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NASA NTRS2008

Intercomparison of SCIAMACHY and OMI Tropospheric NO2 Columns: Observing the Diurnal Evolution of Chemistry and Emissions from Space

Concurrent (August 2006) measurements of tropospheric NO2 columns from OMI aboard Aura (1330 local overpass time) and SCIAMACHY aboard Envisat (1000 local overpass time) offer an opportunity to examine the consistency between the two instruments under tropospheric background conditions and the effect of different observing times. For scenes with tropospheric NO 2 columns <5.0 x 10(exp 15) molecules cm 2, SCIAMACHY and OMI agree within 1.0-2.0 x 10(exp 15) molecules cm 2, consistent with the detection limits of both instruments. We find evidence for a low bias of 0.2 x 10(exp 15) molecules cm 2 in OMI observations over remote oceans. Over the fossil fuel source regions at northern midlatitudes, we find that SCIAMACHY observes up to 40% higher NO2 at 1000 local time (LT) than OMI at 1330 LT. Over biomass burning regions in the tropics, SCIAMACHY observes up to 40% lower NO 2 columns than OMI. These differences are present in the spectral fitting of the data (slant column) and are augmented in the fossil fuel regions and dampened in the tropical biomass burning regions by the expected increase in air mass factor as the mixing depth rises from 1000 to 1330 LT. Using a global 3-D chemical transport model (GEOS-Chem), we show that the 1000-1330 LT decrease in tropospheric NO2 column over fossil fuel source regions can be explained by photochemical loss, dampened by the diurnal cycle of anthropogenic emissions that has a broad daytime maximum. The observed 1000-1330 LT NO2 column increase over tropical biomass burning regions points to a sharp midday peak in emissions and is consistent with a diurnal cycle of emissions derived from geostationary satellite fire counts.

Boersma, K. Folkert↗

NASA NTRS2010

The Utility of the OMI HCHO/NO2 in Air Quality Decision-Making Activities

I will discuss a novel and practical application of the OMI HCHU and NO2 data products to the "weight of evidence" in the air quality decision-making process (e.g., State Implementation Plan (SIP)) for a city, region, or state to demonstrate that it is making progress toward attainment of the National Ambient Air Quality Standard (NAAQS) for ozone. Any trend, or lack thereof, in the observed OMI HCHO/NO2 may support that an emission control strategy implemented to reduce ozone is or is not occurring for a metropolitan area. In addition, the observed OMI HCHO/NO2 may be used to define new emission control strategies as the photochemical environments of urban areas evolve over time. I will demonstrate the utility of the OMI HCHO/NO2 over the U.S. for air quality applications with support from simulations with both a regional model and a photochemical box model. These results support mission planning of an OMI-like instrument for the proposed GEO-CAPE satellite that has as one of its objectives to study air quality from space. However, I'm attending the meeting as the Aura Deputy Project Scientist, so I don't technically need to present anything to justify the travel.

Duncan, Bryan↗

NASA NTRS2011

SO2 Emissions and Lifetimes: Estimates from Inverse Modeling Using In Situ and Global, Space-Based (SCIAMACHY and OMI) Observations

Top-down constraints on global sulfur dioxide (SO2) emissions are inferred through inverse modeling using SO2 column observations from two satellite instruments (SCIAMACHY and OMI). We first evaluated the S02 column observations with surface SO2 measurements by applying local scaling factors from a global chemical transport model (GEOS-Chem) to SO2 columns retrieved from the satellite instruments. The resulting annual mean surface SO2 mixing ratios for 2006 exhibit a significant spatial correlation (r=0.86, slope=0.91 for SCIAMACHY and r=0.80, slope = 0.79 for OMI) with coincident in situ measurements from monitoring networks throughout the United States and Canada. We evaluate the GEOS-Chem simulation of the SO2 lifetime with that inferred from in situ measurements to verity the applicability of GEOS-Chem for inversion of SO2 columns to emissions. The seasonal mean SO2 lifetime calculated with the GEOS-Chem model over the eastern United States is 13 h in summer and 48 h in winter, compared to lifetimes inferred from in situ measurements of 19 +/- 7 h in summer and 58 +/- 20 h in winter. We apply SO2 columns from SCIAMACHY and OMI to derive a top-down anthropogenic SO2 emission inventory over land by using the local GEOS-Chem relationship between SO2 columns and emissions. There is little seasonal variation in the top-down emissions (<15%) over most major industrial regions providing some confidence in the method. Our global estimate for annual land surface anthropogenic SO2 emissions (52.4 Tg S/yr from SCIAMACHY and 49.9 Tg S / yr from OMI) closely agrees with the bottom-up emissions (54.6 Tg S/yr) in the GEOS-Chem model and exhibits consistency in global distributions with the bottom-up emissions (r = 0.78 for SCIAMACHY, and r = 0.77 for OMI). However, there are significant regional differences.

Lee, Chulkyu↗

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 NTRS2011

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

We have developed a relatively simple scheme for simulating retrieved cloud optical centroid pressures (OCP) from satellite solar backscatter observations. We have compared simulator results with those from more detailed retrieval simulators that more fully account for the complex radiative transfer in a cloudy atmosphere. We used this fast simulator to conduct a comprehensive evaluation of cloud OCPs from the two OMI algorithms using collocated data from CloudSat and Aqua MODIS, a unique situation afforded by the A-train formation of satellites. We find that both OMI algorithms perform reasonably well and that the two algorithms agree better with each other than either does with the collocated CloudSat data. This indicates that patchy snow/ice, cloud 3D, and aerosol effects not simulated with the CloudSat data are affecting both algorithms similarly. We note that the collocation with CloudSat occurs mainly on the East side of OMI's swath. Therefore, we are not able to address cross-track biases in OMI cloud OCP retrievals. Our fast simulator may also be used to simulate cloud OCP from output generated by general circulation models (GCM) with appropriate account of cloud overlap. We have implemented such a scheme and plan to compare OMI data with GCM output in the near future.

Joiner, J.↗

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 NTRS2014

Aerosol Remote Sensing from OMI Observations: An Overview

The unique advantage of OMI observations for the characterization of aerosol properties is the availability of radiance measurement at near UV wavelengths. In spite of its coarse spatial resolution, OMI's near UV observations make possible the characterization of aerosol absorption properties. This capability is unavailable in any of the currently operational high spatial resolution aerosol sensors. A unique decadal record of aerosol absorption optical depth and single scattering albedo from near UV observations has been produced from OMI observations. In this presentation we will review the evolution of OMI's aerosol retrieval capability over the past ten years including retrieval algorithm improvements, assessment of retrieved products, and development of new retrieval capabilities to infer the optical depth of aerosol layers located above clouds.

Aerosols↗

NASA NTRS2017

New-Generation NASA Aura Ozone Monitoring Instrument (OMI) Volcanic SO2 Dataset: Algorithm Description, Initial Results, and Continuation with the Suomi-NPP Ozone Mapping and Profiler Suite (OMPS)

Since the fall of 2004, the Ozone Monitoring Instrument (OMI) has been providing global monitoring of volcanic SO2 emissions, helping to understand their climate impacts and to mitigate aviation hazards. Here we introduce a new-generation OMI volcanic SO2 dataset based on a principal component analysis (PCA) retrieval technique. To reduce retrieval noise and artifacts as seen in the current operational linear fit (LF) algorithm, the new algorithm, OMSO2VOLCANO, uses characteristic features extracted directly from OMI radiances in the spectral fitting, thereby helping to minimize interferences from various geophysical processes (e.g., O3 absorption) and measurement details (e.g., wavelength shift). To solve the problem of low bias for large SO2 total columns in the LF product, the OMSO2VOLCANO algorithm employs a table lookup approach to estimate SO2 Jacobians (i.e., the instrument sensitivity to a perturbation in the SO2 column amount) and iteratively adjusts the spectral fitting window to exclude shorter wavelengths where the SO2 absorption signals are saturated. To first order, the effects of clouds and aerosols are accounted for using a simple Lambertian equivalent reflectivity approach. As with the LF algorithm, OMSO2VOLCANO provides total column retrievals based on a set of predefined SO2 profiles from the lower troposphere to the lower stratosphere, including a new profile peaked at 13 km for plumes in the upper troposphere. Examples given in this study indicate that the new dataset shows significant improvement over the LF product, with at least 50% reduction in retrieval noise over the remote Pacific. For large eruptions such as Kasatochi in 2008 (approximately 1700 kt total SO2/ and Sierra Negra in 2005 (greater than 1100DU maximum SO2), OMSO2VOLCANO generally agrees well with other algorithms that also utilize the full spectral content of satellite measurements, while the LF algorithm tends to underestimate SO2. We also demonstrate that, despite the coarser spatial and spectral resolution of the Suomi National Polar-orbiting Partnership (Suomi-NPP) Ozone Mapping and Profiler Suite (OMPS) instrument, application of the new PCA algorithm to OMPS data produces highly consistent retrievals between OMI and OMPS. The new PCA algorithm is therefore capable of continuing the volcanic SO2 data record well into the future using current and future hyperspectral UV satellite instruments.

OMI↗

NASA NTRS2016

Estimates of Lightning NOx Production Based on OMI NO2 Observations Over the Gulf of Mexico

We evaluate nitrogen oxide (NO(sub x) NO + NO2) production from lightning over the Gulf of Mexico region using data from the Ozone Monitoring Instrument (OMI) aboard NASAs Aura satellite along with detection efficiency-adjusted lightning data from the World Wide Lightning Location Network (WWLLN). A special algorithm was developed to retrieve the lightning NOx [(LNO(sub x)] signal from OMI. The algorithm in its general form takes the total slant column NO2 from OMI and removes the stratospheric contribution and tropospheric background and includes an air mass factor appropriate for the profile of lightning NO(sub x) to convert the slant column LNO2 to a vertical column of LNO(sub x). WWLLN flashes are totaled over a period of 3 h prior to OMI overpass, which is the time an air parcel is expected to remain in a 1 deg. x 1 deg. grid box. The analysis is conducted for grid cells containing flash counts greater than a threshold value of 3000 flashes that yields an expected LNO(sub x) signal greater than the background. Pixels with cloud radiance fraction greater than a criterion value (0.9) indicative of highly reflective clouds are used. Results for the summer seasons during 2007-2011 yield mean LNO(sub x) production of approximately 80 +/- 45 mol per flash over the region for the two analysis methods after accounting for biases and uncertainties in the estimation method. These results are consistent with literature estimates and more robust than many prior estimates due to the large number of storms considered but are sensitive to several substantial sources of uncertainty.

Lightning↗

NASA NTRSDate not supplied

Verification of TROPOMI NO2 Product Using OMI NO2 algorithm

We evaluated S5P TROPOspheric Monitoring Instrument (TROPOMI) operational nitrogen dioxide (NO2) product by comparing with the NO2 retrievals from Ozone Monitoring Instrument (OMI) onboard NASA’s Aura satellite. We compared spatially matched NO2 vertical column density (VCD) data from OMI and TROPOMI to identify any discrepancies between the two operational products. We also applied OMI Air-Mass Factor (AMF) algorithm to TROPOMI Slant Column Density (SCD) data (v02.03.01) aiming to create long-term merged NO2 Earth Science Data Record (https://disc.gsfc.nasa.gov/datasets/TROPOMI_MINDS_NO2_1.1/summary ). The algorithm (version 4.0) uses new Geometry-dependent Lambertian Equivalent surface Reflectivity (GLER) product available for each TROPOMI pixel. GLER is pre-calculated using the vector linearized discrete ordinate radiative transfer (VLIDORT) model, which uses as input high-resolution bidirectional reflectance distribution function (BRDF) information from NASA's Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) over land and the wind-dependent Cox–Munk wave-facet slope distribution over water, the latter with a contribution from the water-leaving radiance based on MODIS gap-filled in-water chlorophyll-a data. The GLER data, combined with consistently retrieved cloud parameters, provide improved information for the calculation of the scattering weight profiles. Additional AMF improvements result from using high resolution (0.25o latitude x 0.25o longitude) a priori NO2 profile shapes and other auxiliary information from the Global Modeling Initiative (GMI) Replay simulation sampled using S5P orbital simulator. Our TROPOMI_MINDS_NO2 algorithm employs the stratosphere-troposphere separation scheme, de-striping, and surface snow/ice treatment consistently with the latest OMI NO2 re-processing (version 4). We evaluate the TROPOMI_MINDS_NO2 product by comparing with the S5P operational NO2 product as well as with independent NO2 observations from ground-based Pandora and aircraft observations. Our results indicate that the new TROPOMI_MINDS_NO2 retrievals are generally higher than the S5P operational NO2 product over polluted regions and show improved agreement with independent validation data.

