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The MethaneSAT satellite instrument and its aircraft precursor, MethaneAIR, are imaging spectrometers designed to measure methane concentrations with wide spatial coverage, fine spatial resolution, and high precision compared to currently deployed remote sensing instruments. At 12 960 m cruise altitude above ground (13 850 m above sea level), MethaneAIR datasets have a 4.5 km swath gridded to 10 m × 10 m pixels with 17-20 ppb standard deviation on a flat scene. MethaneAIR was deployed in the summer of 2021 in the Permian Basin to test the accuracy of the retrieved methane concentrations and emission rates using the algorithms developed for MethaneSAT. We report here point source emissions obtained during a single-blind volume-controlled release experiment, using two methods. (1) The modified integrated mass enhancement (mIME) method estimates emission rates using the total mass enhancement of methane in an observed plume combined with winds obtained from Weather Research Forecast driven by High-Resolution Rapid Refresh meteorological data in Large Eddy Simulations mode (WRF-LES-HRRR). WRF-LES-HRRR simulates winds in stochastic eddy-scale (100-1000 m) variability, which is particularly important for low-wind conditions and informing the error budget. The mIME can estimate emission rates of plumes of any size that are detectable by MethaneAIR. (2) The divergence integral (DI) method applies Gauss's theorem to estimate the flux divergence fields through a series of closed surfaces enclosing the sources. The set of boxes grows from the upwind side of the plume through the core of each plume and downwind. No selection of inflow concentration, as used in the mIME, is required. The DI approach can efï¬ ciently determine ï¬ uxes from large sources and clusters of sources but cannot resolve small point emissions. These methods account for the effects of eddy-scale variation in different ways: The DI averages across many eddies, whereas the mIME re-samples many eddies from the LES simulation. The DI directly uses HRRR winds, while mIME uses WRF-LES-HRRR wind products. Emissions estimates from both the mIME and DI methods agreed closely with the single-blind volume-controlled experiments (N Combining double low line 21). The York regression between the estimated emissions and the released emissions has a slope of 0.96 [0.84, 1.08], R Combining double low line 0.83 and N Combining double low line 21, with 30 % mean percentage error for the whole dataset, which indicates that MethaneAIR can quantify point sources emitting more than 200 kg h-1 for the mIME and 500 kg h-1 for the DI method. The two methods also agreed on methane emission estimates from various uncontrolled sources in the Permian Basin. The experiment thus demonstrates the powerful potential of the MethaneAIR instrument and suggests that the quantification method should be transferable to MethaneSAT if it meets the design specifications.

Quantifying the global bromine monoxide (BrO) budget is essential to understand ozone chemistry better. In particular, the tropospheric BrO budget has not been well characterized. Here, we retrieve nearly a decade (February 2012–July 2021) of stratospheric and tropospheric BrO vertical columns from the Ozone Mapping and Profiling Suite Nadir Mapper (OMPS-NM) on board the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite. In quantifying tropospheric BrO enhancements from total slant columns, the key aspects involve segregating them from stratospheric enhancements and applying appropriate air mass factors. To address this concern and improve upon the existing methods, our study proposes an approach that applies distinct BrO vertical profiles based on the presence or absence of tropospheric BrO enhancement at each pixel, identifying it dynamically using a satellite-derived stratospheric-ozone–BrO relationship. We demonstrate good agreement for both stratosphere (r = 0.81–0.83) and troposphere (r = 0.50–0.70) by comparing monthly mean BrO vertical columns from OMPS-NM with ground-based observations from three stations (Lauder, Utqiaġvik, and Harestua). Although algorithm performance is primarily assessed at high latitudes, the OMPS-NM BrO retrievals successfully capture tropospheric enhancements not only in polar regions but also in extrapolar areas, such as the Rann of Kutch and the Great Salt Lake. We also estimate random uncertainties in the retrievals pixel by pixel, which can assist in quantitative applications of the OMPS-NM BrO dataset. Our BrO retrieval algorithm is designed for cross-sensor applications and can be adapted to other space-borne ultraviolet spectrometers, contributing to the creation of continuous long-term satellite BrO observation records. [ABSTRACT FROM AUTHOR]

