Superimposing 2 Wind Roses into 1 Wind Rose

I have 2 dataframes (1 is a climate average and 1 is a current month) of wind speed and wind direction of the same length that I need to combine or superimpose to 1 windrose. The idea is to combine in one image, the climate average wind rose and the current month wind rose. Most of the superimposed images use a common, shared x axis but in my case, that is not possible since each wind direction and wind speed are paired, unique values.
my data looks like this:

df1 (climate average):

day hour  wind_speed  vel_x  vel_y  winddir
1   0     6.4         6.4    0.45   86
1   1     6.7        -6.7   -1.1    261
1   2     6.9        -5.1   -4.7    227
1   3     7.0        -6.3   -2.9    245

df2 (current month/year):

day hour  wind_speed  vel_x  vel_y  winddir
1   0     7.2        -4.3    5.8    323
1   1     7.6         5.9   -4.8    129
1   2     8.0        -6.7   -4.4    237
1   3     8.3        -7.1   -4.3    239

Here are the separate wind roses with the entire data but I am trying to combine them into 1 single/combined/superimposed wind rose.

Here is the code I use to generate the wind rose(s):

ax = WindroseAxes.from_ax()
cmap = plt.get_cmap('viridis')
ax.bar(df1.winddir, df1.wind_speed_ms, normed=True, opening=0.8, bins=np.arange(0, 10, 1),cmap = cmap,edgecolor='white')
ax.set_legend().set_title("Wind Speed (m/s)")

df1:

df2: