Retrieval of NO2 Column Amounts from Ground-Based Hyperspectral Imaging Sensor Measurements

Total column amounts of NO2 (TCN) were estimated from ground-based hyperspectral imaging sensor (HIS) measurements in a polluted urban area (Seoul, Korea) by applying the radiance ratio fitting method with five wavelength pairs from 400 to 460 nm. We quantified the uncertainty of the retrieved TCN based on several factors. The estimated TCN uncertainty was up to 0.09 Dobson unit (DU), equivalent to 2.687 ?? 1020 molecules m???2) given a 1?? error for the observation geometries, including the solar zenith angle, viewing zenith angle, and relative azimuth angle. About 0.1 DU (6.8%) was estimated for an aerosol optical depth (AOD) uncertainty of 0.01. In addition, the uncertainty due to the NO2 vertical profile was 14% to 22%. Compared with the co-located Pandora spectrophotometer measurements, the HIS captured the temporal variation of the TCN during the intensive observation period. The correlation between the TCN from the HIS and Pandora also showed good agreement, with a slight positive bias (bias: 0.6 DU, root mean square error: 0.7 DU)

Stratosphere-troposphere separation of nitrogen dioxide columns from the TEMPO geostationary satellite instrument

Separating the stratospheric and tropospheric contributions in satellite retrievals of atmospheric NO2 column abundance is a crucial step in the interpretation and application of the satellite observations. A variety of stratosphere–troposphere separation algorithms have been developed for sun-synchronous instruments in low Earth orbit (LEO) that benefit from global coverage, including broad clean regions with negligible tropospheric NO2 compared to stratospheric NO2. These global sun-synchronous algorithms need to be evaluated and refined for forthcoming geostationary instruments focused on continental regions, which lack this global context and require hourly estimates of the stratospheric column. Here we develop and assess a spatial filtering algorithm for the upcoming TEMPO geostationary instrument that will target North America. Developments include using independent satellite observations to identify likely locations of tropospheric enhancements, using independent LEO observations for spatial context, consideration of diurnally varying partial fields of regard, and a filter based on stratospheric to tropospheric air mass factor ratios. We test the algorithm with LEO observations from the OMI instrument with an afternoon overpass, and from the GOME-2 instrument with a morning overpass. We compare our TEMPO field of regard algorithm against an identical global algorithm to investigate the penalty resulting from the limited spatial coverage in geostationary orbit, and find excellent agreement in the estimated mean daily tropospheric NO2 column densities (R2=0.999, slope=1.009 for July and R2=0.998, slope=0.999 for January). The algorithm performs well even when only small parts of the continent are observed by TEMPO. The algorithm is challenged the most by east coast morning retrievals in the wintertime (e.g., R2=0.995, slope=1.038 at 14:00 UTC). We find independent global LEO observations (corrected for time of day) provide important context near the field-of-regard edges. We also test the performance of the TEMPO algorithm without these supporting global observations. Most of the continent is unaffected (R2=0.924 and slope=0.973 for July and R2=0.996 and slope=1.008 for January), with 90 % of the pixels having differences of less than ±0.2×1015 molecules cm−2 between the TEMPO tropospheric NO2 column density and the global algorithm. For near-real-time retrieval, even a climatological estimate of the stratospheric NO2 surrounding the field of regard would improve this agreement. In general, the additional penalty of a limited field of regard from TEMPO introduces no more error than normally expected in most global stratosphere–troposphere separation algorithms. Overall, we conclude that hourly near-real-time stratosphere–troposphere separation for the retrieval of NO2 tropospheric column densities by the TEMPO geostationary instrument is both feasible and robust, regardless of the diurnally varying limited field of regard.The authors are grateful to Kelly Chance, Xiong Liu, John Houck, Peter Zoogman, and other members of the TEMPO trace gas retrieval team for their input in preparation of this paper. Work at Dalhousie University was supported by Environment and Climate Change Canada. The authors also gratefully acknowledge the free use of TEMIS NO2 data from the GOME-2 sensor provided by http://www.temis.nl, last access: 12 November 2018, and the NASA Standard Product NO2 data from OMI provided by https://disc.gsfc.nasa.gov/datasets/OMNO2_V003/summary, last access: 9 November 2018. (Environment and Climate Change Canada)https://www.atmos-meas-tech.net/11/6271/2018/Published versio

