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Commit 30e1aac8 authored by tomrink's avatar tomrink
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modules/aeolus/aeolus_amv.py
+ 141
− 15
View file @ 30e1aac8
......@@ -6,12 +6,13 @@ import xarray as xr
from netCDF4 import Dataset, Dimension, Variable
from aeolus.geos_nav import GEOSNavigation
from util.util import haversine_np
from amv.intercompare import best_fit
from amv.intercompare import best_fit, bin_data_by, get_press_bin_ranges, spd_dir_from_uv, uv_from_spd_dir, direction_difference
import math
from metpy.units import units
amv_file_duration = 60 # minutes
half_width = 20 # search box centered on AEOLUS profile (FGF coordinates)
half_width = 25 # search box centered on AEOLUS profile (FGF coordinates)
num_elems = 5424
num_lines = 5424
......@@ -87,8 +88,8 @@ class OPS(AMVFiles):
self.lon_name = 'lon'
self.lat_name = 'lat'
self.out_params = {'Lon', 'Lat', 'Element', 'Line', 'pressure', 'wind_speed', 'wind_direction'}
self.params = {'pressure', 'wind_speed', 'wind_direction'}
self.out_params = ['Lon', 'Lat', 'Element', 'Line', 'pressure', 'wind_speed', 'wind_direction']
self.params = ['pressure', 'wind_speed', 'wind_direction']
self.meta_dict = {'Lon': ('degrees east', 'f4'), 'Lat': ('degrees north', 'f4'), 'Element': (None, 'i4'), 'Line': (None, 'i4'),
'pressure': ('hPa', 'f4'), 'wind_speed': ('m s-1', 'f4'), 'wind_direction': ('degrees', 'f4')}
......@@ -171,8 +172,8 @@ def match_amvs_to_raobs(raob_dict, raob_time, amv_files):
ds = Dataset(fname)
amv_lons = ds[amv_files.lon_name][:]
amv_lats = ds[amv_files.lat_name][:]
amv_lons = ds[amv_files.lon_name][:].data
amv_lats = ds[amv_files.lat_name][:].data
if amv_files.elem_name is None or amv_files.line_name is None:
cc, ll = nav.earth_to_lc_s(amv_lons, amv_lats)
else:
......@@ -184,12 +185,24 @@ def match_amvs_to_raobs(raob_dict, raob_time, amv_files):
param_s.append(amv_lats)
param_s.append(cc)
param_s.append(ll)
vld = None
for param in amv_params:
data = ds[param][:]
if vld is None:
vld = np.invert(data.mask)
else:
vld = np.logical_and(vld, np.invert(data.mask))
if param == 'V_3D':
param_s.append(ds[param][:, 0])
param_s.append(ds[param][:, 1])
param_s.append(data.data[0])
param_s.append(data.data[1])
else:
param_s.append(ds[param][:])
param_s.append(data.data)
param_nd = np.vstack(param_s)
param_nd = param_nd[:, vld]
cc = param_nd[2, :]
ll = param_nd[3, :]
ds.close()
......@@ -215,10 +228,7 @@ def match_amvs_to_raobs(raob_dict, raob_time, amv_files):
num_amvs = np.sum(in_box)
if num_amvs == 0:
continue
# dist = haversine_np(lon, lat, amv_lons[in_box], amv_lats[in_box])
param_nd = np.vstack(param_s)
param_nd = param_nd[:, in_box]
match_dict[key] = param_nd
match_dict[key] = param_nd[:, in_box]
return match_dict
......@@ -317,9 +327,15 @@ def create_file2(filename, raob_to_amv_dct, raob_dct, amv_files):
rootgrp.close()
def bulk_stats(filename):
pass
def analyze2(raob_to_amv_dct, raob_dct):
keys = list(raob_to_amv_dct.keys())
amvs_list = []
bf_list = []
for key in keys:
rlat = key[0]
rlon = key[1]
......@@ -330,6 +346,7 @@ def analyze2(raob_to_amv_dct, raob_dct):
raob_spd = raob[:, 2]
raob_dir = raob[:, 3]
amvs = raob_to_amv_dct.get(key)
amvs_list.append(amvs)
num_amvs = amvs.shape[1]
for i in range(num_amvs):
amv_lon = amvs[0, i]
......@@ -339,9 +356,118 @@ def analyze2(raob_to_amv_dct, raob_dct):
amv_dir = amvs[6, i]
