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NASA NTRS2022

Long-Term Variation of Aerosol Optical Properties Associated With Aerosol Types Over East Asia Using AERONET and Satellite (VIIRS, OMI) Data (2012–2019)

We analyzed annual and seasonal frequency in aerosol type over an 8-year period (2012–2019) to identify aerosol parameter trends over four ground sites and country regions in Korea, China, and Japan by using the Aerosol Robotic Network (AERONET), and the satellite-based Visible Infrared Imaging Radiometer Suite (VIIRS) and Ozone Monitoring Instrument (OMI). Decreasing trends are shown for aerosol optical depth (AOD), Ångstr¨om exponent (AE), and fine mode fraction (FMF) in all countries. The decreasing trend in these data is considered to be due to a decrease in anthropogenic emissions. For the aerosol type frequency, decreases in the proportions of carbonaceous aerosols (CA) and non-absorbing aerosols (NA) were shown in the ground and satellite data, respectively. At most sites, the fractions of low AOD case (LOW) increased, whereas those of the Black and Brown Carbon (BC + BrC) category decreased. In Seoul, the fraction of LOW increased from 48.9% to 70.0%, and that of BC + BrC decreased continuously from 20.4% to 11.1% during 2012–2019. Beijing, on the other hand, showed decreasing LOW from 83.3% (2012) to 52.0% (2019), and that of BC + BrC increased significantly, from 2.4% to 26.2%. The satellite data showed that the percentage of LOW increased continuously, while that of NA aerosols decreased continuously in East Asia. A noticeable decrease in the fraction of CA was detected in China [21.5% (2013) to 11.2% (2019)]. In all countries, CA and NA aerosols had the greatest effect in winter and summer, respectively. We also detected significant differences between the fractions of NA and BC between the ground and satellite data. Changes in aerosol type and properties were observed concurrently in all ground and satellite data, and changes in aerosol type may explain the increasing and decreasing trends that we recorded for most parameters. Consistent results from both ground and satellite data suggest a steady decreasing in fine aerosol pollution in East Asia.

Sujin Eom↗
NASA NTRS2015

The Global Structure of UTLS Ozone in GEOS-5: A Multi-Year Assimilation of EOS Aura Data

Eight years of ozone measurements retrieved from the Ozone Monitoring Instrument (OMI) and the Microwave Limb Sounder, both on the EOS Aura satellite, have been assimilated into the Goddard Earth Observing System version 5 (GEOS-5) data assimilation system. This study thoroughly evaluates this assimilated product, highlighting its potential for science. The impact of observations on the GEOS-5 system is explored by examining the spatial distribution of the observation-minus-forecast statistics. Independent data are used for product validation. The correlation coefficient of the lower-stratospheric ozone column with ozonesondes is 0.99 and the bias is 0.5%, indicating the success of the assimilation in reproducing the ozone variability in that layer. The upper-tropospheric assimilated ozone column is about 10% lower than the ozonesonde column but the correlation is still high (0.87). The assimilation is shown to realistically capture the sharp cross-tropopause gradient in ozone mixing ratio. Occurrence of transport-driven low ozone laminae in the assimilation system is similar to that obtained from the High Resolution Dynamics Limb Sounder (HIRDLS) above the 400 K potential temperature surface but the assimilation produces fewer laminae than seen by HIRDLS below that surface. Although the assimilation produces 5 - 8 fewer occurrences per day (up to approximately 20%) during the three years of HIRDLS data, the interannual variability is captured correctly. This data-driven assimilated product is complementary to ozone fields generated from chemistry and transport models. Applications include study of the radiative forcing by ozone and tracer transport near the tropopause.

GEOS-5↗
NASA NTRS2014

Anthropogenic Emissions of Highly Reactive Volatile Organic Compounds (HRVOCs) Inferred from Oversampling of OMI HCHO Columns

Satellite observations of formaldehyde (HCHO) columns provide top-down constraints on emissions of highly reactive volatile organic compounds (HRVOCs). This approach has been used previously to constrain emissions of isoprene from vegetation, but application to US anthropogenic emissions has been stymied by lack of a discernable HCHO signal. Here we show that oversampling of HCHO data from the Ozone Monitoring Instrument (OMI) for 2005 - 2008 enables quantitative detection of urban and industrial plumes in eastern Texas including Houston, Port Arthur, and Dallas-Fort Worth. By spatially integrating the individual urban-industrial HCHO plumes observed by OMI we can constrain the corresponding HCHO-weighted HRVOC emissions. Application to the Houston plume indicates a HCHO source of 260 plus or minus 110 kmol h-1 and implies a factor of 5.5 plus or minus 2.4 underestimate of anthropogenic HRVOC emissions in the US Environmental Protection Agency inventory. With this approach we are able to monitor the trend in HRVOC emissions over the US, in particular from the oil-gas industry, over the past decade.

