# 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 ) ")