diff --git a/src/stcal/outlier_detection/utils.py b/src/stcal/outlier_detection/utils.py
index bc0485f3..88d27ce0 100644
--- a/src/stcal/outlier_detection/utils.py
+++ b/src/stcal/outlier_detection/utils.py
@@ -77,7 +77,12 @@ def compute_weight_threshold(weight, maskpt):
     weight_masked = np.ma.array(weight, mask=np.logical_or(
         mask_zero_weight, mask_nans))
     # Sigma-clip the unmasked data
-    weight_masked = sigma_clip(weight_masked, sigma=3, maxiters=5)
+    weight_masked = sigma_clip(weight_masked,
+                               sigma=3,
+                               maxiters=5,
+                               masked=False,
+                               copy=False,
+                               )
     mean_weight = np.mean(weight_masked)
     # Mask pixels where weight falls below maskpt percent
     weight_threshold = mean_weight * maskpt
diff --git a/tests/outlier_detection/test_utils.py b/tests/outlier_detection/test_utils.py
index 0a59f3c2..4112391b 100644
--- a/tests/outlier_detection/test_utils.py
+++ b/tests/outlier_detection/test_utils.py
@@ -16,6 +16,7 @@
     reproject,
     medfilt,
 )
+from stcal.testing_helpers import MemoryThreshold
 
 
 @pytest.mark.parametrize("shape,diff", [
@@ -72,6 +73,23 @@ def test_compute_weight_threshold_zeros():
     np.testing.assert_allclose(result, 21)
 
 
+def test_compute_weight_threshold_memory():
+    """Test that weight threshold function modifies
+    the weight array in place"""
+    arr = np.zeros([500, 500], dtype=np.float32)
+    arr[:250, :250] = 42
+    arr[10,10] = 0
+    arr[-10,-10] = np.nan
+
+    # buffer to account for memory overhead needs to be small enough
+    # to ensure that the array was not copied
+    fractional_memory_buffer = 1.9
+    expected_mem = int(arr.nbytes*fractional_memory_buffer)
+    with MemoryThreshold(str(expected_mem) + " B"):
+        result = compute_weight_threshold(arr, 0.5)
+    np.testing.assert_allclose(result, 21)
+
+
 def test_flag_crs():
     sci = np.zeros((10, 10), dtype=np.float32)
     err = np.ones_like(sci)