diff --git a/modules/aeolus/aeolus_amv.py b/modules/aeolus/aeolus_amv.py
index 2a8c9ba2ccd4d5aafc701a59c2366d2f86c8800e..6af7a69441fa9024fb6f700a499fbb3f17464354 100644
--- a/modules/aeolus/aeolus_amv.py
+++ b/modules/aeolus/aeolus_amv.py
@@ -944,6 +944,80 @@ def analyze2(amvs_list, bf_list, raob_match_list, bf_gfs_list, amv_prod_list):
                       np.average(np.abs(bin_spd[i])), np.average(bin_spd[i]), np.average(np.abs(bin_dir[i])), np.average(bin_dir[i])))
 
 
+    # Comparison to Level of Best Fits (LBF) GFS to RAOB ------------------------------------------------------------------
+    # ------------------------------------------------------------------------------------------------------------
+    vld_bf = bfs[:, 3] == 0
+    vld_bf_gfs = bfs_gfs[:, 3] == 0
+    keep_idxs = np.logical_and(vld_bf, vld_bf_gfs)
+
+    amv_p = good_amvs[keep_idxs, pidx]
+    bf_p = bfs[keep_idxs, 2]
+    bf_p_gfs = bfs_gfs[keep_idxs, 2]
+    diff = bf_p - bf_p_gfs
+    mad = np.average(np.abs(diff))
+    bias = np.average(diff)
+    print('********************************************************')
+    print('Number of good best fits to GFS: ', bf_p.shape[0])
+    print('press, MAD: {0:.2f}'.format(mad))
+    print('press, bias: {0:.2f}'.format(bias))
+    pd_std = np.std(diff)
+    pd_mean = np.mean(diff)
+    print('press bias/rms: {0:.2f}  {1:.2f} '.format(pd_mean, np.sqrt(pd_mean**2 + pd_std**2)))
+    print('------------------------------------------')
+
+    bin_pres = bin_data_by(diff, amv_p, bin_ranges)
+
+    amv_spd = good_amvs[keep_idxs, sidx]
+    amv_dir = good_amvs[keep_idxs, didx]
+    bf_gfs_spd, bf_gfs_dir = spd_dir_from_uv(bfs_gfs[keep_idxs, 0], bfs_gfs[keep_idxs, 1])
+    bf_spd, bf_dir = spd_dir_from_uv(bfs[keep_idxs, 0], bfs[keep_idxs, 1])
+
+    diff = bf_spd - bf_gfs_spd
+    diff = diff.magnitude
+    spd_mad = np.average(np.abs(diff))
+    spd_bias = np.average(diff)
+    print('spd, MAD: {0:.2f}'.format(spd_mad))
+    print('spd, bias: {0:.2f}'.format(spd_bias))
+    spd_mean = np.mean(diff)
+    spd_std = np.std(diff)
+    print('spd MAD/bias/rms: {0:.2f}  {1:.2f}  {2:.2f}'.format(np.average(np.abs(diff)), spd_mean, np.sqrt(spd_mean**2 + spd_std**2)))
+    print('-----------------')
+    bin_spd = bin_data_by(diff, amv_p, bin_ranges)
+
+    dir_diff = direction_difference(bf_dir.magnitude, bf_gfs_dir.magnitude)
+    print('dir, MAD: {0:.2f}'.format(np.average(np.abs(dir_diff))))
+    print('dir bias: {0:.2f}'.format(np.average(dir_diff)))
+    print('-------------------------------------')
+    bin_dir = bin_data_by(dir_diff, amv_p, bin_ranges)
+
+    u_diffs = bfs[keep_idxs, 0] - bfs_gfs[keep_idxs, 0]
+    v_diffs = bfs[keep_idxs, 1] - bfs_gfs[keep_idxs, 1]
+
+    vd = np.sqrt(u_diffs**2 + v_diffs**2)
+    vd_mean = np.mean(vd)
+    vd_std = np.std(vd)
+    print('VD bias/rms: {0:.2f}  {1:.2f}'.format(vd_mean, np.sqrt(vd_mean**2 + vd_std**2)))
+    print('******************************************************')
+
+    x_values = []
+    num_pres = []
+    num_spd = []
+    num_dir = []
+    print('level   num cases   hgt MAD/bias    spd MAD/bias     dir MAD/bias')
+    print('-------------------------------------------------------------------')
+    for i in range(len(bin_ranges)):
+        x_values.append(np.average(bin_ranges[i]))
+        num_pres.append(bin_pres[i].shape[0])
+        num_spd.append(bin_spd[i].shape[0])
+        num_dir.append(bin_dir[i].shape[0])
+
+        print('{0:d}        {1:d}       {2:.2f}/{3:.2f}       {4:.2f}/{5:.2f}        {6:.2f}/{7:.2f}'
+              .format(int(x_values[i]), num_pres[i], np.average(np.abs(bin_pres[i])), np.average(bin_pres[i]),
+                      np.average(np.abs(bin_spd[i])), np.average(bin_spd[i]), np.average(np.abs(bin_dir[i])), np.average(bin_dir[i])))
+
+    return bin_ranges, bin_pres, bin_spd, bin_dir
+
+
 def compare_amvs_bestfit(amvs_list, bfs_list, bin_size=200):
     amvs = np.concatenate(amvs_list, axis=1)
     amvs = np.transpose(amvs, axes=[1, 0])