scholarly journals A sulfur dioxide Covariance-Based Retrieval Algorithm (COBRA): application to TROPOMI reveals new emission sources

2021 ◽  
Vol 21 (22) ◽  
pp. 16727-16744
Author(s):  
Nicolas Theys ◽  
Vitali Fioletov ◽  
Can Li ◽  
Isabelle De Smedt ◽  
Christophe Lerot ◽  
...  
Keyword(s):  
Sulfur Dioxide ◽  
Principal Component ◽  
Great Sensitivity ◽  
Detection Methods ◽  
Retrieval Algorithm ◽  
Ozone Monitoring Instrument ◽  
So2 Emissions ◽  
Data Set ◽  
So2 Emission ◽  
Monitoring Instrument

Abstract. Sensitive and accurate detection of sulfur dioxide (SO2) from space is important for monitoring and estimating global sulfur emissions. Inspired by detection methods applied in the thermal infrared, we present here a new scheme to retrieve SO2 columns from satellite observations of ultraviolet back-scattered radiances. The retrieval is based on a measurement error covariance matrix to fully represent the SO2-free radiance variability, so that the SO2 slant column density is the only retrieved parameter of the algorithm. We demonstrate this approach, named COBRA, on measurements from the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor (S-5P) satellite. We show that the method reduces significantly both the noise and biases present in the current TROPOMI operational DOAS SO2 retrievals. The performance of this technique is also benchmarked against that of the principal component algorithm (PCA) approach. We find that the quality of the data is similar and even slightly better with the proposed COBRA approach. The ability of the algorithm to retrieve SO2 accurately is further supported by comparison with ground-based observations. We illustrate the great sensitivity of the method with a high-resolution global SO2 map, considering 2.5 years of TROPOMI data. In addition to the known sources, we detect many new SO2 emission hotspots worldwide. For the largest sources, we use the COBRA data to estimate SO2 emission rates. Results are comparable to other recently published TROPOMI-based SO2 emissions estimates, but the associated uncertainties are significantly lower than with the operational data. Next, for a limited number of weak sources, we demonstrate the potential of our data for quantifying SO2 emissions with a detection limit of about 8 kt yr−1, a factor of 4 better than the emissions derived from the Ozone Monitoring Instrument (OMI). We anticipate that the systematic use of our TROPOMI COBRA SO2 column data set at a global scale will allow missing sources to be identified and quantified and help improve SO2 emission inventories.

scholarly journals A Sulfur Dioxide Covariance-Based Retrieval Algorithm (COBRA): application to TROPOMI reveals new emission sources

2021 ◽  
Author(s):  
Nicolas Theys ◽  
Vitali Fioletov ◽  
Can Li ◽  
Isabelle De Smedt ◽  
Christophe Lerot ◽  
...  
Keyword(s):  
Sulfur Dioxide ◽  
Principal Component ◽  
Great Sensitivity ◽  
Detection Methods ◽  
Retrieval Algorithm ◽  
Ozone Monitoring Instrument ◽  
So2 Emissions ◽  
Data Set ◽  
So2 Emission ◽  
Monitoring Instrument

Abstract. Sensitive and accurate detection of sulfur dioxide (SO2) from space is important for monitoring and estimating global sulfur emissions. Inspired by detection methods applied in the thermal infrared, we present here a new scheme to retrieve SO2 columns from satellite observations of ultraviolet back-scattered radiances. The retrieval is based on a measurement error covariance matrix to fully represent the SO2-free radiance variability, so that the SO2 slant column density is the only retrieved parameter of the algorithm. We demonstrate this approach, named COBRA, on measurements from the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor (S-5P) satellite. We show that the method reduces significantly both the noise and biases present in the current TROPOMI operational DOAS SO2 retrievals. The performance of this technique is also benchmarked against that of the Principal Component Algorithm (PCA) approach. We find that the quality of the data is similar and even slightly better with the proposed COBRA approach. The ability of the algorithm to retrieve SO2 accurately is also further supported by comparison with ground-based observations. We illustrate the great sensitivity of the method with a high-resolution global SO2 map, considering two and a half years of TROPOMI data. In addition to the known sources, we detect many new SO2 emission hotspots worldwide. For the largest sources, we use the COBRA data to estimate SO2 emission rates. Results are comparable to other recently published TROPOMI-based SO2 emissions estimates, but the associated uncertainties are significantly lower than with the operational data. Next, for a limited number of weak sources, we demonstrate the potential of our data for quantifying SO2 emissions with a detection limit of about 8 kt yr-1, a factor of 4 better than the emissions derived from the Ozone Monitoring Instrument (OMI). We anticipate that the systematic use of our TROPOMI COBRA SO2 column data set at a global scale will allow identifying and quantifying missing sources, and help improving SO2 emission inventories.