We describe the new and improved version 2 of the ozone profile research product from the Ozone Monitoring Instrument (OMI) on the Aura satellite. One of the major changes is to switch the OMI L1b data from collection 3 to the recent collection 4 as well as the accompanying auxiliary datasets. The algorithm details are updated on radiative transfer model calculation and measurement calibrations, along with the input changes in meteorological data, and with the use of a tropopause-based ozone profile climatology, an improved high-resolution solar reference spectrum, and a recent ozone absorption cross-section dataset. A super Gaussian is applied to better represent OMI slit functions instead of a normal Gaussian. The effect of slit function errors on the spectral residuals is further accounted for as pseudo-absorbers in the iterative fit process. The OMI irradiances are averaged into monthly composites to reduce noise uncertainties in OMI daily measurements and to cancel out the temporal variations of instrument characteristics that are common in both radiance and irradiance measurements, which was previously neglected due to use of climatological composites. The empirical soft calibration spectra are re-derived to be consistent with the updated implementations and derived annually to remove the time-varying systematic biases between measured and simulated radiances. The "common mode" correction spectra are derived from remaining residual spectra after soft calibration as a function of solar zenith angle. The common mode is included as a pseudo-absorber in the iterative fit process, which helps to reduce the discrepancies of ozone retrieval accuracy between lower and higher solar zenith angles and between nadir and off-nadir pixels. Validation with ozonesonde measurements demonstrates the improvements of ozone profile retrievals in the troposphere, especially around the tropopause. The retrieval quality of tropospheric column ozone is improved with respect to the seasonal consistency between winter and summer as well as the long-term consistency before and after the row-anomaly occurrence. [ABSTRACT FROM AUTHOR]

This work presents the development of the MethaneAIR Level0–Level1B processor, which converts raw L0 data to calibrated and georeferenced L1B data. MethaneAIR is the airborne simulator for MethaneSAT, a new satellite under development by MethaneSAT LLC, a subsidiary of the Environmental Defense Fund (EDF). MethaneSAT's goals are to precisely map over 80 % of the production sources of methane from oil and gas fields across the globe to an accuracy of 2–4 ppb on a 2 km 2 scale. Efficient algorithms have been developed to perform dark corrections, estimate the noise, radiometrically calibrate data, and correct stray light. A forward model integrated into the L0–L1B processor is demonstrated to retrieve wavelength shifts during flight accurately. It is also shown to characterize the instrument spectral response function (ISRF) changes occurring at each sampled spatial footprint. We demonstrate fast and accurate orthorectification of MethaneAIR data in a three-step process: (i) initial orthorectification of all observations using aircraft avionics, a simple camera model, and a medium-resolution digital elevation map; (ii) registration of oxygen (O 2) channel grayscale images to reference Multispectral Instrument (MSI) band 11 imagery via Accelerated-KAZE (A-KAZE) feature extraction and linear transformation, with similar co-registration of methane (CH 4) channel grayscale images to the registered O 2 channel images; and finally (iii) optimization of the aircraft position and attitude to the registered imagery and calculation of viewing geometry. This co-registration technique accurately orthorectifies each channel to the referenced MSI imagery. However, in the pixel domain, radiance data for each channel are offset by almost 150–200 across-track pixels (rows) and need to be aligned for the full-physics or proxy retrievals where both channels are simultaneously used. We leveraged our orthorectification tool to identify tie points with similar geographic locations in both CH 4 and O 2 images in order to produce shift parameters in the across-track and along-track dimensions. These algorithms described in this article will be implemented into the MethaneSAT L0–L1B processor. [ABSTRACT FROM AUTHOR]

