diff --git a/modules/icing/pirep_goes.py b/modules/icing/pirep_goes.py
index f1fa67d7aef69282c20cabd70d1607736ac19011..5e9da6398ed218ade8cabb87084b578ad4edfd8a 100644
--- a/modules/icing/pirep_goes.py
+++ b/modules/icing/pirep_goes.py
@@ -1589,6 +1589,8 @@ def time_filter(icing_dct, dt_str_0=None, dt_str_1=None, format_code='%Y-%m-%d_%
 
     keep_reports = []
     keep_times = []
+    keep_lons = []
+    keep_lats = []
 
     for ts in list(icing_dct.keys()):
         if ts_0 <= ts < ts_1:
@@ -1596,8 +1598,10 @@ def time_filter(icing_dct, dt_str_0=None, dt_str_1=None, format_code='%Y-%m-%d_%
             for idx, tup in enumerate(rpts):
                 keep_reports.append(tup)
                 keep_times.append(ts)
+                keep_lats.append(tup[0])
+                keep_lons.append(tup[1])
 
-    return keep_times, keep_reports
+    return keep_times, keep_lons, keep_lats, keep_reports
 
 
 # dt_str_0: start datetime string in format YYYY-MM-DD_HH:MM (default)
@@ -1697,20 +1701,23 @@ def collect(icing_times, icing_intensity, jdays=['071', '072', '074', '079', '08
 
 # ------------ This code will not be needed when we implement a Fully Connected CNN -----------------------------------
 # Example GOES file to retrieve GEOS parameters in MetPy form (CONUS)
-exmp_file = '/Users/tomrink/data/OR_ABI-L1b-RadC-M6C14_G16_s20193140811215_e20193140813588_c20193140814070.nc'
+exmp_file_conus = '/Users/tomrink/data/OR_ABI-L1b-RadC-M6C14_G16_s20193140811215_e20193140813588_c20193140814070.nc'
+# Full Disk
+exmp_file_fd = '/Users/tomrink/data/OR_ABI-L1b-RadF-M6C16_G16_s20212521800223_e20212521809542_c20212521809596.nc'
 
 
-def make_for_full_domain_predict(clvrx_file, name_list=l1b_ds_list):
+def make_for_full_domain_predict(clvrx_file, name_list=l1b_ds_list, domain='FD'):
     w_x = 16
     w_y = 16
-    xlen = 2500
-    ylen = 1500
 
-    exmpl_ds = xr.open_dataset(exmp_file)
+    if domain == 'CONUS':
+        exmpl_ds = xr.open_dataset(exmp_file_conus)
+    elif domain == 'FD':
+        exmpl_ds = xr.open_dataset(exmp_file_fd)
     mdat = exmpl_ds.metpy.parse_cf('Rad')
     geos = mdat.metpy.cartopy_crs
-    #xlen = mdat.x
-    #ylen = mdat.y
+    xlen = mdat.x.values.size
+    ylen = mdat.y.values.size
     exmpl_ds.close()
 
     h5f = h5py.File(clvrx_file, 'r')