How to use the pysteps.utils.conversion.to_rainrate function in pysteps

# skill score for high-resolution precipitation forecasts.

# Find observations in the data archive
fns = io.archive.find_by_date(
    date,
    root_path,
    path_fmt,
    fn_pattern,
    fn_ext,
    timestep,
    num_prev_files=0,
    num_next_files=n_leadtimes,
)
# Read the radar composites
R_o, _, metadata_o = io.read_timeseries(fns, importer, **importer_kwargs)
R_o, metadata_o = conversion.to_rainrate(R_o, metadata_o, 223.0, 1.53)

# Compute fractions skill score (FSS) for all lead times, a set of scales and 1 mm/h
fss = verification.get_method("FSS")
scales = [2, 4, 8, 16, 32, 64, 128, 256, 512]
thr = 1.0
score = []
for i in range(n_leadtimes):
    score_ = []
    for scale in scales:
        score_.append(fss(R_f[i, :, :], R_o[i + 1, :, :], thr, scale))
    score.append(score_)

figure()
x = np.arange(1, n_leadtimes + 1) * timestep
plot(x, score)
legend(scales, title="Scale [km]")

R_f2 = transformation.dB_transform(R_f2, threshold=-10.0, inverse=True)[0]

# Find the veriyfing observations in the archive
fns = io.archive.find_by_date(
    date,
    root_path,
    path_fmt,
    fn_pattern,
    fn_ext,
    timestep=5,
    num_next_files=12,
)

# Read and convert the radar composites
R_o, _, metadata_o = io.read_timeseries(fns, importer, **importer_kwargs)
R_o, metadata_o = conversion.to_rainrate(R_o, metadata_o)

# Compute Spearman correlation
skill = verification.get_method("corr_s")
score_1 = []
score_2 = []
for i in range(12):
    score_1.append(skill(R_f1[i, :, :], R_o[i + 1, :, :])["corr_s"])
    score_2.append(skill(R_f2[i, :, :], R_o[i + 1, :, :])["corr_s"])

x = (np.arange(12) + 1) * 5  # [min]
plt.plot(x, score_1, label="buffer_mask = 0")
plt.plot(x, score_2, label="buffer_mask = %i" % buffer)
plt.legend()
plt.xlabel("Lead time [min]")
plt.ylabel("Corr. coeff. []")
plt.title("Spearman correlation")