AURA↗
NASA NTRS2019

Midlatitude Lightning NOx Production Efficiency Inferred From OMI and WWLLN Data

Oxides of nitrogen are critical trace gases in the troposphere and are precursors for nitrate aerosol and ozone, which is an important pollutant and greenhouse gas. Lightning is the major source of NOx(NO + NO2) in the mid-to upper troposphere. We estimate the production efficiency (PE) of lightning NOx (LNOx) using satellite data from the Ozone Monitoring Instrument (OMI) and the ground-based WorldWide Lightning Location Network (WWLLN) in three northern midlatitude, primarily continental regions that include much of North America, Europe and East Asia. Data were obtained over 5 boreal summers, 2007 –2011 and comprise the largest number of midlatitude convective events to date for estimating the LNOx PE with satellite NO2and ground-based lightning measurements. In contrast to some previous studies, the algorithm assumes no minimum flash-rate threshold and estimates freshly produced LNOxby subtracting a background of aged NOx estimated from the OMI dataset itself. We infer an average value of 180 ± 100 moles LNOx produced per lightning flash. We also show evidence of a dependence of PE on lightning flash rate and find an approximate empirical power function relating moles LNOxto flashes. PE decreases by an order of magnitude for a 2-order of magnitude increase in flash rate. This phenomenon has not been reported in previous satellite LNOxstudies but is consistent with ground-based observations suggesting an inverse relationship between flash rate and size.

LNOx↗
NASA NTRS2022

A New Machine Learning Based Analysis for Improving Satellite Retrieved Atmospheric Composition Data: OMI SO2 as an Example

Despite recent progress, satellite retrievals of anthropogenic SO2 still suffer from relatively low signal-tonoise ratios. In this study, we demonstrate a new machine learning data analysis method to improve the quality of satellite SO2 products. In the absence of large ground-truth datasets for SO2, we start from SO2 slant column densities (SCDs) retrieved from the Ozone Monitoring Instrument (OMI) using a data-driven, physically based algorithm and calculate the ratio between the SCD and the root mean square (rms) of the fitting residuals for each pixel. To build the training data, we select presumably clean pixels with small SCD / rms ratios (SRRs) and set their target SCDs to zero. For polluted pixels with relatively large SRRs, we set the target to the original retrieved SCDs. We then train neural networks (NNs) to reproduce the target SCDs using predictors including SRRs for individual pixels, solar zenith, viewing zenith and phase angles, scene reflectivity, and O3 column amounts, as well as the monthly mean SRRs. For data analysis, we employ two NNs: (1) one trained daily to produce analyzed SO2 SCDs for polluted pixels each day and (2) the other trained once every month to produce analyzed SCDs for less polluted pixels for the entire month. Test results for 2005 show that our method can significantly reduce noise and artifacts over background regions. Over polluted areas, the monthly mean NN-analyzed and original SCDs generally agree to within ±15 %, indicating that our method can retain SO2 signals in the original retrievals except for large volcanic eruptions. This is further confirmed by running both the NN-analyzed and original SCDs through a topdown emission algorithm to estimate the annual SO2 emissions for ∼ 500 anthropogenic sources, with the two datasets yielding similar results. We also explore two alternative approaches to the NN-based analysis method. In one, we employ a simple linear interpolation model to analyze the original SCD retrievals. In the other, we develop a PCA–NN algorithm that uses OMI measured radiances, transformed and dimension-reduced with a principal component analysis (PCA) technique, as inputs to NNs for SO2 SCD retrievals. While the linear model and the PCA–NN algorithm can reduce retrieval noise, they both underestimate SO2 over polluted areas. Overall, the results presented here demonstrate that our new data analysis method can significantly improve the quality of existing OMI SO2 retrievals. The method can potentially be adapted for other sensors and/or species and enhance the value of satellite data in air quality research and applications.

