Search

Nitrogen dioxide (NO2) is an important trace-gas pollutant and climate agent whose presence also leads to spectral interference in ocean color retrievals. NO2 column densities have been retrieved with satellite UV–Vis spectrometers such as the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI) that typically have spectral resolutions of the order of 0.5 nm or better and spatial footprints as small as 3.6 km × 5.6 km. These NO2 observations are used to estimate emissions, monitor pollution trends, and study effects on human health. Here, we investigate whether it is possible to retrieve NO2 amounts with lower-spectral-resolution hyperspectral imagers such as the Ocean Color Instrument (OCI) that will fly on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite set for launch in early 2024. OCI will have a spectral resolution of 5 nm and a spatial resolution of ∼ 1 km with global coverage in 1–2 d. At this spectral resolution, small-scale spectral structure from NO2 absorption is still present. We use real spectra from the OMI to simulate OCI spectra that are in turn used to estimate NO2 slant column densities (SCDs) with an artificial neural network (NN) trained on target OMI retrievals. While we obtain good results with no noise added to the OCI simulated spectra, we find that the expected instrumental noise substantially degrades the OCI NO2 retrievals. Nevertheless, the NO2 information from OCI may be of value for ocean color retrievals. OCI retrievals can also be temporally averaged over timescales of the order of months to reduce noise and provide higher-spatial-resolution maps that may be useful for downscaling lower-spatial-resolution data provided by instruments such as OMI and TROPOMI; this downscaling could potentially enable higher-resolution emissions estimates and be useful for other applications. In addition, we show that NNs that use coefficients of leading modes of a principal component analysis of radiance spectra as inputs appear to enable noise reduction in NO2 retrievals. Once trained, NNs can also substantially speed up NO2 spectral fitting algorithms as applied to OMI, TROPOMI, and similar instruments that are flying or will soon fly in geostationary orbit.

Despite recent progress, satellite retrievals of anthropogenic SO2 still suffer from relatively low signal-to-noise 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 top-down 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.

Rain events are becoming less frequent, but stronger in many global locations under a changing climate. These intra-seasonal rainfall features have received less attention than changes in mean temperature and total annual rainfall in their influence on the global carbon cycle. Field rainfall manipulation experiments consistently show non-negligible changes to annual photosynthesis in response to rainfall frequency alterations while holding total annual rainfall constant. However, field and modeling experiments show little consensus on the sign and magnitude of change of annual photosynthesis due to changing storm frequency and magnitude. In this study, we ask: based on satellite observations, how is global photosynthesis changing due to shifts in storm frequency and magnitude? What are the soil-plant-atmosphere drivers of the response?Using several global satellite-based photosynthesis proxies, we find that the annual photosynthesis response to storm frequency is as high in magnitude and global spatial extent as its response to total annual rainfall. The satellite-based photosynthesis proxies and field tower sites indicate that years with fewer, stronger storms tend to show decreased photosynthesis in humid ecosystems and increased photosynthesis in drylands. The absolute magnitudes of annual photosynthesis trends show 10-20% per century changes due to rainfall frequency trends over nearly half of vegetated surfaces, which is consistent with the magnitude and extent of total annual rainfall trend effects. The contrasting responses observed in humid locations and drylands are shown to be driven by patterns of plant pulse response, soil texture, and mean atmospheric aridity response to rain frequency. Ultimately, our results indicate that intra-seasonal rainfall variability drives global photosynthesis interannual variability similarly to interannual rainfall variability.

Early versions of satellite nadir-viewing UV SO2 data products assumed snow-free surface conditions. Snow covered terrain, with its high reflectance in the UV, typically enhances satellite sensitivity to boundary layer pollution. However, a significant fraction of high-quality cloud-free measurements over snow is currently excluded from analyses. This leads to increasing the uncertainties of the satellite emissions estimates and introducing potential seasonal biases due to the lack of data in winter months for some high-latitudinal sources. In this study, we investigated how OMI and TROPOMI satellite SO2 measurements over snow-covered surfaces could be used to improve the annual emissions reported in our SO2 emissions catalogue (version 2, Fioletov et al., 2023). Although only 100 out of 759 sources listed in the catalogue have 10% or more of the observations over snow, for 40 high-latitude sources more than 30% of measurements suitable for emission calculations were made over snow-covered surfaces. For example, in the case of Norilsk, the world’s largest SO2 emissions point source, annual emissions estimates in the SO2 catalogue were based only on 3-4 summer months, while addition of data for snow conditions extends that period to 7 months. Emissions in the SO2 catalogue were based on satellite measurements of SO2 slant column densities (SCDs) that were converted to vertical column densities (VCDs) using site-specific clear-sky air mass factors (AMFs), calculated for snow-free conditions. The same approach was applied to measurements with snow on the ground whereby a new set of constant, site-specific, clear-sky with snow AMFs was created, and these were applied to the measured SCDs. Annual emissions were then estimated for each source considering (i) only snow-free days, (ii) only clear-sky with snow days and (iii) a merged dataset (snow and no snow conditions). For individual sources, the difference between emissions estimated for snow and snow-free conditions is within ±20% for three quarters of smelters and oil and gas sources, and with practically no systematic bias. This is excellent consistency given that there is typically a 3-5 times difference between AMFs for snow and snow-free conditions. For coal-fired power plants, however, emissions estimated for snow conditions are on average 25% higher than for no snow conditions; this difference is likely real and due to larger production (consumption of coal) and emissions in wintertime. Reference:Fioletov, V. E., McLinden, C. A., Griffin, D., Abboud, I., Krotkov, N., Leonard, P. J. T., Li, C., Joiner, J., Theys, N., and Carn, S.: Version 2 of the global catalogue of large anthropogenic and volcanic SO2 sources and emissions derived from satellite measurements, Earth Syst. Sci. Data, 15, 75–93, https://doi.org/10.5194/essd-15-75-2023, 2023.

We map high-resolution nitrogen oxides (NOx) emissions in US cities from the retrieved TROPOspheric Monitoring Instrument (TROPOMI) tropospheric nitrogen dioxide (NO2) columns. A new database of gridded emissions at a horizontal spatial resolution of 0.05°×0.05° has been developed using our newly-developed CTM-Independent SATellite-derived Emission estimation Algorithm for Mixed-sources (MISATEAM). We validate the accuracy of MISATEAM using synthetic NO2 observations derived from the NASA-Unified Weather Research and Forecasting (NU-WRF) model at a horizontal spatial resolution of 4 km × 4 km. The validation results demonstrate the excellent agreement between the inferred emissions magnitudes and the NU-WRF given ones with a correlation coefficient (R) of 0.99 and a normalized mean bias (NMB) of -0.08. They also show a consistent spatial pattern with R of 0.88 ± 0.06 for all investigated cities when comparing inferred and given emissions at grid level. The TROPOMI-based database derived in this study includes annual emission maps for 39 US large cities from 2018 to 2021. While there is a good agreement with national emission inventory (NEI) in general, there are noticeable differences in spatial pattern in some cases. The satellite-derived spatiotemporal patterns of NOx emissions complement information difficult to capture in the conventional emission inventories compiled with “bottom-up” methods by suggesting the misallocation of emissions and/or missing sources. We expect to extend the database globally and also include estimates based on NO2 observations from OMI to provide a longer time record. The method could also be applied to data from future geostationary satellites, such as Geostationary Environment Monitoring Spectrometer (GEMS) or the Tropospheric Emissions: Monitoring Pollution (TEMPO) instrument, to provide diurnal variations in NOx emissions.

Raw data files used to generate figures in Global Change Biology 2023 publication Version 2 includes user friendly python scripts that work with the nc file inputs in this repository

Vegetation indices (VIs), which describe remotely sensed vegetation properties such as photosynthetic activity and canopy structure, are widely used to study vegetation dynamics across scales. However, VI-based results can vary between indices, sensors, quality control measures, compositing algorithms, and atmospheric and sun–target–sensor geometry corrections. These variations make it difficult to draw robust conclusions about ecosystem change and highlight the need for consistent VI application and verification. In this Technical Review, we summarize the history and ecological applications of VIs and the linkages and inconsistencies between them. VIs have been used since the early 1970s and have evolved rapidly with the emergence of new satellite sensors with more spectral channels, new scientific demands and advances in spectroscopy. When choosing VIs, the spectral sensitivity and features of VIs and their suitability for target application should be considered. During data analyses, steps must be taken to minimize the impact of artefacts, VI results should be verified with in situ data when possible and conclusions should be based on multiple sets of indicators. Next-generation VIs with higher signal-to-noise ratios and fewer artefacts will be possible with new satellite missions and integration with emerging vegetation metrics such as solar-induced chlorophyll fluorescence, providing opportunities for studying terrestrial ecosystems globally.

Nitrogen dioxide (NO2) is an important trace-gas pollutant and climate agent whose presence also leads to spectral interference in ocean color retrievals. NO2 column densities have been retrieved with satellite UV–Vis spectrometers such as the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI) that typically have spectral resolutions of the order of 0.5 nm or better and spatial footprints as small as 3.6 km × 5.6 km. These NO2 observations are used to estimate emissions, monitor pollution trends, and study effects on human health. Here, we investigate whether it is possible to retrieve NO2 amounts with lower-spectral-resolution hyperspectral imagers such as the Ocean Color Instrument (OCI) that will fly on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite set for launch in early 2024. OCI will have a spectral resolution of 5 nm and a spatial resolution of ∼ 1 km with global coverage in 1–2 d. At this spectral resolution, small-scale spectral structure from NO2 absorption is still present. We use real spectra from the OMI to simulate OCI spectra that are in turn used to estimate NO2 slant column densities (SCDs) with an artificial neural network (NN) trained on target OMI retrievals. While we obtain good results with no noise added to the OCI simulated spectra, we find that the expected instrumental noise substantially degrades the OCI NO2 retrievals. Nevertheless, the NO2 information from OCI may be of value for ocean color retrievals. OCI retrievals can also be temporally averaged over timescales of the order of months to reduce noise and provide higher-spatial-resolution maps that may be useful for downscaling lower-spatial-resolution data provided by instruments such as OMI and TROPOMI; this downscaling could potentially enable higher-resolution emissions estimates and be useful for other applications. In addition, we show that NNs that use coefficients of leading modes of a principal component analysis of radiance spectra as inputs appear to enable noise reduction in NO2 retrievals. Once trained, NNs can also substantially speed up NO2 spectral fitting algorithms as applied to OMI, TROPOMI, and similar instruments that are flying or will soon fly in geostationary orbit.

Sulfur dioxide (SO2) measurements from the Ozone Monitoring Instrument (OMI), Ozone Mapping and Profiler Suite (OMPS), and TROPOspheric Monitoring Instrument (TROPOMI) satellite spectrometers were used to update and extend the previously developed global catalogue of large SO2 emission sources. This version 2 of the global catalogue covers the period of 2005–2021 and includes a total of 759 continuously emitting point sources releasing from about 10 kt yr−1 to more than 4000 kt yr−1 of SO2, that have been identified and grouped by country and primary source origin: volcanoes (106 sources); power plants (477); smelters (74); and sources related to the oil and gas industry (102). There are several major improvements compared to the original catalogue: it combines emissions estimates from three satellite instruments instead of just OMI, uses a new version 2 of the OMI and OMPS SO2 dataset, and updated consistent site-specific air mass factors (AMFs) are used to calculate SO2 vertical column densities (VCDs). The newest TROPOMI SO2 data processed with the Covariance-Based Retrieval Algorithm (COBRA), used in the catalogue, can detect sources with emissions as low as 8 kt yr−1 (in 2018–2021) compared to the 30 kt yr−1 limit for OMI. In general, there is an overall agreement within ±12 % in total emissions estimated from the three satellite instruments for large regions. For individual emission sources, the spread is larger: the annual emissions estimated from OMI and TROPOMI agree within ±13 % in 50 % of cases and within ±28 % in 90 % of cases. The version 2 catalogue emissions were calculated as a weighted average of emission estimates from the three satellite instruments using an inverse-variance weighting method. OMI, OMPS, and TROPOMI data contribute 7 %, 5 %, and 88 % to the average, respectively, for small ( kt yr−1) sources and 33 %, 20 %, and 47 %, respectively, for large (>300 kt yr−1) sources. The catalogue data show an approximate 50 % decline in global SO2 emissions between 2005 and 2021, although emissions were relatively stable during the last 3 years. The version 2 of the global catalogue has been posted at the NASA global SO2 monitoring website (https://doi.org/10.5067/MEASURES/SO2/DATA406, Fioletov et al., 2022).

