert mm.index is e_sun.index
+    assert_allclose(mm, expected, rtol=1e-6)