avr_n2o = n2o.mean(dim="Time")
# HNO3 from MLS
datafile = xr.open_mfdataset(
'/home/kimberlee/Masters/NO2/MLS_HNO3_monthlymeans/MLS-HNO3-*.nc')
datafile = datafile.sel(Time=slice('20050101', '20141231'))
hno3 = datafile.HNO3.where((datafile.Latitude > -10)
& (datafile.Latitude < 10))
hno3['nLevels'] = datafile.Pressure.mean('Time')
hno3 *= 1e9 # ppbv
hno3 = hno3.resample('YS', dim='Time', how='mean')
avr_hno3 = hno3.mean(dim="Time")
# Calculate NOy = HNO3 + NOx. Need NOx on pressure levels
nox_pres = pres.mean(dim='time')
avr_nox_plevels, mls_levels = helper_functions.interpolate_to_mls_pressure(
nox_pres, avr_nox)
# "Magic numbers" 5:16 correspond to pressure levels where MLS data is good and OSIRIS data exists.
# Approx 146 to 3 hPa.
noy = avr_hno3[5:16].values + avr_nox_plevels
# Plot
sns.set(context="talk", style="white", rc={'font.family': [u'serif']})
fig, ax = plt.subplots(figsize=(10, 8))
l1 = ax.semilogx(avr_no2,
avr_no2.altitude,
sns.xkcd_rgb["magenta"],
marker='o',
label="NO$\mathregular{_2}$")
l2 = ax.semilogx(avr_nox,
avr_nox.altitude,
sns.xkcd_rgb["golden"],
import numpy as np
from NO2 import helper_functions, open_data
# Load NOx
nox, pres_nox = open_data.load_osiris_nox_monthly(start_date='20050101',
end_date='20141231',
min_lat=-10,
max_lat=10,
pressure=1)
# get values at 10 hPa
nox_10_hpa = np.zeros(len(nox.time))
for i in range(len(nox.time)):
pressure_i = pres_nox[i, :]
nox_i = nox[i, :]
n, l = helper_functions.interpolate_to_mls_pressure(pressure_i, nox_i)
# l = 10 hPa at index 7, l = 21.5 hPa at index 5
nox_10_hpa[i] = n[7]
nox_10_hpa_dataset = xr.DataArray(nox_10_hpa, coords=[nox.time], dims=["time"])
monthlymeans = nox_10_hpa_dataset.groupby('time.month').mean('time')
anomalies_nox = nox_10_hpa_dataset.groupby('time.month') - monthlymeans
# Load O3
o3, pres_o3 = open_data.load_osiris_ozone_monthly(start_date='20050101',
end_date='20141231',
min_lat=-10,
max_lat=10,
pressure=1)
# get values at 10 hPa
no2 = datafile.N2O.where((datafile.Latitude > -10)
& (datafile.Latitude < 10))
monthlymeans = no2.groupby('Time.month').mean('Time')
anomalies_n2o = no2.groupby('Time.month') - monthlymeans
anomalies_n2o['nLevels'] = datafile.Pressure.mean('Time')
anomalies_n2o *= 1e9 # ppbv
anomalies_n2o = anomalies_n2o[:, 5:
15] # corresponds to range of pressures returned by alt_to_pres
# Put NOx on MLS pressure levels to match HNO3
nox_on_pres_levels = np.zeros(
(len(nox.time), 10)) # alt_to_pres returns 10 pressure levels
mls_levels = []
for i in range(len(nox.time)):
nox_on_pres_levels[
i, :], l = helper_functions.interpolate_to_mls_pressure(
pres_nox[i, :], nox[i, :])
mls_levels = l
nox_dataset = xr.DataArray(nox_on_pres_levels,
coords=[nox.time, mls_levels],
dims=["Time", "nLevels"])
monthlymeans = nox_dataset.groupby('Time.month').mean('Time')
anomalies_nox = nox_dataset.groupby('Time.month') - monthlymeans
# Interpolate missing values so regression works
anomalies_n2o = helper_functions.linearinterp(anomalies_n2o, mls_levels)
anomalies_nox = helper_functions.linearinterp(anomalies_nox, mls_levels)
anomalies_n2o = anomalies_n2o[0:-3, :]
reg_coeff = np.zeros(len(mls_levels))
# Load daily NOx
nox, pres_nox = open_data.load_osiris_nox_monthly(start_date='20050101', end_date='20141231',
min_lat=-10, max_lat=10, pressure=1)
# Load monthly mean HNO3
datafile = xr.open_mfdataset('/home/kimberlee/Masters/NO2/MLS_HNO3_monthlymeans/MLS-HNO3-*.nc')
datafile = datafile.sel(Time=slice('20050101', '20141231'))
hno3 = datafile.HNO3.where((datafile.Latitude > -10) & (datafile.Latitude < 10))
hno3['nLevels'] = datafile.Pressure.mean('Time') # change level numbers to appropriate pressure
hno3 *= 1e9 # ppbv
# Put NOx on MLS pressure levels to match HNO3
nox_on_pres_levels = np.zeros((len(nox.time), 10)) # alt_to_pres returns 10 pressure levels
mls_levels = []
for i in range(len(nox.time)):
nox_on_pres_levels[i, :], l = helper_functions.interpolate_to_mls_pressure(pres_nox[i, :], nox[i, :])
mls_levels = l
# Calculate NOy = HNO3 + NOx
nox_dataset = xr.DataArray(nox_on_pres_levels, coords=[nox.time, mls_levels], dims=["Time", "nLevels"])
noy = np.zeros((len(nox_dataset.Time), 10))
for i in range(len(nox_dataset.Time)):
noy[i, :] = hno3.values[i, 5:15] + nox_dataset.values[i, :]
noy_dataset = xr.DataArray(noy, coords=[nox_dataset.Time, mls_levels], dims=["Time", "nLevels"])
monthlymeans = noy_dataset.groupby('Time.month').mean('Time')
anomalies_noy = noy_dataset.groupby('Time.month') - monthlymeans
# Load O3
o3, pres_o3 = open_data.load_osiris_ozone_monthly(start_date='20050101', end_date='20141231',
min_lat=-10, max_lat=10, pressure=1)