From 2085e9593ec270671a701113a3c876157cd96780 Mon Sep 17 00:00:00 2001
From: rink <rink@ssec.wisc.edu>
Date: Thu, 22 Oct 2020 19:45:55 -0500
Subject: [PATCH] snapshot..
---
modules/amv/intercompare.py | 52 +++++++++++++++++--------------------
1 file changed, 24 insertions(+), 28 deletions(-)
diff --git a/modules/amv/intercompare.py b/modules/amv/intercompare.py
index 7cbfb6f8..8b7418ff 100644
--- a/modules/amv/intercompare.py
+++ b/modules/amv/intercompare.py
@@ -834,7 +834,7 @@ def get_amv_winds_match(dist_threshold=150, cqi=50):
return amvs_all
-def best_fit(amv_spd, amv_dir, amv_prs, amv_lat, amv_lon, fcst_spd, fcst_dir, fcst_prs, test_a=True, test_b=True):
+def best_fit(amv_spd, amv_dir, amv_prs, amv_lat, amv_lon, fcst_spd, fcst_dir, fcst_prs):
"""Finds the background model best fit pressure associated with the AMV.
The model best-fit pressure is the height (in pressure units) where the
@@ -903,11 +903,9 @@ def best_fit(amv_spd, amv_dir, amv_prs, amv_lat, amv_lon, fcst_spd, fcst_dir, fc
SatwindMaxSpeed = max(fcst_spd[kk])
# Compute U anv V for both AMVs and forecast
- amv_uwind = -amv_spd * np.sin((np.pi/180.0)*amv_dir)
- amv_vwind = -amv_spd * np.cos((np.pi/180.0)*amv_dir)
-# fcst_uwind = -fcst_spd[:] * np.sin(math.radians(fcst_dir[:]))
-# fcst_vwind = -fcst_spd[:] * np.cos(math.radians(fcst_dir[:]))
- dr=0.017453
+ dr = 0.0174533 # pi/180
+ amv_uwind = -amv_spd * np.sin(dr*amv_dir)
+ amv_vwind = -amv_spd * np.cos(dr*amv_dir)
fcst_uwind = -fcst_spd * np.sin(dr*fcst_dir)
fcst_vwind = -fcst_spd * np.cos(dr*fcst_dir)
@@ -916,22 +914,19 @@ def best_fit(amv_spd, amv_dir, amv_prs, amv_lat, amv_lon, fcst_spd, fcst_dir, fc
# Find the model level of best-fit pressure, from the minimum vector difference.
MinVecDiff = min(VecDiff[kk])
- imin = -1
- for i, item in enumerate(VecDiff):
- if MinVecDiff == VecDiff[i]:
- if i in kk[0]:
- imin = i
-
- if imin == -1:
+ imin = np.where(VecDiff == MinVecDiff)
+ if imin[0].size == 0:
if verbose:
print('AMV location lat,lon,prs ({0},{1},{2}) failed to find min vector difference in layers around AMV'.format(amv_lat,amv_lon,amv_prs))
return bf_data
+ imin = imin[0][0]
+ imin = kk[0][imin]
+
# TDR - Feb 2020
if imin == fcst_num_levels - 1:
if verbose:
print('AMV location lat,lon,prs ({0},{1},{2}) failed to find min vector difference in layers around AMV'.format(amv_lat,amv_lon,amv_prs))
-
return bf_data
@@ -948,8 +943,10 @@ def best_fit(amv_spd, amv_dir, amv_prs, amv_lat, amv_lon, fcst_spd, fcst_dir, fc
# assumes fcst data level 0 at surface and (fcst_num_levels-1) at model top
# if bottom model level
- if imin == 0:
+ if imin == 0 or imin == fcst_num_levels - 1:
SatwindBestFitPress = p2
+ if verbose:
+ print('AMV location lat,lon,alt ({0},{1},{2}) min vector difference at top/bottom'.format(amv_lat,amv_lon,amv_prs))
else:
p3 = fcst_prs[imin+1]
p1 = fcst_prs[imin-1]
@@ -992,22 +989,20 @@ def best_fit(amv_spd, amv_dir, amv_prs, amv_lat, amv_lon, fcst_spd, fcst_dir, fc
# Check to see if the best fit pressure is constrained.
- SatwindGoodConstraint = 0
- flag = 2
+ SatwindGoodConstraint = 1
+ vdiff = np.sqrt((amv_uwind - SatwindBestFitU) ** 2 + (amv_vwind - SatwindBestFitV) ** 2)
+ if vdiff > 4.0:
+ SatwindGoodConstraint = 0
+ flag = 2
- if not test_a:
- SatwindGoodConstraint = 1
- elif MinVecDiff <= 4.0:
- SatwindGoodConstraint = 1
- else:
+ # Check for a substantial secondary local minimum or if local minimum is too broad
+ mm = np.where(fcst_prs > (SatwindBestFitPress + 100))[0]
+ nn = np.where(fcst_prs < (SatwindBestFitPress - 100))[0]
+
+ if (np.sum(VecDiff[mm] < (vdiff + 2.0)) + np.sum(VecDiff[nn] < (vdiff + 2.0))) > 0:
+ SatwindGoodConstraint = 0
flag = 1
- if test_a and test_b and SatwindGoodConstraint == 1:
- for ilev in range(fcst_num_levels):
- if fcst_prs[ilev] >= TopPress:
- if ((fcst_prs[ilev] < (SatwindBestFitPress - 100.)) or (fcst_prs[ilev] > (SatwindBestFitPress + 100.))) and (VecDiff[ilev] <= (MinVecDiff + 2.0)):
- SatwindGoodConstraint = 0
- flag = 2
if SatwindGoodConstraint == 1:
bfit_prs = SatwindBestFitPress
@@ -1101,6 +1096,7 @@ def best_fit_altitude(amv_spd, amv_dir, amv_alt, amv_lat, amv_lon, fcst_spd, fcs
sat_wind_best_fit_alt = a2
sat_wind_best_fit_u = fcst_uwind[imin]
sat_wind_best_fit_v = fcst_vwind[imin]
+
if imin == 0 or imin == (fcst_num_levels - 1):
if verbose:
print('AMV location lat,lon,alt ({0},{1},{2}) min vector difference at top/bottom'.format(amv_lat,amv_lon,amv_alt))
--
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