diff --git a/uv.lock b/uv.lock
index b00f135..392e89b 100644
--- a/uv.lock
+++ b/uv.lock
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 name = "xarray"
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+version = "2025.10.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
     { name = "packaging" },
     { name = "pandas" },
 ]
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 [[package]]
diff --git a/workflow/Snakefile b/workflow/Snakefile
index cd7ead6..0692dd0 100644
--- a/workflow/Snakefile
+++ b/workflow/Snakefile
@@ -55,6 +55,13 @@ rule showcase_all:
             param=["TOT_PREC", "T_2M", "SP_10M"],
             region=["globe", "europe", "switzerland"],
         ),
+        expand(
+            OUT_ROOT / "showcases/{run_id}/{init_time}/{init_time}_{param}_{sta}.png",
+            init_time=[t.strftime("%Y%m%d%H%M") for t in REFTIMES],
+            run_id=collect_all_candidates(),
+            param=["T_2M"],
+            sta=["GVE", "KLO", "LUG"],
+        ),
 
 
 rule sandbox_all:
diff --git a/workflow/rules/plot.smk b/workflow/rules/plot.smk
index a312f89..3e34a48 100644
--- a/workflow/rules/plot.smk
+++ b/workflow/rules/plot.smk
@@ -9,6 +9,33 @@ include: "common.smk"
 import pandas as pd
 
 
+rule plot_meteogram:
+    input:
+        script="workflow/scripts/plot_meteogram.mo.py",
+        fct_grib=rules.inference_routing.output[0],
+        analysis_zarr=config["analysis"].get("analysis_zarr"),
+    output:
+        OUT_ROOT / "showcases/{run_id}/meteograms/{init_time}_{param}_{sta}.png",
+    # localrule: True
+    resources:
+        slurm_partition="postproc",
+        cpus_per_task=1,
+        runtime="5m",
+    shell:
+        """
+        export ECCODES_DEFINITION_PATH=$(realpath .venv/share/eccodes-cosmo-resources/definitions)
+        python {input.script} \
+            --forecast {input.fct_grib}  --analysis {input.analysis_zarr} \
+            --date {wildcards.init_time} --outfn {output[0]} \
+            --param {wildcards.param}  --station {wildcards.sta}
+        # interactive editing (needs to set localrule: True and use only one core)
+        # marimo edit {input.script} -- \
+        #     --forecast {input.fct_grib}  --analysis {input.analysis_zarr} \
+        #     --date {wildcards.init_time} --outfn {output[0]} \
+        #     --param {wildcards.param}  --station {wildcards.sta}
+        """
+
+
 rule plot_forecast_frame:
     input:
         script="workflow/scripts/plot_forecast_frame.mo.py",
diff --git a/workflow/scripts/plot_meteogram.mo.py b/workflow/scripts/plot_meteogram.mo.py
new file mode 100644
index 0000000..a624072
--- /dev/null
+++ b/workflow/scripts/plot_meteogram.mo.py
@@ -0,0 +1,395 @@
+import marimo
+
+__generated_with = "0.16.5"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def _():
+    import logging
+    from argparse import ArgumentParser
+    from pathlib import Path
+
+    import matplotlib.pyplot as plt
+    import numpy as np
+    import pandas as pd
+    import xarray as xr
+
+    from meteodatalab import data_source, grib_decoder
+
+    return (
+        ArgumentParser,
+        Path,
+        data_source,
+        grib_decoder,
+        logging,
+        np,
+        pd,
+        plt,
+        xr,
+    )
+
+
+@app.cell
+def _(logging):
+    LOG = logging.getLogger(__name__)
+    LOG_FMT = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+    logging.basicConfig(level=logging.INFO, format=LOG_FMT)
+    return
+
+
+@app.cell
+def _(ArgumentParser, Path):
+    ROOT = Path(__file__).parent
+
+    parser = ArgumentParser()
+
+    parser.add_argument(
+        "--forecast", type=str, default=None, help="Directory to forecast grib data"
+    )
+    parser.add_argument(
+        "--analysis", type=str, default=None, help="Path to analysis zarr data"
+    )
+    parser.add_argument("--date", type=str, default=None, help="reference datetime")
+    parser.add_argument("--outfn", type=str, help="output filename")
+    parser.add_argument("--param", type=str, help="parameter")
+    parser.add_argument("--station", type=str, help="station")
+
+    args = parser.parse_args()
+    grib_dir = Path(args.forecast)
+    zarr_dir = Path(args.analysis)
+    init_time = args.date
+    outfn = Path(args.outfn)
+    station = args.station
+    param = args.param
+    return grib_dir, init_time, outfn, param, station, zarr_dir
+
+
+@app.cell
+def _(pd):
+    stations = pd.DataFrame(
+        [
+            (
+                "BAS",
+                "1_75",
+                1,
+                75,
+                "BAS",
+                "Basel / Binningen",
+                1,
+                "MeteoSchweiz",
+                7.583,
+                47.541,
+                316.14,
+                12.0,
+                0.0,
+            ),
+            (
+                "LUG",
+                "1_47",
+                1,
+                47,
+                "LUG",
+                "Lugano",
+                1,
+                "MeteoSchweiz",
+                8.960,
+                46.004,
+                272.56,
+                10.0,
+                27.34,
+            ),
+            (
+                "GVE",
+                "1_58",
+                1,
+                58,
+                "GVE",
+                "Gen\u00e8ve / Cointrin",
+                1,
+                "MeteoSchweiz",
+                6.122,
+                46.248,
+                415.53,
+                10.0,
+                0.0,
+            ),
+            (
+                "GUT",
+                "1_79",
+                1,
+                79,
+                "GUT",
+                "G\u00fcttingen",
+                1,
+                "MeteoSchweiz",
+                9.279,
+                47.602,
+                439.78,
+                12.0,
+                0.0,
+            ),
+            (
+                "KLO",
+                "1_59",
+                1,
+                59,
+                "KLO",
+                "Z\u00fcrich / Kloten",
+                1,
+                "MeteoSchweiz",
+                8.536,
+                47.48,
+                435.92,
+                10.5,
+                0.0,
+            ),
+            (
+                "SCU",
+                "1_30",
+                1,
+                30,
+                "SCU",
+                "Scuol",
+                1,
+                "MeteoSchweiz",
+                10.283,
+                46.793,
+                1304.42,
+                10.0,
+                0.0,
+            ),
+            (
+                "LUZ",
+                "1_68",
+                1,
+                68,
+                "LUZ",
+                "Luzern",
+                1,
+                "MeteoSchweiz",
+                8.301,
+                47.036,
+                454.0,
+                8.41,
+                32.51,
+            ),
+            (
+                "DIS",
+                "1_54",
+                1,
+                54,
+                "DIS",
+                "Disentis",
+                1,
+                "MeteoSchweiz",
+                8.853,
+                46.707,
+                1198.03,
+                10.0,
+                0.0,
+            ),
+            (
+                "PMA",
+                "1_862",
+                1,
+                862,
+                "PMA",
+                "Piz Martegnas",
+                1,
+                "MeteoSchweiz",
+                9.529,
+                46.577,
+                2668.34,
+                10.0,
+                0.0,
+            ),
+            (
+                "CEV",
+                "1_843",
+                1,
+                843,
+                "CEV",
+                "Cevio",
+                1,
+                "MeteoSchweiz",
+                8.603,
+                46.32,
+                420.0,
+                10.0,
+                6.85,
+            ),
+            (
+                "MLS",
+                "1_38",
+                1,
+                38,
+                "MLS",
+                "Le Mol\u00e9son",
+                1,
+                "MeteoSchweiz",
+                7.018,
+                46.546,
+                1977.0,
+                10.0,
+                13.31,
+            ),
+            (
+                "PAY",
+                "1_32",
+                1,
+                32,
+                "PAY",
+                "Payerne",
+                1,
+                "MeteoSchweiz",
+                6.942,
+                46.811,
+                489.17,
+                10.0,
+                0.0,
+            ),
+            (
+                "NAP",
+                "1_48",
+                1,
+                48,
+                "NAP",
+                "Napf",
