Index: trunk/SDToolBox/extract_data.py
===================================================================
diff -u -r193 -r196
--- trunk/SDToolBox/extract_data.py	(.../extract_data.py)	(revision 193)
+++ trunk/SDToolBox/extract_data.py	(.../extract_data.py)	(revision 196)
@@ -110,6 +110,18 @@
         Returns:
             Tuple[np.array, np.array, np.array]: Sea positions, LON, LAT
         """
+        sea_mask, LON, LAT = ExtractData.get_seamask(mask_filepath)
+        if np.where(sea_mask.ravel() == 1)[0].size == 0:
+            logging.error(
+                f"No sea positions were found at the extracted mask from {mask_filepath}."
+            )
+        # positions where we are in the sea
+        logging.info(f"Generating sea mask with mask: {sea_mask}")
+
+        return sea_mask, LON, LAT
+
+    @staticmethod
+    def get_seamask(mask_filepath: Path,) -> Tuple[np.array, np.array, np.array]:
         logging.info(f"Getting masked file from {mask_filepath}.")
         with Dataset(mask_filepath, "r", "netCDF4") as sea_mask:
             logging.info(f"Extracting variable lsm from mask file.")
@@ -126,14 +138,10 @@
 
             LON = np.concatenate((LON[:, loni + 1 :], LON[:, 0 : loni + 1]), axis=1)
             mask = np.concatenate((mask[:, loni + 1 :], mask[:, 0 : loni + 1]), axis=1)
-            # positions where we are in the sea
-            logging.info(f"Generating sea mask with mask: {mask}")
-            possea = np.where(mask.ravel() == 0.0)[0]
-            if possea.size == 0:
-                logging.error(
-                    f"No sea positions were found at the extracted mask from {mask_filepath}."
-                )
-            return possea, LON, LAT
+            mask[mask != 0] = -1
+            mask[mask == 0] = 1
+            mask[mask == -1] = 0
+            return mask, LON, LAT
 
     class BaseExtractor(ABC):
         file_var_key = "variable"
@@ -520,25 +528,41 @@
                     Index of nearest neighbors and a Tuple of corrected Latitude and Longitude
             """
             mask_filepath = Path(mask_dir) / "ERA5_landsea_mask.nc"
-            possea, LON, LAT = ExtractData.get_seamask_positions(mask_filepath)
-            index, vals = self.get_nearest_neighbor_extended(
-                np.vstack((input_data._input_lon, input_data._input_lat)).T,
-                np.vstack((LON.ravel()[possea], LAT.ravel()[possea])).T,
-            )
-            index_abs = possea[index].squeeze()
 
-            if index_abs.size <= 0:
-                return None, None
-            indexes = np.unravel_index(index_abs, LON.shape)
-            unique_values, unique_idx = np.unique(vals, axis=0, return_index=True)
-            nn_idx = indexes[1], indexes[0]
-            if len(unique_values.shape) == 1:
-                return (
-                    nn_idx,
-                    (np.array([unique_values[1]]), np.array([unique_values[0]])),
+            sea_mask, LON, LAT = ExtractData.get_seamask_positions(mask_filepath)
+            possea = np.where(sea_mask.ravel() == 1)[0]
+
+            if not input_data.is_gridded:
+                index, vals = self.get_nearest_neighbor_extended(
+                    np.vstack((input_data._input_lon, input_data._input_lat)).T,
+                    np.vstack((LON.ravel()[possea], LAT.ravel()[possea])).T,
                 )
-            return nn_idx, (unique_values[:, 1], unique_values[:, 0])
+                index_abs = possea[index].squeeze()
 
+                if index_abs.size <= 0:
+                    return None, None
+                indexes = np.unravel_index(index_abs, LON.shape)
+                unique_values, unique_idx = np.unique(vals, axis=0, return_index=True)
+                nn_idx = indexes[1], indexes[0]
+                if len(unique_values.shape) == 1:
+                    return (
+                        nn_idx,
+                        (np.array([unique_values[1]]), np.array([unique_values[0]])),
+                    )
+            else:
+                index, vals = self.get_nearest_neighbor_extended(
+                    np.vstack((input_data._input_lon, input_data._input_lat)).T,
+                    np.vstack((LON.ravel(), LAT.ravel())).T,
+                )
+                indexes = np.unravel_index(index.squeeze(), LON.shape)
+                nn_idx = indexes[1], indexes[0]
+                unique_values, unique_idx = np.unique(vals, axis=0, return_index=True)
+                input_data.sea_mask = sea_mask[
+                    np.ix_(sorted(indexes[0]), sorted(indexes[1]))
+                ]
+                logging.info("Added sea mask to input_data.")
+                return nn_idx, (unique_values[:, 1], unique_values[:, 0])
+
         @staticmethod
         def __get_case_subset(
             dataset: Dataset,
@@ -583,6 +607,11 @@
                     + "[{}]".format(", ".join([str(coord) for coord in lat]))
                     + "[{}]".format(", ".join([str(coord) for coord in lon]))
                 )
+                if hasattr(input_data, "sea_mask"):
+                    return (
+                        np.ma.getdata(dataset[variable_name][:, lat, lon])
+                        * input_data.sea_mask
+                    )
                 return np.ma.getdata(dataset[variable_name][:, lat, lon])
             return np.ma.getdata(dataset[variable_name][:, lat[0], lon[0]])
 
Index: trunk/tests/test_acceptance.py
===================================================================
diff -u -r193 -r196
--- trunk/tests/test_acceptance.py	(.../test_acceptance.py)	(revision 193)
+++ trunk/tests/test_acceptance.py	(.../test_acceptance.py)	(revision 196)
@@ -229,11 +229,11 @@
         self, variable_key: str
     ):
         steplon = 0.5
-        lonl = 3
+        lonl = 5
         lonr = 7.5
         steplat = 0.5
         latl = 52.5
-        latu = 57
+        latu = 55
         coordsBOX = {
             "LON": np.arange(lonl, lonr + steplon, steplon).tolist(),
             "LAT": np.arange(latl, latu + steplat, steplat).tolist(),