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Add a few new metadata variables in tempo for QC and BC #1597

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Add a few new metadata variables in tempo for QC and BC #1597

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eb31f42
add RT variables in tempo product for QC and BC
jeromebarre Dec 26, 2024
7c638ca
fixes
jeromebarre Dec 27, 2024
f1b00e1
update to conventions
jeromebarre Jan 28, 2025
c1fc79c
cleanup and make test for v3 only
jeromebarre Jan 28, 2025
ef72445
Merge branch 'develop' into feature/tempo_sza
BenjaminRuston Jan 30, 2025
f0fd69f
update variables for qc and bc, test for tempo v3
jeromebarre Jan 30, 2025
dc5bc7d
utesttewsti
jeromebarre Jan 30, 2025
09ea292
testitestout
jeromebarre Jan 30, 2025
397e698
Merge branch 'feature/tempo_sza' of https://github.com/jcsda-internal…
jeromebarre Jan 30, 2025
77de4ac
Update src/compo/tempo_nc2ioda.py
jeromebarre Feb 3, 2025
502ba07
identation nicer cmakelist
jeromebarre Feb 3, 2025
29544d5
column options
jeromebarre Feb 3, 2025
d67d690
typos
jeromebarre Feb 3, 2025
4ba3943
cmakelist
jeromebarre Feb 4, 2025
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4 changes: 2 additions & 2 deletions src/compo/gcas_nc2ioda.py
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Original file line number Diff line number Diff line change
Expand Up @@ -114,7 +114,7 @@ def read(self):
var_data = (var_data_below+var_data_above) * MOLECCM_2MOLM_2 # change to mol/m2
var_name = 'nitrogendioxideTotal'
pressure_vertice[:, 0] = np.zeros(time_dim * xtrack_dim, dtype=np.float32)
elif (self.column.strip() == 'tropo'):
elif (self.column.strip() == 'troposphere'):
var_data = dsFlight['no2_vertical_column_below_aircraft'].values.flatten()*MOLECCM_2MOLM_2
var_name = 'nitrogendioxideColumn'
pressure_vertice[:, 0] = aircraft_p
Expand Down Expand Up @@ -271,7 +271,7 @@ def get_parser():

required.add_argument(
'-c', '--column',
help="type of column: total or tropo",
help="type of column: total or troposphere",
type=str, required=True)

optional = parser.add_argument_group(title='optional arguments')
Expand Down
105 changes: 59 additions & 46 deletions src/compo/tempo_nc2ioda.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@
import netCDF4 as nc
import numpy as np
import os
import sys

import pyiodaconv.ioda_conv_engines as iconv
from collections import defaultdict, OrderedDict
Expand All @@ -37,17 +38,16 @@
hPa2Pa = 1E+2
Na = 6.0221408E+23
cm2m2 = 1E+4
molarmass = {"no2": 46.0055, "hcho": 30.031, "o3": 48.0}
molarmass = {"NO2": 46.0055, "HCHO": 30.031, "O3": 48.0}


class tempo(object):
def __init__(self, filenames, varname, columnType, qa_flg, thin, v3, obsVar):
def __init__(self, filenames, varname, columnType, qa_flg, thin, obsVar):
self.filenames = filenames
self.varname = varname
self.columnType = columnType
self.qa_flg = qa_flg
self.thin = thin
self.v3 = v3
self.obsVar = obsVar
self.varDict = defaultdict(lambda: defaultdict(dict))
self.outdata = defaultdict(lambda: DefaultOrderedDict(OrderedDict))
Expand Down Expand Up @@ -87,9 +87,12 @@ def _read(self):
AttrData['sensor'] = ncd.getncattr('project')
AttrData['platform'] = ncd.getncattr('platform')

# coordinates and mask
# coordinates, mask and RT parameters for BC
lats = ncd.groups['geolocation'].variables['latitude'][:].ravel()
lons = ncd.groups['geolocation'].variables['longitude'][:].ravel()
sza = ncd.groups['geolocation'].variables['solar_zenith_angle'][:].ravel()
vza = ncd.groups['geolocation'].variables['viewing_zenith_angle'][:].ravel()
albedo = ncd.groups['support_data'].variables['albedo'][:].ravel()
qc_flag = ncd.groups['support_data'].variables['ground_pixel_quality_flag'][:]\
.ravel()
cld_fra = ncd.groups['support_data'].variables['eff_cloud_fraction'][:]\
Expand All @@ -110,6 +113,12 @@ def _read(self):
cld_fra.mask = False
cld_fra = np.ma.array(cld_fra, mask=mask)
qa_value = np.ma.array(qa_value, mask=mask)
sza.mask = False
sza = np.ma.array(sza, mask=mask)
vza.mask = False
vza = np.ma.array(vza, mask=mask)
albedo.mask = False
albedo = np.ma.array(albedo, mask=mask)

# adding ability to pre filter the data using the qa value
# and also perform thinning using random uniform draw
Expand All @@ -130,7 +139,7 @@ def _read(self):
time = np.ma.array(time, mask=mask, dtype=object)

# NO2 and HCHO
if self.varname == 'no2' or self.varname == 'hcho':
if self.varname == 'NO2' or self.varname == 'HCHO':

# pressure levels
levels = ncd.dimensions['swt_level'].size
Expand All @@ -146,20 +155,22 @@ def _read(self):
# there is a mismatch between the mask in the scattering weights/box amf
# so we need to reset the mask and replace with the mask that is used

if self.varname == 'no2':
if self.v3:
err_name = 'vertical_column_'+self.columnType
else:
err_name = 'vertical_column_total'
if self.varname == 'NO2':
err_name = 'vertical_column_'+self.columnType
obs_name = 'vertical_column_'+self.columnType
col_amf_name = 'amf_'+self.columnType
tot_amf_name = 'amf_total'
if self.columnType == 'total':
group_name = 'support_data'
else:
group_name = 'product'

if self.varname == 'hcho':
if self.varname == 'HCHO':
tot_amf_name = 'amf'
col_amf_name = 'amf'
obs_name = 'vertical_column'
err_name = 'vertical_column'
group_name = 'product'

tot_amf = ncd.groups['support_data'].variables[tot_amf_name][:].ravel()
tot_amf.mask = False
Expand All @@ -172,14 +183,12 @@ def _read(self):
avg_kernel = box_amf / tot_amf[:, np.newaxis]

# for no2 use avk to define strat trop separation
if self.varname == 'no2':
if self.varname == 'NO2':
t_pause = hPa2Pa * ncd.groups['support_data'].variables['tropopause_pressure'][:]\
.ravel()

if self.columnType != "total":
t_diff = np.array(t_pause[:, np.newaxis] - preslev)[:, :-1]
if self.columnType == "stratosphere":
avg_kernel[t_diff <= 0] = 0.0
if self.columnType == "troposphere":
avg_kernel[t_diff > 0] = 0.0

Expand All @@ -191,36 +200,26 @@ def _read(self):
col_amf = ncd.groups['support_data'].variables[col_amf_name][:].ravel()
col_amf.mask = False
col_amf = np.ma.array(col_amf, mask=mask)
obs = ncd.groups['product'].variables[obs_name][:]\
obs = ncd.groups[group_name].variables[obs_name][:]\
.ravel() * conv
obs.mask = False
obs = np.ma.array(obs, mask=mask)

# error calculation:
err = ncd.groups['product'].variables[err_name+'_uncertainty'][:].ravel()
if self.v3:
if self.columnType == "total" or self.columnType == "stratosphere":
sys.exit("no error with total and strato NRT product")
err = err * conv
else:
err = err * conv * col_amf / tot_amf
err = ncd.groups[group_name].variables[err_name+'_uncertainty'][:].ravel()
err = err * conv

err.mask = False
err = np.ma.array(err, mask=mask)

# error tuning for data assimilation, i.e. less weight to low obs values
# experimental
# err = err * (1.0 + err/obs)

# O3
if self.varname == 'o3':
if self.varname == 'O3':
print("O3 product converter not ready yet")
exit()

# clean data
neg_obs = ((obs > 0.0) & (err > 0.0))
neg_obs = err > 0.0
nan_obs = ((obs != np.nan) & (err != np.nan))
nan_obs = (~np.isnan(obs) & ~np.isnan(err) & np.isreal(obs) & np.isreal(err) & np.isfinite(obs) & np.isfinite(err))
cln = np.logical_and(neg_obs, nan_obs)

# final flag before sending this to ioda engines
Expand All @@ -234,6 +233,9 @@ def _read(self):
print('flg: ', np.shape(flg))
print('qa_value: ', np.shape(qa_value))
print('cld_fra: ', np.shape(cld_fra))
print('sza: ', np.shape(sza))
print('vza: ', np.shape(vza))
print('albedo: ', np.shape(albedo))
print('qc_flag: ', np.shape(qc_flag))
print('obs: ', np.shape(obs))
print('err: ', np.shape(err))
Expand All @@ -247,6 +249,9 @@ def _read(self):
flg = np.ma.compressed(flg)
qa_value = np.ma.compressed(qa_value).astype('float32')
cld_fra = np.ma.compressed(cld_fra).astype('float32')
sza = np.ma.compressed(sza).astype('float32')
vza = np.ma.compressed(vza).astype('float32')
albedo = np.ma.compressed(albedo).astype('float32')
qc_flag = np.ma.compressed(qc_flag).astype('int32')
obs = np.ma.compressed(obs).astype('float32')
err = np.ma.compressed(err).astype('float32')
Expand All @@ -266,6 +271,9 @@ def _read(self):
print('flg: ', np.shape(flg))
print('qa_value: ', np.shape(qa_value))
print('cld_fra: ', np.shape(cld_fra))
print('sza: ', np.shape(sza))
print('vza: ', np.shape(vza))
print('albedo: ', np.shape(albedo))
print('qc_flag: ', np.shape(qc_flag))
print('obs: ', np.shape(obs))
print('err: ', np.shape(err))
Expand All @@ -276,8 +284,11 @@ def _read(self):
self.outdata[('dateTime', 'MetaData')] = time[flg]
self.outdata[('latitude', 'MetaData')] = lats[flg]
self.outdata[('longitude', 'MetaData')] = lons[flg]
self.outdata[('quality_assurance_value', 'MetaData')] = qa_value[flg]
self.outdata[('cloud_fraction', 'MetaData')] = cld_fra[flg]
self.outdata[('qualityFlags', 'MetaData')] = qa_value[flg]
self.outdata[('cloudAmount', 'MetaData')] = cld_fra[flg]
self.outdata[('solarZenithAngle', 'MetaData')] = sza[flg]
self.outdata[('viewingZenithAngle', 'MetaData')] = vza[flg]
self.outdata[('albedo', 'MetaData')] = albedo[flg]
self.outdata[('averagingKernel', 'RetrievalAncillaryData')] = avg_kernel[flg]
self.outdata[('pressureVertice', 'RetrievalAncillaryData')] = preslev[flg]
self.outdata[self.varDict[iodavar]['valKey']] = obs[flg]
Expand All @@ -290,10 +301,16 @@ def _read(self):
self.outdata[('latitude', 'MetaData')], lats[flg]))
self.outdata[('longitude', 'MetaData')] = np.concatenate((
self.outdata[('longitude', 'MetaData')], lons[flg]))
self.outdata[('quality_assurance_value', 'MetaData')] = np.concatenate((
self.outdata[('quality_assurance_value', 'MetaData')], qa_value[flg]))
self.outdata[('cloud_fraction', 'MetaData')] = np.concatenate((
self.outdata[('cloud_fraction', 'MetaData')], cld_fra[flg]))
self.outdata[('qualityFlags', 'MetaData')] = np.concatenate((
self.outdata[('qualityFlags', 'MetaData')], qa_value[flg]))
self.outdata[('cloudAmount', 'MetaData')] = np.concatenate((
self.outdata[('cloudAmount', 'MetaData')], cld_fra[flg]))
self.outdata[('solarZenithAngle', 'MetaData')] = np.concatenate((
self.outdata[('solarZenithAngle', 'MetaData')], sza[flg]))
self.outdata[('viewingZenithAngle', 'MetaData')] = np.concatenate((
self.outdata[('viewingZenithAngle', 'MetaData')], vza[flg]))
self.outdata[('albedo', 'MetaData')] = np.concatenate((
self.outdata[('albedo', 'MetaData')], albedo[flg]))
self.outdata[('averagingKernel', 'RetrievalAncillaryData')] = np.concatenate((
self.outdata[('averagingKernel', 'RetrievalAncillaryData')], avg_kernel[flg]))
self.outdata[('pressureVertice', 'RetrievalAncillaryData')] = np.concatenate((
Expand Down Expand Up @@ -351,7 +368,7 @@ def main():
type=str, required=True)
required.add_argument(
'-c', '--column',
help="type of column: total, troposphere or stratosphere",
help="type of column: total, troposphere",
type=str, required=True)
optional = parser.add_argument_group(title='optional arguments')
optional.add_argument(
Expand All @@ -364,27 +381,23 @@ def main():
help="percentage of random thinning from 0.0 to 1.0. Zero indicates"
" no thinning is performed. (default: %(default)s)",
type=float, default=0.0)
optional.add_argument(
'-v3', '--version3',
action='store_true',
help='Read V3 files and not V2 files')

args = parser.parse_args()

if args.variable == "hcho":
if args.variable == "HCHO":
var_name = 'formaldehyde'
if args.column != "troposphere":
print('hcho is only available for troposphere column, reset column to troposphere', flush=1)
args.column = 'troposphere'
elif args.variable == "no2":
elif args.variable == "NO2":
var_name = 'nitrogendioxide'
elif args.variable == "o3":
elif args.variable == "O3":
var_name = 'ozone'

if args.column == "troposphere" or args.column == "stratosphere":
if args.column == "troposphere":

obsVar = {
var_name+'_'+args.column+'spheric_column': var_name+'Column'
var_name+'_'+args.column+'_column': var_name+'Column'
}

varDims = {
Expand All @@ -405,7 +418,7 @@ def main():
varDims['pressureVertice'] = ['Location', 'Vertice']

# Read in the NO2 data
var = tempo(args.input, args.variable, args.column, args.qa_value, args.thin, args.version3, obsVar)
var = tempo(args.input, args.variable, args.column, args.qa_value, args.thin, obsVar)

# setup the IODA writer
writer = iconv.IodaWriter(args.output, locationKeyList, DimDict)
Expand Down
40 changes: 33 additions & 7 deletions src/compo/tropomi_no2_co_nc2ioda.py
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Expand Up @@ -105,7 +105,7 @@ def _read(self):
avg_kernel = ncd.groups['PRODUCT'].variables['averaging_kernel'][:]
avg_kernel = np.flip(np.reshape(avg_kernel, (nlocs, nlevs)), axis=1)

if self.columnType == 'tropo':
if self.columnType == 'troposphere':
trop_layer = ncd.groups['PRODUCT'].variables['tm5_tropopause_layer_index'][:].ravel()
total_airmass = ncd.groups['PRODUCT'].variables['air_mass_factor_total'][:].ravel()
trop_airmass = ncd.groups['PRODUCT'].variables['air_mass_factor_troposphere'][:].ravel()
Expand All @@ -124,6 +124,10 @@ def _read(self):
ps[...].ravel())), axis=1)
top = ak[nlevs-1, 1] + bk[nlevs-1, 1]*ps[...].ravel()

# albedo
albedo = ncd.groups['PRODUCT'].groups['SUPPORT_DATA'].\
groups['INPUT_DATA'].variables['surface_albedo_nitrogendioxide_window'][:].ravel()

elif self.varname == 'co':
# grab the averaging kernel and reshape it
avg_kernel = ncd.groups['PRODUCT'].groups['SUPPORT_DATA'].\
Expand All @@ -136,6 +140,19 @@ def _read(self):
preslv = np.reshape(preslv, (nlocs, nlevs))
top = np.zeros(nlocs, dtype=np.float32)

# albedo
albedo1 = ncd.groups['PRODUCT'].groups['SUPPORT_DATA'].\
groups['DETAILED_RESULTS'].variables['surface_albedo_2325'][:].ravel()
albedo2 = ncd.groups['PRODUCT'].groups['SUPPORT_DATA'].\
groups['DETAILED_RESULTS'].variables['surface_albedo_2335'][:].ravel()
albedo = 0.5 * (albedo1 + albedo2)

# get angles
sza = ncd.groups['PRODUCT'].groups['SUPPORT_DATA'].\
groups['GEOLOCATIONS'].variables['solar_zenith_angle'][:].ravel()
vza = ncd.groups['PRODUCT'].groups['SUPPORT_DATA'].\
groups['GEOLOCATIONS'].variables['viewing_zenith_angle'][:].ravel()

# assemble presvertices with top vertice
preslv = np.append(top[:, np.newaxis], preslv, axis=1)

Expand All @@ -148,7 +165,10 @@ def _read(self):
self.outdata[('dateTime', 'MetaData')] = times[flg]
self.outdata[('latitude', 'MetaData')] = lats[flg]
self.outdata[('longitude', 'MetaData')] = lons[flg]
self.outdata[('quality_assurance_value', 'MetaData')] = qa_value[flg]
self.outdata[('qualityFlag', 'MetaData')] = qa_value[flg]
self.outdata[('solarZenithAngle', 'MetaData')] = sza[flg]
self.outdata[('viewingZenithAngle', 'MetaData')] = vza[flg]
self.outdata[('albedo', 'MetaData')] = albedo[flg]

self.outdata[('averagingKernel', 'RetrievalAncillaryData')] = avg_kernel[flg]
self.outdata[('pressureVertice', 'RetrievalAncillaryData')] = preslv[flg]
Expand All @@ -160,8 +180,14 @@ def _read(self):
self.outdata[('latitude', 'MetaData')], lats[flg]))
self.outdata[('longitude', 'MetaData')] = np.concatenate((
self.outdata[('longitude', 'MetaData')], lons[flg]))
self.outdata[('quality_assurance_value', 'MetaData')] = np.concatenate((
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self.outdata[('quality_assurance_value', 'MetaData')], qa_value[flg]))
self.outdata[('qualityFlag', 'MetaData')] = np.concatenate((
self.outdata[('qualityFlag', 'MetaData')], qa_value[flg]))
self.outdata[('solarZenithAngle', 'MetaData')] = np.concatenate((
self.outdata[('solarZenithAngle', 'MetaData')], sza[flg]))
self.outdata[('viewingZenithAngle', 'MetaData')] = np.concatenate((
self.outdata[('viewingZenithAngle', 'MetaData')], vza[flg]))
self.outdata[('albedo', 'MetaData')] = np.concatenate((
self.outdata[('albedo', 'MetaData')], albedo[flg]))

self.outdata[('averagingKernel', 'RetrievalAncillaryData')] = np.concatenate((
self.outdata[('averagingKernel', 'RetrievalAncillaryData')], avg_kernel[flg]))
Expand Down Expand Up @@ -235,7 +261,7 @@ def main():
type=str, required=True)
required.add_argument(
'-c', '--column',
help="type of column: total or tropo",
help="type of column: total or tropophere",
type=str, required=True)
optional = parser.add_argument_group(title='optional arguments')
optional.add_argument(
Expand All @@ -258,13 +284,13 @@ def main():

if args.variable == "co":
var_name = 'carbonmonoxide'
if args.column == "tropo":
if args.column == "troposphere":
print('CO is only available for total column, reset column to total', flush=1)
args.column = 'total'
elif args.variable == "no2":
var_name = 'nitrogendioxide'

if args.column == "tropo":
if args.column == "troposphere":

obsVar = {
var_name+'_tropospheric_column': var_name+'Column'
Expand Down
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