TROPOMI↗

NASA NTRS2004

The Antarctic Ozone Hole: Initial Results from Aura / OMI Compared with TOMS

A series of TOMS instruments (on November 7 , Meteor 3, and Earth Probe) has been monitoring the annual development of the Antarctic ozone hole since the 1980s. The ozone mapping instrument on Aura, OMI, is expected to take over this record of observation from the aging Earth Probe TOMS instrument. The area of the ozone hole can be taken as a sensitive indicator of the magnitude of ozone destruction each year. The timing of initial formation of the ozone hole and its duration are sensitive to the atmospheric dynamics of the southern polar regions. The entire TOMS data record (1978 - 2004) has recently been reprocessed with the new version 8 algorithm, which includes a revised calibration. The effect has been to slightly increase ozone hole area over earlier estimates, but only by 23%. OMI (ozone monitoring instrument) on Aura is a hyperspectral imaging instrument that operates in a pushbroom mode to measure solar backscattered radiation in the ultraviolet and visible. OMI has higher spatial resolution than TOMS - 14 x 24 km versus 38 km x 38 km from TOMS. OMI has now begin mapping total column ozone on a global basis in a measurement similar to TOMS. The ozone hole measurements for 2003 are compared with those from Earth Probe TOMS.

McPeters, R.↗

NASA NTRS2005

Testing the Archivas Cluster (Arc) for Ozone Monitoring Instrument (OMI) Scientific Data Storage

The Ozone Monitoring Instrument (OMI) launched on NASA's Aura Spacecraft, the third of the major platforms of the EOS program on July 15,2004. In addition to the long term archive and distribution of the data from OM1 through the Goddard Earth Science Distributed Active Archive Center (GESDAAC), we are evaluating other archive mechanisms that can archive the data in a more immediately available method where it can be used for futher data production and analysis. In 2004, Archivas, Inc. was selected by NASA s Small Business Innovative Research (SBIR) program for the development of their Archivas Cluster (ArC) product. Arc is an online disk based system utilizing self-management and automation on a Linux cluster. Its goal is to produce a low cost solution coupled with the ease of management. The OM1 project is an application partner of the SBIR program, and has deployed a small cluster (5TB) based on the beta Archwas software. We performed extensive testing of the unit using production OM1 data since launch. In 2005, Archivas, Inc. was funded in SBIR Phase II for further development, which will include testing scalability with the deployment of a larger (35TB) cluster at Goddard. We plan to include Arc in the OM1 Team Leader Computing Facility (TLCF) hosting OM1 data for direct access and analysis by the OMI Science Team. This presentation will include a brief technical description of the Archivas Cluster, a summary of the SBIR Phase I beta testing results, and an overview of the OMI ground data processing architecture including its interaction with the Phase II Archivas Cluster and hosting of OMI data for the scientists.

Tilmes, Curt↗

NASA NTRS2007

OMI Tropospheric NO2 from Lightning in Observed Convective Events

Lightning is responsible for an estimated 15 percent of total NO emissions, and is one of the most prominent sources in the upper troposphere. In this study, we present evidence of lightning-generated NO2 (LNO2) using data from the Ozone Monitoring Instrument (OMI), which has observed tropospheric NO2 since its launch in 2004. Although LNO2 has been also reported in previous satellite studies from the Global Ozone Monitoring Experiment (GOME) and SCIAMACHY, OMI is better suited for such measurements by virtue of its higher spatial resolution and daily global coverage. We will present data clearly showing the LNO2 signal in the OMI tropospheric NO2 product on two days over and downwind of specific convective systems in the US Midwest. Gridded monthly mean tropospheric NO 2 data are subtracted from the daily gridded data to obtain the presumed LNO2 signal. Observed cloud-to-ground (CG) lightning flashes from the National Lightning Detection Network (NLDN) were counted along middle and upper tropospheric back trajectories that were run from the regions containing the LNO2 signal. A vertically-weighted average number of upwind CG flashes was obtained using a profile of LNO(x) mass obtained from a series of midlatitude cloud-resolved storm chemistry simulations. The number of CG flashes was scaled up to total flashes (intracloud (IC) flashes plus CG) using a climatological IC/CG ratio. The number of moles of LNO(x) in the region considered was estimated by assuming that LNO2 is 30 percent of LNO(x). This value was divided by the number of upwind flashes to obtain an average estimate of the number of moles produced per flash. Results yield values in the range obtained through other estimation techniques (e.g., aircraft measurements, models). We will also present a similar analysis over northern Australia during the SCOUT-O3/ACTIVE field campaigns in November and December 2005, in which we will compare the OMI LNOx signals with aircraft observations from the storm anvils.

Pickering, Kenneth↗

NASA NTRS2007

Air Quality Research and Applications Using AURA OMi Data

The Ozone Monitoring Instrument (OMI) on EOS Aura is a new generation of satellite remote sensing instrument designed to measure trace gas and aerosol absorption at the UV and blue wavelengths. These measurements are made globally at urban scale resolution with no inter-orbital gaps that make them potentially very useful for air quality research, such as the determination of the sources and processes that affect global and regional air quality, and to develop applications such as air quality forecast. However, the use of satellite data for such applications is not as straight forward as satellite data have been for stratospheric research. There is a need for close interaction between the satellite product developers, in-situ measurement programs, and the air quality research community to overcome some of the inherent difficulties in interpreting data from satellite-based remote sensing instruments. In this talk we will discuss the challenges and opportunities in using OMI products for air quality research and applications. A key conclusion of this work is that to realize the full potential of OMI measurements it will be necessary to combine OMI data with data from instruments such as MLS, MODIS, AIRS, and CALIPSO that are currently flying in the "A-train" satellite constellation. In addition similar data taken by satellites crossing the earth at different local times than the A-train (e.g., the recently MetOp satellite) would need to be processed in a consistent manner to study diurnal variability, and to capture the effects on air quality of rapidly changing events such as wild fires.

Bhartia, P.K.↗

NASA NTRS2009

NO2 Total and Tropospheric Vertical Column Densities from OMI on EOS Aura: Update

The Ozone Monitoring Instrument (OMI), which is on the EOS AURA satellite, retrieves vertical column densities (VCDs) of NO2, along with those of several other trace gases. The relatively high spatial resolution and daily global coverage of the instrument make it particularly well-suited to monitoring tropospheric pollution at scales on the order of 20 km. The OMI NO2 algorithm distinguishes polluted regions from background stratospheric NO2 using a separation algorithm that relies on the smoothly varying stratospheric NO2 and estimations of both stratospheric and tropospheric air mass factors (AMFs). Version 1 of OMI NO2 data has been released for public use. An overview of OMI NO2 data, some recent results and a description of the improvements for version 2 of the algorithm will be presented.

Gleason, J.F.↗

NASA NTRS2006

Solutions Network Formulation Report. Integration of OMI and TES Aerosol Products into the EPA Regional Planning Organizations' FASTNET Aerosol Tracking and Analysis Tool

Every year, more than 280 million visitors tour our Nation s most treasured parks and wilderness areas. Unfortunately, many visitors are unable to see the spectacular vistas they expect because of white or brown haze in the air. Most of this haze is not natural; it is air pollution, carried by the wind often hundreds of miles from its origin. Some of the pollutants have been linked to serious health problems, such as asthma and other lung disorders, and even premature death. In addition, nitrates and sulfates contribute to acid rain formation, which contaminates rivers and lakes and erodes buildings and historical monuments. The U.S. Environmental Protection Agency RPOs (Regional Planning Organizations) have been tasked with monitoring and determining the nature and origin of haze in Class I scenic areas, and finding ways to reduce haze in order to improve visibility in these areas. The RPOs have developed an Internet-based air quality DST (Decision Support Tool) called FASTNET (Fast Aerosol Sensing Tools for Natural Event Tracking). While FASTNET incorporates a few satellite datasets, most of the data utilized by this DST comes from ground-based instrument networks. The problem is that in many areas the sensors are sparsely located, with long distances between them, causing difficulties in tracking haze over the United States, determining its source, and analyzing its content. Satellite data could help to fill in the data gaps and to supplement and verify ground-recorded air quality data. Although satellite data are now being used for air quality research applications, such data are not routinely used for environmental decision support, in part because of limited resources, difficulties with interdisciplinary data interpretation, and the need for advanced inter-agency partnerships. As a result, the validation and verification of satellite data for air quality operational system applications has been limited This candidate solution evaluates the usefulness of OMI (Ozone Monitoring Instrument) and TES (Tropospheric Emission Spectrometer) air quality data for the RPOs by comparing OMI and TES data with ground-based data that are acquired during identified episodes of air pollution. The air quality data from OMI and TES are of different spectral ranges than data from satellites currently included in FASTNET, giving them potential advantages over the existing satellites. If the OMI and TES data are shown to be useful to the RPOs, they would then be integrated into the FASTNET DST for use on an operational basis.

Knowlton, Kelly↗

NASA NTRS2010

Applications of OMI Tropospheric NO(Sub 2) Data: Air Quality Trends in the US and Lightning NO(x) Source Strength

This talk will be presented in two parts: 1) an analysis of tropospheric column NO2 trends in the eastern half of the United States over the period 2005 to 2009 and 2) estimation of lightning NO(x) production rates based on OMI observations and lightning flash rate data. The air quality trends in the eastern US will be determined for specific subregions using tropospheric column NO2 data from OMI for 2005 through 2008 and from GOME-2 for 2007 through 2009. This period is characterized by significant NO(x) emission reductions at power plants within most of this region. The air quality trends will be compared with those estimated from continuous emission monitoring data from the power plants compiled by the US Environmental Protection Agency. OMI NO2 data have also been used to estimate lightning NOx production per flash in selected storms near Costa Rica and Panama during the 2007 NASA TC4 field campaign and over the continental US, Gulf of Mexico, and western Atlantic during the summers of 2005 and 2006. The lightning signal is extracted from the OMI data through a custom retrieval in which an NO2 profile representative of convective outflow is used in the airmass factor calculation and the background NO2 column is subtracted from the tropospheric column. When combined with NO(x)/NO2 ratios from the NASA GMT model and observed flash rates, the resulting estimates of NO(x) production per flash are comparable to those estimated obtained from analyses of aircraft data and cloud-resolving modeling.

Pickering, Kenneth↗

NASA NTRS2010

Polar Mesospheric Clouds (PMCs) Observed by the Ozone Monitoring Instrument (OMI) on Aura

Backscattered ultraviolet (BUV) instruments designed for measuring stratospheric ozone profiles have proven to be robust tools for observing polar mesospheric clouds (PMCs). These measurements are available for more than 30 years, and have been used to demonstrate the existence of long-term variations in PMC occurrence frequency and brightness. The Ozone Monitoring Instrument (OMI) on the EOS Aura satellite provides new and improved capabilities for PMC characterization. OMI uses smaller pixels than previous BUV instruments, which increases its ability to identify PMCs and discern more spatial structure, and its wide cross-track viewing swath provides full polar coverage up to 90 latitude every day in both hemispheres. This cross-track coverage allows the evolution of PMC regions to be followed over several consecutive orbits. Localized PMC variations determined from OMI measurements are consistent with coincident SBUV/2 measurements. Nine seasons of PMC observations from OMI are now available, and clearly demonstrate the advantages of these measurements for PMC analysis.

DeLand, Matthew T.↗