diff --git a/docs/api.rst b/docs/api.rst
index 6abdc27e..72ae5f4e 100644
--- a/docs/api.rst
+++ b/docs/api.rst
@@ -144,6 +144,20 @@ Quality checks for weather data.
    quality.weather.temperature_limits
    quality.weather.wind_limits
 
+In addition to validating temperature by comparing with limits, module
+temperature should be positively correlated with irradiance. Poor
+correlation could indicate that the sensor has become detached from
+the module, for example. Unlike other functions in the
+:py:mod:`quality` module which return Boolean masks over the input
+series, this function returns a single Boolean value indicating
+whether the entire series has passed (``True``) or failed (``False``)
+the quality check.
+
+.. autosummary::
+   :toctree: generated/
+
+   quality.weather.module_temperature_check
+
 .. rubric:: References
 
 .. [1]  C. N. Long and Y. Shi, An Automated Quality Assessment and Control
diff --git a/pvanalytics/quality/weather.py b/pvanalytics/quality/weather.py
index caa7d888..5e331bab 100644
--- a/pvanalytics/quality/weather.py
+++ b/pvanalytics/quality/weather.py
@@ -1,5 +1,6 @@
 """Quality control functions for weather data."""
 from pvanalytics.quality import util
+from scipy import stats
 
 
 def temperature_limits(air_temperature, limits=(-35.0, 50.0)):
@@ -97,3 +98,30 @@ def wind_limits(wind_speed, limits=(0.0, 50.0)):
         upper_bound=limits[1],
         inclusive_lower=True
     )
+
+
+def module_temperature_check(module_temperature, irradiance,
+                             correlation_min=0.5):
+    """Test whether the module temperature is correlated with irradiance.
+
+    Parameters
+    ----------
+    module_temperature : Series
+        Time series of module temperature.
+    irradiance : Series
+        Time series of irradiance with the same index as
+        `module_temperature`. This should be of relatively high
+        quality (outliers and other problems removed).
+    correlation_min : float, default 0.5
+        Minimum correlation between `module_temperature` and
+        `irradiance` for the module temperature sensor to 'pass'
+
+    Returns
+    -------
+    bool
+        True if the correlation between `module_temperature` and
+        `irradiance` exceeds `correlation_min`.
+
+    """
+    _, _, r, _, _ = stats.linregress(module_temperature, irradiance)
+    return r > correlation_min
diff --git a/pvanalytics/tests/quality/test_weather.py b/pvanalytics/tests/quality/test_weather.py
index c2807e30..a32138e4 100644
--- a/pvanalytics/tests/quality/test_weather.py
+++ b/pvanalytics/tests/quality/test_weather.py
@@ -2,6 +2,7 @@
 import pytest
 import pandas as pd
 import numpy as np
+from pvlib import location
 from pandas.util.testing import assert_series_equal
 from pvanalytics.quality import weather
 
@@ -87,3 +88,23 @@ def test_wind_limits(weather_data):
         weather_data['extreme_wind_flag'],
         check_names=False
     )
+
+
+def test_module_temperature():
+    """Module temperature is correlated with GHI."""
+    albuquerque = location.Location(
+        35.0844, -106.6504, altitude=5312, tz='MST'
+    )
+    times = pd.date_range(
+        start='01/01/2020',
+        end='03/01/2020',
+        freq='H',
+        tz='MST'
+    )
+    clearsky = albuquerque.get_clearsky(times, model='simplified_solis')
+    assert weather.module_temperature_check(
+        clearsky['ghi']*0.6, clearsky['ghi']
+    )
+    assert not weather.module_temperature_check(
+        clearsky['ghi']*(-0.6), clearsky['ghi']
+    )