+                1,
+                "MeteoSchweiz",
+                7.94,
+                47.005,
+                1404.03,
+                15.0,
+                0.0,
+            ),
+        ],
+        columns=[
+            "station",
+            "name",
+            "type_id",
+            "point_id",
+            "nat_abbr",
+            "fullname",
+            "owner_id",
+            "owner_name",
+            "longitude",
+            "latitude",
+            "height_masl",
+            "pole_height",
+            "roof_height",
+        ],
+    )
+    return (stations,)
+
+
+@app.cell
+def load_grib_data(data_source, grib_decoder, grib_dir, init_time, param):
+    if param == "SP_10M":
+        paramlist = ["U_10M", "V_10M"]
+    elif param == "SP":
+        paramlist = ["U", "V"]
+    else:
+        paramlist = [param]
+
+    grib_files = sorted(grib_dir.glob(f"{init_time}*.grib"))
+    fds = data_source.FileDataSource(datafiles=grib_files)
+    ds_grib = grib_decoder.load(fds, {"param": paramlist})
+    ds_grib = ds_grib[param].squeeze()
+    ds_grib
+    return ds_grib, paramlist
+
+
+@app.cell
+def load_zarr_data(ds_grib, np, param, paramlist, xr, zarr_dir):
+    ds_zarr = xr.open_zarr(zarr_dir, consolidated=False)
+    ds_zarr = ds_zarr.set_index(time="dates")
+    ds_zarr = ds_zarr.sel(time=ds_grib.valid_time.values)
+    ds_zarr = ds_zarr.assign_coords({"variable": ds_zarr.attrs["variables"]})
+    ds_zarr = ds_zarr.sel(variable=[p for p in paramlist]).squeeze(
+        "ensemble", drop=True
+    )
+
+    # recover original 2D shape
+    ny, nx = ds_zarr.attrs["field_shape"]
+    y_idx, x_idx = np.unravel_index(np.arange(ny * nx), shape=(ny, nx))
+    ds_zarr = ds_zarr.assign_coords({"y": ("cell", y_idx), "x": ("cell", x_idx)})
+    ds_zarr = ds_zarr.set_index(cell=("y", "x"))
+    ds_zarr = ds_zarr.unstack("cell")
+
+    # set lat lon as coords
+    ds_zarr = ds_zarr.rename({"latitudes": "lat", "longitudes": "lon"})
+    ds_zarr = ds_zarr.set_coords(["lat", "lon"])
+    ds_zarr = ds_zarr.sel(variable=param).squeeze()["data"]
+    ds_zarr
+    return (ds_zarr,)
+
+
+@app.cell
+def _(ds_grib, ds_zarr, np, pd, stations):
+    def nearest_yx_euclid(ds_grib, lat_s, lon_s):
+        """
+        Return (y_idx, x_idx) of the grid point nearest to (lat_s, lon_s)
+        using Euclidean distance in degrees.
+        """
+        lat2d = ds_grib["lat"]  # (y, x)
+        lon2d = ds_grib["lon"]  # (y, x)
+
+        dist2 = (lat2d - lat_s) ** 2 + (lon2d - lon_s) ** 2
+        flat_idx = np.nanargmin(dist2.values)
+        y_idx, x_idx = np.unravel_index(flat_idx, dist2.shape)
+        return int(y_idx), int(x_idx)
+
+    def get_grib_idx_row(row):
+        return nearest_yx_euclid(ds_grib, row["latitude"], row["longitude"])
+
+    idxs_grib = stations.apply(get_grib_idx_row, axis=1, result_type="expand")
+    idxs_grib.columns = ["grib_y_idx", "grib_x_idx"]
+
+    def get_zarr_idx_row(row):
+        return nearest_yx_euclid(ds_zarr, row["latitude"], row["longitude"])
+
+    idxs_zarr = stations.apply(get_zarr_idx_row, axis=1, result_type="expand")
+    idxs_zarr.columns = ["zarr_y_idx", "zarr_x_idx"]
+
+    sta_idxs = pd.concat([stations, idxs_grib, idxs_zarr], axis=1).set_index("station")
+    sta_idxs
+    return (sta_idxs,)
+
+
+@app.cell
+def _(ds_grib, ds_zarr, outfn, plt, sta_idxs, station):
+    grib_x_idx, grib_y_idx = (
+        sta_idxs.loc[station].grib_x_idx,
+        sta_idxs.loc[station].grib_y_idx,
+    )
+    zarr_x_idx, zarr_y_idx = (
+        sta_idxs.loc[station].zarr_x_idx,
+        sta_idxs.loc[station].zarr_y_idx,
+    )
+
+    # analysis
+    ds_zarr.isel(x=zarr_x_idx, y=zarr_y_idx).plot(
+        x="time", label="analysis", color="k", ls="--"
+    )
+
+    # TODO: plot actual forecaster data...
+    ds_grib.isel(x=grib_x_idx, y=grib_y_idx).isel(
+        lead_time=list(range(0, 126, 6))
+    ).plot(x="valid_time", marker="o", linestyle="None", color="k", label="forecaster")
+
+    # interpolator
+    ds_grib.isel(x=grib_x_idx, y=grib_y_idx).plot(x="valid_time", label="interpolator")
+
+    plt.legend()
+    plt.ylabel(
+        f"{ds_grib.attrs['parameter']['shortName']} ({ds_grib.attrs['parameter']['units']})"
+    )
+    plt.title(f"{init_time} {ds_grib.attrs['parameter']['name']} at {station}")
+    plt.savefig(outfn)
+    return
+
+
+if __name__ == "__main__":
+    app.run()