We use formaldehyde (HCHO) vertical column measurements from the Scanning Imaging Absorption spectrometer for Atmospheric Chartography (SCIAMACHY) and Ozone Monitoring Instrument (OMI), and a nested-grid version of the GEOS-Chem chemistry transport model, to infer an ensemble of top-down isoprene emission estimates from tropical South America during 2006, using different model configurations and assumptions in the HCHO air-mass factor (AMF) calculation. Scenes affected by biomass burning are removed on a daily basis using fire count observations, and we use the local model sensitivity to identify locations where the impact of spatial smearing is small, though this comprises spatial coverage over the region. We find that the use of the HCHO column data more tightly constrains the ensemble isoprene emission range from 27-61 Tg C to 31-38 Tg C for SCIAMACHY, and 45-104 Tg C to 28-38 Tg C for OMI. Median uncertainties of the top-down emissions are about 60-260% for SCIAMACHY, and 10-90% for OMI. We find that the inferred emissions are most sensitive to uncertainties in cloud fraction and cloud top pressure (differences of ±10%), the a priori isoprene emissions (±20%), and the HCHO vertical column retrieval (±30%). Construction of continuous top-down emission maps generally improves GEOS-Chem's simulation of HCHO columns over the region, with respect to both the SCIAMACHY and OMI data. However, if local time top-down emissions are scaled to monthly mean values, the annual emission inferred from SCIAMACHY are nearly twice those from OMI. This difference cannot be explained by the different sampling of the sensors or uncertainties in the AMF calculation. Key Points Satellite HCHO data constrain range of uncertainty in Amazon isoprene emissions Top-down emissions are sensitive to prior isoprene inventory and HCHO retrieval Top-down emissions from SCIAMACHY are about twice those derived from OMI © 2013. American Geophysical Union. All Rights Reserved.

We use a nested-grid version of the GEOS-Chem chemistry transport model, constrained by isoprene emissions from the Model of Emissions of Gases and Aerosols from Nature (MEGAN), and the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) bottom-up inventories, to evaluate the impact that surface isoprene emissions have on formaldehyde (HCHO) air-mass factors (AMFs) and vertical column densities (VCDs) over tropical South America during 2006, as observed by the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) and Ozone Monitoring Instrument (OMI). Although the large-scale seasonal variability of monthly mean HCHO VCDs is typically unaffected by the choice of bottom-up inventory, large relative differences of up to ±45% in the HCHO VCD can occur for individual regions and months, but typically most VCD differences are of order ±20%. These relative changes are comparable to those produced by other sources of uncertainty in the AMF including aerosols and surface albedo, but less than those from clouds. In a sensitivity test, we find that top-down annual isoprene emissions inferred from SCIAMACHY and OMI HCHO vertical columns can vary by as much as ±30-50% for each instrument respectively, depending on the region studied and the a priori isoprene emissions used. Our analysis suggests that the influence of the a priori isoprene emissions on HCHO AMFs and VCDs is therefore non-negligible and must be carefully considered when inferring top-down isoprene emissions estimates over this, or potentially any other, region. © 2012. American Geophysical Union.

We present an evaluation of a nested high-resolution Goddard Earth Observing System (GEOS)-Chem chemistry transport model simulation of tropospheric chemistry over tropical South America. The model has been constrained with two isoprene emission inventories: (1) the canopy-scale Model of Emissions of Gases and Aerosols from Nature (MEGAN) and (2) a leaf-scale algorithm coupled to the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) dynamic vegetation model, and the model has been run using two different chemical mechanisms that contain alternative treatments of isoprene photo-oxidation. Large differences of up to 100 Tg C yr-1 exist between the isoprene emissions predicted by each inventory, with MEGAN emissions generally higher. Based on our simulations we estimate that tropical South America (30-85°W, 14°N-25°S) contributes about 15-35% of total global isoprene emissions. We have quantified the model sensitivity to changes in isoprene emissions, chemistry, boundary layer mixing, and soil NOx emissions using ground-based and airborne observations. We find GEOS-Chem has difficulty reproducing several observed chemical species; typically hydroxyl concentrations are underestimated, whilst mixing ratios of isoprene and its oxidation products are overestimated. The magnitude of model formaldehyde (HCHO) columns are most sensitive to the choice of chemical mechanism and isoprene emission inventory. We find GEOS-Chem exhibits a significant positive bias (10-100%) when compared with HCHO columns from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) and Ozone Monitoring Instrument (OMI) for the study year 2006. Simulations that use the more detailed chemical mechanism and/or lowest isoprene emissions provide the best agreement to the satellite data, since they result in lower-HCHO columns. Copyright © 2011 by the American Geophysical Union.

