diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index fa121fe83f635c817bea93a74589b9a99d294c1a..6cf92c08a8d3d81ac57eaa0e3618c8eb8ace67c7 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -641,13 +641,12 @@ class PlotSectorialSkillScore(AbstractPlotClass):  # pragma: no cover
         self._reference_dim = reference_dim
         self._labels = None
         self._model_name_for_plots = model_name_for_plots
+        self._sns_settings = {"hue": "ahead", "whis": 1., "palette": "Blues_r",
+                              "showmeans": False, "flierprops": {"marker": "."}}
         self._data, self._reference_model = self._prepare_data(data)
-        logging.info("PlotSectorialSkillScore: finished _prepare_data(data)")
         self._plot()
-        logging.info("PlotSectorialSkillScore: finished _plot()")
         self.plot_name = self.plot_name + "_vertical"
         self._plot_vertical()
-        logging.info("PlotSectorialSkillScore: finished _plot_vertical()")
 
     @TimeTrackingWrapper
     def _prepare_data(self, data: xr.DataArray):
@@ -659,6 +658,7 @@ class PlotSectorialSkillScore(AbstractPlotClass):  # pragma: no cover
             name="data")
         return data, reference_model
 
+    @TimeTrackingWrapper
     def _plot(self):
         size = max([len(np.unique(self._data.sector)), 6])
         data = self._data
@@ -667,16 +667,17 @@ class PlotSectorialSkillScore(AbstractPlotClass):  # pragma: no cover
         for ref in self._reference_model:
             ref_data = data[data[self._reference_dim]==ref]
             fig, ax = plt.subplots(figsize=(size, size * 0.8))
-            sns.boxplot(x="sector", y="data", hue="ahead", data=ref_data,
-                        whis=1, ax=ax, palette="Blues_r",
-                        showmeans=False, #meanprops={"markersize": 3, "markeredgecolor": "k"},
-                        flierprops={"marker": "."},
-                        )
+            # sns.boxplot(x="sector", y="data", hue="ahead", data=ref_data,
+            #             whis=1, ax=ax, palette="Blues_r",
+            #             showmeans=False, #meanprops={"markersize": 3, "markeredgecolor": "k"},
+            #             flierprops={"marker": "."},
+            #             )
+            sns.boxplot(x="sector", y="data", data=ref_data, ax=ax, **self._sns_settings)
             ax.axhline(y=0, color="grey", linewidth=.5)
             ax.set(ylabel=f"skill score ({self._model_setup} vs. {ref})", xlabel="sector",
                    title="summary of all stations", ylim=self._lim(ref_data))
             handles, _ = ax.get_legend_handles_labels()
-            plt.xticks(rotation=45, horizontalalignment="right")
+            # plt.xticks(rotation=45, horizontalalignment="right")
             ax.legend(handles, self._labels)
             plt.tight_layout()
             pdf_pages.savefig()
@@ -684,19 +685,26 @@ class PlotSectorialSkillScore(AbstractPlotClass):  # pragma: no cover
         pdf_pages.close()
         plt.close('all')
 
+    @TimeTrackingWrapper
     def _plot_vertical(self):
         """Plot skill scores of the comparisons, but vertically aligned."""
-        fig, ax = plt.subplots()
         data = self._data
-        sns.boxplot(y="sector", x="data", hue="ahead", data=data, whis=1.5, ax=ax, palette="Blues_r",
-                    showmeans=False, meanprops={"markersize": 3, "markeredgecolor": "k"}, flierprops={"marker": "."},
-                    )
-        ax.axvline(x=0, color="grey", linewidth=.5)
-        ax.set(xlabel=f"skill score ({self._model_setup} vs. {self._reference_model})", ylabel="sector",
-               title="summary of all stations", xlim=self._lim(data))
-        handles, _ = ax.get_legend_handles_labels()
-        ax.legend(handles, self._labels)
-        plt.tight_layout()
+        plot_path = os.path.join(os.path.abspath(self.plot_folder), f"{self.plot_name}.pdf")
+        pdf_pages = matplotlib.backends.backend_pdf.PdfPages(plot_path)
+        for ref in self._reference_model:
+            ref_data = data[data[self._reference_dim] == ref]
+            fig, ax = plt.subplots()
+            sns.boxplot(y="sector", x="data", data=ref_data, ax=ax, **self._sns_settings)
+            ax.axvline(x=0, color="grey", linewidth=.5)
+            ax.set(xlabel=f"skill score ({self._model_setup} vs. {ref})", ylabel="sector",
+                   title="summary of all stations", xlim=self._lim(ref_data))
+            handles, _ = ax.get_legend_handles_labels()
+            ax.legend(handles, self._labels)
+            plt.tight_layout()
+            pdf_pages.savefig()
+        # close all open figures / plots
+        pdf_pages.close()
+        plt.close('all')
 
     @staticmethod
     def _lim(data) -> Tuple[float, float]: