diff --git a/video_prediction_tools/other_scripts/visualize_postprocess_era5_template.sh b/video_prediction_tools/other_scripts/visualize_postprocess_era5_template.sh
index 337a817bad5e602ebaff0215f6fecf89788bedc2..a9d9c621d4c090f17669650613d340a6bf9a5e7a 100644
--- a/video_prediction_tools/other_scripts/visualize_postprocess_era5_template.sh
+++ b/video_prediction_tools/other_scripts/visualize_postprocess_era5_template.sh
@@ -6,16 +6,18 @@ VIRT_ENV_NAME=venv_test
 echo "Activating virtual environment..."
 source ../virtual_envs/${VIRT_ENV_NAME}/bin/activate
 
-#the source directory contains the tfrecords
-checkpoint_dir=/home/b.gong/model/checkpoint_89
-results_dir=/home/b.gong/results/
-lquick=1
+#checkpoint_dir: the checkpoint directory from train step
+checkpoint_dir=/path/to/checkpoint/directory
+#Results_dir: the output dir to save the results
+results_dir=/path/to/results/directory
+#Climate_file: the netcdf file point to the climtology, which you can download along with the samples data
 climate_file=/home/b.gong/data_era5/T2monthly/climatology_t2m_1991-2020.nc
 #select models
 model=convLSTM
-#mkdir ${results_dir}
+
+# The --lquick_evaluation enable you to quick test and generate the results. If you want to test on full large test dataset, please remove the --lquick_evaluation argument
 python3 ../main_scripts/main_visualize_postprocess.py --checkpoint  ${checkpoint_dir} --mode test  \
                                                            --results_dir ${results_dir} --batch_size 4 \
                                                            --num_stochastic_samples 1 \
-							   --lquick_evaluation ${lquick} --climatology_file ${climate_file}
+							   --lquick_evaluation --climatology_file ${climate_file}