From ebb1ea860471132de0b5f0ae73d13ea3cdff5eb1 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 7 Dec 2020 14:23:21 -0600
Subject: [PATCH] snapshot...
---
modules/aeolus/aeolus_amv.py | 76 ++++++++++++++++++++++++++++++++++++
1 file changed, 76 insertions(+)
diff --git a/modules/aeolus/aeolus_amv.py b/modules/aeolus/aeolus_amv.py
index c53e52b2..35c16db7 100644
--- a/modules/aeolus/aeolus_amv.py
+++ b/modules/aeolus/aeolus_amv.py
@@ -686,6 +686,82 @@ def analyze2(raob_to_amv_dct, raob_dct, gfs_filename=None):
np.average(np.abs(bin_spd[i])), np.average(bin_spd[i]), np.average(np.abs(bin_dir[i])), np.average(bin_dir[i])))
+def compare_amvs_bestfit(amvs, bfs):
+ didx = 4
+ sidx = 3
+ pidx = 2
+
+ vld_bf = bfs[:, 3] == 0
+ keep_idxs = vld_bf
+
+ bin_size = 200.0
+ bin_ranges = get_press_bin_ranges(50, 1050, bin_size=bin_size)
+
+ amv_p = amvs[keep_idxs, pidx]
+ bf_p = bfs[keep_idxs, 2]
+ diff = amv_p - bf_p
+ mad = np.average(np.abs(diff))
+ bias = np.average(diff)
+ print('********************************************************')
+ print('Number of good best fits: ', bf_p.shape[0])
+ print('press, MAD: {0:.2f}'.format(mad))
+ print('press, bias: {0:.2f}'.format(bias))
+ pd_std = np.std(diff)
+ pd_mean = np.mean(diff)
+ print('press bias/rms: {0:.2f} {1:.2f} '.format(pd_mean, np.sqrt(pd_mean**2 + pd_std**2)))
+ print('------------------------------------------')
+
+ bin_pres = bin_data_by(diff, amv_p, bin_ranges)
+
+ amv_spd = amvs[keep_idxs, sidx]
+ amv_dir = amvs[keep_idxs, didx]
+ bf_spd, bf_dir = spd_dir_from_uv(bfs[keep_idxs, 0], bfs[keep_idxs, 1])
+
+ diff = amv_spd * units('m/s') - bf_spd
+ diff = diff.magnitude
+ spd_mad = np.average(np.abs(diff))
+ spd_bias = np.average(diff)
+ print('spd, MAD: {0:.2f}'.format(spd_mad))
+ print('spd, bias: {0:.2f}'.format(spd_bias))
+ spd_mean = np.mean(diff)
+ spd_std = np.std(diff)
+ print('spd MAD/bias/rms: {0:.2f} {1:.2f} {2:.2f}'.format(np.average(np.abs(diff)), spd_mean, np.sqrt(spd_mean**2 + spd_std**2)))
+ print('-----------------')
+ bin_spd = bin_data_by(diff, amv_p, bin_ranges)
+
+ dir_diff = direction_difference(amv_dir, bf_dir.magnitude)
+ print('dir, MAD: {0:.2f}'.format(np.average(np.abs(dir_diff))))
+ print('dir bias: {0:.2f}'.format(np.average(dir_diff)))
+ print('-------------------------------------')
+ bin_dir = bin_data_by(dir_diff, amv_p, bin_ranges)
+
+ amv_u, amv_v = uv_from_spd_dir(amvs[keep_idxs, sidx], amvs[keep_idxs, didx])
+ u_diffs = amv_u - (bfs[keep_idxs, 0] * units('m/s'))
+ v_diffs = amv_v - (bfs[keep_idxs, 1] * units('m/s'))
+
+ vd = np.sqrt(u_diffs**2 + v_diffs**2)
+ vd_mean = np.mean(vd)
+ vd_std = np.std(vd)
+ print('VD bias/rms: {0:.2f} {1:.2f}'.format(vd_mean, np.sqrt(vd_mean**2 + vd_std**2)))
+ print('******************************************************')
+
+ x_values = []
+ num_pres = []
+ num_spd = []
+ num_dir = []
+ print('level num cases hgt MAD/bias spd MAD/bias dir MAD/bias')
+ print('-------------------------------------------------------------------')
+ for i in range(len(bin_ranges)):
+ x_values.append(np.average(bin_ranges[i]))
+ num_pres.append(bin_pres[i].shape[0])
+ num_spd.append(bin_spd[i].shape[0])
+ num_dir.append(bin_dir[i].shape[0])
+
+ print('{0:d} {1:d} {2:.2f}/{3:.2f} {4:.2f}/{5:.2f} {6:.2f}/{7:.2f}'
+ .format(int(x_values[i]), num_pres[i], np.average(np.abs(bin_pres[i])), np.average(bin_pres[i]),
+ np.average(np.abs(bin_spd[i])), np.average(bin_spd[i]), np.average(np.abs(bin_dir[i])), np.average(bin_dir[i])))
+
+
# imports the S4 NOAA output
# filename: full path as a string, '/home/user/filename'
# returns a dict: time -> list of profiles (a profile is a list of levels)
--
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