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NASA NTRS2011

Lessons Learned from OMI Observations of Point Source SO2 Pollution

The Ozone Monitoring Instrument (OMI) on NASA Aura satellite makes global daily measurements of the total column of sulfur dioxide (SO2), a short-lived trace gas produced by fossil fuel combustion, smelting, and volcanoes. Although anthropogenic SO2 signals may not be detectable in a single OMI pixel, it is possible to see the source and determine its exact location by averaging a large number of individual measurements. We describe new techniques for spatial and temporal averaging that have been applied to the OMI SO2 data to determine the spatial distributions or "fingerprints" of SO2 burdens from top 100 pollution sources in North America. The technique requires averaging of several years of OMI daily measurements to observe SO2 pollution from typical anthropogenic sources. We found that the largest point sources of SO2 in the U.S. produce elevated SO2 values over a relatively small area - within 20-30 km radius. Therefore, one needs higher than OMI spatial resolution to monitor typical SO2 sources. TROPOMI instrument on the ESA Sentinel 5 precursor mission will have improved ground resolution (approximately 7 km at nadir), but is limited to once a day measurement. A pointable geostationary UVB spectrometer with variable spatial resolution and flexible sampling frequency could potentially achieve the goal of daily monitoring of SO2 point sources and resolve downwind plumes. This concept of taking the measurements at high frequency to enhance weak signals needs to be demonstrated with a GEOCAPE precursor mission before 2020, which will help formulating GEOCAPE measurement requirements.

Krotkov, N.↗
NASA NTRS2018

A New Global Anthropogenic SO2 Emission Inventory for the Last Decade: A Mosaic of Satellite-Derived and Bottom-Up Emissions

Sulfur dioxide (SO2) measurements from the Ozone Monitoring Instrument (OMI) satellite sensor have been used to detect emissions from large point sources. Emissions from over 400 sources have been quantified individually based on OMI observations, accounting for about a half of total reported anthropogenic SO2 emissions. Here we report a newly developed emission inventory, OMI-HTAP, by combining these OMI-based emission estimates and the conventional bottom-up inventory, HTAP, for smaller sources that OMI is not able to detect. OMI-HTAP includes emissions from OMI-detected sources that are not captured in previous leading bottom-up inventories, enabling more accurate emission estimates for regions with such missing sources. In addition, our approach offers the possibility of rapid updates to emissions from large point sources that can be detected by satellites. Our methodology applied to OMI-HTAP can also be used to merge improved satellite-derived estimates with other multi-year bottom-up inventories, which may further improve the accuracy of the emission trends. OMI-HTAP SO2 emissions estimates for Persian Gulf, Mexico, and Russia are 59%, 65%, and 56% larger than HTAP estimates, respectively, in year 2010. We have evaluated the OMI-HTAP inventory by performing simulations with the Goddard Earth Observing System version 5 (GEOS-5) model. The GEOS-5 simulated SO2 concentrations driven by both HTAP and OMI-HTAP were compared against in situ measurements. We focus for the validation on year 2010 for which HTAP is most valid and for which a relatively large number of in situ measurements are available. Results show that the OMI-HTAP inventory improves the agreement between the model and observations, in particular over the US, with the normalized mean bias decreasing from 0.41 (HTAP) to -0.03 (OMI-HTAP) for year 2010. Simulations with the OMI-HTAP inventory capture the worldwide major trends of large anthropogenic SO2 emissions that are observed with OMI. Correlation coefficients of the observed and modelled surface SO2 in 2014 increase from 0.16 (HTAP) to 0.59 (OMI-HTAP) and the normalized mean bias dropped from 0.29 (HTAP) to 0.05 (OMI-HTAP), when we updated 2010 HTAP emissions with 2014 OMI-HTAP emissions in the model.

Sulfur dioxide (SO2)↗
NASA NTRS2013

A Fast and Sensitive New Satellite SO2 Retrieval Algorithm based on Principal Component Analysis: Application to the Ozone Monitoring Instrument

We describe a new algorithm to retrieve SO2 from satellite-measured hyperspectral radiances. We employ the principal component analysis technique in regions with no significant SO2 to capture radiance variability caused by both physical processes (e.g., Rayleigh and Raman scattering and ozone absorption) and measurement artifacts. We use the resulting principal components and SO2 Jacobians calculated with a radiative transfer model to directly estimate SO2 vertical column density in one step. Application to the Ozone Monitoring Instrument (OMI) radiance spectra in 310.5-340 nm demonstrates that this approach can greatly reduce biases in the operational OMI product and decrease the noise by a factor of 2, providing greater sensitivity to anthropogenic emissions. The new algorithm is fast, eliminates the need for instrument-specific radiance correction schemes, and can be easily adapted to other sensors. These attributes make it a promising technique for producing longterm, consistent SO2 records for air quality and climate research.

Principal component analysis↗
NASA NTRS2007

Using Satellite Measurements to Investigate Regional-scale Chemistry: The Case for Geostationary Observations

One of the recommendations of the Decadal Survey that was recently released by the National Academy of Science was that of a geostationary platform from which to obtain trace gas measurements. The use of such a platform is particularly advantageous when applied to understanding the formation of regional air pollution. This study demonstrates the challenges of trying to utilize information from instruments on satellites in low-earth orbit (LEO). We also demonstrate the advantage gained through a simulation that would provide hourly observations. In this case study, we take advantage of the high resolution Level-2 orbital data available from the Ozone Monitoring Instrument (OMI), in conjunction with assimilated stratospheric column ozone fields, to evaluate if meaningful tropospheric ozone information can be obtained on a regional scale. We focus on a period on late June 2005 when a widespread pollution episode enveloped the Houston metropolitan area as well as a large region in southeast Texas.

Fishman, Jack↗
NASA NTRS2006

Exploration of OMI Products for Air Quality Applications Through Comparisons with Models and Observations

The Ozone Monitoring Instrument (OMI) on board NASA s Aura satellite was launched in July 2004, and is now providing daily global observations of total column ozone, NO2, and SO2, as well as aerosol information. Algorithms have also been developed to produce daily tropospheric ozone and NO2 products. The tropospheric ozone product reported here is a tropospheric residual computed through use of Aura Microwave Limb Sounder (MLS) ozone profile data to quantify stratospheric ozone. We are investigating the applicability of OMI products for use in air quality modeling, forecasting, and analysis. These investigations include comparison of the OMI tropospheric O3 and NO2 products with global and regional models and with lower tropospheric aircraft observations. Large-scale transport of pollution seen in the OM1 tropospheric O3 data is compared with output from NASA's Global Modeling Initiative global chemistry and transport model. On the regional scale we compare the OMI tropospheric O3 and NO2 with fields from the National Oceanic and Atmospheric Administration and Environmental Protection Agency (NOAA/EPA) operational Eta/CMAQ air quality forecasting model over the eastern United States. This 12-km horizontal resolution model output is roughly of equivalent resolution to the OMI pixel data. Correlation analysis between lower tropospheric aircraft O3 profile data taken by the University of Maryland over the Mid-Atlantic States and OMI tropospheric column mean volume mixing ratio for O3 will be presented. These aircraft data are representative of the lowest 3 kilometers of the atmosphere, the region in which much of the locally-generated and regionally-transported ozone exists.

Pickering, K. E.↗
NASA NTRSDate not supplied

A Principal Component and Machine Learning Approach to Spatially Gap Fill Hyperspectral Ocean Color Satellite Retrievals

Retrievals of ocean color properties from space are important for monitoring the health of the ocean ecosystem but such retrievals tend to be limited in spatial coverage due to conditions such as clouds, aerosols, and sun glint. Gap filling of ocean color retrievals is typically performed by combining retrievals from multiple satellites or temporally averaging multiple days of retrievals but despite these techniques large gaps still exist posing challenges for near real time monitoring of events like harmful algae blooms. To address these limitations, we propose a spatial gap filling approach using machine learning to learn how to perform an atmospheric correction under challenging retrieval conditions. In this approach a principal component analysis is used to decompose the hyperspectral measurements into spectral features that describe the scattering and absorption of the atmosphere as well as the underlying surface. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from a standard ocean color algorithm such as the MODIS atmospheric correction algorithm. This machine learning approach is independent of a priori information and does not rely on any radiative transfer modeling. We apply the approach to two hyperspectral UV/VIS instruments, the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can be used to estimate ocean color properties such as chlorophyll, remote sensing reflectance, and fluorescence line height. This method could be used as a gap-filling technique for the future Ocean Color Instrument (OCI) which will be onboard NASA's Plankton, Aerosol Cloud, ocean Ecosystem (PACE) ocean color satellite to provide additional information for monitoring the health of our global oceans. Additionally, it could be applied to the geostationary satellite Tropospheric Emissions: Monitoring of Pollution (TEMPO) to better understand diurnal variability in ocean ecology.

