diff --git a/src/esmac_diags/preprocessing/prep_ACEENA_sfc.py b/src/esmac_diags/preprocessing/prep_ACEENA_sfc.py
index 80a337d..7015eb3 100644
--- a/src/esmac_diags/preprocessing/prep_ACEENA_sfc.py
+++ b/src/esmac_diags/preprocessing/prep_ACEENA_sfc.py
@@ -155,7 +155,7 @@ def prep_ccn(ccnpath, predatapath, dt=3600):
     time = ccndata['time'].load()
     coefs = ccndata['N_CCN_fit_coefs'].load()
     ccn = ccndata['N_CCN'].load()
-    qc_ccn = ccndata['qc_N_CCN'].load()
+    qc_ccns = ccndata['qc_N_CCN'].load()
     ss = ccndata['supersaturation_calculated'].load()
     ccndata.close()
     
@@ -207,22 +207,22 @@ def prep_ccn(ccnpath, predatapath, dt=3600):
     # data resolution is hourly, so interpolate for finer resolution
     # note that ENA code includes polynomial fits (should make consistent in future)
     if dt >= 3600:
-        ccn1_measure = median_time_1d(time1, ccn1, time_new, arraytype='xarray')
-        ss1_i = median_time_1d(time1, ss1, time_new, arraytype='xarray')
-        ccn2_measure = median_time_1d(time2, ccn2, time_new, arraytype='xarray')
-        ss2_i = median_time_1d(time2, ss2, time_new, arraytype='xarray')
-        ccn5_measure = median_time_1d(time5, ccn5, time_new, arraytype='xarray')
-        ss5_i = median_time_1d(time5, ss5, time_new, arraytype='xarray')
+        ccn1_measure = median_time_1d(time, ccn1, time_new, arraytype='xarray')
+        ss1_i = median_time_1d(time, ss1, time_new, arraytype='xarray')
+        ccn2_measure = median_time_1d(time, ccn2, time_new, arraytype='xarray')
+        ss2_i = median_time_1d(time, ss2, time_new, arraytype='xarray')
+        ccn5_measure = median_time_1d(time, ccn5, time_new, arraytype='xarray')
+        ss5_i = median_time_1d(time, ss5, time_new, arraytype='xarray')
         ccn1_fit_i = median_time_1d(time, ccn1_fit, time_new, arraytype='xarray')
         ccn2_fit_i = median_time_1d(time, ccn2_fit, time_new, arraytype='xarray')
         ccn5_fit_i = median_time_1d(time, ccn5_fit, time_new, arraytype='xarray')
     if dt < 3600:
-        ccn1_measure = interp_time_1d(time1, ccn1, time_new, arraytype='xarray')
-        ss1_i = interp_time_1d(time1, ss1, time_new, arraytype='xarray')
-        ccn2_measure = interp_time_1d(time2, ccn2, time_new, arraytype='xarray')
-        ss2_i = interp_time_1d(time2, ss2, time_new, arraytype='xarray')
-        ccn5_measure = interp_time_1d(time5, ccn5, time_new, arraytype='xarray')
-        ss5_i = interp_time_1d(time5, ss5, time_new, arraytype='xarray')
+        ccn1_measure = interp_time_1d(time, ccn1, time_new, arraytype='xarray')
+        ss1_i = interp_time_1d(time, ss1, time_new, arraytype='xarray')
+        ccn2_measure = interp_time_1d(time, ccn2, time_new, arraytype='xarray')
+        ss2_i = interp_time_1d(time, ss2, time_new, arraytype='xarray')
+        ccn5_measure = interp_time_1d(time, ccn5, time_new, arraytype='xarray')
+        ss5_i = interp_time_1d(time, ss5, time_new, arraytype='xarray')
         ccn1_fit_i = interp_time_1d(time, ccn1_fit, time_new, arraytype='xarray')
         ccn2_fit_i = interp_time_1d(time, ccn2_fit, time_new, arraytype='xarray')
         ccn5_fit_i = interp_time_1d(time, ccn5_fit, time_new, arraytype='xarray')