diff --git a/docs/whatsnew/v0.2.3.rst b/docs/whatsnew/v0.2.3.rst
index 216f9f59..d309d726 100644
--- a/docs/whatsnew/v0.2.3.rst
+++ b/docs/whatsnew/v0.2.3.rst
@@ -15,6 +15,10 @@ Bug Fixes
 * Remove freq parameter from :py:func:`pvanalytics.quality.gaps.completeness`
   and :py:func:`pvanalytics.quality.gaps.completeness_score`. Frequency is now
   always calculated from the input data's DatetimeIndex. (:pull:`236`) 
+* Suppress divide-by-zero warnings in
+  :mod:`pvanalytics.features.snow` and precision-loss warnings in
+  :mod:`pvanalytics.quality.outliers` by extending ``np.errstate`` to
+include ``invalid='ignore'``. (:issue:`#237`, :pull:`YYY`)
 
 Requirements
 ~~~~~~~~~~~~
@@ -39,3 +43,4 @@ Testing
 Contributors
 ~~~~~~~~~~~~
 * Cliff Hansen (:ghuser:`cwhanse`)
+* Omesh Chandure (:ghuser: `Omesh37`)
\ No newline at end of file
diff --git a/pvanalytics/features/snow.py b/pvanalytics/features/snow.py
index c3b542db..84c98c37 100644
--- a/pvanalytics/features/snow.py
+++ b/pvanalytics/features/snow.py
@@ -224,7 +224,7 @@ def categorize(transmission, measured_voltage,
     modeled_voltage_with_snow_copy = np.where(
         transmission == 0, 0, modeled_voltage_with_snow)
 
-    with np.errstate(divide='ignore'):
+    with np.errstate(divide='ignore', invalid='ignore'):
         vmp_ratio =\
             measured_voltage /\
             modeled_voltage_with_snow_copy
diff --git a/pvanalytics/quality/outliers.py b/pvanalytics/quality/outliers.py
index 4efc140d..ead57f96 100644
--- a/pvanalytics/quality/outliers.py
+++ b/pvanalytics/quality/outliers.py
@@ -1,5 +1,6 @@
 """Functions for identifying and labeling outliers."""
 import pandas as pd
+import numpy as np
 from scipy import stats
 from statsmodels import robust
 
@@ -75,7 +76,8 @@ def zscore(data, zmax=1.5, nan_policy='raise'):
                              "nan_policy. Expected 'raise' or 'omit'.")
 
     is_outlier = pd.Series(False, index=data.index)
-    is_outlier.loc[~nan_mask] = abs(stats.zscore(data[~nan_mask])) > zmax
+    with np.errstate(invalid='ignore'):
+        is_outlier.loc[~nan_mask] = abs(stats.zscore(data[~nan_mask])) > zmax
     return is_outlier
 
 
diff --git a/pvanalytics/util/_fit.py b/pvanalytics/util/_fit.py
index 65a61559..479be44a 100644
--- a/pvanalytics/util/_fit.py
+++ b/pvanalytics/util/_fit.py
@@ -127,4 +127,7 @@ def _quartic(x, a, b, c, e):
     )
     model = _quartic(x, params[0], params[1], params[2], params[3])
     residuals = y - model
-    return 1 - (np.sum(residuals**2) / np.sum((y - np.mean(y))**2))
+    ss_tot = np.sum((y - np.mean(y))**2)
+    if ss_tot == 0:
+        return 0.0
+    return 1 - (np.sum(residuals**2) / ss_tot)