diff --git a/modules/amv/intercompare.py b/modules/amv/intercompare.py
index 96bed187a38ea51f62c3725dac51c77904751566..0cb3ab5c02130240fd584429cf3174d9321f82d9 100644
--- a/modules/amv/intercompare.py
+++ b/modules/amv/intercompare.py
@@ -973,6 +973,7 @@ def best_fit(amv_spd, amv_dir, amv_prs, amv_lat, amv_lon, fcst_spd, fcst_dir, fc
        History:
        10/2012 - Steve Wanzong - Created in Fortran
        10/2013 - Sharon Nebuda - rewritten for python
+       10/2021 - Tom Rink - adapted from previous version to leverage more of NumPy
     """
 
     undef = -9999.0
@@ -1001,7 +1002,7 @@ def best_fit(amv_spd, amv_dir, amv_prs, amv_lat, amv_lon, fcst_spd, fcst_dir, fc
             print('AMV location lat,lon,prs ({0},{1},{2}) failed to find fcst prs around AMV'.format(amv_lat, amv_lon, amv_prs))
         return bf_data
 
-# Diagnostic field: Find the model minimum speed and maximum speed within PressDiff of the AMV.
+# Diagnostic field: Find the forecast/model minimum speed and maximum speed within PressDiff of the AMV.
     if verbose:
         SatwindMinSpeed = min(fcst_spd[kk])
         SatwindMaxSpeed = max(fcst_spd[kk])
@@ -1013,7 +1014,7 @@ def best_fit(amv_spd, amv_dir, amv_prs, amv_lat, amv_lon, fcst_spd, fcst_dir, fc
     fcst_uwind = -fcst_spd * np.sin(dr*fcst_dir)
     fcst_vwind = -fcst_spd * np.cos(dr*fcst_dir)
 
-# Calculate the vector difference between the AMV and model background at all levels.
+# Calculate the vector difference between the AMV and forecast/model background at all levels.
     VecDiff = np.sqrt((amv_uwind - fcst_uwind) ** 2 + (amv_vwind - fcst_vwind) ** 2)
 
 # Find the model level of best-fit pressure, from the minimum vector difference.