A New Retrieval Algorithm for OMI NO2: Tropospheric Results and Comparisons with Measurements and Models

Nitrogen oxides (NOx =NO+NO2) are important atmospheric trace constituents that impact tropospheric air pollution chemistry and air quality. We have developed a new NASA algorithm for the retrieval of stratospheric and tropospheric NO2 vertical column densities using measurements from the nadir-viewing Ozone Monitoring Instrument (OMI) on NASA's Aura satellite. The new products rely on an improved approach to stratospheric NO2 column estimation and stratosphere-troposphere separation and a new monthly NO2 climatology based on the NASA Global Modeling Initiative chemistry-transport model. The retrieval does not rely on daily model profiles, minimizing the influence of a priori information. We evaluate the retrieved tropospheric NO2 columns using surface in situ (e.g., AQS/EPA), ground-based (e.g., DOAS), and airborne measurements (e.g., DISCOVER-AQ). The new, improved OMI tropospheric NO2 product is available at high spatial resolution for the years 200S-present. We believe that this product is valuable for the evaluation of chemistry-transport models, examining the spatial and temporal patterns of NOx emissions, constraining top-down NOx inventories, and for the estimation of NOx lifetimes

An improved retrieval of tropospheric nitrogen dioxide from GOME

We present a retrieval of tropospheric nitrogen dioxide (NO2) columns from the Global Ozone Monitoring Experiment (GOME) satellite instrument that improves in several ways over previous retrievals, especially in the accounting of Rayleigh and cloud scattering. Slant columns, which are directly fitted without low-pass filtering or spectral smoothing, are corrected for an artificial offset likely induced by spectral structure on the diffuser plate of the GOME instrument. The stratospheric column is determined from NO2 columns over the remote Pacific Ocean to minimize contamination from tropospheric NO2. The air mass factor (AMF) used to convert slant columns to vertical columns is calculated from the integral of the relative vertical NO2 distribution from a global 3-D model of tropospheric chemistry driven by assimilated meteorological data (Global Earth Observing System (GEOS)-CHEM), weighted by altitude-dependent scattering weights computed with a radiative transfer model (Linearized Discrete Ordinate Radiative Transfer), using local surface albedos determined from GOME observations at NO2 wavelengths. The AMF calculation accounts for cloud scattering using cloud fraction, cloud top pressure, and cloud optical thickness from a cloud retrieval algorithm (GOME Cloud Retrieval Algorithm). Over continental regions with high surface emissions, clouds decrease the AMF by 20–30% relative to clear sky. GOME is almost twice as sensitive to tropospheric NO2 columns over ocean than over land. Comparison of the retrieved tropospheric NO2 columns for July 1996 with GEOS-CHEM values tests both the retrieval and the nitrogen oxide radical (NOx) emissions inventories used in GEOS-CHEM. Retrieved tropospheric NO2 columns over the United States, where NOx emissions are particularly well known, are within 18% of GEOS-CHEM columns and are strongly spatially correlated (r = 0.78, n = 288, p < 0.005). Retrieved columns show more NO2 than GEOS-CHEM columns over the Transvaal region of South Africa and industrial regions of the northeast United States and Europe. They are lower over Houston, India, eastern Asia, and the biomass burning region of central Africa, possibly because of biases from absorbing aerosols

Global inventory of nitrogen oxide emissions constrained by space-based observations of NO2 columns

sions (37.7 Tg N yr #1 ) agrees closely with the GEIAbased a priori (36.4) and with the EDGAR 3.0 bottom-up inventory (36.6), but there are significant regional differences. A posteriori NO x emissions are higher by 50 -- 100% in the Po Valley, Tehran, and Riyadh urban areas, and by 25 -- 35% in Japan and South Africa. Biomass burning emissions from India, central Africa, and Brazil are lower by up to 50%; soil NO x emissions are appreciably higher in the western United States, the Sahel, and southern Europe

