x44a = np.array([x44, y44])
x44b = x44a[:, ~np.isnan(x44a).any(axis=0)]
x4 = x44b[0]
y4 = x44b[1]
x55 = gc_data_HNO3_djf
y55 = sites_HNO3_djf
x55a = np.array([x55, y55])
x55b = x55a[:, ~np.isnan(x55a).any(axis=0)]
x5 = x55b[0]
y5 = x55b[1]
#Regression ~ using bootstrap
#ind=np.where((gc_data_HNO3_annual!=0)&(sites_HNO3_AM!=0))
#print (ind)
regres_annual = rma(x1, y1, (len(x1)), 1000)
print('slope annual: ', regres_annual[0])
print('Intercept annual: ', regres_annual[1])
print('slope error annual: ', regres_annual[2])
print('Intercept error annual: ', regres_annual[3])
regres_mam = rma(x2, y2, (len(x2)), 1000)
print('slope mam: ', regres_mam[0])
print('Intercept mam: ', regres_mam[1])
print('slope error mam: ', regres_mam[2])
print('Intercept error mam: ', regres_mam[3])
regres_jja = rma(x3, y3, (len(x3)), 1000)
print('slope jja: ', regres_jja[0])
print('Intercept jja: ', regres_jja[1])
print('slope error jja: ', regres_jja[2])
x1 = x11b[0]
y1 = x11b[1]
print(x1, 'x1')
print(y1, 'y1')
x1y1 = np.vstack([x1, y1])
z1 = gaussian_kde(x1y1)(x1y1)
slope1, intercept1, r_value1, p_value1, std_err1 = stats.linregress(x1, y1)
line1 = slope1 * x1 + intercept1
correlate_annual = stats.pearsonr(x1, y1)
#Regression ~ using bootstrap
#ind=np.where((gc_data_ammonia_annual!=0)&(sites_ammonia_AM!=0))
#print (ind)
regres_annual = rma(x1, y1, (len(x1)), 1000)
print('slope annual: ', regres_annual[0])
print('Intercept annual: ', regres_annual[1])
print('slope error annual: ', regres_annual[2])
print('Intercept error annual: ', regres_annual[3])
#plotting scatter plot
title_list1 = 'NAEI and IASI NH$_3$ Emission Annual'
fig1 = plt.figure(facecolor='White', figsize=[11, 11])
pad = 1.1
ax = plt.subplot(111)
plt.title(title_list1, fontsize=20, y=1)
plt.plot(x1, y1, 'o', color='black', markersize=3)
xvals = np.arange(-1e3, 1e5, 5)
def cldslice(pcolno2,cldtophgt):
"""
Compute upper troposphere NO2 using partial columns above
cloudy scenes.
Determine NO2 mixing ratio by regressing NO2 partial columns
against cloud-top heights over cloudy scenes.
INPUT: vectors of partial columns in molec/m2 and corresponding
cloud top heights in hPa.
OUTPUT: NO2 volumetric mixing ratio, corresponding estimated error on the
cloud-sliced NO2 value, a number to identify which filtering
criteria led to loss of data in the case that the cloud-sliced
NO2 value ia nan, and the mean cloud pressure of data retained
after 10th and 90th percentile filtering.
