diff --git a/src/pydartdiags/matplots/matplots.py b/src/pydartdiags/matplots/matplots.py
index 8b08675..f2bccf0 100644
--- a/src/pydartdiags/matplots/matplots.py
+++ b/src/pydartdiags/matplots/matplots.py
@@ -227,33 +227,26 @@ def plot_profile(
     return fig
 
 
-def plot_rank_histogram(obs_seq, levels, type, ens_size):
-
+def plot_rank_histogram(obs_seq, type, ens_size, levels=None):
     qc0 = stats.select_used_qcs(obs_seq.df)  # filter only qc=0, qc=2
-    qc0 = qc0[qc0["type"] == type]  # filter by type
-    if qc0.empty:
-        print(f"No rows found for type: {type}")
-        return None
-
-    if qc0["vert_unit"].nunique() > 1:
-        print(
-            f"Multiple vertical units found in the data: {qc0['vert_unit'].unique()} for type: {type}"
-        )
-        return None
-
-    vert_unit = qc0.iloc[0]["vert_unit"]
-    conversion, unit = _get_plot_unit(vert_unit)  # multiplier and unit for y-axis
+    print("type: ", type)
 
-    stats.bin_by_layer(qc0, levels, verticalUnit=vert_unit)  # bin by level
+    if (isinstance(type, int) and type < 0) or (type == "IDENTITY_OBS"):
+        print("Observation type is for identity observations.") # No filtering by type for identity obs
+        if qc0.empty:
+            print(f"No rows found for type: {type}")
+            return None
+        type = "IDENTITY_OBS"
 
-    midpoints = qc0["midpoint"].unique()
+    else:
+        qc0 = qc0[qc0["type"] == type]  # filter by type
 
-    for level in sorted(midpoints):
+        if qc0.empty:
+            print(f"No rows found for type: {type}")
+            return None
 
-        df = qc0[qc0["midpoint"] == level]
-        # convert to hPa only for Pressure (Pa)
-        df["midpoint"] = df["midpoint"].astype(float)
-        df["midpoint"] = df["midpoint"] * conversion
+    if levels is None:
+        print(f"Observation sequence does not use vertical coordinate. Proceeding without level binning.")
 
         df = stats.calculate_rank(qc0)
 
@@ -284,6 +277,62 @@ def plot_rank_histogram(obs_seq, levels, type, ens_size):
             ax2.set_xlabel("Observation Rank (among ensemble members)")
             ax2.set_ylabel("Count")
 
+        fig.suptitle(f"{type}", fontsize=14)
+
+        plt.tight_layout(rect=[0, 0.03, 1, 0.95])
+        plt.show()
+        return None
+
+    elif qc0["vert_unit"].nunique() > 1:
+        print(
+            f"Multiple vertical units found in the data: {qc0['vert_unit'].unique()} for type: {type}"
+        )
+        return None
+
+    else:
+        vert_unit = qc0.iloc[0]["vert_unit"]
+        conversion, unit = _get_plot_unit(vert_unit)  # multiplier and unit for y-axis
+
+        stats.bin_by_layer(qc0, levels, verticalUnit=vert_unit)  # bin by level
+
+        midpoints = qc0["midpoint"].unique()
+
+        for level in sorted(midpoints):
+
+            df = qc0[qc0["midpoint"] == level]
+            # convert to hPa only for Pressure (Pa)
+            df["midpoint"] = df["midpoint"].astype(float)
+            df["midpoint"] = df["midpoint"] * conversion
+
+            df = stats.calculate_rank(qc0)
+
+            if "posterior_rank" in df.columns:
+                fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))
+            else:
+                fig, ax1 = plt.subplots()
+
+            # Plot the prior rank histogram
+            bins = list(range(1, ens_size + 2))
+            ax1.hist(
+                df["prior_rank"], bins=bins, color="blue", alpha=0.5, label="prior rank"
+            )
+            ax1.set_title("Prior Rank Histogram")
+            ax1.set_xlabel("Observation Rank (among ensemble members)")
+            ax1.set_ylabel("Count")
+
+            # Plot the posterior rank histogram if it exists
+            if "posterior_rank" in df.columns:
+                ax2.hist(
+                    df["posterior_rank"],
+                    bins=bins,
+                    color="green",
+                    alpha=0.5,
+                    label="posterior rank",
+                )
+                ax2.set_title("Posterior Rank Histogram")
+                ax2.set_xlabel("Observation Rank (among ensemble members)")
+                ax2.set_ylabel("Count")
+
         fig.suptitle(f"{type} at Level {round(level, 1)} {unit}", fontsize=14)
 
         plt.tight_layout(rect=[0, 0.03, 1, 0.95])