diff --git a/py/frequenz/client/weather/_types.py b/py/frequenz/client/weather/_types.py
index deb1809..1501496 100644
--- a/py/frequenz/client/weather/_types.py
+++ b/py/frequenz/client/weather/_types.py
@@ -192,36 +192,48 @@ def to_ndarray_vlf(
             # get the location indexes of the proto for the filtered locations
             if locations:
                 for location in locations:
+                    found = False
                     for l_index, location_forecast in enumerate(
                         self._forecasts_pb.location_forecasts
                     ):
                         if location == Location.from_pb(location_forecast.location):
                             location_indexes.append(l_index)
+                            found = True
                             break
+                    if not found:
+                        location_indexes.append(-1)
             else:
                 location_indexes = list(range(num_locations))
 
             # get the val indexes of the proto for the filtered validity times
             if validity_times:
                 for req_validitiy_time in validity_times:
+                    found = False
                     for t_index, val_time in enumerate(
                         self._forecasts_pb.location_forecasts[0].forecasts
                     ):
                         if req_validitiy_time == val_time.valid_at_ts.ToDatetime():
                             validity_times_indexes.append(t_index)
+                            found = True
                             break
+                    if not found:
+                        validity_times_indexes.append(-1)
             else:
                 validity_times_indexes = list(range(num_times))
 
             # get the feature indexes of the proto for the filtered features
             if features:
                 for req_feature in features:
+                    found = False
                     for f_index, feature in enumerate(
                         self._forecasts_pb.location_forecasts[0].forecasts[0].features
                     ):
                         if req_feature == ForecastFeature.from_pb(feature.feature):
                             feature_indexes.append(f_index)
+                            found = True
                             break
+                    if not found:
+                        feature_indexes.append(-1)
             else:
                 feature_indexes = list(range(num_features))
 
@@ -243,12 +255,13 @@ def to_ndarray_vlf(
                     array_f_index = 0
 
                     for f_index in feature_indexes:
-                        array[array_t_index, array_l_index, array_f_index] = (
-                            self._forecasts_pb.location_forecasts[l_index]
-                            .forecasts[t_index]
-                            .features[f_index]
-                            .value
-                        )
+                        if l_index >= 0 and t_index >= 0 and f_index >= 0:
+                            array[array_t_index, array_l_index, array_f_index] = (
+                                self._forecasts_pb.location_forecasts[l_index]
+                                .forecasts[t_index]
+                                .features[f_index]
+                                .value
+                            )
                         array_f_index += 1
 
                     array_t_index += 1