"""
# Initialize:
utmrno2=0.0
utmrno2err=0.0
error_state=0
# Define factor to convert slope of NO2 partial column vs pressure
# to VMR:
den2mr=np.divide((np.multiply(g,mmair)),na)
# Get 10th and 90th percentiles of data population:
p10=np.percentile(pcolno2,10)
p90=np.percentile(pcolno2,90)
# Remove outliers determined as falling outside the 10th and 90th
# percentile range. Not include this or instead using 5th and 95th leads
# to overestimate in cloud-sliced UT NO2 compared to the "truth":
sind=np.where((pcolno2>p10)&(pcolno2<p90))[0]
# Trim the data to remove ouliers:
pcolno2=pcolno2[sind]
cldtophgt=cldtophgt[sind]
# Cloud pressure mean:
mean_cld_pres=np.mean(cldtophgt)
# Get number of points in vector:
npoints=len(cldtophgt)
# Only consider data with more than 5 points for reasonably
# robust statistics. This step is added to account for data loss
# removing outliers:
if npoints<=10:
error_state=1
utmrno2=np.nan
utmrno2err=np.nan
return (utmrno2, utmrno2err, error_state, mean_cld_pres)
# Get cloud top height standard deviation:
stdcld=np.std(cldtophgt)
# Get cloud top height range:
diffcld=np.nanmax(cldtophgt)-np.nanmin(cldtophgt)
# Only consider scenes with a dynamic range of clouds:
# (i) Cloud range:
if diffcld<=140:
error_state=2
utmrno2=np.nan
utmrno2err=np.nan
return (utmrno2, utmrno2err, error_state, mean_cld_pres)
# (ii) Cloud standard deviation:
if stdcld<=30:
error_state=3
utmrno2=np.nan
utmrno2err=np.nan
return (utmrno2, utmrno2err, error_state, mean_cld_pres)
# Get regression statistics:
# Partial NO2 column (molec/m2) vs cloud top height (hPa):
# 300 iterations of regression chosen to compromise between
# statistics and computational efficiency:
result=rma(cldtophgt*1e2,pcolno2,len(pcolno2),300)
# Remove data with relative error > 100%:
if np.absolute(np.divide(result[2], result[0]))>1.0:
error_state=4
utmrno2=np.nan
utmrno2err=np.nan
return (utmrno2, utmrno2err, error_state, mean_cld_pres)
# Account for negative values:
# Set points with sum of slope and error less than zero to nan.
# This is to account for noise in the data that hover near zero.
if result[0]<0 and (not np.isnan(utmrno2)):
if (np.add(result[0],result[2])<0):
error_state=5
utmrno2=np.nan
utmrno2err=np.nan
return (utmrno2, utmrno2err, error_state, mean_cld_pres)
# Proceed with estimating NO2 mixing ratios for retained data:
#if not np.isnan(utmrno2):
slope=result[0]
#slope=np.multiply(slope,sf)
slope_err=result[2]
#slope_err=np.multiply(slope_err,sf)
# Convert slope to mol/mol:
utmrno2=np.multiply(slope,den2mr)
# Convert error to mol/mol:
utmrno2err=np.multiply(slope_err,den2mr)
# Convert UT NO2 from mol/mol to ppt:
utmrno2=np.multiply(utmrno2,1e+12)
# Convert UT NO2 error from mol/mol to ppt
utmrno2err=np.multiply(utmrno2err,1e+12)