Satellite-based visible and near-infrared imaging of the Earth's surface is generally not performed in moderate to highly cloudy conditions; images that look visibly cloud covered to the human eye are typically discarded. Here, we expand upon previous work that employed machine learning (ML) to estimate underlying land surface reflectances at red, green, and blue (RGB) wavelengths in cloud contaminated spectra using a low spatial resolution satellite spectrometer. Specifically, we apply the ML methodology to a case study at much higher spatial resolution with the Hyperspectral Imager for the Coastal Ocean (HICO) that flew on the International Space Station (ISS). HICO spatial sampling is of the order of 90 m. The purpose of our case study is to test whether high spatial resolution features can be captured using multi-spectral imaging in lightly cloudy and overcast conditions. We selected one clear and one cloudy image over a portion ofthe panhandle coastline of Florida to demonstrate that land features are partially recoverable in overcast conditions. Many high contrast features are well recovered in the presence of optically thin clouds. However, some of the low contrast features, such as narrow roads, are smeared out in the heavily clouded part of the reconstructed image. This case study demonstrates that our approach may be useful for many science and applications that are being developed for current and upcoming satellite missions including precision agriculture and natural vegetation analysis, water quality assessment as well as disturbance, change, hazard, and disaster detection.

Nitrogen oxides ( NO x ≡ NO + NO 2 ) in the NOx -limited upper troposphere (UT) are long-lived and so have a large influence on the oxidizing capacity of the troposphere and formation of the greenhouse gas ozone. Models misrepresent NOx in the UT, and observations to address deficiencies in models are sparse. Here we obtain a year of near-global seasonal mean mixing ratios of NO2 in the UT (450–180 hPa) at 1 ∘ × 1 ∘ by applying cloud-slicing to partial columns of NO2 from TROPOMI. This follows refinement of the cloud-slicing algorithm with synthetic partial columns from the GEOS-Chem chemical transport model. TROPOMI, prior to cloud-slicing, is corrected for a 13 % underestimate in stratospheric NO2 variance and a 50 % overestimate in free-tropospheric NO2 determined by comparison to Pandora total columns at high-altitude free-tropospheric sites at Mauna Loa, Izana, and Altzomoni and MAX-DOAS and Pandora tropospheric columns at Izana. Two cloud-sliced seasonal mean UT NO2 products for June 2019 to May 2020 are retrieved from corrected TROPOMI total columns using distinct TROPOMI cloud products that assume clouds are reflective boundaries (FRESCO-S) or water droplet layers (ROCINN-CAL). TROPOMI UT NO2 typically ranges from 20–30 pptv over remote oceans to >80 pptv over locations with intense seasonal lightning. Spatial coverage is mostly in the tropics and subtropics with FRESCO-S and extends to the midlatitudes and polar regions with ROCINN-CAL, due to its greater abundance of optically thick clouds and wider cloud-top altitude range. TROPOMI UT NO2 seasonal means are spatially consistent ( R=0.6 –0.8) with an existing coarser spatial resolution (5 ∘ latitude × 8 ∘ longitude) UT NO2 product from the Ozone Monitoring Instrument (OMI). UT NO2 from TROPOMI is 12–26 pptv more than that from OMI due to increase in NO2 with altitude from the OMI pressure ceiling (280 hPa) to that for TROPOMI (180 hPa), but possibly also due to altitude differences in TROPOMI and OMI cloud products and NO2 retrieval algorithms. The TROPOMI UT NO2 product offers potential to evaluate and improve representation of UT NOx in models and supplement aircraft observations that are sporadic and susceptible to large biases in the UT.

Despite recent progress, satellite retrievals of anthropogenic SO2 still suffer from relatively low signal-to-noise 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-drive, 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 analysed SO2 SCDs for polluted pixels each day and 2) the other trained once every month to produce analysed 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 analysed 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 analysed and the original SCDs through a top-down 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 analyse 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.

Retrievals of ocean color from space are important for better understanding of the ocean ecosystem but can be limited under conditions such as clouds, aerosols, and sunglint. Many ocean color algorithms use a few selected spectral bands to perform an atmospheric correction and then derive the upwelling radiance from the ocean. The limitations in the atmospheric correction under certain conditions lead to many gaps in daily spatial coverage of ocean color retrievals. To address these limitations, we introduce a new approach that uses machine learning to estimate ocean color from top of atmosphere radiances or reflectance measurements. In this approach, a principal component analysis is used to decompose the hyperspectral measurements into spectral features that describe the scattering and absorption of the atmosphere and the underlying surface. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from the MODIS atmospheric correction algorithm. This machine learning approach is independent of a priori information and does not rely on any radiative transfer modeling. We apply the approach to two hyperspectral UV/VIS instruments, the ozone monitoring instrument (OMI) and the TROPOspheric Monitoring Instrument (TROPOMI), using measurements from 320–500 nm to show that it can be used to reproduce ocean color properties in less-than-ideal conditions. This machine learning approach complements the current atmospheric correction ocean color retrievals by filling in the gaps resulting from cloud, aerosol, and sunglint contamination. This method can be applied to the future hyperspectral Ocean Color Instrument (OCI), which will be onboard NASA’s Plankton, Aerosol Cloud, ocean Ecosystem (PACE) ocean color satellite set to launch in 2024.

The Ozone Monitoring Instrument (OMI) has been providing global observations of SO 2 pollution since 2004. Here we introduce the new anthropogenic SO 2 vertical column density (VCD) dataset in the version 2 OMI SO 2 product (OMSO2 V2). As with the previous version (OMSO2 V1.3), the new dataset is generated with an algorithm based on principal component analysis of OMI radiances but features several updates. The most important among those is the use of expanded lookup tables and model a priori profiles to estimate SO 2 Jacobians for individual OMI pixels, in order to better characterize pixel-to-pixel variations in SO 2 sensitivity including over snow and ice. Additionally, new data screening and spectral fitting schemes have been implemented to improve the quality of the spectral fit. As compared with the planetary boundary layer SO 2 dataset in OMSO2 V1.3, the new dataset has substantially better data quality, especially over areas that are relatively clean or affected by the South Atlantic Anomaly. The updated retrievals over snow/ice yield more realistic seasonal changes in SO 2 at high latitudes and offer enhanced sensitivity to sources during wintertime. An error analysis has been conducted to assess uncertainties in SO 2 VCDs from both the spectral fit and Jacobian calculations. The uncertainties from spectral fitting are reflected in SO 2 slant column densities (SCDs) and largely depend on the signal-to-noise ratio of the measured radiances, as implied by the generally smaller SCD uncertainties over clouds or for smaller solar zenith angles. The SCD uncertainties for individual pixels are estimated to be ∼ 0.15–0.3 DU (Dobson units) between ∼ 40 ∘ S and ∼ 40 ∘ N and to be ∼ 0.2–0.5 DU at higher latitudes. The uncertainties from the Jacobians are approximately ∼ 50 %–100 % over polluted areas and are primarily attributed to errors in SO 2 a priori profiles and cloud pressures, as well as the lack of explicit treatment for aerosols. Finally, the daily mean and median SCDs over the presumably SO 2 -free equatorial east Pacific have increased by only ∼ 0.0035 DU and ∼ 0.003 DU respectively over the entire 15-year OMI record, while the standard deviation of SCDs has grown by only ∼ 0.02 DU or ∼ 10%. Such remarkable long-term stability makes the new dataset particularly suitable for detecting regional changes in SO 2 pollution.

NASA's Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ, conducted in 2011–2014) campaign in the United States and the joint NASA and National Institute of Environmental Research (NIER) Korea–United States Air Quality Study (KORUS-AQ, conducted in 2016) in South Korea were two field study programs that provided comprehensive, integrated datasets of airborne and surface observations of atmospheric constituents, including nitrogen dioxide (NO2), with the goal of improving the interpretation of spaceborne remote sensing data. Various types of NO2 measurements were made, including in situ concentrations and column amounts of NO2 using ground- and aircraft-based instruments, while NO2 column amounts were being derived from the Ozone Monitoring Instrument (OMI) on the Aura satellite. This study takes advantage of these unique datasets by first evaluating in situ data taken from two different instruments on the same aircraft platform, comparing coincidently sampled profile-integrated columns from aircraft spirals with remotely sensed column observations from ground-based Pandora spectrometers, intercomparing column observations from the ground (Pandora), aircraft (in situ vertical spirals), and space (OMI), and evaluating NO2 simulations from coarse Global Modeling Initiative (GMI) and high-resolution regional models. We then use these data to interpret observed discrepancies due to differences in sampling and deficiencies in the data reduction process. Finally, we assess satellite retrieval sensitivity to observed and modeled a priori NO2 profiles. Contemporaneous measurements from two aircraft instruments that likely sample similar air masses generally agree very well but are also found to differ in integrated columns by up to 31.9 %. These show even larger differences with Pandora, reaching up to 53.9 %, potentially due to a combination of strong gradients in NO2 fields that could be missed by aircraft spirals and errors in the Pandora retrievals. OMI NO2 values are about a factor of 2 lower in these highly polluted environments due in part to inaccurate retrieval assumptions (e.g., a priori profiles) but mostly to OMI's large footprint (>312 km2).

2019 was the hottest and driest year on record for southeast Australia leading to bushfires of unprecedented extent. Ecosystem carbon losses due to drought and fire are believed to have been substantial, but have not been well quantified. Here, we utilize space-based measurements of trace gases (TROPOspheric Monitoring Instrument XCO, Orbiting Carbon Observatory 2 XCO2) and up-scaled GPP (FluxSat GPP) to quantify the carbon cycle anomalies resulting from drought and fire in southeast Australia during the 2019–2020 growing season. We find that biomass burning released 113–236 TgC of CO2 while drought and fire-induced anomalies in net ecosystem exchange reduced growing season carbon uptake by an additional 19–52 TgC of CO2. These carbon losses were concentrated during the spring and early summer, when hot-dry conditions were most severe. A shift to cooler conditions with above average rainfall during February is found to result in a partial recovery and greening in unburned ecosystems, but not in fire-impacted areas. The net 2019–2020 carbon loss substantially exceeded interannual variations in net uptake over 2010–2019 estimated from top-down constraints (~5σ anomaly), and exceeded Australia’s annual fossil fuel emissions (~104 TgC year−1). Top-down constraints show that the regional carbon budget is strongly regulated by climate variability, and suggest cool–wet conditions are required for a rapid recovery of carbon stocks. This has implications for the regional carbon budget as more frequent climate-change-driven heat and drought events may increase the frequency of fire events and the recovery time of ecosystems, threatening the carbon stocks of the region.

Urbanization affects vegetation within city administrative boundary and nearby rural areas. Gross primary production (GPP) of vegetation in global urban areas is one of important metrics for assessing the impacts of urbanization on terrestrial ecosystems. To date, very limited data and information on the spatial-temporal dynamics of GPP in the global urban areas are available. In this study, we reported the spatial distribution and temporal dynamics of annual GPP during 2000–2016 from 8,182 gridcells (0.5° by 0.5° latitude and longitude) that have various proportion of urban areas. Approximately 79.3% of these urban gridcells had increasing trends of annual GPP during 2000-2016. As urban area proportion (%) within individual urban gridcells increased, the means of annual GPP trends also increased. Our results suggested that for those urban gridcells, the negative effect of urban expansion (often measured by impervious surfaces) on GPP was to large degree compensated by increased vegetation within the gridcells, mostly driven by urban management and local climate and environment. Our findings on the continued increases of annual GPP in most of urban gridcells shed new insight on the importance of urban areas on terrestrial carbon cycle and the potential of urban management and local climate and environment on improving vegetation in urban areas.

Satellite-based visible and near-infrared imaging of the Earth’s surface is generally not performed in moderate to highly cloudy conditions; images that look visibly cloud covered to the human eye are typically discarded. Here, we expand upon previous work that employed machine learning (ML) to estimate underlying land surface reflectances at red, green, and blue (RGB) wavelengths in cloud contaminated spectra using a low spatial resolution satellite spectrometer. Specifically, we apply the ML methodology to a case study at much higher spatial resolution with the Hyperspectral Imager for the Coastal Ocean (HICO) that flew on the International Space Station (ISS). HICO spatial sampling is of the order of 90 m. The purpose of our case study is to test whether high spatial resolution features can be captured using hyper-spectral imaging in lightly cloudy and overcast conditions. We selected one clear and one cloudy image over a portion of the panhandle coastline of Florida to demonstrate that land features are partially recoverable in overcast conditions. Many high contrast features are well recovered in the presence of optically thin clouds. However, some of the low contrast features, such as narrow roads, are smeared out in the heavily clouded part of the reconstructed image. This case study demonstrates that our approach may be useful for many science and operational applications that are being developed for current and upcoming satellite missions including precision agriculture and natural vegetation analysis, water quality assessment, as well as disturbance, change, hazard, and disaster detection.

Far‐red solar‐induced chlorophyll fluorescence (SIF) has been retrieved from multiple satellites with nearly continuous global coverage since 1996. Multiple official and research‐grade retrievals provide a means for cross validation across sensors and algorithms, but produces substantial variation across products due to differences in instrument characteristics and retrieval algorithm. The lack of a consistent, calibrated SIF data set hampers scientific interpretation of planetary photosynthesis. NASA's Orbiting Carbon Observatory 2 (OCO‐2) offers small sampling footprints, high data acquisition, and repeating spatially resolved targets at bioclimatically diverse locations, providing a unique benchmark for spaceborne sensors traceable to ground data. We leverage overlap between the longer running Global Ozone Monitoring Instrument version 2 (GOME‐2) SIF time series, and more recent state‐of‐the‐art OCO‐2 and TROPOspheric Monitoring Instrument (TROPOMI) data, in a first attempt to reconcile inconsistencies in the long‐term record. After screening and correcting for key instrument differences (time of day, wavelength, Sun‐sensor geometry, cloud effects, footprint area), we find that Global Ozone Monitoring Instrument version 2 and TROPOspheric Monitoring Instrument perform exceedingly well in capturing spatial, seasonal, and interannual variability across OCO‐2 targets. However, Global Ozone Monitoring Instrument version 2 retrieval methods differ by up to a factor of 2 in signal‐to‐noise and magnitude. Magnitude differences are largely attributed to retrieval window choice, with wider windows producing higher magnitudes. The assumed SIF spectral shape has negligible effect. Substantial research is needed to understand remaining sensitivities to atmospheric absorption and reflectance. We conclude that OCO‐2 and TROPOspheric Monitoring Instrument have opened up the possibility to produce a multidecadal SIF record with well‐characterized uncertainty and error quantification for overlapping instruments, enabling back‐calibration of previous instruments and production of a consistent, research‐grade, harmonized time series.

Dry season length strongly influences tropical rainforest vegetation and is largely determined by precipitation patterns1,2. Over the Amazon, the dry season length has increased since 1979 and severe short-term droughts have occurred3,4. However, similar changes have not been investigated for the world’s second largest rainforest, the Congo Basin, where long-term drying and large-scale declines in forest greenness and canopy water content were reported5. Here we present observational evidence for widespread increases in the boreal summer (June–August) dry season length over the Congo Basin since the 1980s, from both hydrological and ecological perspectives. We analysed both dry season onset and dry season end via multiple independent precipitation and satellite-derived vegetation datasets for the period 1979–2015. The dry season length increased by 6.4–10.4 days per decade in the period 1988–2013, primarily attributed to an earlier dry season onset and a delayed dry season end. The earlier dry season onset was caused by long-term droughts due to decreased rainfall in the pre-dry season (April–June). The delayed dry season end resulted from insufficiently replenished soil moisture, which postpones the start of the next wet season and hinders vegetation regrowth. If such changes continue, the enhanced water stress in a warming climate may affect the carbon cycle and alter the composition and structure of evergreen rainforest1,6. The length of the dry season over tropical forests is a determining factor of ecosystem health and drought risk. Hydroclimate and vegetation data show that dry season length has increased over the Congo rainforest since the 1980s, owing to both an earlier dry season onset and a delayed end.

Indian Ocean Dipole (IOD), a major climate variability in the tropics which drives the abiotic stress associated with heavy rainfalls and severe droughts, is not much understood in terms of its role in the carbon cycle, while El Nino-Southern Oscillation (ENSO)-related terrestrial carbon cycle variation has been intensively studied. Here, we investigate IOD's impact on land photosynthesis over the Indian ocean rim countries during austral spring using satellite-based gross primary productivity (GPP) and Earth System Model simulations produced in the Coupled Model Intercomparison Project Phase 6 (CMIP6). IOD independently affects GPP with significant positive partial correlation coefficients (urn:x-wiley:23284277:media:eft2795:eft2795-math-0001) over most of Africa and India, and negative urn:x-wiley:23284277:media:eft2795:eft2795-math-0002 over southern China, Indo-China peninsula, maritime continent, and Australia, mostly driven by precipitation variations; this obviously differs from the widespread significant negative urn:x-wiley:23284277:media:eft2795:eft2795-math-0003 pattern induced by ENSO. The recent extremely positive IOD in 2019 caused the canonical IOD-affected GPP patterns, however, with its extreme impacts. Furthermore, though large inter-model spreads exist, the CMIP6 multimodel median can basically capture the main characteristics of IOD-affected precipitation and GPP patterns. Importantly, IOD is predicted to occur more frequently in future warming scenarios. Model future projections suggest that it will exert larger impacts on GPP variations over central and eastern Africa, Sumatra, western and southeastern Australia with stronger urn:x-wiley:23284277:media:eft2795:eft2795-math-0004 and enhanced explained variance, but less impacts over southern Africa, east India, Indo-China peninsula, and northeastern Australia. Therefore, besides ENSO, understanding the IOD impacts can provide us new insights into regional and global carbon cycle interannual variability.

The June 21, 2019 eruption of the Raikoke volcano (Kuril Islands, Russia, 48°N, 153°E) produced significant amounts of volcanic aerosols (sulfate and ash) and sulfur dioxide (SO2) gas that penetrated into the lower stratosphere. The dispersed SO2 and sulfate aerosols in the stratosphere were still detectable by multiple satellite sensors for three months after the eruption. For this study of SO2 and aerosol clouds we use data obtained from two of the Ozone Mapping Profiler Suite (OMPS) sensors on the Suomi National Polar-orbiting Partnership (SNPP) satellite: total column SO2 from the Nadir Mapper (NM) and aerosol extinction profiles from the Limb Profiler (LP) as well as other satellite data sets. The LP standard aerosol extinction product at 674 nm has been re-processed with an adjustment correcting for limb viewing geometry effects. It was shown that the amount of SO2 decreases with a characteristic period of 8–18 days and the peak of sulfate aerosol recorded at a wavelength of 674 nm lags the initial peak of SO2 by 1.5 months. Using satellite observations and a trajectory model, we examined the dynamics of unusual atmospheric feature that was observed, a stratospheric coherent circular cloud (CCC) of SO2 and aerosol from July 18 to September 22, 2019.

Across temperate North America, interannual variability (IAV) in gross primary production (GPP) and net ecosystem exchange (NEE) and their relationship with environmental drivers are poorly understood. Here, we examine IAV in GPP and NEE and their relationship to environmental drivers using two state‐of‐the‐science flux products: NEE constrained by surface and space‐based atmospheric CO2 measurements over 2010–2015 and satellite up‐scaled GPP from FluxSat over 2001–2017. We show that the arid western half of temperate North America provides a larger contribution to IAV in GPP (104% of east) and NEE (127% of east) than the eastern half, in spite of smaller magnitude of annual mean GPP and NEE. This occurs because anomalies in western ecosystems are temporally coherent across the growing season leading to an amplification of GPP and NEE. In contrast, IAV in GPP and NEE in eastern ecosystems is dominated by seasonal compensation effects, associated with opposite responses to temperature anomalies in spring and summer. Terrestrial biosphere models in the MsTMIP ensemble generally capture these differences between eastern and western temperate North America, although there is considerable spread between models., Key Points GPP and NEE IAV in western temperate North America is characterized by amplification, with enhance uptake in cooler‐wetter conditionsGPP and NEE IAV in eastern temperate North America is characterized by compensating anomalies between spring and summerThe MsTMIP models generally capture these east‐west differences in NEE and GPP IAV

Photosynthetic capacity is often quantified by the Rubisco-limited photosynthetic capacity (i.e. maximum carboxylation rate, V(cmax)). It is a key plant functional trait that is widely used in Earth System Models for simulation of the global carbon and water cycles. Measuring V(cmax) is time-consuming and laborious; therefore, the spatiotemporal distribution of V(cmax) is still poorly understood due to limited measurements of V(cmax). In this study, we used a data assimilation approach to map the spatial variation of V(cmax) for global terrestrial ecosystems from a 11-year-long satellite-observed solar-induced chlorophyll fluorescence (SIF) record. In this SIF-derived V(cmax) map, the mean V(cmax) value for each plant function type (PFT) is found to be comparable to a widely used N-derived V(cmax) dataset by Kattge et al. (2009). The gradient of V(cmax) along PFTs is clearly revealed even without land cover information as an input. Large seasonal and spatial variations of V(cmax) are found within each PFT, especially for diverse crop rotation systems. The distribution of major crop belts, characterized with high V(cmax) values, is highlighted in this V(cmax) map. Legume plants are characterized with high V(cmax) values. This V(cmax) map also clearly illustrates the emerging soybean revolution in South America where V(cmax) is the highest among the world. The gradient of V(cmax) in Amazon is found to follow the transition of soil types with different soil N and P contents. This study suggests that satellite-observed SIF is powerful in deriving the important plant functional trait, i.e. V(cmax), for global climate change studies.

Nitrogen oxides (NOx ≡ NO + NO2) in the NOx-limited upper troposphere (UT) are long-lived and so have a large influence on the oxidizing capacity of the troposphere and formation of the greenhouse gas ozone. Models misrepresent NOx in the UT and observations to address deficiencies in models are sparse. Here we obtain a year of near-global seasonal mean mixing ratios of NO2 in the UT (450–180 hPa) at 1 ° x 1° by applying cloud-slicing to partial columns of NO2 from TROPOMI. This follows refinement of the cloud-slicing algorithm with synthetic partial columns from the GEOS-Chem chemical transport model. We find that synthetic cloud-sliced UT NO2 are spatially consistent (R = 0.64) with UT NO2 calculated across the same cloud pressure range and scenes as are cloud-sliced (“true” UT NO2), but the cloud-sliced UT NO2 is 11–22 % more than the "true" all-sky seasonal mean. The largest contributors to differences between synthetic cloud-sliced and “true” UT NO2 are target resolution of the cloud-sliced product and uniformity of overlying stratospheric NO2. TROPOMI, prior to cloud-slicing, is corrected for a 13 % underestimate in stratospheric NO2 variance and a 50 % overestimate in free tropospheric NO2 determined by comparison to Pandora total columns at high-altitude sites in Mauna Loa, Izaña and Altzomoni, and MAX-DOAS and Pandora tropospheric columns at Izaña. Two cloud-sliced seasonal mean UT NO2 products for June 2019 to May 2020 are retrieved from corrected TROPOMI total columns using distinct TROPOMI cloud products that assume clouds are reflective boundaries (FRESCO-S) or water droplet layers (ROCINN-CAL). TROPOMI UT NO2 typically ranges from 20-30 pptv over remote oceans to > 80 pptv over locations with intense seasonal lightning. Spatial coverage is mostly in the tropics and subtropics with FRESCO-S and extends to the midlatitudes and polar regions with ROCINN-CAL, due to its greater abundance of optically thick clouds and wider cloud top altitude range. TROPOMI UT NO2 seasonal means are spatially consistent (R = 0.6–0.8) with an existing coarser spatial resolution (5° latitude x 8° longitude) UT NO2 product from the Ozone Monitoring Instrument (OMI). UT NO2 from TROPOMI is 12–26 pptv more than that from OMI due to increase in NO2 with altitude from the OMI pressure ceiling (280 hPa) to that for TROPOMI (180 hPa), but possibly also systematic altitude differences between the TROPOMI and OMI cloud products. The TROPOMI UT NO2 product offers potential to evaluate and improve representation of UT NOx in models and supplement aircraft observations that are sporadic and susceptible to large biases in the UT.

Non-linear effects, such as from saturation, stray light, or obstruction of light, negatively impact satellite measured ultraviolet and visible Earthshine radiance spectra and downstream retrievals of atmospheric and surface properties derived from these spectra. In addition, excessive noise such as from cosmic ray impacts, prevalent within the South Atlantic Anomaly, can also degrade satellite radiance measurements. Saturation specifically pertains to observations of very bright surfaces such as sun glint over water surfaces or thick clouds. Related residual electronic cross-talk or blooming effects may occur in spatial pixels adjacent to a saturated area. Obstruction of light can occur within the zones of solar eclipses as well as from material located outside of the satellite instrument. The latter may also produce unintended scattered light into a satellite instrument. When these effects cannot be corrected to an acceptable level for science quality retrievals, it is desirable to flag the affected pixels. Here, we introduce a new detection method that is based on the correlation, r, between the observed Earthshine radiance and solar irradiance spectra over a 10 nm-spectral range; our Decorrelation Index (DI for brevity) is simply defined as DI=1−r. DI increases with non-linear effects or excessive noise in either radiances (the most likely cause in OMI data) or irradiances. DI is relatively straight-forward to use and interpret and can be computed for different wavelength intervals. We developed a set of DIs for two spectral channels of the Ozone Monitoring Instrument (OMI), a hyperspectral pushbroom imaging spectrometer. For each OMI spatial measurement, we define 14 wavelength-dependent DIs within the OMI visible channel (350–498 nm) and 6 DIs in its ultraviolet 2 (UV2) channel (310–370 nm). As defined, DIs reflect a continuous range of deviations of observed spectra from the reference irradiance spectrum that are complementary to the binary Saturation Possibility Warning (SPW) flags currently provided for each individual spectral/spatial pixels in the OMI radiance data set. Smaller values of DI are also caused by a number of geophysical factors; this allows one to obtain interesting physical results on the global distribution of spectral variations.

TEMPO was selected in 2012 by NASA as the first Earth Venture Instrument, for launch between 2018 and 2021. It will measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO observes from Mexico City, Cuba, and the Bahamas to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution (~2.1 km N/S×4.4 km E/W at 36.5°N, 100°W). TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry, as well as contributing to carbon cycle knowledge. Measurements are made hourly from geostationary (GEO) orbit, to capture the high variability present in the diurnal cycle of emissions and chemistry that are unobservable from current low-Earth orbit (LEO) satellites that measure once per day. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a commercial GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve ozone (O), nitrogen dioxide (NO), sulfur dioxide (SO), formaldehyde (HCO), glyoxal (CHO), bromine monoxide (BrO), IO (iodine monoxide), water vapor, aerosols, cloud parameters, ultraviolet radiation, and foliage properties. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O chemistry cycle. Multi-spectral observations provide sensitivity to O in the lowermost troposphere, substantially reducing uncertainty in air quality predictions. TEMPO quantifies and tracks the evolution of aerosol loading. It provides these near-real-time air quality products that will be made publicly available. TEMPO will launch at a prime time to be the North American component of the global geostationary constellation of pollution monitoring together with the European Sentinel-4 (S4) and Korean Geostationary Environment Monitoring Spectrometer (GEMS) instruments.

Observations of solar‐induced chlorophyll a fluorescence (SIF) from spaceborne spectrometers can advance our understanding of terrestrial and aquatic carbon cycles. Here we present the first global retrievals of SIF at red wavelengths from the TROPOspheric Monitoring Instrument (TROPOMI). Despite the weak signal level, considerable uncertainties, and subtle measurement artifacts, spatial patterns and magnitudes agree with independent data sets. Over land, spatial patterns of our red SIF estimates covary with the far‐red SIF data. Red SIF over the ocean is highly consistent with the normalized fluorescence line height (nFLH) inferred from measurements of the MODerate resolution Imaging Spectroradiometer (MODIS), even when comparing single days and fine spatial scales. Major advantages of our Fraunhofer line‐based SIF retrievals include the capability to sense SIF through optically thin cloud/aerosol layers and an insensitivity to ocean color. This opens up new avenues for studying ocean biogeochemistry from space.

The Ozone Monitoring Instrument (OMI) has been providing global observations of SO2 pollution since 2004. Here we introduce the new anthropogenic SO2 vertical column density (VCD) dataset in the version 2 OMI SO2 product (OMSO2 V2). As with the previous version (OMSO2 V1.3), the new dataset is generated with an algorithm based on principal component analysis of OMI radiances, but features several updates. The most important among those is the use of expanded lookup tables and model a priori profiles to estimate SO2 Jacobians for individual OMI pixels, in order to better characterize pixel-to-pixel variations in SO2 sensitivity, including over snow and ice. Additionally, new data screening and spectral fitting schemes have been implemented to improve the quality of the spectral fit. As compared with the planetary boundary layer SO2 dataset in OMSO2 V1.3, the new dataset has substantially better data quality, especially over areas that are relatively clean or affected by the south Atlantic anomaly. The updated retrievals over snow/ice yield more realistic seasonal changes in SO2 at high latitudes and offer enhanced sensitivity to sources during wintertime. An error analysis has been conducted to assess uncertainties in SO2 VCDs from both the spectral fit and Jacobian calculations. The uncertainties from spectral fitting are reflected in SO2 slant column densities (SCDs) and largely depend on the signal-to-noise ratio of the measured radiances, as implied by the generally smaller SCD uncertainties over clouds or for lower solar zenith angles. The SCD uncertainties for individual pixels are estimated to be ~ 0.15–0.3 DU (Dobson Units) between ~ 40° S and ~ 40° N and to be ~ 0.2–0.5 DU at higher latitudes. The uncertainties from the Jacobians are approximately ~ 50–100 % over polluted areas, and primarily attributed to errors in SO2 a priori profiles and cloud pressures, as well as the lack of explicit treatment for aerosols. Finally, the daily mean and median SCDs over the presumably SO2-free equatorial East Pacific have increased by only ~ 0.0035 DU and ~ 0.003 DU respectively over the entire 15-year OMI record; while the standard deviation of SCDs has grown by only ~ 0.02 DU or ~ 10 %. Such remarkable long-term stability makes the new dataset particularly suitable for detecting regional changes in SO2 pollution.

Nitrogen oxides (NO x = NO + NO2) in the upper troposphere (~10-12 km) are effective at producing ozone in the upper troposphere where ozone is a potent greenhouse gas. Observations of NOx in the upper troposphere are limited in time to a few intensive research aircraft campaigns and in space to commercial aircraft campaigns. There are satellite-derived observations of NO2 in the upper troposphere from the Ozone Monitoring Instrument (OMI), but these are at very coarse resolutions (seasonal, > 2,000 km). The high-resolution Sentinel-5P/TROPOMI instrument offers higher spatial resolution and better cloud-resolving capability than OMI. Here we use synthetic columns of NO2 from the GEOS-Chem chemical transport model to assess feasibility of deriving NO2 in the upper troposphere using partial columns of NO2 above cloudy scenes (the so-called cloud-slicing technique). The model is also used to quantify errors induced by uncertainties in cloud-top height and to determine whether NO2 over cloudy scenes is representative of all-sky conditions (the "truth"). We find that the cloud-slicing approach is spatially consistent (R =0.5) with the "truth", but with a small (10 pptv) bias in background NO2. Cloud-slicing is then applied to TROPOMI total columns of NO2 to derive near-global observations of NO2 in the upper troposphere and assessed against the existing OMI products and aircraft observations from NASA DC8 aircraft campaigns.

Semi-arid ecosystems have been recognized as an important driver of interannual variability (IAV) in the growth rate of atmospheric CO2. However, the importance of these ecosystems for IAV in gross primary productivity (GPP) and net ecosystem exchange (NEE) over North America is not well characterized. In this study, we examine IAV over temperate North America using NEE constrained by surface-based and space-based atmospheric CO2 measurements over 2010–2015 and upscaled GPP from FluxSat over 2001–2017. We show that the arid west of North America provides a larger contribution to IAV in GPP and NEE than the more productive eastern half of North America. This occurs because flux anomalies in western North America are temporally coherent across the growing season leading to an amplification of GPP and NEE for wet years. In contrast, IAV in eastern North America shows seasonal compensation effects, wherein positive anomalies during April–June are compensated for by negative anomalies during July–September.

We present our new and improved version (version 4.0) of the NASA standard nitrogen dioxide (NO2) product from the Ozone Monitoring Instrument (OMI) on the Aura satellite. This version incorporates the most important improvements proposed for regional OMI NO2 products by expert users, and enhances NO2 data quality on a global scale through improvements in the Air Mass Factors (AMFs) in several ways. The algorithm is based on a conceptually new, geometry-dependent Lambertian surface equivalent reflectivity (GLER) operational product. GLER is calculated using the vector radiative transfer model VLIDORT, which uses as input high–resolution bidirectional reflectance distribution function (BRDF) information from NASA’s Aqua MODIS instrument 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 and our corresponding, consistently retrieved effective cloud fraction and O2-O2 optical centroid cloud pressures provide inputs to the new NO2 AMF algorithm, which increases tropospheric NO2 by up to 50% in highly polluted areas; the differences include both cloud and surface BRDF effects as well as biases between the MODIS and OMI-based surface reflectance data sets. We assess the new product using independent observations from ground-based and airborne instruments. The improved NO2 data record could be beneficial for studies related to emissions and trends of nitrogen oxides (NOx) and co-emitted gases.

Across North America, interannual variability (IAV) in gross primary production (GPP) and net ecosystem exchange (NEE), and their relationship with environmental drivers, are poorly understood. Her...