MODIS atmospheric correction algorithm↗
NASA NTRS2017

Continuation of Long-Term Global SO2 Pollution Monitoring from OMI to OMPS

Over the past 20 years, advances in satellite remote sensing of pollution-relevant species have made space-borne observations an increasingly important part of atmospheric chemistry research and air quality management. This progress has been facilitated by advanced UV-vis spectrometers, such as the Ozone Monitoring Instrument (OMI) on board the NASA Earth Observing System (EOS) Aura satellite, and continues with new instruments, such as the Ozone Mapping and Profiler Suite (OMPS) on board the NASA-NOAA Suomi National Polar-orbiting Partnership (SNPP) satellite. In this study, we demonstrate that it is possible, using our state-of-the-art principal component analysis (PCA) retrieval technique, to continue the long-term global SO2 pollution monitoring started by OMI with the current and future OMPS instruments that will fly on the NOAA Joint Polar Satellite System (JPSS) 1, 2, 3, and 4 satellites in addition to SNPP, with a very good consistency of retrievals from these instruments. Since OMI SO2 data have been primarily used for (1) providing regional context on air pollution and long-range transport on a daily basis and (2) providing information on point emission sources on an annual basis after data averaging, we focused on these two aspects in our OMI-OMPS comparisons. Four years of retrievals (2012-2015) have been compared for three regions: eastern China, Mexico, and South Africa. In general, the comparisons show relatively high correlations (r = 0. 79-0.96) of daily regional averaged SO2 mass between the two instruments and near-unity regression slopes (0.76-0.97). The annual averaged SO2 loading differences between OMI and OMPS are small (< 0.03 Dobson unit (DU) over South Africa and up to 0.1 DU over eastern China). We also found a very good correlation (r = 0. 92-0.97) in the spatial distribution of annual averaged SO2 between OMI and OMPS over the three regions during 2012-2015. The emissions from ∼ 400 SO2 sources calculated with the two instruments also show a very good correlation (r = ∼ 0.9) in each year during 2012-2015. OMPS-detected SO2 point source emissions are slightly lower than those from OMI, but OMI-OMPS differences decrease with increasing strength of source. The OMI-OMPS SO2 mass differences on a pixel by pixel (daily) basis in each region can show substantial differences. The two instruments have a spatial correlation coefficient of 0.7 or better on < ∼ 50 % of the days. It is worth noting that consistent SO2 retrievals were achieved without any explicit adjustments to OMI or OMPS radiance data and that the retrieval agreement may be further improved by introducing a more comprehensive Jacobian lookup table than is currently used.

satellite remote sensing of pollution↗
NASA NTRS2013

Assessment and Applications of NASA Ozone Data Products Derived from Aura OMI-MLS Satellite Measurements in Context of the GMI Chemical Transport Model

Measurements from the Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS), both onboard the Aura spacecraft, have been used to produce daily global maps of column and profile ozone since August 2004. Here we compare and evaluate three strategies to obtain daily maps of tropospheric and stratospheric ozone from OMI and MLS measurements: trajectory mapping, direct profile retrieval, and data assimilation. Evaluation is based upon an assessment that includes validation using ozonesondes and comparisons with the Global Modeling Initiative (GMI) chemical transport model (CTM). We investigate applications of the three ozone data products from near-decadal and inter-annual timescales to day-to-day case studies. Zonally averaged inter-annual changes in tropospheric ozone from all of the products in any latitude range are of the order 1-2 Dobson Units while changes (increases) over the 8-year Aura record investigated http://eospso.gsfc.nasa.gov/atbd-category/49 vary approximately 2-4 Dobson Units. It is demonstrated that all of the ozone products can measure and monitor exceptional tropospheric ozone events including major forest fire and pollution transport events. Stratospheric ozone during the Aura record has several anomalous inter-annual events including stratospheric warming split events in the Northern Hemisphere extra-tropics that are well captured using the data assimilation ozone profile product. Data assimilation with continuous daily global coverage and vertical ozone profile information is the best of the three strategies at generating a global tropospheric and stratospheric ozone product for science applications.