Can Li↗
NASA NTRS2016

Interpreting the Ultraviolet Aerosol Index Observed with the OMI Satellite Instrument to Understand Absorption by Organic Aerosols: Implications for Atmospheric Oxidation and Direct Radiative Effects

Satellite observations of the ultraviolet aerosol index (UVAI) are sensitive to absorption of solar radiation by aerosols; this absorption affects photolysis frequencies and radiative forcing. We develop a global simulation of the UVAI using the 3-D chemical transport model GEOSChem coupled with the Vector Linearized Discrete Ordinate Radiative Transfer model (VLIDORT). The simulation is applied to interpret UVAI observations from the Ozone Monitoring Instrument (OMI) for the year 2007. Simulated and observed values are highly consistent in regions where mineral dust dominates the UVAI, but a large negative bias (-0.32 to -0.97) exists between simulated and observed values in biomass burning regions. We determine effective optical properties for absorbing organic aerosol, known as brown carbon (BrC), and implement them into GEOS-Chem to better represent observed UVAI values over biomass burning regions. The inclusion of absorbing BrC decreases the mean bias between simulated and OMI UVAI values from -0.57 to -0.09 over West Africa in January, from -0.32 to +0.0002 over South Asia in April, from -0.97 to -0.22 over southern Africa in July, and from -0.50 to +0.33 over South America in September. The spectral dependence of absorption after including BrC in the model is broadly consistent with reported observations for biomass burning aerosol, with absorbing Angstrom exponent (AAE) values ranging from 2.9 in the ultraviolet (UV) to 1.3 across the UV-Near IR spectrum. We assess the effect of the additional UV absorption by BrC on atmospheric photochemistry by examining tropospheric hydroxyl radical (OH) concentrations in GEOS-Chem. The inclusion of BrC decreases OH by up to 30% over South America in September, up to 20% over southern Africa in July, and up to 15% over other biomass burning regions. Global annual mean OH concentrations in GEOS-Chem decrease due to the presence of absorbing BrC, increasing the methyl chloroform lifetime from 5.62 to 5.68 years, thus reducing the bias against observed values. We calculate the direct radiative effect (DRE) of BrC using GEOS-Chem coupled with the radiative transfer model RRTMG (GC-RT). Treating organic aerosol as containing more strongly absorbing BrC changes the global annual mean all-sky top of atmosphere (TOA) DRE by +0.03Wm(exp -2) and all-sky surface DRE by -0.08Wm(exp -2). Regional changes of up to +0.3Wm(exp -2) at TOA and down to -1.5Wm(exp -2) at the surface are found over major biomass burning regions.

Air Pollution↗
NASA NTRS2019

Lightning NOx Production in the Tropics as Determined Using OMI NO2 Retrievals and WWLLN Stroke Data

Nitrogen oxide (NOx) production by lightning in the tropics is estimated using tropospheric NOx amounts (LNOx*) over deep convective grid boxes derived from OMI (Ozone Monitoring Instrument) nitrogen dioxide (NO2) slant columns and detection efficiency adjusted WWLLN (World Wide Lightning Location Network) flashes. The lightning NOx production efficiency (LNOx PE) in the tropics is determined for the austral and boreal summers of 2007 to 2011 by regressing regional mean daily values of LNOx* for individual seasons against daily flash totals during flash windows prior to the OMI overpass. LNOx PE is determined to be approximately two times larger over marine locations than over continental locations possibly because marine flashes are more energetic. Overall, the mean LNOx PE for the tropics is calculated to be 170 ± 100 mol per flash with values over the tropical Pacific (low flash rate region) being largest. The main contributors to uncertainties in PE are uncertainties in WWLLN flash detection efficiency, upper tropospheric NOx lifetime in the near field of convection, and air mass factor biases.