Retrieval and molecule sensitivity studies for the global ozone monitoring experiment and the scanning imaging absorption spectrometer for atmospheric chartography

The Global Ozone Monitoring Experiment (GOME) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) are diode based spectrometers that will make atmospheric constituent and aerosol measurements from European satellite platforms beginning in the mid 1990's. GOME measures the atmosphere in the UV and visible in nadir scanning, while SCIAMACHY performs a combination of nadir, limb, and occultation measurements in the UV, visible, and infrared. A summary is presented of the sensitivity studies that were performed for SCIAMACHY measurements. As the GOME measurement capability is a subset of the SCIAMACHY measurement capability, the nadir, UV, and visible portion of the studies is shown to apply to GOME as well

Tunable far infrared studies of molecular parameters in support of stratospheric measurements

Lab studies were made in support of far infrared spectroscopy of the stratosphere using the Tunable Far InfraRed (TuFIR) method of ultrahigh resolution spectroscopy and, more recently, spectroscopic and retrieval calculations performed in support of satellite-based atmospheric measurement programs: the Global Ozone Monitoring Experiment (GOME), and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY)

Analysis of Canadian Tropospheric Ozone Measurements from Geostationary Orbit and An Assessment of Non-Coincident Limb-Nadir Matching for Measuring Tropospheric Nitrogen Dioxide

This thesis work attempts to improve the quality of surface-level pollutant concentrations retrieved from satellite-borne optical instruments. In the first part of the present work, an analysis is performed to determine potential benefits of implementing a different radiative transfer model than the one planned for retrieving Canadian tropospheric ozone concentrations with future measurements from the Tropospheric Emissions: Monitoring of Pollution (TEMPO) optical instrument, planned to be launched in 2022 into geostationary orbit to measure tropospheric pollutants over the majority of North America. The plane-parallel Earth-atmosphere geometry assumption for multiple-scattered electromagnetic radiation in the planned radiative transfer model for the TEMPO ozone retrieval algorithm has minimal effect for heritage instruments that look at angles close to straight down and measure at local times where the Sun is far above the horizon. However, it is demonstrated in the present work for simulated TEMPO measurements over the Canadian Oil Sands that the retrieval error for a radiative transfer model with a plane-parallel geometry can reach approximately 15% at 13:00 local time, 25% in March or September near local sunrise, 50% in June near local sunrise, and 80% in December near local sunrise, while a radiative transfer model with a spherical geometry results in error up to an order of magnitude smaller in each case. Further work is required to assess the effects of the geometry assumptions on different orders of scattering and of measurement noise. In the second part of the present work, a novel method of estimating tropospheric NO2 pollution using non-coincident limb- and nadir-viewing instrument measurements is further assessed with a reanalysis using new datasets produced by the Ozone Monitoring Instrument (OMI), the Optical Spectrograph and Infrared Imager System (OSIRIS), and a photochemical box model, and an analysis using OSIRIS and the TROPOspheric Monitoring Instrument (TROPOMI). A bias is demonstrated in the current publicly available OSIRIS NO2 density profile data, leading to the development of an updated dataset that is shown to agree with a previously validated dataset within retrieval error bounds above the tropopause. The OSIRIS-OMI reanalysis demonstrates biases of up to 0.5*10^15 molecules/cm^2 due to the different photochemical box model input parameters and up to 0.2*10^15 molecules/cm^2 due to the use of the latest OMI NO2 dataset. The OSIRIS-TROPOMI analysis demonstrates a positive average bias of approximately 0.5*10^15 molecules/cm^2 in the limb-nadir matching with TROPOMI relative to that with OMI due to TROPOMI-OMI tropospheric and stratospheric NO2 column density biases. Error range estimates of photochemical box model input parameters and of different versions of OMI datasets, further analysis of local and yearly dependencies of OSIRIS-OMI limb-nadir matching biases, and further studies on latitudinal and seasonal dependencies of TROPOMI-OMI dataset biases are recommended for future work