GMI↗
NASA NTRS2006

The High Resolution Tropospheric Ozone Residual

The co-flight of the MLS stratospheric limb sounder and the Ozone Monitoring Instrument (OMI) provides the capability of computing the Tropospheric Ozone Residual (TOR) in much greater detail [Ziemke et al., 2006]. Using forward trajectory calculations of MLS ozone measurements combined with OMI column ozone we have developed a high horizontal resolution tropospheric ozone residual (HTOR) which can provide even more detail than the standard TOR product. HTOR is especially useful for extra-tropical studies of tropospheric ozone transport. We find that both the Pacific pollution corridor (East Asia to Alaska) and the Atlantic pollution corridor (North America east coast to Europe) are also preferred locations for strat-trop folds leading to systematic overestimates of pollution amounts. In fact, fold events appear to dominate extra-tropical Northern Hemisphere day-to-day maps of HTOR. Model estimates of the tropospheric column are in reasonable agreement with the HTOR amounts when offsets due to different tropopause height calculations are taken into consideration.

Schoeberl, Mark R.↗
NASA NTRS2014

Estimating Uncertainty in Long Term Total Ozone Records from Multiple Sources

Total ozone measurements derived from the TOMS and SBUV backscattered solar UV instrument series cover the period from late 1978 to the present. As the SBUV series of instruments comes to an end, we look to the 10 years of data from the AURA Ozone Monitoring Instrument (OMI) and two years of data from the Ozone Mapping Profiler Suite (OMPS) on board the Suomi National Polar-orbiting Partnership satellite to continue the record. When combining these records to construct a single long-term data set for analysis we must estimate the uncertainty in the record resulting from potential biases and drifts in the individual measurement records. In this study we present a Monte Carlo analysis used to estimate uncertainties in the Merged Ozone Dataset (MOD), constructed from the Version 8.6 SBUV2 series of instruments. We extend this analysis to incorporate OMI and OMPS total ozone data into the record and investigate the impact of multiple overlapping measurements on the estimated error. We also present an updated column ozone trend analysis and compare the size of statistical error (error from variability not explained by our linear regression model) to that from instrument uncertainty.

TOMS and SBUV↗
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 NTRS2019

Monitoring Volcanic Eruptions by OMI/OMPS, ASTER, and MERRA-2

Volcanic lava flows and/or the gas eruptions are the most common characteristics that can be remotely monitored with satellite technology in the global perspective and on different timescales. Atmospheric Sulfur Dioxide (SO2), one of the most abundant gases from volcanic eruptions apart from atmospheric common gases Carbon Dioxide and water vapor, can be directly detected by space-based sensors on satellites. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is one of the 12 Distributed Active Archive Centers (DAACs) within NASA's Earth Observing System Data and Information System (EOSDIS), which archives SO2 data sets from the Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) in 1978, till the ongoing Ozone Monitoring Instrument (OMI) on NASA's EOS-Aura satellite, the Ozone Mapping Profiler Suite (OMPS) Nadir Mapper (NM) on both the Suomi National Polar-Orbiting Partnership (Suomi-NPP or SNPP) and the Joint Polar-orbiting Satellite System-1 (JPSS-1) satellites, into the future JPSS missions. In addition to the standard OMI/Aura and OMPS/S-NPP SO2 products, SO2 products created under the charter of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) project, are also archived at GES DISC, through which NASA enacts to expand understanding the Earth system using consistent data records. The Land Processes Distributed Active Archive Center (LP DAAC) is another EOSDIS's DAAC that provides land data products and operates as a partnership with the U.S. Geological Survey (USGS). The LP DAAC has been archiving the satellite imagery from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard NASA's EOS-Terra satellite, a high spatial resolution (15 meters) and 14 band multispectral instrument. The ASTER imagery is one of the land products contributing to the application for monitoring hot spots and land terrain changes caused by volcanic eruption events. The data potential in GES DISC and LP DAAC to monitor volcanic sources of SO2 and the influence of wind fields on the gas plume spread will be demonstrated with the most recent 2018 May-July Kilauea Volcano eruption.

Zeng, Jian↗
NASA NTRS2011

A Long-Term Merged Global Ozone Data Set

Using data from a series of space-based ozone measurement systems we are producing a consistent, calibrated ozone time series that can be used for trend analyses and other studies. To produce the total column ozone time series we use data from Total Ozone Mapping Spectrometer (TOMS) instruments on Nimbus 7 and Earth Probe, and data from the Ozone Monitoring Instrument (OMI) flying on the Aura spacecraft. Both total column ozone and the ozone vertical profile data are produced from the Solar Backscatter Ultraviolet (SBUV) instruments on Nimbus 7 and SBUV/2 instruments on a series of NOAA spacecraft. A global zonal mean merged ozone data time series covering the period from 1970-1972 and 1978-2010 has been produced for total column ozone. A similar profile data set has been constructed, but is still in preliminary form due to uncertainty in calibration adjustments as a function of latitude and altitude.