Dale J. Allen↗
NASA NTRS2009

Lightning-Generated NO(x) Seen By OMI during NASA's TC-4 Experiment: First Results

We present here case studies identifying upper-tropospheric NO2 produced in convective storms during NASA's Tropical Composition, Cloud and Climate Coupling Experiment (TC~i)n July and August 2007. DC8 aircraft missions, flown from the mission base in Costa Rica, recorded in situ NO2 profiles near active storms and in relatively quiet areas. We combine these data with measurements from the Ozone Monitoring Instrument (OMI) on the Aura satellite to estimate the amount of NO2 produced by lightning (LN02) above background levels in the regions influenced by storms. In our analysis, improved off-line processing techniques are employed to minimize known artifacts in the OM1 data. Information on lightning flashes (primarily CG) observed by the surface network operated by the Instituto Costarricense de Electricidad are examined upwind of regions where OM1 indicates enhanced LNO2. Comparisons of the observed flash data with measurements by the TRMM/LIS satellite instrument are used to obtain the lightning detection efficiency for total flashes. Finally, using the NO/NO2 ratio estimated from DC-8 observations, we estimate the average NO(x) production per lightning flash for each case in this study. The magnitudes of the measured NO(x) enhancements are compared with those observed by the DC-8 and with similar OM1 measurements analyzed in mid-latitude experiments.

Bucsela, Eric↗
NASA NTRS2014

Application of Spectral Analysis Techniques in the Intercomparison of Aerosol Data: Part III. Using Combined PCA to Compare Spatiotemporal Variability of MODIS, MISR and OMI Aerosol Optical Depth

Satellite measurements of global aerosol properties are very useful in constraining aerosol parameterization in climate models. The reliability of different data sets in representing global and regional aerosol variability becomes an essential question. In this study, we present the results of a comparison using combined principal component analysis (CPCA), applied to monthly mean, mapped (Level 3) aerosol optical depth (AOD) product from Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), and Ozone Monitoring Instrument (OMI). This technique effectively finds the common space-time variability in the multiple data sets by decomposing the combined AOD field. The results suggest that all of the sensors capture the globally important aerosol regimes, including dust, biomass burning, pollution, and mixed aerosol types. Nonetheless, differences are also noted. Specifically, compared with MISR and OMI, MODIS variability is significantly higher over South America, India, and the Sahel. MODIS deep blue AOD has a lower seasonal variability in North Africa, accompanied by a decreasing trend that is not found in either MISR or OMI AOD data. The narrow swath of MISR results in an underestimation of dust variability over the Taklamakan Desert. The MISR AOD data also exhibit overall lower variability in South America and the Sahel. OMI does not capture the Russian wild fire in 2010 nor the phase shift in biomass burning over East South America compared to Central South America, likely due to cloud contamination and the OMI row anomaly. OMI also indicates a much stronger (boreal) winter peak in South Africa compared with MODIS and MISR.

satellite observation↗
NASA NTRS2014

Satellite Monitoring Over the Canadian Oil Sands: Highlights from Aura OMI and TES

Satellite remote sensing provides a unique perspective for air quality monitoring in and around the Canadian Oil Sands as a result of its spatial and temporal coverage. Presented are Aura satellite observations of key pollutants including nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), ammonia (NH3), methanol (CH3OH), and formic acid (HCOOH) over the Canadian Oil Sands. Some of the highlights include: (i) the evolution of NO2 and SO2 from the Ozone Monitoring Instrument (OMI), including comparisons with other nearby sources, (ii) two years of ammonia, carbon monoxide, methanol, and formic acid observations from 240 km North-South Tropospheric Emission Spectrometer (TES) transects through the oils sands, and (iii) preliminary insights into emissions derived from these observations.

NO2↗
NASA NTRS2009

Temperature Dependence of Factors Controlling Isoprene Emissions

We investigated the relationship of variability in the formaldehyde (HCHO) columns measured by the Aura Ozone Monitoring Instrument (OMI) to isoprene emissions in the southeastern United States for 2005-2007. The data show that the inferred, regional-average isoprene emissions varied by about 22% during summer and are well correlated with temperature, which is known to influence emissions. Part of the correlation with temperature is likely associated with other causal factors that are temperature-dependent. We show that the variations in HCHO are convolved with the temperature dependence of surface ozone, which influences isoprene emissions, and the dependence of the HCHO column to mixed layer height as OMI's sensitivity to HCHO increases with altitude. Furthermore, we show that while there is an association of drought with the variation in HCHO, drought in the southeastern U.S. is convolved with temperature.

