Improved aerosol correction for OMI tropospheric NO2 retrieval over East Asia : Constraint from CALIOP aerosol vertical profile
Abstract. Satellite retrieval of vertical column densities (VCDs) of tropospheric
nitrogen dioxide ( NO2 ) is critical for NOx pollution and impact
evaluation. For regions with high aerosol loadings, the retrieval accuracy is
greatly affected by whether aerosol optical effects are treated implicitly
(as additional “effective” clouds) or explicitly, among other factors. Our
previous POMINO algorithm explicitly accounts for aerosol effects to improve
the retrieval, especially in polluted situations over China, by using aerosol
information from GEOS-Chem simulations with further monthly constraints by
MODIS/Aqua aerosol optical depth (AOD) data. Here we present a major
algorithm update, POMINO v1.1, by constructing a monthly climatological dataset of aerosol extinction profiles, based on level 2 CALIOP/CALIPSO data over
2007–2015, to better constrain the modeled aerosol vertical profiles. We find that GEOS-Chem captures the month-to-month variation in CALIOP
aerosol layer height (ALH) but with a systematic underestimate by about 300–600 m
(season and location dependent), due to a too strong negative vertical
gradient of extinction above 1 km. Correcting the model aerosol extinction
profiles results in small changes in retrieved cloud fraction, increases in
cloud-top pressure (within 2 %–6 % in most cases), and increases in
tropospheric NO2 VCD by 4 %–16 % over China on a monthly basis in
2012. The improved NO2 VCDs (in POMINO v1.1) are more consistent with
independent ground-based MAX-DOAS observations ( R2=0.80 , NMB  = −3.4  %, for
162 pixels in 49 days) than POMINO ( R2=0.80 , NMB  = −9.6  %), DOMINO v2 ( R2=0.68 , NMB  = −2.1  %), and QA4ECV
( R2=0.75 , NMB  = −22.0  %) are. Especially on haze days, R2 
reaches 0.76 for POMINO v1.1, much higher than that for POMINO (0.68),
DOMINO v2 (0.38), and QA4ECV (0.34). Furthermore, the increase in cloud
pressure likely reveals a more realistic vertical relationship between cloud
and aerosol layers, with aerosols situated above the clouds in certain
months instead of always below the clouds. The POMINO v1.1 algorithm is a
core step towards our next public release of the data product (POMINO v2), and
it will also be applied to the recently launched S5P-TROPOMI sensor.