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The retrieval of cloud parameters from the atmospheric Sentinel missions require Earth reflectance measurements from a set of spectral bands. Frequently, the ground pixel footprints of the involved spectral bands are not fully aligned and therefore, special treatment is required within the operational algorithms. This so-called inter-band spatial mis-registration of passive spectrometers is present when the Earth reflectance measurements in different spectral bands are captured by different spectrometers. The cloud retrieval algorithm requires reflectance measurements in the UV (ultraviolet)/VIS (visible) band, where the first cloud parameter (i.e., radiometric cloud fraction) is retrieved from the OCRA (Optical Cloud Recognition Algorithm) algorithm. In addition, Earth reflectances in the NIR (near-infrared) band are needed for the retrieval of two additional cloud parameters (i.e., cloud height and cloud albedo or cloud-top height and optical thickness) from the ROCINN (Retrieval of Cloud Information using Neural Networks) algorithm. In the former TROPOMI (TROPOspheric Monitoring Instrument)/S5P (Sentinel-5 Precursor) retrieval, a co-registration scheme of the derived cloud parameters from the source band to the target band based on pre-calculated mapping weights from UV/VIS to NIR, and vice versa, is applied. In this paper we present a new scheme for the co-registration of the TROPOMI cloud parameters using collocated VIIRS (Visible Infrared Imaging Radiometer Suite)/SNPP (Suomi National Polar-orbiting Partnership) information. A great benefit of the new co-registration scheme based on the VIIRS data is that it improves the overall quality of the TROPOMI cloud products and, in addition, it allows the re-construction of the cloud parameters on the first UV/VIS detector pixel, which was impossible with the former scheme based on the static mapping tables. The latter practically means that a significant number of valid data points are added to the TROPOMI cloud, total ozone, SO2 and HCHO product since November 26th 2023 (orbit 31705), when the UPAS version 2.6 with the new co-registration scheme was activated operationally. From a comparison analysis between the two techniques, we found that the largest differences mainly appear for inhomogeneous scenes. From a validation exercise of TROPOMI against VIIRS in the across-track flight direction, we found that the old co-registration scheme tends to smooth out cloud structures along the scanline, whereas such structures can be maintained with the new scheme. The need to implement a similar inter-band spatial co-registration scheme is foreseen for the Sentinel-4/MTG-S (Meteosat Third Generation - Sounder) and Sentinel-5/MetOp-SG (Meteorological Operational Satellite - Second Generation) missions. In the case of Sentinel-4 instrument, the external cloud information will originate from collocated FCI (Flexible Combined Imager) data, on board the MTG-I (Meteosat Third Generation - Imager) satellite. [ABSTRACT FROM AUTHOR]

Clouds have a strong impact on satellite measurements of tropospheric trace gases in the ultraviolet, visible, and near-infrared spectral ranges from space. Therefore, trace gas retrievals rely on information on cloud fraction, cloud albedo, and cloud height from cloud products. In this study, the cloud parameters from different cloud retrieval algorithms for Sentinel-5 Precursor (S5P) TROPOMI are compared: the OCRA a priori cloud fraction, the ROCINN CAL cloud fraction and cloud top and base height, the ROCINN CRB cloud fraction and cloud height, the FRESCO cloud fraction, the interpolated FRESCO cloud height from the TROPOMI NO2 product, the cloud fraction from the NO2 fitting window, the O2-O2 cloud fraction and cloud height, the MICRU cloud fraction, and the VIIRS cloud fraction. Two different versions of the TROPOMI cloud products OCRA/ROCINN, FRESCO, and the TROPOMI NO2 product are included in the comparisons (processor version 1.x and 2.x). Overall, the cloud parameters retrieved by the different algorithms show qualitative consistency in version 1.x and good agreement in version 2.x with the exception of the VIIRS cloud fraction, which cannot be directly compared to the other data. Differences between the cloud retrievals are found especially for small cloud heights with a cloud fraction threshold of 0.2, i.e., clouds that are particularly relevant for tropospheric trace gas retrievals. The cloud fractions of the different version 2 cloud products primarily differ over snow- and ice-covered pixels and scenes with sun glint, for which only MICRU includes an explicit treatment. All cloud parameters show some systematic problems related to the across-track dependence, where larger values are found at the edges of the satellite view. The consistency between the cloud parameters from different algorithms depends strongly on how the data is filtered for the comparison, e.g., what quality value is used or whether snow and ice-covered pixels are excluded from the analysis. In summary, clear differences were found between the results of various algorithms, but these differences are reduced in the most recent versions of the cloud data. [ABSTRACT FROM AUTHOR]