Duncan, Bryan N.↗
NASA NTRS2019

Trends and Seasonal Variability of Atmospheric NO2 and HNO3 Concentrations Across Three Major African Biomes Inferred from Long-Term Series of Ground-Based and Satellite Measurements

In the framework of the INDAAF (International Network to study Deposition and Atmospheric chemistry in AFrica) programme, part of the ACTRIS (Aerosols, Clouds, and Trace gases Research InfraStructure), European Research Infrastructure for the long-term observation of Aerosol, Cloud, and Trace gases, this paper aims to study trends and seasonal variability of surface atmospheric NO2 and HNO3 concentrations, and OMI (Ozone Monitoring Instrument) NO2 over 6 sites in tropical Africa. Sites are located in west and central Africa to represent the major African biomes: dry savanna (Banizoumbou, Niger and Katibougou, Mali), wet savanna (Djougou, Benin and Lamto, Côte d’Ivoire) and forest (Bomassa, Republic of Congo and Zoétélé, Cameroon). Ground-based NO2 and HNO3 concentrations were obtained over the period 1998–2015 using INDAAF passive samplers at a monthly basis, and NO2 Vertical Column Densities (VCDs) from OMI for a 1-degree grid cell around each sites were obtained from 2005 to 2015. Mean annual NO2 concentrations ranged from 2.3 plus or minus 1.2 to 0.9 plus or minus 0.4 parts per billion (ppb) from dry savannas to forests, representing a north-south gradient. In dry savannas, we observe two concentration peaks of NO2 appearing at the beginning and the end of the wet season both for ground-based and satellite measurements, whereas at wet savannas and forest sites, NO2 concentrations are highest in the dry season. The seasonality of surface NO2 observations provide further evidence for a large role of microbial soil NOx emissions in dry savannas and of biomass burning NOx emissions in wet savanna and forest sites. Mean annual HNO3 concentrations ranged from 0.4 to 0.5 ppb in dry and wet savannas to 0.2 to 0.3 ppb in forest. In dry ecosystems, higher HNO3 concentrations are measured in the early wet season, consistent with NO2 results. The analysis of a long-term dataset of surface O3 concentrations indicates that HNO3 production can mainly be explained by the photo-oxidation of NOx. Mann-Kendall and Seasonal Kendall statistical tests showed that NO2 surface concentrations have a significant decreasing seasonal and annual trends at multiple sites (p-value less than 0.05) ranging from -2.96 percent per year (at Zoétélé) to -0.64 percent per year (at Banizoumbou). HNO3 results indicate no trends except at Bomassa (1.07 percent per year). The decreasing NO2 ground-based concentration trends observed in wet savannas sites are correlated with OMI NO2 decreasing trends at these sites. Trends obtained for NO2 concentrations in wet savanna and forest ecosystems are consistent with trends of NOₓ biomass burning emissions.

Trends↗
NASA NTRS2007

Forecasting Plant Productivity and Health Using Diffuse-to-Global Irradiance Ratios Extracted from the OMI Aerosol Product

Atmospheric aerosols are a major contributor to diffuse irradiance. This Candidate Solution suggests using the OMI (Ozone Monitoring Instrument) aerosol product as input into a radiative transfer model, which would calculate the ratio of diffuse to global irradiance at the Earth s surface. This ratio can significantly influence the rate of photosynthesis in plants; increasing the ratio of diffuse to global irradiance can accelerate photosynthesis, resulting in greater plant productivity. Accurate values of this ratio could be useful in predicting crop productivity, thereby improving forecasts of regional food resources. However, disagreements exist between diffuse-to-global irradiance values measured by different satellites and ground sensors. OMI, with its unique combination of spectral bands, high resolution, and daily global coverage, may be able to provide more accurate aerosol measurements than other comparable sensors.

Knowlton, Kelly↗
NASA NTRS2017

Glyoxal Yield from Isoprene Oxidation and Relation to Formaldehyde: Chemical Mechanism, Constraints from SENEX Aircraft Observations, and Interpretation of OMI Satellite Data

Glyoxal (CHOCHO) is produced in the atmosphere by the oxidation of volatile organic compounds(VOCs). Like formaldehyde (HCHO), another VOC oxidation product, it is measurable from space by solar backscatter. Isoprene emitted by vegetation is the dominant source of CHOCHO and HCHO in most of the world. We use aircraft observations of CHOCHO and HCHO from the Southeast Nexus (SENEX) campaign over the southeast US in summer 2013 to better understand the CHOCHO time-dependent yield from isoprene oxidation, its dependence on nitrogen oxides (NO (sub x) triple bonded to NO plus NO2), the behavior of the CHOCHO-HCHO relationship, the quality of Ozone Monitoring Instrument (OMI) CHOCHO satellite observations, and the implications for using CHOCHO observations from space as constraints on isoprene emissions. We simulate the SENEX and OMI observations with the Goddard Earth Observing System chemical transport model (GEOSChem) featuring a new chemical mechanism for CHOCHO formation from isoprene. The mechanism includes prompt CHOCHO formation under low-NO (sub x) conditions following the isomerization of the isoprene peroxy radical (ISOPO2).The SENEX observations provide support for this prompt CHOCHO formation pathway, and are generally consistent with the GEOS-Chem mechanism. Boundary layer CHOCHO and HCHO are strongly correlated in the observations and the model, with some departure under low-NO (sub x) conditions due to prompt CHOCHO formation. SENEX vertical profiles indicate a free-tropospheric CHOCHO background that is absent from the model. The OMI CHOCHO data provide some support for this free-tropospheric background and show southeast US enhancements consistent with the isoprene source but a factor of 2 too low. Part of this OMI bias is due to excessive surface reflectivities assumed in the retrieval. The OMI CHOCHO and HCHO seasonal data over the southeast US are tightly correlated and provide redundant proxies of isoprene emissions. Higher temporal resolution in future geostationary satellite observations may enable detection of the prompt CHOCHO production under low-NO (sub x) conditions apparent in the SENEX data.

OMI↗
NASA NTRS2014

Increase of Ozone Concentrations, Its Temperature Sensitivity and the Precursor Factor in South China

Concerns have been raised about the possible connections between the local and regional photochemical problem and global warming. The current study assesses the trend of ozone in Hong Kong and the Pearl River Delta (PRD) in South China and investigates the interannual changes of sensitivity of ozone to air temperature, as well as the trends in regional precursors. Results reveal, at the three monitoring sites from the mid-1990s to 2010, an increase in the mean ozone concentrations from 1.0 to 1.6 microgram m(exp -3) per year. The increase occurred in all seasons, with the highest rate in autumn. This is consistent with trends and temperature anomalies in the region. The increase in the sensitivity of ozone to temperature is clearly evident from the correlation between ozone (OMI [Ozone Monitoring Instrument] column amount) and surface air temperature (from the Atmospheric Infrared Sounder) displayed in the correlation maps for the PRD during the prominently high ozone period of July September. It is observed to have increased from 2005 to 2010, the latter being the hottest year on record globally. To verify this temporal change in sensitivity, the ground-level trends of correlation coefficients/regression slopes are analysed. As expected, results reveal a statistically significant upward trend over a 14-year period (19972010). While the correlation revealed in the correlation maps is in agreement with the corresponding OMI ozone maps when juxtaposed, temperature sensitivity of surface ozone also shows an association with ozone concentration, with R0.5. These characteristics of ozone sensitivity are believed to have adverse implications for the region. As shown by ground measurements and/or satellite analyses, the decrease in nitrogen oxides (NO2) and NO(sub x) in Hong Kong is not statistically significant whileNO2 of thePRD has only very slightly changed. However, carbon dioxide has remarkably declined in the whole region. While these observations concerning precursors do not seem to adequately support an increasing ozone trend, measured surface levels of formaldehyde, a proxy for volatile organic compound (VOC) emissions, have risen significantly in the PRD (20042010). Hence, the reactive VOCs in the PRD are likely to be the main culprit for the increase of ozone, as far as precursors are concerned. Despite the prevailing problem, model simulations suggest prospects for improvement in the future.

South China↗
NASA NTRS2012

Biases in Long-term NO2 Averages Inferred from Satellite Observations Due to Cloud Selection Criteria

Retrievals of atmospheric trace gas column densities from space are compromised by the presence of clouds, requiring most studies to exclude observations with significant cloud fractions in the instrument's field of view. Using NO2 observations at three ground stations representing urban, suburban, and rural environments, and tropospheric vertical column densities measured by the Ozone Monitoring Instrument (OMI) over each site, we show that the observations from space represent monthly averaged ground-level pollutant conditions well (R=0.86) under relatively cloud-free conditions. However, by analyzing the ground-level data and applying the OMI cloud fraction as a filter, we show there is a significant bias in long-term averaged NO2 as a result of removing the data during cloudy conditions. For the ground-based sites considered in this study, excluding observations on days when OMI-derived cloud fractions were greater than 0.2 causes 12:00-14:00 mean summer mixing ratios to be underestimated by 12%+/-6%, 20%+/-7%, and 40%+/-10% on average (+/-1 standard deviation) at the urban, suburban, and rural sites respectively. This bias was investigated in particular at the rural site, a region where pollutant transport is the main source of NO2, and where longterm observations of NOy were also available. Evidence of changing photochemical conditions and a correlation between clear skies and the transport of cleaner air masses play key roles in explaining the bias. The magnitude of a bias is expected to vary from site to site depending on meteorology and proximity to NOx sources, and decreases when longer averaging times of ground station data (e.g. 24-h) are used for the comparison.

SEASONAL-VARIATIONS↗
NASA NTRS2021

Southeast Michigan Health & Air Quality: Identifying Trends of Ground-level Ozone Precursors in Southeast Michigan and Northern Ohio

Pollutants resulting from industrial activity can react with sunlight to create ground-level ozone, a harmful pollutant that can exacerbate respiratory health issues such as asthma. Due to a history of heavy industrialization, residents of southeast Michigan and northern Ohio are especially susceptible to ground-level ozone. NASA DEVELOP, in partnership with the Michigan Department of Environment, Great Lakes, and Energy’s (EGLE) Air Quality Division and the Lake Michigan Air Directors Consortium (LADCO), investigated the effectiveness of Earth observations (EO) in monitoring pollutants that contribute to ground-level ozone. The team used the European Space Agency’s TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel-5P, and NASA’s Ozone Monitoring Instrument (OMI) aboard Aura, to measure nitrogen dioxide (NO2), formaldehyde (HCHO), and methane (CH4) from 2019 to 2021 (May-September). The team oversampled the EO data on monthly, yearly, and 3-year time scales where possible to enhance more localized pollutant trends. Specifically, TROPOMI effectively monitored NO2 from space, indicating sub-city distributions on a monthly timescale. Contrastingly, the measurements of HCHO were predominately noisy, and therefore failed to show any distribution trends. Lastly, CH4 trends were identifiable yet coarse, hinting that EO monitoring in the case of CH4 is not beneficial for our partners. The end products provide the partners with insight on the utility of EO in measuring certain ozone precursors and can be used to guide ground-level ozone reduction strategies throughout the region.

Mariam Moeen↗
NASA NTRS2012

Fast Simulators for Satellite Cloud Optical Centroid Pressure Retrievals, 1. Evaluation of OMI Cloud Retrievals

The cloud Optical Centroid Pressure (OCP), also known as the effective cloud pressure, is a satellite-derived parameter that is commonly used in trace-gas retrievals to account for the effects of clouds on near-infrared through ultraviolet radiance measurements. Fast simulators are desirable to further expand the use of cloud OCP retrievals into the operational and climate communities for applications such as data assimilation and evaluation of cloud vertical structure in general circulation models. In this paper, we develop and validate fast simulators that provide estimates of the cloud OCP given a vertical profile of optical extinction. We use a pressure-weighting scheme where the weights depend upon optical parameters of clouds and/or aerosol. A cloud weighting function is easily extracted using this formulation. We then use fast simulators to compare two different satellite cloud OCP retrievals from the Ozone Monitoring Instrument (OMI) with estimates based on collocated cloud extinction profiles from a combination of CloudS at radar and MODIS visible radiance data. These comparisons are made over a wide range of conditions to provide a comprehensive validation of the OMI cloud OCP retrievals. We find generally good agreement between OMI cloud OCPs and those predicted by CloudSat. However, the OMI cloud OCPs from the two independent algorithms agree better with each other than either does with the estimates from CloudSat/MODIS. Differences between OMI cloud OCPs and those based on CloudSat/MODIS may result from undetected snow/ice at the surface, cloud 3-D effects, low altitude clouds missed by CloudSat, and the fact that CloudSat only observes a relatively small fraction of an OMI field-of-view.

Joiner, J.↗