diff --git a/pysteps/xarray_helpers.py b/pysteps/xarray_helpers.py
index a1049f32..33ec2f40 100644
--- a/pysteps/xarray_helpers.py
+++ b/pysteps/xarray_helpers.py
@@ -83,6 +83,7 @@ def convert_input_to_xarray_dataset(
     precip: np.ndarray,
     quality: np.ndarray | None,
     metadata: dict[str, str | float | None],
+    startdate: datetime | None = None,
 ) -> xr.Dataset:
     """
     Read a precip, quality, metadata tuple as returned by the importers
@@ -99,6 +100,8 @@ def convert_input_to_xarray_dataset(
     metadata: dict
         Metadata dictionary containing the attributes described in the
         documentation of :py:mod:`pysteps.io.importers`.
+    startdate: datetime, None
+        Datetime object containing the start date and time for the nowcast
 
     Returns
     -------
@@ -107,7 +110,31 @@ def convert_input_to_xarray_dataset(
 
     """
     var_name, attrs = cf_parameters_from_unit(metadata["unit"])
-    h, w = precip.shape
+
+    dims = None
+    timesteps = None
+    ens_number = None
+
+    if precip.ndim == 4:
+        ens_number, timesteps, h, w = precip.shape
+        dims = ["ens_number", "time", "y", "x"]
+
+        if startdate is None:
+            raise Exception("startdate missing")
+
+    elif precip.ndim == 3:
+        timesteps, h, w = precip.shape
+        dims = ["time", "y", "x"]
+
+        if startdate is None:
+            raise Exception("startdate missing")
+
+    elif precip.ndim == 2:
+        h, w = precip.shape
+        dims = ["y", "x"]
+    else:
+        raise Exception(f"Precip field shape: {precip.shape} not supported")
+
     x_r = np.linspace(metadata["x1"], metadata["x2"], w + 1)[:-1]
     x_r += 0.5 * (x_r[1] - x_r[0])
     y_r = np.linspace(metadata["y1"], metadata["y2"], h + 1)[:-1]
@@ -142,25 +169,33 @@ def convert_input_to_xarray_dataset(
 
     data_vars = {
         var_name: (
-            ["y", "x"],
+            dims,
             precip,
             {
                 "units": attrs["units"],
                 "standard_name": attrs["standard_name"],
                 "long_name": attrs["long_name"],
                 "grid_mapping": "projection",
-                "transform": metadata["transform"],
-                "accutime": metadata["accutime"],
-                "threshold": metadata["threshold"],
-                "zerovalue": metadata["zerovalue"],
-                "zr_a": metadata["zr_a"],
-                "zr_b": metadata["zr_b"],
             },
         )
     }
+
+    metadata_keys = [
+        "transform",
+        "accutime",
+        "threshold",
+        "zerovalue",
+        "zr_a",
+        "zr_b",
+    ]
+
+    for metadata_field in metadata_keys:
+        if metadata_field in metadata:
+            data_vars[var_name][2][metadata_field] = metadata[metadata_field]
+
     if quality is not None:
         data_vars["quality"] = (
-            ["y", "x"],
+            dims,
             quality,
             {
                 "units": "1",
@@ -210,6 +245,26 @@ def convert_input_to_xarray_dataset(
             },
         ),
     }
+
+    if ens_number is not None:
+        coords["ens_number"] = (
+            ["ens_number"],
+            list(range(1, ens_number + 1, 1)),
+            {
+                "long_name": "ensemble member",
+                "standard_name": "realization",
+                "units": "",
+            },
+        )
+
+    if timesteps is not None:
+        startdate_str = datetime.strftime(startdate, "%Y-%m-%d %H:%M:%S")
+
+        coords["time"] = (
+            ["time"],
+            list(range(1, timesteps + 1, 1)),
+            {"long_name": "forecast time", "units": "seconds since %s" % startdate_str},
+        )
     if grid_mapping_var_name is not None:
         coords[grid_mapping_name] = (
             [],
@@ -223,7 +278,7 @@ def convert_input_to_xarray_dataset(
         "precip_var": var_name,
     }
     dataset = xr.Dataset(data_vars=data_vars, coords=coords, attrs=attrs)
-    return dataset.sortby(["y", "x"])
+    return dataset.sortby(dims)
 
 
 def convert_output_to_xarray_dataset(