McPeters, Richard D.↗
NASA NTRS2022

An Examination of the Recent Stability of Ozonesonde Global Network Data

The recent Assessment of Standard Operating Procedures for OzoneSondes (ASOPOS 2.0; WMO/GAW Report #268) addressed questions of homogeneity and long-term stability in global electrochemical concentration cell (ECC) ozone sounding network time series. Among its recommendations was adoption of a standard for evaluating data quality in ozonesonde time series. Total column ozone (TCO) derived from the sondes compared to TCO from Aura’s Ozone Monitoring Instrument (OMI) is a primary quality indicator. Comparisons of sonde ozone with Aura’s Microwave Limb Sounder (MLS) are used to assess the stability of stratospheric ozone. This paper provides a comprehensive examination of global ozonesonde network data stability and accuracy since 2004 in light of the sudden post-2013 TCO “dropoff” of ~3-4% that was reported previously at select stations (Stauffer et al., 2020). Comparisons with Aura OMI TCO averaged across the network of 60 stations are stable within about ±2% over the past 18 years. Sonde TCO has similar stability compared to three other TCO satellite instruments, and the stratospheric ozone measurements average to within ±5% of MLS from 50 to 10 hPa. Thus, sonde data are reliable for trends, but with a caveat applied for a subset of dropoff stations in the tropics and subtropics. The dropoff is associated with only one of two major ECC instrument types. A detailed examination of ECC serial numbers pinpoints the timing of the dropoff. However, we find that overall, ozonesonde data are stable and accurate compared to independent measurements over the past two decades.

Ryan M. Stauffer↗
NASA NTRS2014

Aura OMI Observations of Global SO2 and NO2 Pollution from 2005 to 2013

The Ozone Monitoring Instrument (OMI), a NASA partnership with the Netherlands and Finland, flies on the NASA Aura satellite and uses reflected sunlight to measure the two critical atmospheric trace gases: nitrogen dioxide (NO2) and sulfur dioxide (SO2) characterizing daily air quality. Both gases and the secondary pollutants they produce (particulate matter, PM2.5, and tropospheric ozone) are USEPA designated criteria pollutants, posing serious threats to human health and the environment (e.g., acid rain, plant damage and reduced visibility). Our group at NASA GSFC has developed and maintained OMI standard SO2 and NO2 data products. We have recently released an updated version of the standard NO2 L2 and L3 products (SP v2.1) and continue improving the algorithm. We are currently in the process of releasing next generation pollution SO2 product, based on an innovative Principal Component Analysis (PCA) algorithm, which greatly reduces the noise and biases. These new standard products provide valuable datasets for studying anthropogenic pollution on local to global scales. Here we highlight some of the OMI observed changes in air quality over several regions. Over the US average NO2 and SO2 pollution levels had decreased dramatically as a result of both technological improvements (e.g., catalytic converters on cars) and stricter regulations of emissions. We see continued decline in pollution over Europe. Over China OMI observed an increase of about 60 percent in NO2 pollution between 2005 and 2013, despite a temporal reversal of the growing trend due to both 2008 Olympic Games and the economic recession in 2009. Chinese SO2 pollution seems to have stabilized since peaking in 2007, probably due to government efforts to curb SO2 emissions from the power sector. We have also observed large increases in both SO2 and NO2 pollution particularly in Eastern India where a number of large new coal power plants had been built in recent years. We expect that further improvements in the OMI NO2 and SO2 products will allow more robust quantification of long-term trends in global air quality.

SO2↗
NASA NTRS2011

An Intercomparison of Tropospheric Ozone Retrievals Derived from Two Aura Instruments and Measurements in Western North America in 2006

Two recently developed methods for quantifying tropospheric ozone abundances based on Aura data, the Trajectory‐enhanced Tropospheric Ozone Residual (TTOR) and an assimilation of Aura data into Goddard Earth Observing System Version 4 (ASM), are compared to ozone measurements from ozonesonde data collected in April-May 2006 during the INTEX Ozonesonde Network Study 2006 (IONS‐06) campaign. Both techniques use Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) observations. Statistics on column ozone amounts for both products are presented. In general, the assimilation compares better to sonde integrated ozone to 200 hPa (28.6% difference for TTOR versus 2.7% difference for ASM), and both products are biased low. To better characterize the performance of ASM, ozone profiles based on the assimilation are compared to those from ozonesondes. We noted slight negative biases in the lower troposphere, and slight positive biases in the upper troposphere/lower stratosphere (UT/ LS), where we observed the greatest variability. Case studies were used to further understand ASM performance. We examine one case from 17 April 2006 at Bratt's Lake, Saskatchewan, where geopotential height gradients appear to be related to an underestimation in the ASM in the UT/LS region. A second case, from 21 April 2006 at Trinidad Head, California, is a situation where the overprediction of ozone in the UT/LS region does not appear to be due to current dynamic conditions but seems to be related to uncertainty in the flow pattern and large differences in MLS observations upstream.

UTLS↗
NASA NTRS2016

Comparison of OMI NO2 Observations and Their Seasonal and Weekly Cycles with Ground-Based Measurements in Helsinki

We present the comparison of satellite-based OMI (Ozone Monitoring Instrument) NO2 products with ground-based observations in Helsinki. OMI NO2 total columns, available from standard product (SP) and DOMINO algorithm, are compared with the measurements performed by the Pandora spectrometer in Helsinki in 2012. The relative difference between Pandora 21 and OMI SP retrievals is 4 and 6 for clear sky and all sky conditions, respectively. DOMINO NO2 retrievals showed slightly lower total columns with median differences about 5 and 14 for clear sky and all sky conditions, respectively. Large differences often correspond to cloudy autumn-winter days with solar zenith angles above 65. Nevertheless, the differences remain within the retrieval uncertainties. Furthermore, the weekly and seasonal cycles from OMI, Pandora and NO2 surface concentrations are compared. Both satellite- and ground-based data show a similar weekly cycle, with lower NO2 levels during the weekend compared to the weekdays as result of reduced emissions from traffic and industrial activities. Also the seasonal cycle shows a similar behavior, even though the results are affected by the fact that most of the data are available during spring-summer because of cloud cover in other seasons. This is one of few works in which OMI NO2 retrievals are evaluated in an urban site at high latitudes (60N). Despite the city of Helsinki having relatively small pollution sources, OMI retrievals have proved to be able to describe air quality features and variability similar to surface observations. This adds confidence in using satellite observations for air quality monitoring also at high latitudes.

Validation↗
NASA NTRS2019

A New Discrete Wavelength BUV Algorithm for Consistent Volcanic SO2 Retrievals from Multiple Satellite Missions

This paper describes a new discrete wavelength algorithm developed for retrieving volcanic sulfur dioxide (SO2) vertical column density (VCD) from UV observing satellites. The Multi-Satellite SO2 algorithm (MS_SO2) simultaneously retrieves column densities of sulfur dioxide, ozone, and Lambertian effective reflectivity (LER) and its spectral dependence. It is used operationally to process measurements from the heritage Total Ozone Mapping Spectrometer (TOMS) onboard NASA's Nimbus-7 satellite (N7/TOMS: 1978-1993) and from the current Earth Polychromatic Imaging Camera (EPIC) onboard Deep Space Climate Observatory (DSCOVR: 2015-) from the Earth-Sun Lagrange (L1) orbit. Results from MS_SO2 algorithm for several volcanic cases were assessed using the more sensitive principal component analysis (PCA) algorithm. The PCA is an operational algorithm used by NASA to retrieve SO2 from hyperspectral UV spectrometers, such as the Ozone Monitoring Instrument (OMI) onboard NASA's Earth Observing System Aura satellite and Ozone Mapping and Profiling Suite (OMPS) onboard NASA-NOAA Suomi National Polar Partnership (SNPP) satellite. For this comparative study, the PCA algorithm was modified to use the discrete wavelengths of the Nimbus-7/TOMS instrument, described in Sect. S1 of the Supplement. Our results demonstrate good agreement between the two retrievals for the largest volcanic eruptions of the satellite era, such as the 1991 Pinatubo eruption. To estimate SO2 retrieval systematic uncertainties, we use radiative transfer simulations explicitly accounting for volcanic sulfate and ash aerosols. Our results suggest that the discrete-wavelength MS_SO2 algorithm, although less sensitive than hyperspectral PCA algorithm, can be adapted to retrieve volcanic SO2 VCDs from contemporary hyperspectral UV instruments, such as OMI and OMPS, to create consistent, multi-satellite, long-term volcanic SO2 climate data records.

Bradford L Fisher↗