scholarly journals Retrieval of absolute SO<sub>2</sub> column amounts from scattered-light spectra: implications for the evaluation of data from automated DOAS networks

2016 ◽  
Vol 9 (12) ◽  
pp. 5677-5698 ◽  
Author(s):  
Peter Lübcke ◽  
Johannes Lampel ◽  
Santiago Arellano ◽  
Nicole Bobrowski ◽  
Florian Dinger ◽  
...  
Keyword(s):  
Scattered Light ◽  
Principal Component ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Light Sources ◽  
Reference Spectrum ◽  
Emission Rates ◽  
Instrumental Effects ◽  
So2 Emissions ◽  
So2 Emission

Abstract. Scanning spectrometer networks using scattered solar radiation in the ultraviolet spectral region have become an increasingly important tool for monitoring volcanic sulfur dioxide (SO2) emissions. Often measured spectra are evaluated using the differential optical absorption spectroscopy (DOAS) technique. In order to obtain absolute column densities (CDs), the DOAS evaluation requires a Fraunhofer reference spectrum (FRS) that is free of absorption structures of the trace gas of interest. For measurements at volcanoes such a FRS can be readily obtained if the scan (i.e. series of measurements at different elevation angles) includes viewing directions where the plume is not seen. In this case, it is possible to use these viewing directions (e.g. zenith) as FRS. Possible contaminations of the FRS by the plume can then be corrected by calculating and subtracting an SO2 offset (e.g. the lowest SO2 CD) from all viewing directions of the respective scan. This procedure is followed in the standard evaluations of data from the Network for Observation of Volcanic and Atmospheric Change (NOVAC). While this procedure is very efficient in removing Fraunhofer structures and instrumental effects it has the disadvantage that one can never be sure that there is no SO2 from the plume in the FRS. Therefore, using a modelled FRS (based on a high-resolution solar atlas) has a great advantage. We followed this approach and investigated an SO2 retrieval algorithm using a modelled FRS. In this paper, we present results from two volcanoes that are monitored by NOVAC stations and which frequently emit large volcanic plumes: Nevado del Ruiz (Colombia) recorded between January 2010 and June 2012 and from Tungurahua (Ecuador) recorded between January 2009 and December 2011. Instrumental effects were identified with help of a principal component analysis (PCA) of the residual structures of the DOAS evaluation. The SO2 retrieval performed extraordinarily well with an SO2 DOAS retrieval error of 1 − 2 × 1016 [molecules cm−2]. Compared to a standard evaluation, we found systematic differences of the differential slant column density (dSCD) of only up to  ≈ 15 % when looking at the variation of the SO2 within one scan. The major advantage of our new retrieval is that it yields absolute SO2 CDs and that it does not require complicated instrumental calibration in the field (e.g. by employing calibration cells or broadband light sources), since the method exploits the information available in the measurements.We compared our method to an evaluation that is similar to the NOVAC approach, where a spectrum that is recorded directly before the scan is used as an FRS and an SO2 CD offset is subtracted from all retrieved dSCD in the scan to correct for possible SO2 contamination of the FRS. The investigation showed that 21.4 % of the scans (containing significant amounts of SO2) at Nevado del Ruiz and 7 % of the scans at Tungurahua showed much larger SO2 CDs when evaluated using modelled FRS (more than a factor of 2). For standard evaluations the overall distribution of the SO2 CDs in a scan can in some cases indicate whether the plume affects all viewing directions and thus these scans need to be discarded for NOVAC emission rate evaluation. However, there are other cases where this is not possible and thus the reported SO2 emission rates would be underestimated. The new method can be used to identify these cases and thus it can considerably improve SO2 emission budgets.

