try it using mean

metobs/data/filter.py

 """Array filtering functions
 """
-from numpy import ndarray, array, diff
-from numpy.ma import MaskedArray, masked_array, masked_where
+from numpy import ndarray, array, diff, zeros
+from numpy.ma import MaskedArray, masked_array, masked_where, zeros as mzeros
 
 __docformat__ = 'Epytext'
 __author__ = 'David Hoese'
@@ -19,15 +19,23 @@ def filter_arr(arr, threshold, fill_value=None):
     @param fill_value: Value to replace the filtered values with (ex. NaN)
     @type fill_value: anything
     """
-    if type.arr != ndarray or type.arr != MaskedArray:
+    if type(arr) != ndarray and type(arr) != MaskedArray:
         if fill_value != None: arr = array(arr)
         if fill_value == None: arr = masked_array(arr)
 
-    if fill_value != None: diff_arr = zeros(arr.shape[0])
-    if fill_value == None: diff_arr = ma.zeros(arr.shape[0])
-    diff_arr[1:] = diff(arr, n=1)
+    step = 10
+    m = zeros(arr.shape[0])
+    for i in range(0, arr.shape[0], step):
+        for j in range(step):
+            mean = (arr[i:(i+step)].cumsum()[-1] - arr[i+j])/(step-1)
+            df = abs(mean - arr[i+j])
+            if df > threshold: m[i+j] = 1
 
-    arr = masked_where(diff_arr >= threshold, arr)
+    #if fill_value != None: diff_arr = zeros(arr.shape[0])
+    #if fill_value == None: diff_arr = mzeros(arr.shape[0])
+    #diff_arr[1:] = diff(arr, n=1)
+
+    arr = masked_array(data = arr, mask = m)
     if fill_value != None:
         arr.fill_value = fill_value
         arr = arr.filled()