diff --git a/visualizer/plotting.py b/visualizer/plotting.py
index 0b5afe7272165a8193239545b47122055dde1055..c86a44ce24e160be219b39a7f7ce8fdf91339958 100644
--- a/visualizer/plotting.py
+++ b/visualizer/plotting.py
@@ -80,11 +80,7 @@ class TimeSeries(Plotter):
         fig.set_figwidth(12)
         axes.plot(data[:, 0], data[:, meas.field])
         avg = np.nanmean(data[:, meas.field], dtype='float32')
-        axes.hlines(y=avg,
-                    xmin=data[0, 0],
-                    xmax=data[-1, 0],
-                    linestyle='-',
-                    alpha=0.7)
+        axes.hlines(y=avg, xmin=data[0, 0], xmax=data[-1, 0])
         axes.set_ylabel(f'{meas.title.title()} ({meas.units})')
         axes.grid(True)
         maximum = max(data, key=lambda row: row[meas.field])
@@ -152,22 +148,21 @@ class Overlay(Plotter):
         for dset in datasets:
             raw_data = read_data(dset.station, dset.year)
             dset.data = np.array(ignore_feb_29(raw_data))
+            dset.avg = np.nanmean(dset.data[:, meas.field], dtype='float32')
 
         fig, axes = plt.subplots()
         fig.set_figheight(6)
         fig.set_figwidth(12)
         for i, dset in enumerate(datasets):
-            alpha_kw = {'alpha': 0.6} if i else {}
             axes.plot(datasets[0].data[:, 0],
                       dset.data[:, meas.field],
-                      **alpha_kw,
-                      label=f'{dset.name} {dset.data[0, 0].year}')
-        for dset in datasets:
-            dset.avg = np.nanmean(dset.data[:, meas.field], dtype='float32')
+                      alpha=0.875 if i else 1.0,
+                      label=f'{dset.name}, {dset.data[0, 0].year}')
             axes.hlines(y=dset.avg,
                         xmin=datasets[0].data[0, 0],
                         xmax=datasets[0].data[-1, 0],
-                        alpha=0.7)
+                        colors=f'C{i}',
+                        alpha=0.875 if i else 1.0)
         axes.set_ylabel(f'{meas.title.title()} ({meas.units})')
 
         for dset in datasets: