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NASA NTRS2011

An Investigation of Interaction of Saharan Dust and Atlantic ITCZ Using Cloudsat-Calipso and A-Train Data

In this study, we investigate the radiative forcing of Saharan dust, its interactions with the Atlantic Intertropical Convergence Zone (ITCZ), through African easterly waves (AEW), African easterly jets (AEJ), and its impacts in short term numerical forecasts of tropical cyclogenesis using the GOCART-GEOS5 forecast system. Our approach is to develop and use an A-Train satellite simulator (ATSS) to constrain the observed aerosol index of refraction and particle size distribution by finding the values that simultaneously minimize the difference between observed CALIOP, CloudSat, OMI, and MODIS radiances and simulated radiances inverted from atmospheric model output using procedures and physical principles consistent with those used in corresponding retrieval algorithms. We use observations from the A-train and TRMM to determine relationships among the Saharan dust layer, transport by the AEW, and possible responses to dust radiative forcing in developing tropical cyclones in the A-ITCZ. Preliminary model results showing physical processes associated with the generation and transport of the Saharan dust layer, their interactions with the incipient moisture, clouds and rainfall in developing tropical cyclones will be presented. Also presented will be results of a case study of possible radiative impacts on AEW and AEJ during the NAMMA field campaign.

Lau, W.↗
NASA NTRS2000

Science Requirements Document for OMI-EOS

A Dutch-Finnish scientific and industrial consortium is supplying the Ozone Monitoring Instrument (OMI) for Earth Observing System-Aura (EOS-Aura). EOS-Aura is the next NASA mission to study the Earth's atmosphere extensively, and successor to the highly successful UARS (Upper Atmospheric Research Satellite) mission. The 'Science Requirements Document for OMI-EOS' presents an overview of the Aura and OMI mission objectives. It describes how OMI fits into the Aura mission and it reviews the synergy with the other instruments onboard Aura to fulfill the mission. This evolves in the Scientific Requirements for OMI (Chapter 3), stating which trace gases have to be measured with what necessary accuracy, in order for OMI to meet Aura's objectives. The most important data product of OMI, the ozone vertical column, densities shall have a better accuracy and an improved global coverage than the predecessor instruments TOMS (Total Ozone Monitoring Spectrometer) and GOME (Global Ozone Monitoring Experiment), which is a.o. achieved by a better signal to noise ratio, improved calibration and a wide field-of-view. Moreover, in order to meet its role on Aura, OMI shall measure trace gases, such as NO2, OClO, BrO, HCHO and SO2, aerosols, cloud top height and cloud coverage. Improved accuracy, better coverage, and finer ground grid than has been done in the past are goals for OMI. After the scientific requirements are defined, three sets of subordinate requirements are derived. These are: the algorithm requirements, i.e. what do the algorithms need in order to meet the scientific requirements; the instrument and calibration requirements, i.e. what has to be measured and how accurately in order to provide the quality of data necessary for deriving the data products; and the validation requirements, i.e. a strategy of how the OMI program will assure that its data products are valid in the atmosphere, at least to the required accuracy.

Levelt, P. F.↗
NASA NTRS2014

Global Free Tropospheric NO2 Abundances Derived Using a Cloud Slicing Technique Applied to Satellite Observations from the Aura Ozone Monitoring Instrument (OMI)

We derive free-tropospheric NO2 volume mixing ratios (VMRs) and stratospheric column amounts of NO2 by applying a cloud slicing technique to data from the Ozone Monitoring Instrument (OMI) on the Aura satellite. In the cloud-slicing approach, the slope of the above-cloud NO2 column versus the cloud scene pressure is proportional to the NO2 VMR. In this work, we use a sample of nearby OMI pixel data from a single orbit for the linear fit. The OMI data include cloud scene pressures from the rotational-Raman algorithm and above-cloud NO2 vertical column density (VCD) (defined as the NO2 column from the cloud scene pressure to the top-of-the-atmosphere) from a differential optical absorption spectroscopy (DOAS) algorithm. Estimates of stratospheric column NO2 are obtained by extrapolating the linear fits to the tropopause. We compare OMI-derived NO2 VMRs with in situ aircraft profiles measured during the NASA Intercontinental Chemical Transport Experiment Phase B (INTEX-B) campaign in 2006. The agreement is generally within the estimated uncertainties when appropriate data screening is applied. We then derive a global seasonal climatology of free-tropospheric NO2 VMR in cloudy conditions. Enhanced NO2 in the free troposphere commonly appears near polluted urban locations where NO2 produced in the boundary layer may be transported vertically out of the boundary layer and then horizontally away from the source. Signatures of lightning NO2 are also shown throughout low and middle latitude regions in summer months. A profile analysis of our cloud slicing data indicates signatures of uplifted and transported anthropogenic NO2 in the middle troposphere as well as lightning-generated NO2 in the upper troposphere. Comparison of the climatology with simulations from the Global Modeling Initiative (GMI) for cloudy conditions (cloud optical thicknesses > 10) shows similarities in the spatial patterns of continental pollution outflow. However, there are also some differences in the seasonal variation of free-tropospheric NO2 VMRs near highly populated regions and in areas affected by lightning-generated NOx. Stratospheric column NO2 obtained from cloud slicing agrees well with other independently-generated estimates, providing further confidence in the free-tropospheric results.

NO2↗
NASA NTRS2013

Modeling of 2008 Kasatochi Volcanic Sulfate Direct Radiative Forcing: Assimilation of OMI SO2 Plume Height Data and Comparison with MODIS and CALIOP Observations

Volcanic SO2 column amount and injection height retrieved from the Ozone Monitoring Instrument (OMI) with the Extended Iterative Spectral Fitting (EISF) technique are used to initialize a global chemistry transport model (GEOS-Chem) to simulate the atmospheric transport and lifecycle of volcanic SO2 and sulfate aerosol from the 2008 Kasatochi eruption, and to subsequently estimate the direct shortwave, top-of-the-atmosphere radiative forcing of the volcanic sulfate aerosol. Analysis shows that the integrated use of OMI SO2 plume height in GEOS-Chem yields: (a) good agreement of the temporal evolution of 3-D volcanic sulfate distributions between model simulations and satellite observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP), and (b) an e-folding time for volcanic SO2 that is consistent with OMI measurements, reflecting SO2 oxidation in the upper troposphere and stratosphere is reliably represented in the model. However, a consistent (approx. 25 %) low bias is found in the GEOS-Chem simulated SO2 burden, and is likely due to a high (approx.20 %) bias of cloud liquid water amount (as compared to the MODIS cloud product) and the resultant stronger SO2 oxidation in the GEOS meteorological data during the first week after eruption when part of SO2 underwent aqueous-phase oxidation in clouds. Radiative transfer calculations show that the forcing by Kasatochi volcanic sulfate aerosol becomes negligible 6 months after the eruption, but its global average over the first month is -1.3W/sq m, with the majority of the forcing-influenced region located north of 20degN, and with daily peak values up to -2W/sq m on days 16-17. Sensitivity experiments show that every 2 km decrease of SO2 injection height in the GEOS-Chem simulations will result in a approx.25% decrease in volcanic sulfate forcing; similar sensitivity but opposite sign also holds for a 0.03 m increase of geometric radius of the volcanic aerosol particles. Both sensitivities highlight the need to characterize the SO2 plume height and aerosol particle size from space. While more research efforts are warranted, this study is among the first to assimilate both satellite-based SO2 plume height and amount into a chemical transport model for an improved simulation of volcanic SO2 and sulfate transport.

Wang, J.↗
NASA NTRS2019

Ceramic Industry at Morbi as a Large Source of SO2 Emissions in India

Observations from the Ozone Monitoring Instrument (OMI), onboard the NASA's Earth Observing System (EOS) Aura satellite, reveal a large SO2 "hotspot" over Morbi, Gujarat, India, while the available emissions inventories do not report any major sources in this region. There are no industries that are typically associated with the elevated SO2 such as large power plants, smelters, or oil refineries in the Morbi region. Our analysis shows that the elevated SO2 source is attributed to the ceramic industries in an area of ~7 × 7 km2 near the city. OMI-estimated SO2 emissions from the Morbi ceramic industries have been near or above 100 kt y1 since 2009, which are similar to emission levels from larger Indian oil refineries (such as Essar and Jamnagar) and power stations (such as Mundra Thermal Power Station and Ultra Mega Power Plant) located in Gujarat, India. According to OMI measurements, the SO2 emissions from the Morbi ceramic industries are presently five times higher than they were in 2005. This study demonstrates that in the absence of any other information about SO2 emissions from the ceramic industry, these satellite-based estimates can fill the gap in emission inventories in a timely fashion.

S K Kharol↗
NASA NTRS2010

Impact of Asian Dust on Climate and Air Quality

Dust generated from Asian permanent desert and desertification areas can be efficiently transported around the globe, making significant radiative impact through their absorbing and scattering solar radiation and through their deposition on snow and ice to modify the surface albedo. Asian dust is also a major concern of surface air quality not only in the source and immediate downwind regions but also areas thousands of miles away across the Pacific. We present here a global model, GOCART, analysis of data from satellite remote sensing instrument (MODIS, MISR, CALIPSO, OMI) and other observations on Asian dust sources, transport, and deposition, and use the model to assess the Asian dust impact on global climate and air quality.

Chin, Mian↗
NASA NTRS2015

Constraining Black Carbon Aerosol over Asia using OMI Aerosol Absorption Optical Depth and the Adjoint of GEOS-Chem

Accurate estimates of the emissions and distribution of black carbon (BC) in the region referred to here as Southeastern Asia (70degE-l50degE, 11degS-55degN) are critical to studies of the atmospheric environment and climate change. Analysis of modeled BC concentrations compared to in situ observations indicates levels are underestimated over most of Southeast Asia when using any of four different emission inventories. We thus attempt to reduce uncertainties in BC emissions and improve BC model simulations by developing top-down, spatially resolved, estimates of BC emissions through assimilation of OMI observations of aerosol absorption optical depth (AAOD) with the GEOS-Chem model and its adjoint for April and October of 2006. Overwhelming enhancements, up to 500%, in anthropogenic BC emissions are shown after optimization over broad areas of Southeast Asia in April. In October, the optimization of anthropogenic emissions yields a slight reduction (1-5%) over India and parts of southern China, while emissions increase by 10-50% over eastern China. Observational data from in situ measurements and AERONET observations are used to evaluate the BC inversions and assess the bias between OMI and AERONET AAOD. Low biases in BC concentrations are improved or corrected in most eastern and central sites over China after optimization, while the constrained model still underestimates concentrations in Indian sites in both April and October, possibly as a. consequence of low prior emissions. Model resolution errors may contribute up to a factor of 2.5 to the underestimate of surface BC concentrations over northern India. We also compare the optimized results using different anthropogenic emission inventories and discuss the sensitivity of top-down constraints on anthropogenic emissions with respect to biomass burning emissions. In addition, the impacts of brown carbon, the formulation of the observation operator, and different a priori constraints on the optimization are investigated. Overall, despite these limitations and uncertainties, using OMI AAOD to constrain BC sources improves model representation of BC distributions, particularly over China.

Aerosols↗
NASA NTRS2014

Improvements to the OMI Near-uv Aerosol Algorithm Using A-train CALIOP and AIRS Observations

The height of desert dust and carbonaceous aerosols layers and, to a lesser extent, the difficulty in assessing the predominant size mode of these absorbing aerosol types, are sources of uncertainty in the retrieval of aerosol properties from near UV satellite observations. The availability of independent, near-simultaneous measurements of aerosol layer height, and aerosol-type related parameters derived from observations by other A-train sensors, makes possible the direct use of these parameters as input to the OMI (Ozone Monitoring Instrument) near UV retrieval algorithm. A monthly climatology of aerosol layer height derived from observations by the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) sensor, and real-time AIRS (Atmospheric Infrared Sounder) CO observations are used in an upgraded version of the OMI near UV aerosol algorithm. AIRS CO measurements are used as a reliable tracer of carbonaceous aerosols, which allows the identification of smoke layers in areas and times of the year where the dust-smoke differentiation is difficult in the near-UV. The use of CO measurements also enables the identification of elevated levels of boundary layer pollution undetectable by near UV observations alone. In this paper we discuss the combined use of OMI, CALIOP and AIRS observations for the characterization of aerosol properties, and show a significant improvement in OMI aerosol retrieval capabilities.

Improvements to OMI↗
NASA NTRS2023

Satellite Remote-Sensing Capability to Assess Tropospheric-Column Ratios of Formaldehyde and Nitrogen Dioxide: Case Study During the Long Island Sound Tropospheric Ozone Study 2018 (LISTOS 2018) Field Campaign

Satellite retrievals of tropospheric-column formaldehyde (HCHO) and nitrogen dioxide (NO 2 ) are frequently used to investigate the sensitivity of ozone (O 3 ) production to emissions of nitrogen oxides and volatile organic carbon compounds. This study inter-compared the systematic biases and uncertainties in retrievals of NO 2 and HCHO, as well as resulting HCHO–NO 2 ratios (FNRs), from two commonly applied satellite sensors to investigate O 3 production sensitivities (Ozone Monitoring Instrument, OMI, and TROPOspheric Monitoring Instrument, TROPOMI) using airborne remote-sensing data taken during the Long Island Sound Tropospheric Ozone Study 2018 between 25 June and 6 September 2018. Compared to aircraft-based HCHO and NO 2 observations, the accuracy of OMI and TROPOMI were magnitude-dependent with high biases in clean environments and a tendency towards more accurate comparisons to even low biases in moderately polluted to polluted regions. OMI and TROPOMI NO 2 systematic biases were similar in magnitude (normalized median bias, NMB = 5 %–6 %; linear regression slope ≈ 0.5–0.6), with OMI having a high median bias and TROPOMI resulting in small low biases. Campaign-averaged uncertainties in the three satellite retrievals (NASA OMI; Quality Assurance for Essential Climate Variables, QA4ECV OMI; and TROPOMI) of NO 2 were generally similar, with TROPOMI retrievals having slightly less spread in the data compared to OMI. The three satellite products differed more when evaluating HCHO retrievals. Campaign-averaged tropospheric HCHO retrievals all had linear regression slopes ∼0.5 and NMBs of 39 %, 17 %, 13 %, and 23 % for NASA OMI, QA4ECV OMI, and TROPOMI at finer (0.05° x 0.05°) and coarser (0.15° x 0.15°) spatial resolution, respectively. Campaign-averaged uncertainty values (root mean square error, RMSE) in NASA and QA4ECV OMI HCHO retrievals were ~9.0 x 10 15 molecules cm –2 (∼ 50 %–55 % of mean column abundance), and the higher-spatial-resolution retrievals from TROPOMI resulted in RMSE values ∼30 % lower. Spatially averaging TROPOMI tropospheric-column HCHO, along with NO 2 and FNRs, to resolutions similar to the OMI reduced the uncertainty in these retrievals. Systematic biases in OMI and TROPOMI NO 2 and HCHO retrievals tended to cancel out, resulting in all three satellite products comparing well to observed FNRs. However, while satellite-derived FNRs had minimal campaign-averaged median biases, unresolved errors in the indicator species did not cancel out in FNR calculations, resulting in large RMSE values compared to observations. Uncertainties in HCHO retrievals were determined to drive the unresolved biases in FNR retrievals.

Matthew S. Johnson↗
NASA NTRS2014

Solar Spectral Irradiance Changes During Cycle 24

We use solar spectra obtained by the Ozone Monitoring Instrument (OMI) on board the Aura satellite to detect and follow long-term (years) and short-term (weeks) changes in the solar spectral irradiance (SSI) in the 265-500 nm spectral range. During solar Cycle 24, in the relatively line-free regions the SSI changed by approximately 0.6% +/- 0.2% around 265 nm. These changes gradually diminish to 0.15% +/- 0.20% at 500 nm. All strong spectral lines and blends, with the notable exception of the upper Balmer lines, vary in unison with the solar "continuum." Besides the lines with strong chromospheric components, the most involved species include Fe I blends and all prominent CH, NH, and CN spectral bands. Following the general trend seen in the solar "continuum," the variability of spectral lines also decreases toward longer wavelengths. The long-term solar cycle SSI changes are closely, to within the quoted 0.1%-0.2% uncertainties, matched by the appropriately adjusted short-term SSI variations derived from the 27 day rotational modulation cycles. This further strengthens and broadens the prevailing notion about the general scalability of the UV SSI variability to the emissivity changes in the Mg II 280 nm doublet on timescales from weeks to years. We also detect subtle deviations from this general rule: the prominent spectral lines and blends at lambda approximately or greater than 350 nm show slightly more pronounced 27 day SSI changes when compared to the long-term (years) trends. We merge the solar data from Cycle 21 with the current Cycle 24 OMI and GOME-2 observations and provide normalized SSI variations for the 170-795 nm spectral region.

Aura OMI↗
NASA NTRS2002

Science Objectives of EOS-Aura's Ozone Monitoring Instrument (OMI)

OMI is a UV/VIS nadir solar backscatter spectrograph, which provides near global coverage in one day with a spatial resolution of 13 x 24 sq km. OMI is a new instrument, with a heritage from the European satellite instruments GOME, GOMOS and SCIAMACHY. OMI's unique capabilities for measuring important trace gases with a small footprint and daily global coverage, in conjunction with the other Aura instruments, will make a major contribution to our understanding of stratospheric and tropospheric chemistry and climate change. OMI will measure solar irradiance and Earth radiances in the wavelength range of 270 to 500 nm with spectral resolution of about 0.5 nm and a spectral sampling of about 2-3 per FWHM. From these observations, total columns of O3, NO2, BrO and SO2 will be derived from the back-scattered solar radiance using differential absorption spectroscopy (DOAS). The TOMS total ozone record will also be continued by employing the well established TOMS algorithm. Because of the high accuracy and spatial resolution of the measurements, a good estimate of tropospheric amounts of ozone and NO2 are expected. Ozone profiles will be derived using the optimal estimation method. The spectral aerosol optical depth will be determined from measurements between 340 and 500 nm. This will provide information on aerosol concentration, aerosol size distribution and aerosol type. This wavelength range makes it possible to retrieve aerosol information over both land and sea. OMI observations will also allow retrievals of cloud coverage and cloud heights. From these products, the UV-B flux at the surface can then be derived with high spatial resolution.

Levelt, P. F.↗
NASA NTRS2007

Simulations of Tropospheric NO2 by the Global Modeling Initiative (GMI) Model Utilizing Assimilated and Forecast Meteorological Fields: Comparison to Ozone Monitoring Instrument (OMI) Measurements

We present simulations of the tropospheric composition for the years 2004 and 2005, carried out by the GMI Combined Stratosphere-Troposphere (Combo) model, at a resolution of 2degx2.5deg. The model includes a new parameterization of lightning sources of NO(x) which is coupled to the cloud mass fluxes in the adopted meteorological fields. These simulations use two different sets of input meteorological fields: a)late-look assimilated fields from the Global Modeling and Assimilation Office (GMAO), GEOS-4 system and b) 12-hour forecast fields initialized with the assimilated data. Comparison of the forecast to the assimilated fields indicates that the forecast fields exhibit less vigorous convection, and yield tropical precipitation fields in better agreement with observations. Since these simulations include a complete representation of the stratosphere, they provide realistic stratosphere-tropospheric fluxes of O3 and NO(y). Furthermore, the stratospheric contribution to total columns of different troposheric species can be subtracted in a consistent fashion, and the lightning production of NO(y) will depend on the adopted meteorological field. We concentrate here on the simulated tropospheric columns of NO2, and compare them to observations by the OM1 instrument for the years 2004 and 2005. The comparison is used to address these questions: a) is there a significant difference in the agreement/disagreement between simulations for these two different meteorological fields, and if so, what causes these differences?; b) how do the simulations compare to OMI observations, and does this comparison indicate an improvement in simulations with the forecast fields? c) what are the implications of these simulations for our understanding of the NO2 emissions over continental polluted regions?

Rodriquez, J. M.↗
NASA NTRS2016

Response of SO2 and Particulate Air Pollution to Local and Regional Emission Controls: A Case Study in Maryland

This paper addresses the questions of what effect local regulations can have on pollutants with different lifetimes and how surface observations and remotely sensed data can be used to determine the impacts. We investigated the decadal trends of tropospheric sulfur dioxide (SO2) and aerosol pollution over Maryland and its surrounding states, using surface, aircraft, and satellite measurements. Aircraft measurements indicated fewer isolated SO2 plumes observed in summers, a 40 decrease of column SO2, and a 20 decrease of atmospheric optical depth (AOD) over Maryland after the implementation of local regulations on sulfur emissions from power plants (90 reduction from 2010). Surface observations of SO2 and particulate matter (PM) concentrations in Maryland show similar trends. OMI SO2 and MODIS AOD observations were used to investigate the column contents of air pollutants over the eastern U.S.; these indicate decreasing trends in column SO2 (60 decrease) and AOD (20 decrease). The decrease of upwind SO2 emissions also reduced aerosol loadings over the downwind Atlantic Ocean near the coast by 20, while indiscernible changes of the SO2 column were observed. A step change of SO2 emissions in Maryland starting in 20092010 had an immediate and profound benefit in terms of local surface SO2 concentrations but a modest impact on aerosol pollution, indicating that short-lived pollutants are effectively controlled locally, while long-lived pollutants require regional measures.

satellite measurements↗
NASA NTRS2023

Tracking NO2 Pollution Changes Over Texas: Synthesis of In Situ and Satellite Observations

Nitrogen oxides (NO x ) are major air pollutants that play a crucial role in atmospheric chemistry. We compare Ozone Measuring Instrument's (OMI) NO 2 records with the in situ surface measurements from the Air Quality System of the US Environmental Protection Agency and the Texas Commission on Environmental Quality network in the state of Texas with the goal of understanding the correspondence of satellite and in situ surface observations and identifying the potential synergies between the two observing systems. Our analysis of over 40 in situ daily surface site observations, mostly from urban areas, and OMI daily observed data suggests a correlation ( r ) ranging between 0.2 and 0.8. The correlation improves considerably ( r > 0.5) for monthly average data. Weekly variation of surface NO 2 with a Sunday minimum is well captured by OMI tropospheric NO 2 column observations with similar weekend reductions. NO 2 trend in Texas during 2005–2019 is characterized by significant reductions of 20%–36% in highly populated cities and urban centers. However, a significant (up to 80%) increase was observed in oil and gas producing regions of the Permian and Eagle Ford Basins over the same period. In March–April of 2020, like the other US and global cities, Texas experienced up to 60% reduction in NO 2 levels in major cities due to travel restrictions imposed at local and national levels to contain the spread of COVID-19. Though such reduction is temporary, these reductions were significantly larger than those achieved in the past 16 years of OMI record suggesting that technological advancement can curtail NO x emissions.

Nitrogen dioxide (NO2)↗
NASA NTRS2025

Validation of Formaldehyde Products From Three Satellite Retrievals (OMI SAO, OMPS-NPP SAO, and OMI BIRA) in the Marine Atmosphere With Four Seasons of Atom Aircraft Observations

Formaldehyde (HCHO) in the atmosphere is an intermediate product from the oxidation of methane and non-methane volatile organic compounds. In remote marine regions, HCHO variability is closely related to atmospheric oxidation capacity and modeled HCHO in these regions is usually added as a global satellite HCHO background. Thus, it is important to understand and validate the levels of satellite HCHO over the remote oceans. Here we intercompare three satellite retrievals of total HCHO columns (OMI-SAO (v004), OMPS-NPP SAO, and OMI BIRA) and validate them against in situ observations from the NASA Atmospheric Tomography Mission (ATom) mission. All retrievals are correlated with ATom integrated columns over remote oceans, with OMI SAO (v004) showing the best agreement. This is also reflected in the mean bias (MB) for OMI SAO (-0.73±0.87) x 10 15 molec cm -2 , OMPS SAO (-0.76±0.88) x 10 15 molec cm -2 , and OMI BIRA (-1.40±1.11) x 10 15 molec cm -2 . We recommend the OMI-SAO (v004) retrieval for remote ocean atmosphere studies. Three satellite HCHO retrievals and in situ ATom columns all generally captured the spatial and seasonal distributions of HCHO in the remote ocean atmosphere. Retrieval bias varies by latitude and season, but a persistent low bias is found in all products at high latitudes and the general low bias is most severe for the OMI BIRA product. Examination of retrieval components reveals slant column corrections have a larger impact on the retrievals over remote marine regions while AMFs play a smaller role. This study informs that the potential latitude-dependent biases in the retrievals require further investigation for improvement and should be considered when using marine HCHO satellite data, and vertical profiles from in situ instruments are crucial for validating satellite retrievals.

Atom↗
NASA NTRSDate not supplied

Continuing Global SO2 Data Record from OMI and SNPP/OMPS to JPSS-1/NOAA-20/OMPS

Since 2004, the Ozone Monitoring Instrument (OMI) aboard NASA's Earth Observing System (EOS) Aura spacecraft has been providing global observations that help to constrain the sources, transport, and environmental impacts of anthropogonic and volcanic SO2. The OMI SO2 data record is now being continued with the NASA/NOAA Suomi National Polar-orbiting Partnership (SNPP)/Ozone Mapping and Profiler Suite (OMPS) launched in 2011. Both OMI and SNPP/OMPS SO2 products are produced with the Goddard principal component analysis (PCA) based spectral fitting algorithm. This data-driven technique inherently accounts for various instrumental factors and geophysical interferences, leading to high-quality, consistent SO2 retrievals between OMI and SNPP/OMPS, despite coarser spectral (~0.5 nm vs. ~1 nm) and spatial (13  24 km2 vs. 50  50 km2 at nadir) resolution for the latter. In this presentation, we describe our effort to continue the long-term SO2 climate data record using measurements from the Joint Polar Satellite System (JPSS)-1/NOAA-20 (N20)/OMPS. Launched in 2017, the N20/OMPS is a follow-on for SNPP/OMPS but features a spatial resolution (17  13 km2) that is comparable with OMI. We will discuss our progress implementing the PCA SO2 algorithm with N20/OMPS, especially algorithmic improvements to further reduce retrieval noise and bias for large volcanic eruptions. We will present examples for both continuously emitting sources (e.g., power plants in India and oil/gas fields in the Middle East) and volcanic eruptions (e.g., Raikoke in 2019). We will also compare N20/OMPS SO2 retrievals with OMI and SNPP/OMPS, as well as other instruments such as the ESA Copernicus Sentinel-5 Precursor (S5P)/TROPOspheric Monitoring Instrument (TROPOMI). To assess the ability of N20/OMPS to monitor and quantify SO2 sources, we will run the level 2 retrievals through a top-down emission algorithm to estimate the SO2 emission strengths for a number of point sources. Finally, we will outline our plan for further algorithm refinement and public data release.

SO2↗
NASA NTRS2014

Assessment of OMI Near-UV Aerosol Optical Depth over Land

This is the first comprehensive assessment of the aerosol optical depth (AOD) product retrieved from the near-UV observations by the Ozone Monitoring Instrument (OMI) onboard the Aura satellite. The OMI-retrieved AOD by the ultraviolet (UV) aerosol algorithm (OMAERUV version 1.4.2) was evaluated using collocated Aerosol Robotic Network (AERONET) level 2.0 direct Sun AOD measurements over 8 years (2005-2012). A time series analysis of collocated satellite and ground-based AOD observations over 8 years shows no discernible drift in OMI's calibration. A rigorous validation analysis over 4 years (2005-2008) was carried out at 44 globally distributed AERONET land sites. The chosen locations are representative of major aerosol types such as smoke from biomass burning or wildfires, desert mineral dust, and urban/industrial pollutants. Correlation coefficient (p) values of 0.75 or better were obtained at 50 percent of the sites with about 33 percent of the sites in the analysis reporting regression line slope values larger than 0.70 but always less than unity. The combined AERONET-OMAERUV analysis of the 44 sites yielded a p of 0.81, slope of 0.79, Y intercept of 0.10, and 65 percent OMAERUV AOD falling within the expected uncertainty range (largest of 30 percent or 0.1) at 440 nanometers. The most accurate OMAERUV retrievals are reported over northern Africa locations where the predominant aerosol type is desert dust and cloud presence is less frequent. Reliable retrievals were documented at many sites characterized by urban-type aerosols with low to moderate AOD values, concentrated in the boundary layer. These results confirm that the near-ultraviolet observations are sensitive to the entire aerosol column. A simultaneous comparison of OMAERUV, Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue, and Multiangle Imaging Spectroradiometer (MISR) AOD retrievals to AERONET measurements was also carried out to evaluate the OMAERUV accuracy in relation to those of the standard aerosol satellite products. The outcome of the comparison indicates that OMAERUV, MODIS Deep Blue, and MISR retrieval accuracies in arid and semiarid environments are statistically comparable.

aerosol optical depth↗
NASA NTRS2014

In-Situ and Remote-Sensing Data Fusion Using Machine Learning Techniques to Infer Urban and Fire Related Pollution Plumes

Airmass type characterization is key in understanding the relative contribution of various emission sources to atmospheric composition and air quality and can be useful in bottom-up model validation and emission inventories. However, classification of pollution plumes from space is often not trivial. Sub-orbital campaigns, such as SEAC4RS (Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys) give us a unique opportunity to study atmospheric composition in detail, by using a vast suite of in-situ instruments for the detection of trace gases and aerosols. These measurements allow identification of spatial and temporal atmospheric composition changes due to various pollution plumes resulting from urban, biogenic and smoke emissions. Nevertheless, to transfer the knowledge gathered from such campaigns into a global spatial and temporal context, there is a need to develop workflow that can be applicable to measurements from space. In this work we rely on sub-orbital in-situ and total column remote sensing measurements of various pollution plumes taken aboard the NASA DC-8 during 2013 SEAC4RS campaign, linking them through a neural-network (NN) algorithm to allow inference of pollution plume types by input of columnar aerosol and trace-gas measurements. In particular, we use the 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research) airborne measurements of wavelength dependent aerosol optical depth (AOD), particle size proxies, O3, NO2 and water vapor to classify different pollution plumes. Our method relies on assigning a-priori ground-truth labeling to the various plumes, which include urban pollution, different fire types (i.e. forest and agriculture) and fire stage (i.e. fresh and aged) using cluster analysis of aerosol and trace-gases in-situ and auxiliary (e.g. trajectory) data and the training of a NN scheme to fit the best prediction parameters using 4STAR measurements as input. We explore our misclassification rates as related to our ground-truth labels, and with multi-layered pollution plume cases. The next step in our analysis is to optimize parameter selection for a scheme that can be applied to space-borne aerosol and trace-gas observation platforms such as OMI, and future geostationary satellites such as TEMPO and GEO-CAPE.

Neural-network↗