scholarly journals A global catalogue of large SO<sub>2</sub> sources and emissions derived from the Ozone Monitoring Instrument

2016 ◽  
Author(s):  
Vitali E. Fioletov ◽  
Chris A. McLinden ◽  
Nickolay Krotkov ◽  
Can Li ◽  
Joanna Joiner ◽  
...  
Keyword(s):  
Power Plants ◽  
Oil And Gas ◽  
Principal Component ◽  
Oil And Gas Industry ◽  
Point Sources ◽  
Ozone Monitoring Instrument ◽  
So2 Emissions ◽  
Gas Industry ◽  
Monitoring Instrument ◽  
Ozone Monitoring

Abstract. Sulphur dioxide (SO2) measurements from the Ozone Monitoring Instrument (OMI) satellite sensor processed with the new Principal Component Analysis (PCA) algorithm were used to detect large point emission sources or clusters of sources. The total of 491 continuously emitting point sources releasing from about 30 kt yr−1 to more than 4000 kt yr−1 of SO2 per year have been identified and grouped by country and by primary source origin: volcanoes (76 sources); power plants (297); smelters (53); and sources related to the oil and gas industry (65). The sources were identified using different methods, including through OMI measurements themselves applied to a new emissions detection algorithm, and their evolution during the 2005–2014 period was traced by estimating annual emissions from each source. For volcanic sources, the study focused on continuous degassing, and emissions from explosive eruptions were excluded. Emissions from degassing volcanic sources were measured, many for the first time, and collectively they account for about 30 % of total SO2 emissions estimated from OMI measurements, but that fraction has increased in recent years given that cumulative global emissions from power plants and smelters are declining while emissions from oil and gas industry remained nearly constant. Anthropogenic emissions from the USA declined by 80 % over the 2005–2014 period as did emissions from western and central Europe, whereas emissions from India nearly doubled, and emissions from other large SO2-emitting regions (South Africa, Russia, Mexico, and the Middle East) remained fairly constant. In total, OMI-based estimates account for about a half of total reported anthropogenic SO2 emissions; the remaining half is likely related to sources emitting less than 30 kt yr−1 and not detected by OMI.

scholarly journals A global catalogue of large SO<sub>2</sub> sources and emissions derived from the Ozone Monitoring Instrument

2016 ◽  
Vol 16 (18) ◽  
pp. 11497-11519 ◽  
Author(s):  
Vitali E. Fioletov ◽  
Chris A. McLinden ◽  
Nickolay Krotkov ◽  
Can Li ◽  
Joanna Joiner ◽  
...  
Keyword(s):  
Power Plants ◽  
Oil And Gas ◽  
Principal Component ◽  
Oil And Gas Industry ◽  
Point Sources ◽  
Ozone Monitoring Instrument ◽  
So2 Emissions ◽  
Gas Industry ◽  
Monitoring Instrument ◽  
Ozone Monitoring

Abstract. Sulfur dioxide (SO2) measurements from the Ozone Monitoring Instrument (OMI) satellite sensor processed with the new principal component analysis (PCA) algorithm were used to detect large point emission sources or clusters of sources. The total of 491 continuously emitting point sources releasing from about 30 kt yr−1 to more than 4000 kt yr−1 of SO2 per year have been identified and grouped by country and by primary source origin: volcanoes (76 sources); power plants (297); smelters (53); and sources related to the oil and gas industry (65). The sources were identified using different methods, including through OMI measurements themselves applied to a new emission detection algorithm, and their evolution during the 2005–2014 period was traced by estimating annual emissions from each source. For volcanic sources, the study focused on continuous degassing, and emissions from explosive eruptions were excluded. Emissions from degassing volcanic sources were measured, many for the first time, and collectively they account for about 30 % of total SO2 emissions estimated from OMI measurements, but that fraction has increased in recent years given that cumulative global emissions from power plants and smelters are declining while emissions from oil and gas industry remained nearly constant. Anthropogenic emissions from the USA declined by 80 % over the 2005–2014 period as did emissions from western and central Europe, whereas emissions from India nearly doubled, and emissions from other large SO2-emitting regions (South Africa, Russia, Mexico, and the Middle East) remained fairly constant. In total, OMI-based estimates account for about a half of total reported anthropogenic SO2 emissions; the remaining half is likely related to sources emitting less than 30 kt yr−1 and not detected by OMI.

scholarly journals Separating damage from environmental effects affecting civil structures for near real-time damage detection

2017 ◽  
Vol 17 (4) ◽  
pp. 850-868 ◽  
Author(s):  
William Soo Lon Wah ◽  
Yung-Tsang Chen ◽  
Gethin Wyn Roberts ◽  
Ahmed Elamin
Keyword(s):  
Damage Detection ◽  
Real Time ◽  
Environmental Effects ◽  
Environmental Conditions ◽  
Principal Component ◽  
Detection Methods ◽  
Real Time Monitoring ◽  
Civil Structures ◽  
Data Set ◽  
Wide Range

Analyzing changes in vibration properties (e.g. natural frequencies) of structures as a result of damage has been heavily used by researchers for damage detection of civil structures. These changes, however, are not only caused by damage of the structural components, but they are also affected by the varying environmental conditions the structures are faced with, such as the temperature change, which limits the use of most damage detection methods presented in the literature that did not account for these effects. In this article, a damage detection method capable of distinguishing between the effects of damage and of the changing environmental conditions affecting damage sensitivity features is proposed. This method eliminates the need to form the baseline of the undamaged structure using damage sensitivity features obtained from a wide range of environmental conditions, as conventionally has been done, and utilizes features from two extreme and opposite environmental conditions as baselines. To allow near real-time monitoring, subsequent measurements are added one at a time to the baseline to create new data sets. Principal component analysis is then introduced for processing each data set so that patterns can be extracted and damage can be distinguished from environmental effects. The proposed method is tested using a two-dimensional truss structure and validated using measurements from the Z24 Bridge which was monitored for nearly a year, with damage scenarios applied to it near the end of the monitoring period. The results demonstrate the robustness of the proposed method for damage detection under changing environmental conditions. The method also works despite the nonlinear effects produced by environmental conditions on damage sensitivity features. Moreover, since each measurement is allowed to be analyzed one at a time, near real-time monitoring is possible. Damage progression can also be given from the method which makes it advantageous for damage evolution monitoring.

scholarly journals New-generation NASA Aura Ozone Monitoring Instrument (OMI) volcanic SO<sub>2</sub> dataset: Algorithm description, initial results, and continuation with the Suomi-NPP Ozone Mapping and Profiler Suite (OMPS)

2016 ◽  
Author(s):  
Can Li ◽  
Nickolay A. Krotkov ◽  
Simon Carn ◽  
Yan Zhang ◽  
Robert J. D. Spurr ◽  
...  
Keyword(s):  
Lower Troposphere ◽  
Climate Impacts ◽  
Wavelength Shift ◽  
Ozone Monitoring Instrument ◽  
So2 Emissions ◽  
Monitoring Instrument ◽  
Spectral Fitting ◽  
Ozone Monitoring ◽  
Pca Algorithm ◽  
New Generation

Abstract. 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 pre-defined 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 (~ 1700 kt total SO2) and Sierra Negra in 2005 (> 1100 DU maximal 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.

scholarly journals Updated SO<sub>2</sub> emission estimates over China using OMI/Aura observations

2018 ◽  
Vol 11 (3) ◽  
pp. 1817-1832 ◽  
Author(s):  
Maria Elissavet Koukouli ◽  
Nicolas Theys ◽  
Jieying Ding ◽  
Irene Zyrichidou ◽  
Bas Mijling ◽  
...  
Keyword(s):  
Emission Inventory ◽  
Satellite Observations ◽  
Ozone Monitoring Instrument ◽  
So2 Emissions ◽  
Transport Modelling ◽  
So2 Emission ◽  
Bottom Up ◽  
Beijing Area ◽  
Entire Domain ◽  
Particulate Matter Emissions

Abstract. The main aim of this paper is to update existing sulfur dioxide (SO2) emission inventories over China using modern inversion techniques, state-of-the-art chemistry transport modelling (CTM) and satellite observations of SO2. Within the framework of the EU Seventh Framework Programme (FP7) MarcoPolo (Monitoring and Assessment of Regional air quality in China using space Observations) project, a new SO2 emission inventory over China was calculated using the CHIMERE v2013b CTM simulations, 10 years of Ozone Monitoring Instrument (OMI)/Aura total SO2 columns and the pre-existing Multi-resolution Emission Inventory for China (MEIC v1.2). It is shown that including satellite observations in the calculations increases the current bottom-up MEIC inventory emissions for the entire domain studied (15–55° N, 102–132° E) from 26.30 to 32.60 Tg annum−1, with positive updates which are stronger in winter ( ∼  36 % increase). New source areas were identified in the southwest (25–35° N, 100–110° E) as well as in the northeast (40–50° N, 120–130° E) of the domain studied as high SO2 levels were observed by OMI, resulting in increased emissions in the a posteriori inventory that do not appear in the original MEIC v1.2 dataset. Comparisons with the independent Emissions Database for Global Atmospheric Research, EDGAR v4.3.1, show a satisfying agreement since the EDGAR 2010 bottom-up database provides 33.30 Tg annum−1 of SO2 emissions. When studying the entire OMI/Aura time period (2005 to 2015), it was shown that the SO2 emissions remain nearly constant before the year 2010, with a drift of −0.51 ± 0.38 Tg annum−1, and show a statistically significant decline after the year 2010 of −1.64 ± 0.37 Tg annum−1 for the entire domain. Similar findings were obtained when focusing on the greater Beijing area (30–40° N, 110–120° E) with pre-2010 drifts of −0.17 ± 0.14 and post-2010 drifts of −0.47 ± 0.12 Tg annum−1. The new SO2 emission inventory is publicly available and forms part of the official EU MarcoPolo emission inventory over China, which also includes updated NOx, volatile organic compounds and particulate matter emissions.

scholarly journals Validation and update of OMI Total Column Water Vapor product

2016 ◽  
Vol 16 (17) ◽  
pp. 11379-11393 ◽  
Author(s):  
Huiqun Wang ◽  
Gonzalo Gonzalez Abad ◽  
Xiong Liu ◽  
Kelly Chance
Keyword(s):  
Water Vapor ◽  
Liquid Water ◽  
Retrieval Algorithm ◽  
Ozone Monitoring Instrument ◽  
Validation Data ◽  
Data Set ◽  
Wide Range ◽  
Key Factor ◽  
The Mean ◽  
Total Column

Abstract. The collection 3 Ozone Monitoring Instrument (OMI) Total Column Water Vapor (TCWV) data generated by the Smithsonian Astrophysical Observatory's (SAO) algorithm version 1.0 and archived at the Aura Validation Data Center (AVDC) are compared with NCAR's ground-based GPS data, AERONET's sun-photometer data, and Remote Sensing System's (RSS) SSMIS data. Results show that the OMI data track the seasonal and interannual variability of TCWV for a wide range of climate regimes. During the period from 2005 to 2009, the mean OMI−GPS over land is −0.3 mm and the mean OMI−AERONET over land is 0 mm. For July 2005, the mean OMI−SSMIS over the ocean is −4.3 mm. The better agreement over land than over the ocean is corroborated by the smaller fitting residuals over land and suggests that liquid water is a key factor for the fitting quality over the ocean in the version 1.0 retrieval algorithm. We find that the influence of liquid water is reduced using a shorter optimized retrieval window of 427.7–465 nm. As a result, the TCWV retrieved with the new algorithm increases significantly over the ocean and only slightly over land. We have also made several updates to the air mass factor (AMF) calculation. The updated version 2.1 retrieval algorithm improves the land/ocean consistency and the overall quality of the OMI TCWV data set. The version 2.1 OMI data largely eliminate the low bias of the version 1.0 OMI data over the ocean and are 1.5 mm higher than RSS's “clear” sky SSMIS data in July 2005. Over the ocean, the mean of version 2.1 OMI−GlobVapour is 1 mm for July 2005 and 0 mm for January 2005. Over land, the version 2.1 OMI data are about 1 mm higher than GlobVapour when TCWV  <  15 mm and about 1 mm lower when TCWV  >  15 mm.

scholarly journals A new discrete wavelength BUV algorithm for consistent volcanic SO<sub>2</sub> retrievals from multiple satellite missions

2019 ◽  
Author(s):  
Bradford L. Fisher ◽  
Nickolay A. Krotkov ◽  
Pawan K. Bhartia ◽  
Can Li ◽  
Simon Carn ◽  
...  
Keyword(s):  
Sulfur Dioxide ◽  
Principal Component ◽  
Volcanic Eruptions ◽  
Climate Data ◽  
Ozone Monitoring Instrument ◽  
Vertical Column ◽  
Earth Observing System ◽  
Paper Supplement ◽  
Process Measurements ◽  
Pca Algorithm

Abstract. 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, Lambertian Effective Reflectivity (LER) and its spectral dependence. It is used operationally to process measurements from the heritage Total Ozone Mapping Spectrometer (TOMS) on board NASA's Nimbus-7 satellite (N7/TOMS: 1978–1993) and from the current Earth Polychromatic Imaging Camera (EPIC) on board Deep Space Climate Observatory (DSCOVR: 2015–) from the Earth-Sun Lagrange (L1) orbit. Results from MS_SO2 algorithm for several volcanic cases were validated 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 Ozone Monitoring Instrument (OMI) on board NASA’s Earth Observing System Aura satellite and Ozone Mapping and Profiling Suite (OMPS) on board NASA-NOAA Suomi National Polar Partnership (S-NPP) satellite. For this comparative study, the PCA algorithm was modified to use the discrete wavelengths of the Nimbus7/TOMS instrument, described in S1 of the paper supplement. Our results demonstrate good agreement between the two retrievals for the largest volcanic eruptions of the satellite era, such as 1991 Pinatubo eruption. To estimate SO2 retrieval 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.

scholarly journals Ozone Monitoring Instrument (OMI) Aura nitrogen dioxide standard product version 4.0 with improved surface and cloud treatments

2021 ◽  
Vol 14 (1) ◽  
pp. 455-479
Author(s):  
Lok N. Lamsal ◽  
Nickolay A. Krotkov ◽  
Alexander Vasilkov ◽  
Sergey Marchenko ◽  
Wenhan Qin ◽  
...  
Keyword(s):  
Nitrogen Dioxide ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Ozone Monitoring Instrument ◽  
Monitoring Instrument ◽  
Standard Product ◽  
Independent Observations ◽  
Moderate Resolution ◽  
Ozone Monitoring ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. We present a new and improved version (V4.0) of the NASA standard nitrogen dioxide (NO2) product from the Ozone Monitoring Instrument (OMI) on the Aura satellite. This version incorporates the most salient improvements for OMI NO2 products suggested by expert users and enhances the NO2 data quality in several ways through improvements to the air mass factors (AMFs) used in the retrieval algorithm. The algorithm is based on the geometry-dependent surface Lambertian equivalent reflectivity (GLER) operational product that is available on an OMI pixel basis. GLER is 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) instruments over land and the wind-dependent Cox–Munk wave-facet slope distribution over water, the latter with a contribution from the water-leaving radiance. The GLER combined with consistently retrieved oxygen dimer (O2–O2) absorption-based effective cloud fraction (ECF) and optical centroid pressure (OCP) provide improved information to the new NO2 AMF calculations. The new AMFs increase the retrieved tropospheric NO2 by up to 50 % in highly polluted areas; these differences arise from both cloud and surface BRDF effects as well as biases between the new MODIS-based and previously used OMI-based climatological surface reflectance data sets. We quantitatively evaluate the new NO2 product using independent observations from ground-based and airborne instruments. The new V4.0 data and relevant explanatory documentation are publicly available from the NASA Goddard Earth Sciences Data and Information Services Center (https://disc.gsfc.nasa.gov/datasets/OMNO2_V003/summary/, last access: 8 November 2020), and we encourage their use over previous versions of OMI NO2 products.

Sign in / Sign up

Export Citation Format

Share Document