Space-borne formaldehyde (HCHO) column measurements from the Ozone Monitoring Instrument (OMI), with 13 × 24 km2 nadir footprint and daily global coverage, provide new constraints on the spatial distribution of biogenic isoprene emission from North America. OMI HCHO columns for June-August 2006 are consistent with measurements from the earlier GOME satellite sensor (1996-2001) but OMI is 2-14% lower. The spatial distribution of OMI HCHO columns follows that of isoprene emission; anthropogenic hydrocarbon emissions are undetectable except in Houston. We develop updated relationships between HCHO columns and isoprene emission from a chemical transport model (GEOS-Chem), and use these to infer top-down constraints on isoprene emissions from the OMI data. We compare the OMI-derived emissions to a state-of-science bottom-up isoprene emission inventory (MEGAN) driven by two land cover databases, and use the results to optimize the MEGAN emission factors (EFs) for broadleaf trees (the main isoprene source). The OMI-derived isoprene emissions in North America (June-August 2006) with 1° × 1° resolution are spatially consistent with MEGAN (R2 = 0.48-0.68) but are lower (by 4-25% on average). MEGAN overestimates emissions in the Ozarks and the Upper South. A better fit to OMI (R2 = 0.73) is obtained in MEGAN by using a uniform isoprene EF from broadleaf trees rather than variable EFs. Thus MEGAN may overestimate emissions in areas where it specifies particularly high EFs. Within-canopy isoprene oxidation may also lead to significant differences between the effective isoprene emission to the atmosphere seen by OMI and the actual isoprene emission determined by MEGAN. Copyright 2008 by the American Geophysical Union.

We estimate isoprene emissions over tropical South America during 1997-2001 using column measurements of formaldehyde (HCHO) from the Global Ozone Monitoring Experiment (GOME) satellite instrument, the GEOS-Chem chemistry transport model, and the MEGAN (Model of Emissions of Gases and Aerosols from Nature) bottom-up isoprene inventory. GEOS-Chem is qualitatively consistent with in situ ground-based and aircraft concentration profiles of isoprene and HCHO, and GOME HCHO column data (r = 0.41; bias = +35%), but has less skill in reproducing wet season observations. Observed variability of GOME HCHO columns over South America is determined largely by isoprene and biomass burning. We find that the column contributions from other biogenic volatile organic compounds (VOC) are typically smaller than the column fitting uncertainty. HCHO columns influenced by biomass burning are removed using Along Track Scanning Radiometer (ATSR) firecounts and GOME NO2 columns. We find that South America can be split into eastern and western regions, with fires concentrated over the eastern region. A monthly mean linear transfer function, determined by GEOS-Chem, is used to infer isoprene emissions from observed HCHO columns. The seasonal variation of GOME isoprene emissions over the western region is broadly consistent with MEGAN (r = 0.41; bias = -256%), with largest isoprene emissions during the dry season when the observed variability is consistent with knowledge of temperature dependence. During the wet season other unknown factors play a significant role in determining observed variability. Copyright 2008 by the American Geophysical Union.