# Finally, remove outliers in the cloud-sliced NO2
# 200 pptv threshold is chosen, as far from likely range.
# Scale factor applied to TROPOMI UT NO2 to account for
# positive bias in free tropospheric NO2:
if utmrno2>200:
error_state=6
utmrno2=np.nan
utmrno2err=np.nan
return (utmrno2, utmrno2err, error_state, mean_cld_pres)
else:
return (utmrno2, utmrno2err, error_state, mean_cld_pres)
plt.subplot(2, 3, 3)
cs = m.pcolor(xi, yi, np.squeeze(gcutno2), vmin=0, vmax=80, cmap='jet')
m.drawparallels(np.arange(-80., 81., 45.), labels=[1, 0, 0, 0], fontsize=8)
m.drawmeridians(np.arange(-180., 181., 45.), labels=[0, 0, 0, 1], fontsize=8)
m.drawcoastlines()
m.drawcountries()
cbar = m.colorbar(cs, location='bottom', pad="10%")
plt.title('True NO2 VMRs under all-sky conditions')
plt.subplot(2, 3, 4)
plt.plot(trueno2, gno2vmr, 'o', color='black', markersize=6)
r=stats.pearsonr(trueno2[~np.isnan(gno2vmr)],\
gno2vmr[~np.isnan(gno2vmr)])
#print('Correlation = ', r[0])
result=rma(trueno2[~np.isnan(gno2vmr)],gno2vmr[~np.isnan(gno2vmr)],\
len(trueno2[~np.isnan(gno2vmr)]),1000)
print(result, flush=True)
xvals = np.arange(0, 100, 5)
yvals = result[1] + xvals * result[0]
plt.plot(xvals, yvals, '-')
plt.xlim(-4, 80)
plt.ylim(-4, 80)
plt.xlabel('True NO2 (cloudy)')
plt.ylabel('Cloud-sliced NO2')
print('===== True (cloudy) vs cloud-sliced UT NO2 ====')
print('R = ', r[0], flush=True)
print('Slope = ', result[0])
print('Slope Err = ', result[2], flush=True)
print('Intercept = ', result[1], flush=True)
print('Intercept Err = ', result[3], flush=True)
add2plt=("y = {a:6.2f}x + {b:6.3f}".\
def plot_data(self):
# Plot the data:
m = Basemap(resolution='l', projection='merc',
lat_0=0, lon_0=0, llcrnrlon=self.minlon,
llcrnrlat=self.minlat, urcrnrlon=self.maxlon, urcrnrlat=self.maxlat)
xi, yi = m(self.X, self.Y)
plt.subplot(2, 3, 1)
cs = m.pcolor(xi, yi, np.squeeze(self.g_no2_vmr), vmin=0, vmax=80, cmap='jet')
m.drawparallels(np.arange(-80., 81., 45.), labels=[1, 0, 0, 0], fontsize=8)
m.drawmeridians(np.arange(-180., 181., 45.), labels=[0, 0, 0, 1], fontsize=8)
m.drawcoastlines()
m.drawcountries()
cbar = m.colorbar(cs, location='bottom', pad="10%")
plt.title('Cloud-sliced NO2 VMRs')
plt.subplot(2, 3, 2)
cs = m.pcolor(xi, yi, np.squeeze(self.true_no2), vmin=0.0, vmax=80, cmap='jet')
m.drawparallels(np.arange(-80., 81., 45.), labels=[1, 0, 0, 0], fontsize=8)
m.drawmeridians(np.arange(-180., 181., 45.), labels=[0, 0, 0, 1], fontsize=8)
m.drawcoastlines()
m.drawcountries()
cbar = m.colorbar(cs, location='bottom', pad="10%")
plt.title('True cloudy NO2')
plt.subplot(2, 3, 3)
cs = m.pcolor(xi, yi, np.squeeze(self.g_askut_no2), vmin=0, vmax=80, cmap='jet')
m.drawparallels(np.arange(-80., 81., 45.), labels=[1, 0, 0, 0], fontsize=8)
m.drawmeridians(np.arange(-180., 181., 45.), labels=[0, 0, 0, 1], fontsize=8)
m.drawcoastlines()
m.drawcountries()
cbar = m.colorbar(cs, location='bottom', pad="10%")
plt.title('True NO2 VMRs under all-sky conditions')
plt.subplot(2, 3, 4)
plt.plot(self.true_no2, self.g_no2_vmr, 'o', color='black', markersize=6)
