diff --git a/src/weathergen/datasets/data_reader_synop.py b/src/weathergen/datasets/data_reader_synop.py
new file mode 100644
index 000000000..913efb624
--- /dev/null
+++ b/src/weathergen/datasets/data_reader_synop.py
@@ -0,0 +1,274 @@
+# (C) Copyright 2025 WeatherGenerator contributors.
+#
+# This software is licensed under the terms of the Apache Licence Version 2.0
+# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# In applying this licence, ECMWF does not waive the privileges and immunities
+# granted to it by virtue of its status as an intergovernmental organisation
+# nor does it submit to any jurisdiction.
+
+import logging
+from pathlib import Path
+from typing import override
+
+import numpy as np
+import xarray as xr
+from numpy.typing import NDArray
+
+from weathergen.datasets.data_reader_base import (
+    DataReaderTimestep,
+    ReaderData,
+    TimeWindowHandler,
+    TIndex,
+    check_reader_data,
+)
+
+_logger = logging.getLogger(__name__)
+
+
+class DataReaderSynop(DataReaderTimestep):
+    "Wrapper for SYNOP datasets from MetNo in NetCDF"
+
+    def __init__(
+        self,
+        tw_handler: TimeWindowHandler,
+        filename: Path,
+        stream_info: dict,
+    ) -> None:
+        """
+        Construct data reader for anemoi dataset
+
+        Parameters
+        ----------
+        filename :
+            filename (and path) of dataset
+        stream_info :
+            information about stream
+
+        Returns
+        -------
+        None
+        """
+
+        np32 = np.float32
+
+        # open  dataset to peak that it is compatible with requested parameters
+        ds = xr.open_dataset(filename, engine="netcdf4")
+
+        # If there is no overlap with the time range, the dataset will be empty
+        if tw_handler.t_start >= ds.time.max() or tw_handler.t_end <= ds.time.min():
+            name = stream_info["name"]
+            _logger.warning(f"{name} is not supported over data loader window. Stream is skipped.")
+            super().__init__(tw_handler, stream_info)
+            self._init_empty()
+            return
+
+        if "frequency" in stream_info:
+            assert False, "Frequency sub-sampling currently not supported"
+
+        period = (ds.time[1] - ds.time[0]).values
+        data_start_time = ds.time[0].values
+        data_end_time = ds.time[-1].values
+        assert data_start_time is not None and data_end_time is not None, (
+            data_start_time,
+            data_end_time,
+        )
+        super().__init__(
+            tw_handler,
+            stream_info,
+            data_start_time,
+            data_end_time,
+            period,
+        )
+        # If there is no overlap with the time range, no need to keep the dataset.
+        if tw_handler.t_start >= data_end_time or tw_handler.t_end <= data_start_time:
+            self._init_empty()
+            return
+        else:
+            self.ds = ds
+            self.len = len(ds)
+
+        self.offset_data_channels = 4
+        self.fillvalue = ds["air_temperature"][0, 0].values.item()
+        self.channels_file = [k for k in self.ds.keys()]
+
+        # caches lats and lons
+        lat_name = stream_info.get("latitude_name", "latitude")
+        self.latitudes = _clip_lat(np.array(ds[lat_name], dtype=np32))
+        lon_name = stream_info.get("longitude_name", "longitude")
+        self.longitudes = _clip_lon(np.array(ds[lon_name], dtype=np32))
+
+        self.geoinfo_channels = stream_info.get("geoinfos", [])
+        self.geoinfo_idx = [self.channels_file.index(ch) for ch in self.geoinfo_channels]
+        # cache geoinfos
+        self.geoinfo_data = np.stack([np.array(ds[ch], dtype=np32) for ch in self.geoinfo_channels])
+        self.geoinfo_data = self.geoinfo_data.transpose()
+
+        # select/filter requested source channels
+        self.source_idx = self.select_channels(ds, "source")
+        self.source_channels = [self.channels_file[i] for i in self.source_idx]
+
+        # select/filter requested target channels
+        self.target_idx = self.select_channels(ds, "target")
+        self.target_channels = [self.channels_file[i] for i in self.target_idx]
+
+        ds_name = stream_info["name"]
+        _logger.info(f"{ds_name}: source channels: {self.source_channels}")
+        _logger.info(f"{ds_name}: target channels: {self.target_channels}")
+        _logger.info(f"{ds_name}: geoinfo channels: {self.geoinfo_channels}")
+
+        self.properties = {
+            "stream_id": 0,
+        }
+
+        # TODO: this should be stored/cached
+        self.mean, self.stdev = self._compute_mean_stdev()
+        self.mean_geoinfo = self.mean[self.geoinfo_idx]
+        self.stdev_geoinfo = self.stdev[self.geoinfo_idx]
+
+    def _compute_mean_stdev(self) -> (np.array, np.array):
+        _logger.info("Starting computation of mean and stdev.")
+
+        mean, stdev = [], []
+
+        for ch in self.channels_file:
+            data = np.array(self.ds[ch], np.float64)
+            mask = data == self.fillvalue
+            data[mask] = np.nan
+            mean += [np.nanmean(data.flatten())]
+            stdev += [np.nanstd(data.flatten())]
+
+        mean = np.array(mean)
+        stdev = np.array(stdev)
+
+        _logger.info("Finished computation of mean and stdev.")
