diff --git a/toargridding/metadata.py b/toargridding/metadata.py
index 2c608850a2e3cebbee09f4ca1278a3ba137feb4e..2ba49787c7947f83df6153d4500b52fcfe7e5b84 100644
--- a/toargridding/metadata.py
+++ b/toargridding/metadata.py
@@ -1,4 +1,4 @@
-from datetime import datetime
+from datetime import datetime, timedelta
 from enum import Enum
 from dataclasses import dataclass
 
@@ -33,7 +33,8 @@ class TimeSample:
 
     @property
     def daterange_option(self):
-        return f"{self.start.isoformat()},{self.end.isoformat()}"
+        end_with_padding = self.end + timedelta(1)
+        return f"{self.start.isoformat()},{end_with_padding.isoformat()}"
 
     @property
     def frequency(self):
diff --git a/toargridding/toar_rest_client.py b/toargridding/toar_rest_client.py
index 26b3d3b4a455b2bfc37eb3e452b7feaaec495dcd..28fb9fc4a96b4853da26e23a1d794c14492aa701 100644
--- a/toargridding/toar_rest_client.py
+++ b/toargridding/toar_rest_client.py
@@ -154,16 +154,16 @@ class AnalysisService:
         return coords[valid_coords]
 
     def get_clean_timeseries(self, timeseries, metadata: Metadata):
+        # TODO maybe use cf-index here already ?
+        first, last = timeseries.columns[0], timeseries.columns[-1]
+        # remove data where utc -> sun/local ? time conversion leads to dateshift
+        timeseries.drop(columns=[first, last], inplace=True)
+        timeseries.columns = metadata.time.as_datetime_index()
+
         all_na = timeseries.isna().all(axis=1)
         timeseries = timeseries[~all_na]
         timeseries = timeseries.fillna(0)
-        response_timesteps = timeseries.columns
-        expected_timesteps = metadata.time.as_datetime_index()
-        if not (response_timesteps == expected_timesteps).all():
-            raise ValueError("foo")
 
-        # TODO maybe use cf-index here already ?
-        timeseries.columns = metadata.time.as_datetime_index()
         return timeseries
 
     def load_data(