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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
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32 changes: 30 additions & 2 deletions src/compo/tempo_nc2ioda.py
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Original file line number Diff line number Diff line change
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 Down Expand Up @@ -234,6 +243,9 @@ 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 +259,9 @@ 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 +281,9 @@ 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 @@ -278,6 +296,9 @@ def _read(self):
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[('solar_zenith_angle', 'MetaData')] = sza[flg]
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are these the right IODA names? I thought snake case was frowned upon?

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thanks @CoryMartin-NOAA ,, you are correct the name to use is:
solarZenithAngle

https://github.com/JCSDA-internal/ioda/blob/202aafb341afdd667562ded96447abb34199bf43/share/ioda/yaml/validation/ObsSpace.yaml#L955

note the first name in the list is the current standard with the other names ones that would be changed into the leading entry if the upgrader were run something like:

${JEDI_BUILD}/bin/ioda-upgrade-v2-to-v3.x ${ioda_v2} ${ioda_v3} ${JEDI_SRC}/ioda/share/ioda/yaml/validation/ObsSpace.yaml

self.outdata[('viewing_zenith_angle', '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 @@ -294,6 +315,12 @@ def _read(self):
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[('solar_zenith_angle', 'MetaData')] = np.concatenate((
self.outdata[('solar_zenith_angle', 'MetaData')], sza[flg]))
self.outdata[('viewing_zenith_angle', 'MetaData')] = np.concatenate((
self.outdata[('viewing_zenith_angle', '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 @@ -367,6 +394,7 @@ def main():
optional.add_argument(
'-v3', '--version3',
action='store_true',
default=True,
help='Read V3 files and not V2 files')

args = parser.parse_args()
Expand All @@ -384,7 +412,7 @@ def main():
if args.column == "troposphere" or args.column == "stratosphere":

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

varDims = {
Expand Down