+
+        return mean, stdev
+
+    @override
+    def _init_empty(self) -> None:
+        super()._init_empty()
+        self.ds = None
+        self.len = 0
+
+    @override
+    def length(self) -> int:
+        """
+        Length of dataset
+
+        Return :
+        Length
+        """
+        return self.len
+
+    @override
+    def _get(self, idx: TIndex, channels_idx: list[int]) -> ReaderData:
+        """
+        Get data for window (for either source or target, through public interface)
+
+        Parameters
+        ----------
+        idx : int
+            Index of temporal window
+        channels_idx : np.array
+            Selection of channels
+
+        Returns
+        -------
+        ReaderData providing coords, geoinfos, data, datetimes
+        """
+
+        (t_idxs, dtr) = self._get_dataset_idxs(idx)
+
+        if self.ds is None or self.len == 0 or len(t_idxs) == 0:
+            return ReaderData.empty(
+                num_data_fields=len(channels_idx), num_geo_fields=len(self.geoinfo_idx)
+            )
+
+        assert t_idxs[0] >= 0, "index must be non-negative"
+        didx_start = t_idxs[0]
+        # End is inclusive
+        didx_end = t_idxs[-1] + 1
+
+        # extract number of time steps and collapse ensemble dimension
+        # ds is a wrapper around zarr with get_coordinate_selection not being exposed since
+        # subsetting is pushed to the ctor via frequency argument; this also ensures that no sub-
+        # sampling is required here
+        sel_channels = [self.channels_file[i] for i in channels_idx]
+        data = self.ds[sel_channels].isel(time=slice(didx_start, didx_end)).to_array().values
+        # flatten along time dimension
+        data = data.transpose([1, 2, 0]).reshape((data.shape[1] * data.shape[2], data.shape[0]))
+        # set invalid values to NaN
+        mask = data == self.fillvalue
+        data[mask] = np.nan
+
+        # construct lat/lon coords
+        latlon = np.concatenate(
+            [
+                np.expand_dims(self.latitudes, 0),
+                np.expand_dims(self.longitudes, 0),
+            ],
+            axis=0,
+        ).transpose()
+
+        # repeat len(t_idxs) times
+        coords = np.vstack((latlon,) * len(t_idxs))
+        geoinfos = np.vstack((self.geoinfo_data,) * len(t_idxs))
+
+        # date time matching #data points of data
+        datetimes = np.repeat(self.ds.time[didx_start:didx_end].values, len(data) // len(t_idxs))
+
+        rd = ReaderData(
+            coords=coords,
+            geoinfos=geoinfos,
+            data=data,
+            datetimes=datetimes,
+        )
+        check_reader_data(rd, dtr)
+
+        return rd
+
+    def select_channels(self, ds, ch_type: str) -> NDArray[np.int64]:
+        """
+        Select source or target channels
+
+        Parameters
+        ----------
+        ds0 :
+            raw anemoi dataset with available channels
+        ch_type :
+            "source" or "target", i.e channel type to select
+
+        Returns
+        -------
+        ReaderData providing coords, geoinfos, data, datetimes
+
+        """
+
+        channels = self.stream_info.get(ch_type)
+        assert channels is not None, f"{ch_type} channels need to be specified"
+        # sanity check
+        is_empty = len(channels) == 0 if channels is not None else False
+        if is_empty:
+            stream_name = self.stream_info["name"]
+            _logger.warning(f"No channel for {stream_name} for {ch_type}.")
+
+        chs_idx = np.sort([self.channels_file.index(ch) for ch in channels])
+
+        return np.array(chs_idx)
+
+
+# TODO: move to base class
+def _clip_lat(lats: NDArray) -> NDArray[np.float32]:
+    """
+    Clip latitudes to the range [-90, 90] and ensure periodicity.
+    """
+    return (2 * np.clip(lats, -90.0, 90.0) - lats).astype(np.float32)
+
+
+# TODO: move to base class
+def _clip_lon(lons: NDArray) -> NDArray[np.float32]:
+    """
+    Clip longitudes to the range [-180, 180] and ensure periodicity.
+    """
+    return ((lons + 180.0) % 360.0 - 180.0).astype(np.float32)
diff --git a/src/weathergen/datasets/multi_stream_data_sampler.py b/src/weathergen/datasets/multi_stream_data_sampler.py
index ef2d6be24..20228b7b2 100644
--- a/src/weathergen/datasets/multi_stream_data_sampler.py
+++ b/src/weathergen/datasets/multi_stream_data_sampler.py
@@ -23,6 +23,7 @@
 )
 from weathergen.datasets.data_reader_fesom import DataReaderFesom
 from weathergen.datasets.data_reader_obs import DataReaderObs
+from weathergen.datasets.data_reader_synop import DataReaderSynop
 from weathergen.datasets.icon_dataset import IconDataset
 from weathergen.datasets.masking import Masker
 from weathergen.datasets.stream_data import StreamData
@@ -108,6 +109,9 @@ def __init__(
                     case "icon":
                         dataset = IconDataset
                         datapath = cf.data_path_icon
+                    case "station":
+                        dataset = DataReaderSynop
+                        datapath = cf.data_path_obs
                     case _:
                         msg = f"Unsupported stream type {stream_info['type']}"
                         f"for stream name '{stream_info['name']}'."