# 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")
plt.tight_layout()
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]")
xlabel("Lead time [min]")
ylabel("FSS ( > 1.0 mm/h ) ")