Search NASA⌕ Search

Engineering topics

Vitali Fioletov

Publications and source records attributed to Vitali Fioletov.

13 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Comment on "Observation of Large and All-Season Ozone Losses Over the Tropics" [AIP Advances 12, 075066 (2022)]

As discussed above, and supported by extensive literature, there is no robust, credible observational evidence for substantial ozone depletion (i.e., an “ozone hole”) in the tropics. It is well known that climatological total ozone in the tropics is much lower than that in the mid-latitudes (e.g., Sahai et al., 2000; Weber et al., 2022). Satellite and ozonesonde measurements indicate a 3%–5% per decade decline of tropical lower stratosphere ozone prior to 2000, far smaller than that reported by L2022. The stronger decline reported by L2022 is caused by inappropriate use of the gap-filled version of the TOST ozone dataset, which is based on sparse tropical ozone sondes before the 1990s. This misuse of data (TOST and total column ozone) shows the importance of collaboratively engaging with groups who obtain the measurements and create climatological datasets before performing such analyses. Furthermore, the study by L2022 has multiple flaws in its discussion of atmospheric chemistry and dynamics, particularly in the proposed, and previously refuted (see Sec. III A), cosmicray- driven electron induced (CRE) mechanism. Evidence for the occurrence of tropical stratospheric clouds, as needed for the tropical CRE mechanism, is lacking, nor do CFC-12 observations show signatures of depletion in the tropical lower stratosphere, which could be associated with dissociative electron attachment-induced loss of CFC-12 on particulate matter (i.e., the CRE mechanism). Finally, it is worth reiterating that the CRE mechanism is also not responsible for polar LS ozone depletion. Polar ozone loss can be well explained by the gas phase and heterogeneous chemistry, based on extensive observations and modeling studies documented in many thousands of scientific papers on the topic [e.g., see WMO (2018) and references therein], which is not acknowledged by L2022. L2022’s research paper is a severely flawed one. There is no tropical ozone hole, and the CRE mechanism does not explain observed changes in stratospheric ozone either in the polar regions or in the tropics.

Quality assurance↗
NASA NTRS2022

A New Machine Learning Based Analysis for Improving Satellite Retrieved Atmospheric Composition Data: OMI SO2 as an Example

Despite recent progress, satellite retrievals of anthropogenic SO2 still suffer from relatively low signal-tonoise ratios. In this study, we demonstrate a new machine learning data analysis method to improve the quality of satellite SO2 products. In the absence of large ground-truth datasets for SO2, we start from SO2 slant column densities (SCDs) retrieved from the Ozone Monitoring Instrument (OMI) using a data-driven, physically based algorithm and calculate the ratio between the SCD and the root mean square (rms) of the fitting residuals for each pixel. To build the training data, we select presumably clean pixels with small SCD / rms ratios (SRRs) and set their target SCDs to zero. For polluted pixels with relatively large SRRs, we set the target to the original retrieved SCDs. We then train neural networks (NNs) to reproduce the target SCDs using predictors including SRRs for individual pixels, solar zenith, viewing zenith and phase angles, scene reflectivity, and O3 column amounts, as well as the monthly mean SRRs. For data analysis, we employ two NNs: (1) one trained daily to produce analyzed SO2 SCDs for polluted pixels each day and (2) the other trained once every month to produce analyzed SCDs for less polluted pixels for the entire month. Test results for 2005 show that our method can significantly reduce noise and artifacts over background regions. Over polluted areas, the monthly mean NN-analyzed and original SCDs generally agree to within ±15 %, indicating that our method can retain SO2 signals in the original retrievals except for large volcanic eruptions. This is further confirmed by running both the NN-analyzed and original SCDs through a topdown emission algorithm to estimate the annual SO2 emissions for ∼ 500 anthropogenic sources, with the two datasets yielding similar results. We also explore two alternative approaches to the NN-based analysis method. In one, we employ a simple linear interpolation model to analyze the original SCD retrievals. In the other, we develop a PCA–NN algorithm that uses OMI measured radiances, transformed and dimension-reduced with a principal component analysis (PCA) technique, as inputs to NNs for SO2 SCD retrievals. While the linear model and the PCA–NN algorithm can reduce retrieval noise, they both underestimate SO2 over polluted areas. Overall, the results presented here demonstrate that our new data analysis method can significantly improve the quality of existing OMI SO2 retrievals. The method can potentially be adapted for other sensors and/or species and enhance the value of satellite data in air quality research and applications.

