Improved aerosol correction for OMI tropospheric NO<sub>2</sub> retrieval over East Asia: constraint from CALIOP aerosol vertical profile

Abstract

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.

High-resolution (0.05° × 0.05°) NOx emissions in the Yangtze River Delta inferred from OMI

“…), consistent retrievals of clouds (a prerequisite for the NO 2 retrieval) and NO 2 , and use of a parallelized, LIDORTdriven AMFv6 package. POMINO NO 2 VCDs are consistent with ground-based MAX-DOAS data (Liu et al, 2019b).…”

“…Also, for near-ground NO 2 over the YRD area, the contribution of downward vertical transport is negligible compared to the contribution of ground sources. Aircraft emissions contribute little to the total ground source, because 78 % of aircraft emissions occur at the high altitudes (9-12 km) (Ma and Xiuji, 2000). Therefore, PHLET only accounts for near-ground NO 2 from ground soil, biomass burning, and anthropogenic sources (energy, industry, transportation, and residential).…”

Effect of changing NO<sub><i>x</i></sub> lifetime on the seasonality and long-term trends of satellite-observed tropospheric NO<sub>2</sub> columns over China

“…In ECV, polluted scenes with high aerosol optical depths (and likely high NO2) can be misclassified as cloudy and excluded from the seasonal-mean, which leads to a negative sampling bias (Lin et al, 2014Liu et al, 2019). On the other hand, retrieving NO2 columns under high-aerosol 160 conditions can be uncertain because of the strong sensitivity to the vertical distribution of NO2 relative to that of aerosols, although this is less of a problem in POMINO as it uses the CALIOP-observed aerosol vertical profiles (Lin et al, 2014Liu et al, 2019). MAX-DOAS NO2 than ECV (-3% versus -22% bias) and on hazy days (+4% versus -26% bias), but on clear (cloud fraction=0) days ECV performed better (-6% bias) than POMINO (+30% bias).…”

“…We use tropospheric NO2 columns from two retrievals: the European Quality Assurance for Essential Climate Variables (QA4ECV) project's NO2 ECV precursor product and the Peking University POMINO product, version 2 Liu et al, 2019). NO2 tropospheric column retrieval in the ECV product involves (1) spectral fit of the backscattered solar radiation in the 405-465 nm window to obtain the total NO2 slant column (SC), (2) removal of the 90 stratospheric component by data assimilation with the TM5-MP chemical transport model, and (3) conversion of the tropospheric SC to a tropospheric vertical column (VC) with an air mass factor (AMF = SC/VC) that depends on viewing geometry, surface albedo, retrieved cloud properties, and the NO2 vertical profile (taken from the TM5-MP model).…”

“…In polluted areas (aerosol optical depth greater than 0.5), the choice of aerosol correction method can affect the AMF by 45% (Lorente et al, 2017) and the sign of the correction depends on the vertical distribution of aerosols relative to NO2 (Palmer et 100 al., 2001;Martin et al, 2003;Liu et al, 2019). POMINO also includes angular dependence of surface reflectance, online radiative transfer calculation, and consistency in retrievals of cloud properties and NO2 Liu et al, 2019).…”