bf = best_fit(amv_spd, amv_dir, amv_prs, amv_lat, amv_lon, raob_spd, raob_dir, raob_prs)
bf_list.append(bf)
if bf[3] == 0:
print(nlevs, amv_prs, bf[2])
bspd, bdir = spd_dir_from_uv(bf[0], bf[1])
#print(amv_spd, bspd, amv_dir, bdir)
amvs = np.concatenate(amvs_list, axis=1)
amvs = np.transpose(amvs, axes=[1, 0])
bfs = np.stack(bf_list, axis=0)
good_amvs = amvs
num_good = good_amvs.shape[0]
didx = 6
sidx = 5
pidx = 4
spd_min = good_amvs[:, sidx].min()
spd_max = good_amvs[:, sidx].max()
print('spd min/max/mean: ', spd_min, spd_max, np.average(good_amvs[:, sidx]))
p_min = good_amvs[:, pidx].min()
p_max = good_amvs[:, pidx].max()
print('pres min/max/mean: ', p_min, p_max, np.average(good_amvs[:, pidx]))
low = good_amvs[:, pidx] >= 700
mid = np.logical_and(good_amvs[:, pidx] < 700, good_amvs[:, pidx] > 400)
hgh = good_amvs[:, pidx] <= 400
n_low = np.sum(low)
n_mid = np.sum(mid)
n_hgh = np.sum(hgh)
print('% low: ', 100.0*(n_low/num_good))
print('% mid: ', 100.0*(n_mid/num_good))
print('% hgh: ', 100.0*(n_hgh/num_good))
print('---------------------------')
print('Low Spd min/max/mean: ', good_amvs[low, sidx].min(), good_amvs[low, sidx].max(), good_amvs[low,sidx].mean())
print('Low Press min/max/mean: ', good_amvs[low, pidx].min(), good_amvs[low, pidx].max(), good_amvs[low, pidx].mean())
print('---------------------------')
print('Mid Spd min/max/mean: ', good_amvs[mid, sidx].min(), good_amvs[mid, sidx].max(), good_amvs[mid, sidx].mean())
print('Mid Press min/max/mean: ', good_amvs[mid, pidx].min(), good_amvs[mid, pidx].max(), good_amvs[mid, pidx].mean())
print('---------------------------')
print('Hgh Spd min/max/mean: ', good_amvs[hgh, sidx].min(), good_amvs[hgh, sidx].max(), good_amvs[hgh, sidx].mean())
print('Hgh Press min/max/mean: ', good_amvs[hgh, pidx].min(), good_amvs[hgh, pidx].max(), good_amvs[hgh, pidx].mean())
bin_size = 200.0
vld_bf = bfs[:, 3] == 0
keep_idxs = vld_bf
amv_p = good_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('num of best fits: ', bf_p.shape[0])
print('press, MAD: ', mad)
print('press, bias: ', bias)
pd_std = np.std(diff)
pd_mean = np.mean(diff)
print('press bias/rms ', pd_mean, np.sqrt(pd_mean**2 + pd_std**2))
print('------------------------------------------')
bin_ranges = get_press_bin_ranges(50, 1050, bin_size=bin_size)
bin_pres = bin_data_by(diff, amv_p, bin_ranges)
amv_spd = good_amvs[keep_idxs, sidx]
amv_dir = good_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
spd_mad = np.average(np.abs(diff))
spd_bias = np.average(diff)
print('spd, MAD: ', spd_mad)
print('spd, bias: ', spd_bias)
spd_mean = np.mean(diff)
spd_std = np.std(diff)
print('spd MAD/bias/rms: ', 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: ', np.average(np.abs(dir_diff)))
print('dir bias: ', np.average(dir_diff))
print('-------------------------------------')
bin_dir = bin_data_by(dir_diff, amv_p, bin_ranges)
amv_u, amv_v = uv_from_spd_dir(good_amvs[keep_idxs, sidx], good_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: ', vd_mean, np.sqrt(vd_mean**2 + vd_std**2))
print('------------------------------------------')
x_values = []
num_pres = []
num_spd = []
num_dir = []
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])
#return x_values, bin_pres, num_pres, bin_spd, num_spd, bin_dir, num_dir
return amvs, bfs
# imports the S4 NOAA output
......
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