r = stats.pearsonr(self.true_no2[~np.isnan(self.g_no2_vmr)],
self.g_no2_vmr[~np.isnan(self.g_no2_vmr)])
# print('Correlation = ', r[0])
result = rma(self.true_no2[~np.isnan(self.g_no2_vmr)], self.g_no2_vmr[~np.isnan(self.g_no2_vmr)],
len(self.true_no2[~np.isnan(self.g_no2_vmr)]), 1000)
print(result, flush=True)
xvals = np.arange(0, 100, 5)
yvals = result[1] + xvals * result[0]
plt.plot(xvals, yvals, '-')
plt.xlim(-4, 80)
plt.ylim(-4, 80)
plt.xlabel('True NO2 (cloudy)')
plt.ylabel('Cloud-sliced NO2')
print('===== True (cloudy) vs cloud-sliced UT NO2 ====')
print('R = ', r[0], flush=True)
print('Slope = ', result[0])
print('Slope Err = ', result[2], flush=True)
print('Intercept = ', result[1], flush=True)
print('Intercept Err = ', result[3], flush=True)
add2plt = ("y = {a:6.2f}x + {b:6.3f}".format(a=result[0], b=result[1]))
plt.text(2, 75, add2plt, fontsize=8,
ha='left', va='center') # , transform=ax.transAxes)
add2plt = ("R = {a:6.2f}".format(a=r[0]))
plt.text(2, 65, add2plt, fontsize=8,
ha='left', va='center') # , transform=ax.transAxes)
plt.subplot(2, 3, 5)
plt.plot(self.g_askut_no2, self.g_no2_vmr, 'o', color='black', markersize=6)
r = stats.pearsonr(self.g_askut_no2[(~np.isnan(self.g_no2_vmr)) & (~np.isnan(self.g_askut_no2))],
self.g_no2_vmr[(~np.isnan(self.g_no2_vmr)) & (~np.isnan(self.g_askut_no2))])
result = rma(self.g_askut_no2[(~np.isnan(self.g_no2_vmr)) & (~np.isnan(self.g_askut_no2))],
self.g_no2_vmr[(~np.isnan(self.g_no2_vmr)) & (~np.isnan(self.g_askut_no2))],
len(self.g_askut_no2[(~np.isnan(self.g_no2_vmr)) & (~np.isnan(self.g_askut_no2))]),
1000)
xvals = np.arange(0, 100, 5)
yvals = result[1] + xvals * result[0]
plt.plot(xvals, yvals, '-')
plt.xlim(-4, 80)
plt.ylim(-4, 80)
plt.xlabel('True NO2 (all-sky)')
plt.ylabel('Cloud-sliced NO2')
add2plt = ("y = {a:6.2f}x + {b:6.3f}". \
format(a=result[0], b=result[1]))
plt.text(2, 75, add2plt, fontsize=8, \
ha='left', va='center') # , transform=ax.transAxes)
add2plt = ("R = {a:6.2f}".format(a=r[0]))
plt.text(2, 65, add2plt, fontsize=8, \
ha='left', va='center') # , transform=ax.transAxes)
plt.subplot(2, 3, 6)
plt.plot(self.g_askut_no2, self.true_no2, 'o', color='black', markersize=6)
r = stats.pearsonr(self.g_askut_no2[(~np.isnan(self.true_no2)) & (~np.isnan(self.g_askut_no2))],
self.true_no2[(~np.isnan(self.true_no2)) & (~np.isnan(self.g_askut_no2))])
result = rma(self.g_askut_no2[(~np.isnan(self.true_no2)) & (~np.isnan(self.g_askut_no2))],
self.true_no2[(~np.isnan(self.true_no2)) & (~np.isnan(self.g_askut_no2))],
len(self.g_askut_no2[(~np.isnan(self.true_no2)) & (~np.isnan(self.g_askut_no2))]),
1000)
xvals = np.arange(0, 100, 5)
yvals = result[1] + xvals * result[0]
plt.plot(xvals, yvals, '-')
plt.xlim(-4, 80)
plt.ylim(-4, 80)
plt.xlabel('True NO2 (all-sky)')
plt.ylabel('True NO2 (cloudy)')
add2plt = ("y = {a:6.2f}x + {b:6.3f}".format(a=result[0], b=result[1]))
plt.text(2, 75, add2plt, fontsize=8, ha='left', va='center')
add2plt = ("R = {a:6.2f}".format(a=r[0]))
plt.text(2, 65, add2plt, fontsize=8, ha='left', va='center')
plt.show()