We use a continuous 6-year record (1996 - 2001) of GOME satellite measurements of formaldehyde (HCHO) columns over east and south Asia to improve regional emission estimates of reactive nonmethane volatile organic compounds (NMVOCs), including isoprene, alkenes, HCHO, and xylenes. Mean monthly HCHO observations are compared to simulated HCHO columns from the GEOS-Chem chemical transport model using state-of-science, "bottom-up" emission inventories from Streets et al. (2003a) for anthropogenic and biomass burning emissions and Guenther et al. (2006) for biogenic emissions (MEGAN). We find that wintertime GOME observations can diagnose anthropogenic reactive NMVOC emissions from China, leading to an estimate 25% higher than Streets et al. (2003a). We attribute the difference to vehicular emissions. The biomass burning source for east and south Asia is almost 5 times the estimate of Streets et al. (2003a). GOME reveals a large source from agricultural burning in the North China Plain in June missing from current inventories. This source may reflect a recent trend toward in-field burning of crop residues as the need for biofuels diminishes. Biogenic isoprene emission in east and south Asia derived from GOME is 56 ± 30 Tg yr-1, similar to 52 Tg yr-1 from MEGAN. We find, however, that MEGAN underestimates emissions in China and overestimates emissions in the tropics. The higher Chinese biogenic and biomass burning emissions revealed by GOME have important implications for ozone pollution. We find 5 to 20 ppb seasonal increases in surface ozone in GEOS-Chem for central and northern China when using GOME-derived versus bottom-up emissions. Our methodology can be adapted for other regions of the world to provide top-down constraints on NMVOC emissions where multiple emission source types overlap in space and time. Copyright 2007 by the American Geophysical Union.

Reducing methane (CH4) emissions from the oil and gas (O&G) sector is key to mitigating climate change in the near-term. MethaneSAT is an upcoming satellite mission designed to monitor basin-wide O&G emissions globally, providing estimates of emission rates and helping identify the underlying processes leading to methane release to the atmosphere. MethaneSAT data will help advocacy and policy efforts to help track methane reduction commitments and targets made by countries and industry. Here we introduce the CH4 retrieval algorithm for MethaneSAT based on the CO2 proxy method. We apply the algorithm to observations from the maiden campaign of MethaneAIR, an airborne precursor to the satellite with similar instrument specifications. The campaign was conducted during winter 2019 and summer 2021 over three major US oil and gas basins. Analysis of the MethaneAIR data shows that measurement precision is typically better than 2 % for 20 × 20 m2 pixel resolution, with no strong dependence on geophysical variables such as surface reflectance. We show that detector focus drifts over the course of each flight likely due to thermal gradients that develop across the optical bench. The impacts of this drift on retrieved CH4 can mostly be mitigated by including a parameter that squeezes the laboratory tabulated instrument spectral response function in the spectral fit. Validation against coincident EM27/SUN retrievals shows that MethaneAIR values are generally within 1 %. MethaneAIR retrievals were also intercompared with those of TROPOMI; the latitudinal gradients for the two datasets are in good agreement, with a 2.5 ppb mean bias between instruments. We evaluate the accuracy of MethaneAIR estimates of point source emissions using observations made over the Permian O&G basin, based on the integrated mass enhancement approach coupled with a plume-masking algorithm based on total variational denoising. We estimate that the median point source detection threshold is 100–150 kg h−1 at the aircraft's nominal 12 km above-surface observation altitude, based on an ensemble WRF large eddy simulations used to mimic the campaign conditions with the threshold for quantification about 2× the detection threshold. Retrievals from repeated basin surveys indicate the presence of both persistent and intermittent sources, and we highlight an example from each case. For the persistent source we infer emissions from a large O&G processing facility, and estimate a leak rate between 1.6 and 2.1 %, higher than any previously-reported emission from a facility of its size. We also identify a ruptured pipeline that alone would constitute 2 % of estimated basin emissions, two weeks before it was found by its operator, highlighting the importance of regular monitoring from the future satellite mission. The results showcase the capability of MethaneAIR to make highly accurate, precise measurements of methane dry-air mole fractions in the atmosphere, with fine spatial resolution over large swaths on the ground. The results provide confidence that MethaneSAT can make such measurements at unprecedentedly fine scales from space (∼ 130 × 400 m2), thereby delivering quantitative data on basin-wide methane emissions. [ABSTRACT FROM AUTHOR]