Can Li↗
NASA NTRS2022

Quantifying urban, industrial, and background changes in NO2 during the COVID-19 lockdown period based on TROPOMI satellite observations

The COVID-19 lockdown had a large impact on anthropogenic emissions of air pollutants and particularly on nitrogen dioxide (NO 2 ). While the overall NO 2 decline over some large cities is well-established, understanding the details remains a challenge since multiple source categories contribute. In this study, a new method of isolation of three components (background NO 2 , NO 2 from urban sources, and NO 2 from industrial point sources) is applied to estimate the impact of the COVID-19 lockdown on each of them. The approach is based on fitting satellite data by a statistical model with empirical plume dispersion functions driven by a meteorological reanalysis. Population density and surface elevation data as well as coordinates of industrial sources were used in the analysis. The tropospheric NO 2 vertical column density (VCD) values measured by the Tropospheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor over 261 urban areas for the period from 16 March to 15 June 2020 were compared with the average VCD values for the same period in 2018 and 2019. While the background NO 2 component remained almost unchanged, the urban NO 2 component declined by −18 % to −28 % over most regions. India, South America, and a part of Europe (particularly, Italy, France, and Spain) demonstrated a −40 % to −50 % urban emission decline. In contrast, the decline over urban areas in China, where the lockdown was over during the analysed period, was, on average, only -4.4± 8 %. Emissions from large industrial sources in the analysed urban areas varied greatly from region to region from -4.8 ± 6 % for China to -40 ± 10 % for India. Estimated changes in urban emissions are correlated with changes in Google mobility data (the correlation coefficient is 0.62) confirming that changes in traffic were one of the key elements in the decline in urban NO2 emissions. No correlation was found between changes in background NO2 and Google mobility data. On the global scale, the background and urban components were remarkably stable in 2018, 2019, and 2021, with averages of all analysed areas all being within ±2.5 % and suggesting that there were no substantial drifts or shifts in TROPOMI data. The 2020 data are clearly an outlier: in 2020, the mean background component for all analysed areas (without China) was -6.0 % ± 1.2 % and the mean urban component was -26.7 ± 2.6 % or 20σ below the baseline level from the other years.

COVID-19↗
NASA NTRS2021

A Sulfur Dioxide Covariance-Based Retrieval Algorithm (COBRA): Application to TROPOMI Reveals New Emission Sources

Sensitive and accurate detection of sulfur dioxide (SO 2 ) 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 SO 2 columns from satellite observations of ultraviolet back-scattered radiances. The retrieval is based on a measurement error covariance matrix to fully represent the SO 2 -free radiance variability, so that the SO 2 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 SO 2 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 SO 2 accurately is also further supported by comparison with ground-based observations. We illustrate the great sensitivity of the method with a high-resolution global SO 2 map, considering two and a half years of TROPOMI data. In addition to the known sources, we detect many new SO 2 emission hotspots worldwide. For the largest sources, we use the COBRA data to estimate SO 2 emission rates. Results are comparable to other recently published TROPOMI-based SO 2 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 SO 2 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 SO 2 column data set at a global scale will allow identifying and quantifying missing sources, and help improving SO 2 emission inventories.

SO2↗
NASA NTRS2020

Inconsistencies in Sulphur Dioxide Emissions from the Canadian Oil Sands and Potential Implications

Satellite-derived and reported sulfur dioxide (SO2) emissions from the Canadian oil sands are shown to have been consistent up to 2013. Post-2013, these sources of emissions data diverged, with reported emissions dropping by a factor of two, while satellite-derived emissions for the region remained relatively constant, with the discrepancy (satellite-derived emissions minus reported emissions) peaking at 50 kt(SO2) yr−1 around 2016. The 2013–2014 period corresponds to when new flue-gas desulfurization units came on-line. Previous work has established a high level of consistency between at-stack SO2 emissions observations and satellite estimates, and surface monitoring network SO2 concentrations over the same multi-year period show similar trends as the satellite data, with a slight increase in concentrations post-2013. No clear explanation for this discrepancy currently exists. The implications of the discrepancy towards estimated total sulfur deposition to downwind ecosystems were estimated relative to 2013 emissions levels, with the satellite-derived values leaving the area of regional critical load exceedances of aquatic ecosystems largely unchanged from 2013 values, 335 000 km2, and reported values potentially decreasing this area to 185,000 km2.

Sulphur dioxide (SO2) emissions↗
NASA NTRS2018

High‐Resolution Mapping of Nitrogen Dioxide With TROPOMI: First Results and Validation Over the Canadian Oil Sands

TROPOspheric Monitoring Instrument (TROPOMI), on-board the Sentinel-5 Precursersatellite, is a nadir-viewing spectrometer measuring reflected sunlight in the ultraviolet, visible,near-infrared, and shortwave infrared. From these spectra several important air quality and climate-relatedatmospheric constituents are retrieved, including nitrogen dioxide (NO2) at unprecedented spatialresolution from a satellite platform. We present the first retrievals of TROPOMI NO2over the Canadian OilSands, contrasting them with observations from the Ozone Monitoring Instrument satellite instrument,and demonstrate TROPOMI's ability to resolve individual plumes and highlight its potential for derivingemissions from individual mining facilities. Further, the first TROPOMI NO2validation is presented,consisting of aircraft and surface in situ NO2observations, and ground-based remote-sensingmeasurements between March and May 2018. Our comparisons show that the TROPOMI NO2verticalcolumn densities are highly correlated with the aircraft and surface in situ NO2observations, and theground-based remote-sensing measurements with a low bias (15–30 %); this bias can be reduced byimproved air mass factors. Plain Language Summary: Nitrogen dioxide (NO2) is a pollutant that is linked to respiratoryhealth issues and has negative environmental impacts such as soil and water acidification. Near thesurface the most significant sources of NO2are fossil fuel combustion and biomass burning. With a recentlylaunched satellite instrument (TROPOspheric Monitoring Instrument [TROPOMI]), NO2can be measuredwith an unprecedented combination of accuracy, spatial coverage, and resolution. This work presents thefirst TROPOMI NO2measurements near the Canadian Oil Sands and shows that these measurements havean outstanding ability to detect NO2on a very high horizontal resolution that is unprecedented for satelliteNO2observations. Further, these satellite measurements are in excellent agreement with aircraft andground-based measurements.

TROPOspheric Monitoring Instrument (TROPOMI)↗
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 NTRSDate not supplied

Global SO 2 Data Record from OMPS Instruments on the JPSS Constellation

NASA’s Earth Observing System (EOS) SO 2 climate data record (CDR) started in 2004, with the launch of the Aura/Ozone Monitoring Instrument (OMI) and is now being continued with the SNPP/Ozone Mapping and Profiler Suite (OMPS) launched in 2011. Both OMI and SNPP/OMPS SO 2 CDRs are produced with the Goddard principal component analysis (PCA) spectral fitting algorithm. An advantage of the data-driven PCA retrieval technique is that it enables highly consistent retrievals from different instruments, by inherently accounting for various instrumental factors. To further extend the EOS SO 2 CDR, we are implementing the PCA SO 2 retrieval algorithm with the L1B measurements from OMPS instruments flying on the Joint Polar Satellite System (JPSS) constellation. In this presentation, we will provide an update on our progress in NOAA-20 (launched in 2017) and NOAA-21 (launched in 2022) PCA SO2 retrievals. We will focus on our new NOAA-20/OMPS PCA SO 2 EOS continuity product, to be publicly released in fall of 2023. We will present statistical analyses on the quality of NOAA-20 PCA SO 2 product, including retrieval noise, biases over background areas, and long-term stability. We will compare our PCA SO 2 retrievals from NOAA-20 with those from OMI, SNPP/OMPS, and S5P/TROPOMI (TROPOspheric Monitoring Instrument) for anthropogenic sources as well as large volcanic plumes. We will also discuss the application of a new machine learning technique that helps to further reduce the noise of NOAA-20 SO 2 retrievals. In addition, we will present preliminary PCA SO 2 retrievals from NOAA-21/OMPS, including those from direct readout implementation for aviation disaster avoidance. Finally, we will share some first results applying the PCA algorithm to NASA’s geostationary TEMPO (Tropospheric Emissions: Monitoring of Pollution) instrument to obtain hourly, high resolution SO 2 data over North America.

SO2↗
NASA NTRSDate not supplied

Continuing Long-term Global SO 2 Data Record with JPSS OMPS Instruments

NASA’s long-term Earth Observing System (EOS) SO 2 climate data record (CDR) started with Aura/Ozone Monitoring Instrument (OMI, launched in 2004) and is now being continued with the SNPP/Ozone Mapping and Profiler Suite (OMPS, launched in 2011). Both OMI and SNPP/OMPS SO 2 CDRs are produced with the Goddard principal component analysis (PCA) spectral fitting algorithm. By inherently accounting for various instrumental factors, the PCA technique enables highly consistent retrievals between different instruments. In this presentation, we will provide an overview on our effort to further extend the EOS SO 2 CDR, by implementing the PCA SO 2 algorithm with multiple OMPS instruments flying on the Joint Polar Satellite System (JPSS) constellation, including NOAA-20 (launched in 2017) and NOAA-21 (launched in 2022). We will present results analyzing our new NOAA-20/OMPS PCA SO 2 EOS continuity product, to be publicly released in fall of 2023. We will show statistical analyses on the quality of NOAA-20 PCA SO 2 product, such as retrieval noise, biases over background areas, and long-term stability. We will employ a previously established top-down method to estimate SO2 emissions from selected large point sources, using NOAA-20 SO 2 retrievals and assimilated wind fields as input. The SO 2 emission estimates derived from NOAA-20 retrievals will be compared with those from OMI, SNPP/OMPS, and S5P/TROPOMI (TROPOspheric Monitoring Instrument). We will also demonstrate the application of a new machine learning technique that further reduces the noise of NOAA-20 SO 2 retrievals. Finally, we will present preliminary PCA SO 2 retrievals from recently launched satellite sensors, including NOAA-21/OMPS and NASA’s geostationary TEMPO (Tropospheric Emissions: Monitoring of Pollution) instrument.

SO2↗