From 952f68731fabfed14f38d23c6c36176dcb68effe Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Thu, 12 Nov 2020 01:16:21 -0600
Subject: [PATCH] snapshot...
---
modules/aeolus/aeolus_amv.py | 64 +++++++++++++++++++++++++++++-------
1 file changed, 53 insertions(+), 11 deletions(-)
diff --git a/modules/aeolus/aeolus_amv.py b/modules/aeolus/aeolus_amv.py
index 327d5eeb..1bd33a5e 100644
--- a/modules/aeolus/aeolus_amv.py
+++ b/modules/aeolus/aeolus_amv.py
@@ -671,7 +671,7 @@ def subset_by_time(match_file, dt_str_0, dt_str_1, amv_var_names):
return prof_da, amvs_da, prof_locs_da
-def analyze(prof_da, amvs_da, prof_locs_da):
+def analyze(prof_da, amvs_da, prof_locs_da, dist_threshold=5.0):
# sort amvs by distance to profile
dst = amvs_da.sel(num_params='dist_to_prof')
s_i = np.argsort(dst, axis=1)
@@ -686,14 +686,11 @@ def analyze(prof_da, amvs_da, prof_locs_da):
for k in range(prof_da.shape[0]):
prof_da[k] = prof_da[k, s_i[k, ::-1]]
+ # analyze single cloud layer profiles
one_lyr = prof_locs_da.sel(space='num_levels') == 1
- dst = amvs_da.sel(num_params='dist_to_prof')
- close = np.logical_and(dst > 0.0, dst < 5.0)
-
- # compare for one cloud layer profile
-
one_lyr_profs = prof_da[one_lyr]
one_lyr_amvs = amvs_da[one_lyr]
+ print('number of one layer profs: ', one_lyr_profs.shape[0])
hgt_vld = one_lyr_amvs.sel(num_params='H_3D') > 0
hgt_vld = hgt_vld.values
@@ -701,19 +698,64 @@ def analyze(prof_da, amvs_da, prof_locs_da):
one_lyr_amvs = one_lyr_amvs[has_vld]
one_lyr_profs = one_lyr_profs[has_vld]
+ print('number of one layer profs with at least one valid AMV height', one_lyr_profs.shape[0])
# compare profile highest cloud layer top to closest within 5km
dst = one_lyr_amvs.sel(num_params='dist_to_prof')
- close = (np.logical_and(dst > 0.0, dst < 2.0)).values
+ close = (np.logical_and(dst > 0.0, dst < dist_threshold)).values
close = close.sum(1) > 0
one_lyr_amvs = one_lyr_amvs[close]
one_lyr_profs = one_lyr_profs[close]
+ print('number of one layer profs with at least one AMV within 5km: ', one_lyr_profs.shape[0])
+ cnt = 0
for k in range(one_lyr_profs.shape[0]):
- dst = one_lyr_amvs[k, :, 2]
- b = np.logical_and(dst > 0.0, dst < 2.0)
- diffs = one_lyr_amvs[k, b, 0] - one_lyr_profs[k, 0, 1]
- print(diffs.values, one_lyr_amvs[k, b, 2].values)
+ dst = one_lyr_amvs[k, :, ].sel(num_params='dist_to_prof')
+ b = np.logical_and(dst > 0.0, dst < dist_threshold)
+ h_3d = one_lyr_amvs[k, :].sel(num_params='H_3D')
+ h_3d = h_3d[b]
+ vld = h_3d > 0
+ h_3d = h_3d[vld]
+ if len(h_3d) > 0:
+ in_lyr = np.logical_and(h_3d > one_lyr_profs[k, 0, 0], h_3d < one_lyr_profs[k, 0, 1])
+ cnt += np.sum(in_lyr)
+
+ print(cnt)
+
+ # Do calculations for single and multi-layer combined
+ hgt_vld = amvs_da.sel(num_params='H_3D') > 0
+ hgt_vld = hgt_vld.values
+ has_vld = hgt_vld.sum(1) > 0
+
+ amvs_da = amvs_da[has_vld]
+ prof_da = prof_da[has_vld]
+ prof_locs_da = prof_locs_da[has_vld]
+ print('number of profs with at least one valid AMV height', prof_da.shape[0])
+ # compare profile highest cloud layer top to closest within 5km
+ dst = amvs_da.sel(num_params='dist_to_prof')
+ close = (np.logical_and(dst > 0.0, dst < dist_threshold)).values
+ close = close.sum(1) > 0
+
+ amvs_da = amvs_da[close]
+ prof_da = prof_da[close]
+ prof_locs_da = prof_locs_da[close]
+ print('number of profs with at least one AMV within 5km: ', prof_da.shape[0])
+
+ cnt = 0
+ for k in range(prof_da.shape[0]):
+ dst = amvs_da[k, :, ].sel(num_params='dist_to_prof')
+ b = np.logical_and(dst > 0.0, dst < dist_threshold)
+ h_3d = amvs_da[k, :].sel(num_params='H_3D')
+ h_3d = h_3d[b]
+ vld = h_3d > 0
+ h_3d = h_3d[vld]
+ if len(h_3d) > 0:
+ nlevs = prof_locs_da[k].values.astype(int)[3]
+ for j in range(nlevs):
+ in_lyr = np.logical_and(h_3d > prof_da[k, j, 0], h_3d < prof_da[k, j, 1])
+ cnt += np.sum(in_lyr)
+
+ print(cnt)
return one_lyr_profs, one_lyr_amvs
--
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