Quantifying isoprene emissions using satellite observations of the formaldehyde (HCHO) columns is subject to errors involving the column retrieval and the assumed relationship between HCHO columns and isoprene emissions,taken here from the GEOSCHEM chemical transport model. Here we use a 6-year (1996-2001) HCHO column data set from the Global Ozone Monitoring Experiment (GOME) satellite instrument to (1) quantify these errors, (2) evaluate GOME-derived isoprene emissions with in situ flux measurements and a process-based emission inventory (Model of Emissions of Gases and Aerosols from Nature, MEGAN), and (3) investigate the factors driving the seasonal and interannual variability of North American isoprene emissions. The error in the GOME HCHO column retrieval is estimated to be 40%. We use the Master Chemical Mechanism (MCM) to quantify the time-dependent HCHO production from isoprene, α-and β-pinenes, and methylbutenol and show that only emissions of isoprene are detectable by GOME. The time-dependent HCHO yield from isoprene oxidation calculated by MCM is 20-30% larger than in GEOS-CHEM. GOME-derived isoprene fluxes track the observed seasonal variation of in situ measurements at a Michigan forest site with a -30% bias. The seasonal variation of North American isoprene emissions during 2001 inferred from GOME is similar to MEGAN, with GOME emissions typically 25% higher (lower) at the beginning (end) of the growing season. GOME and MEGAN both show a maximum over the southeastern United States, but they differ in the precise location. The observed interannual variability of this maximum is 20-30%, depending on month. The MEGAN isoprene emission dependence on surface air temperature explains 75% of the month-to-month variability in GOME-derived isoprene emissions over the southeastern United States during May-September 1996-2001. Copyright 2006 by the American Geophysical Union.

Halogen-driven ozone and hydrocarbon losses in springtime Arctic boundary layer are investigated using a regional chemical transport model. Surface observations of ozone at Alert and Barrow and aircraft observations of ozone and hydrocarbons during the Tropospheric Ozone Production about the Spring Equinox (TOPSE) experiment from February to May in 2000 are analyzed. We prescribe halogen radical distributions on the basis of GOME BrO observations. Tropospheric GOME BrO column shows an apparent anticorrelation with surface temperature over high-BrO regions. The enhancements of tropospheric BrO columns coincide with movements of cold polar air masses. While GOME BrO measurements reach the maximum in March, simulated near-surface ozone loss peaks in April because of the increasing daylight hours and hence the time for chemical processing. At its peak, the area of simulated near-surface ozone depletions (O3 < 20 ppbv) covers >50% of the northern high latitudes. Analysis of surface measurements at Alert and Barrow points to the importance of long-range transport of ozone-poor air from high-BrO regions. We find that specifying a BrO layer thickness of 300 m results in the best overall agreement between observed and simulated ozone. The apparent halogen-driven ozone loss up to 1 km was reproduced in the model because of vertical transport of ozone-poor air from low altitudes. When the empirical Cl/Br ratios derived from previous observations are used, the model can reproduce the observed halogen loss of light alkanes and acetylene. The Cl/Br ratios from a recent box model study using an accepted chemical mechanism are, however, much higher than the empirical results. We show that the hydrocarbon loss is not as sensitive to the prescribed thickness of the halogen layer as the ozone loss, therefore representing a more robust measure for evaluating satellite BrO column measurements. Copyright 2006 by the American Geophysical Union.

Formaldehyde (HCHO) columns measured from space by solar UV backscatter allow mapping of reactive hydrocarbon emissions. The principal contributor to these emissions during the growing season is the biogenic hydrocarbon isoprene, which is of great importance for driving regional and global tropospheric chemistry. We present seven years (1995-2001) of HCHO column data for North America from the Global Ozone Monitoring Experiment (GOME), and show that the general seasonal and interannual variability of these data is consistent with knowledge of isoprene emission. There are some significant regional discrepancies with the seasonal patterns predicted from current isoprene emission models, and we suggest that these may reflect flaws in the models. The interannual variability of HCHO columns observed by GOME appears to follow the interannual variability of surface temperature, as expected from current isoprene emission models.

