diff --git a/azmp_genReport2024.py b/azmp_genReport2024.py
index 9488a12..b3b614e 100644
--- a/azmp_genReport2024.py
+++ b/azmp_genReport2024.py
@@ -65,8 +65,16 @@
 
 ## ---- 2023 update ---- ##
 # 0. Update CASTS and CABOTS.
-# (separate scripts)    
-    
+# (separate scripts)
+
+# Update CASTS from FRDR if wanted
+print('Would you like to update CASTS? [yes/no]')
+CASTS_up = input()
+if CASTS_up == 'yes':
+    azgen.CASTS_update(years=np.arange(1912,int(yoi)).astype(str),version='CASTS_2023')
+elif 'no':
+    print('No update to CASTS made.')
+
 
 # 1.  NAO, AO, AMO
 azgen.nao(int(yoi),'~/data/AZMP/climate_indices/nao_data.csv')
diff --git a/azmp_modules/azmp_genreport.py b/azmp_modules/azmp_genreport.py
index 9fd8341..d05208a 100644
--- a/azmp_modules/azmp_genreport.py
+++ b/azmp_modules/azmp_genreport.py
@@ -23,6 +23,7 @@
 __version__ = '0.1'
 
 import os
+import wget
 import re
 import numpy as np
 import pandas as pd
@@ -44,7 +45,26 @@
 os.environ['PROJ_LIB'] = '/home/cyrf0006/anaconda3/share/proj'
 #from mpl_toolkits.basemap import Basemap
 
-    
+
+
+def CASTS_update(
+    years,
+    version,
+    out_path='~/data/CASTS/',
+    url='https://g-772fa5.cd4fe.0ec8.data.globus.org/1/published/publication_734/submitted_data/',):
+    '''
+    Update CASTS using wget directly from FRDR.
+    Dowloads the yearly files for specified years.
+    '''
+    for year in years:
+        if os.path.exists(os.path.expanduser(out_path+year+'.nc')):
+            os.remove(url+version+'/'+year+'.nc')
+        wget.download(
+            url+version+'/'+year+'.nc',
+            os.path.expanduser(out_path))
+        print(year+' CASTS files updated.')
+
+
 def nao(
     YEAR,
     nao_file_loc,
diff --git a/azmp_modules/azmp_report_tools.py b/azmp_modules/azmp_report_tools.py
index e7ce0f2..fde3a6d 100644
--- a/azmp_modules/azmp_report_tools.py
+++ b/azmp_modules/azmp_report_tools.py
@@ -3927,6 +3927,9 @@ def bottomS_scorecards(path, years, clim_year=[2006, 2021]):
     df = df[(df.index.year>=years[0]) & (df.index.year<=years[-1])]
     percent_coverage = df.S_percent_coverage.values.copy().round(0)
     # Flag bad years (no or weak sampling):
+    bad_years = np.array([1981, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 2020, 2023])
+    for i in bad_years:
+        df[df.index.year==i]=np.nan
     bad_years = df.index.year.values[percent_coverage < 80]
     for i in bad_years:
         df[df.index.year==i]=np.nan