diff --git a/mlair/helpers/statistics.py b/mlair/helpers/statistics.py
index 9a0346e4f09bbbe75d2c8dd70dac2d26d1b5b146..aa89da0ea66e263d076af9abd578ba125c260bec 100644
--- a/mlair/helpers/statistics.py
+++ b/mlair/helpers/statistics.py
@@ -525,8 +525,8 @@ def calculate_average(data: xr.DataArray, **kwargs) -> xr.DataArray:
     return data.mean(**kwargs)
 
 
-def create_n_bootstrap_realizations(data: xr.DataArray, dim_name_time, dim_name_model, n_boots: int = 1000,
-                                    dim_name_boots='boots') -> xr.DataArray:
+def create_n_bootstrap_realizations(data: xr.DataArray, dim_name_time: str, dim_name_model: str, n_boots: int = 1000,
+                                    dim_name_boots: str = 'boots') -> xr.DataArray:
     """
     Create n bootstrap realizations and calculate averages across realizations
 
@@ -546,8 +546,7 @@ def create_n_bootstrap_realizations(data: xr.DataArray, dim_name_time, dim_name_
     for boot in range(n_boots):
         res[boot] = (calculate_average(
             create_single_bootstrap_realization(data, dim_name_time=dim_name_time),
-            dim=dim_name_time
-        ))
+            dim=dim_name_time, skipna=True))
     return res
 
 
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index e5e6b77196368a571124efc0844ba7f1bb8ed97f..e7ed04b2f8694e7e4e2c90d215cb042cb33beef8 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -131,10 +131,15 @@ class PostProcessing(RunEnvironment):
         self.plot()
 
     def estimate_sample_uncertainty(self, separate_ahead=False):
+        #todo: set n_boots
+        #todo: visualize
+        #todo: write results on disk
         block_length = self.data_store.get_default("uncertainty_estimate_block_length", default="1m")
         evaluate_competitors = self.data_store.get_default("uncertainty_estimate_evaluate_competitors", default=True)
         block_mse = self.calculate_block_mse(evaluate_competitors=evaluate_competitors, separate_ahead=separate_ahead,
                                              block_length=block_length)
+        res = statistics.create_n_bootstrap_realizations(block_mse, self.index_dim, self.model_type_dim, n_boots=10)
+        res
 
     def calculate_block_mse(self, evaluate_competitors=True, separate_ahead=False, block_length="1m"):
         path = self.data_store.get("forecast_path")