We describe the new and improved version (V2) of the ozone profile research product from the Ozone Monitoring Instrument (OMI) on the Aura satellite. One of the major changes is to switch the OMI L1b data from collection 3 to the recent collection 4 as well as the accompanying auxiliary datasets. The algorithm details are updated on radiative transfer (RT) model calculation and measurement calibrations, along with the input changes of meteorological data, and with the use of a tropopause-based ozone profile climatology, an improved high-resolution solar reference spectrum, and a recent ozone absorption cross- section dataset. A super Gaussian is applied to better represent OMI slit functions, instead of a normal Gaussian. The effect of slit function errors on the spectral residuals is further accounted for as pseudo absorbers in the iterative fit process. The OMI irradiances are averaged into monthly composites to reduce noise uncertainties in OMI daily measurements and to cancel out the common degradation of radiance and irradiance measurements which was previously neglected due to use of climatological composites. The empirical soft calibration spectra are re-derived to be consistent with the updated implementations and derived annually to remove the time-dependent systematic biases between measured and simulated radiances. The "common mode" correction spectra are derived from remaining residual spectra after soft calibration as a function of solar zenith angle. The common mode is included as a pseudo absorber in the iterative fit process, which helps to reduce the discrepancies of ozone retrieval accuracy between lower and higher solar zenith angles and between nadir and off-nadir pixels. Validation with ozonesonde measurements demonstrates the improvements of ozone profile retrievals in the troposphere, especially around the tropopause. The retrieval quality of tropospheric column ozone is improved with respect to the seasonal consistency between winter and summer as well as the long-term consistency before and after the row-anomaly occurrence. [ABSTRACT FROM AUTHOR]

We report the development of an algorithm for the retrieval of Total Column Water Vapor (TCWV) from blue spectra obtained by satellite instruments such as the Ozone Monitoring Instrument (OMI). The algorithm is implemented in an automatic processing pipeline and will be used to generate a long-term data record as part of a MEaSUREs project. TCWV is calculated as the ratio between the Slant Column Density (SCD) and Air Mass Factor (AMF). Both these factors are improved upon previous work by incorporating more constraints or physical processes. For the SCD, we have optimized the retrieval window to 432 - 466 nm, performed a temperature correction, and employed a new stripe-removal post-processing routine. The use of OMI collection 4 spectra reduces the SCD fitting uncertainty by ~9% with respect to collection 3. For the AMF, we perform on-line radiative transfer using VLIDORT. Over land surfaces, we use bi directional reflectances based on MODIS products. Over the oceans, we consider surface roughness and water-leaving radiance, and we find that water-leaving radiance is important for avoiding large TCWV biases over the oceans. Under relatively clear conditions, the MEaSUREs OMI data are well correlated with the reference datasets, having correlation coefficients of r ~0.9. Over the oceans, MEaSUREs-AMSR_E has an overall mean (median) of ~ 1 mm (0.6 mm) with a standard deviation of a ~6.5 mm, though large systematic differences in certain regions are also found. Over land surfaces, MEaSUREs-GPS has an overall mean (median) of -0.7 mm (-0.8 mm) with a ~5.7 mm. Even a small amount of cloud can introduce large bias and scatter; thus, without further correction, strict data filtering criteria are required. However, the MEaSUREs TCWV data can be corrected through machine learning when accurate measurements are abundant. In this regard, under all-sky conditions, the mean bias of MEaSUREs over the oceans reduces from 4.5 mm (without correction) to -0.3 mm (with correction using LightGBM models), and the standard deviation decreases from 11.8 mm to 3.8 mm. We also examined the representation error of the GPS stations using the dense GEONET data. The within-pixel variance of TCWV varies with grid size following a power law dependence. At 0.25°x0.25° resolution, the derived representation error is about 1.4 mm. [ABSTRACT FROM AUTHOR]