Water vapor plays a very important role on the greenhouse effect, rendering it an atmospheric constituent that requires continuous and global monitoring by different types of remote sensing instruments. The TROPOMI/S5P Total Column Water Vapor (TCWV) is a new product retrieved from the blue wavelength band (435 -455nm), using an algorithm that was originally developed for the GOME-2/Metop sensors. For the purposes of this work, 2.5 years of continuous satellite observations at high spatial resolution are validated against co-located (in space and in time) precipitable water Level 2.0 (quality-assured) ground-based measurements from the NASA AERONET (AErosol RObotic NETwork). The network uses CIMEL sunphotometers located at approximately 1300 stations globally to monitor precipitable water among other products. The two datasets, satellite and ground-based, were co-located and the percentage differences of the comparisons were calculated and statistically analyzed. The correlation coefficient of the two products is found to be 0.9 and the mean bias of the relative percentage differences is of the order of only -3 % for the mid-latitudes and the tropics (±60°). The effect of various influence quantities, such as air mass factor, solar zenith angle, clouds and albedo are also presented and discussed. It was found that the cloud properties affect the validation results, leading the TCWV to a dry bias of -19 % for low cloudiness (CTP > 800hPa). The cloud albedo introduces a wet bias of 10 % when the cloud albedo is below 0.3 and a dry bias up to -20 % when the clouds are more reflective. Overall, the TROPOMI/S5P TCWV product, on a global scale and for moderate albedo and cloudiness, agrees well at -4.0 ± 4.3 % with the ground-truth. [ABSTRACT FROM AUTHOR]

We present a new tropospheric ozone data set based on TROMOMI/Sentinel-5 Precursor (S5P) total ozone mea- surements combined stratospheric ozone data from the Belgian Assimilation System for Chemical ObsErvations (BASCOE). BASCOE is constrained by assimilating ozone observations from the microwave limb sounder (MLS). The tropospheric ozone algorithm is similar to the well established OMI-MLS or OMPS-MERRA-2 retrieval. Compared to this we gain spatial resolu- tion when applying the algorithm to TROPOMI data (5.5 x 3.5 km²) and extend these data record into the future. Compared to the OMPS-MERRA-2 data a mean positive bias of ≈ 3 DU is found. A small negative bias of about 2 DU is observed in the tropics relative to the operational TROPOMI tropospheric (S5P_O3_TCL) data based on the CCD algorithm. The new tropospheric ozone data (S5P-BASCOE) is compared to a set of globally distributed ozone sondes data integrated up to the tropopause level. For the comparison both the mean of the satellite observations around the sounding station and the closest column data are used. Depending on the latitude the S5P-BASCOE deviate from the sondes and between 0 and 5 DU, indicating a good agreement. However, some exceptional larger positive deviation up to ±10 DU are found. The monthly mean tropospheric column as well as time series for selected places showed the expected spatial and temporal pattern. [ABSTRACT FROM AUTHOR]

Lightning is one of the major sources of nitrogen oxides (NOx) in the atmosphere, contributing to the tropospheric concentration of ozone and to the oxidising capacity of the atmosphere. Lightning produces between 2-8 Tg N per year globally and on average about 250 ± 150 mol NOx per flash. In this work, we estimate the moles of NOx produced per flash (LNOx production efficiency) in the Pyrenees (Spain, France and Andorra) and in the Ebro Valley (Spain) by using nitrogen dioxide (NO2) and cloud properties from the TROPOspheric Monitoring Instrument (TROPOMI) and lightning data from the Earth Networks Global Lightning Network (ENGLN) and from the EUropean Co-operation for LIghtning Detection (EUCLID). The Pyrenees is one of the areas in Europe with the highest lightning frequency and, due to its remoteness as well as experiencing very low NOx background, enables us to better distinguish the LNOx signal produced by recent lightning in TROPOMI NO2 measurements. We compare the LNOx production efficiency estimates for 8 convective systems in 2018 using two different sets of TROPOMI research products, provided by the Royal Netherlands Meteorological Institute (KNMI) and the Deutsches Zentrum für Luft- und Raumfahrt (DLR), respectively. According to our results, the mean LNOx production efficiency in the Pyrenees and in the Ebro Valley, using a three-hour chemical lifetime, ranges between 14 and 103 mol NOx per flash from the 8 systems. The mean LNOx production efficiency estimates obtained using both TROPOMI products and ENGLN lightning data differ by ~23 %, while it differs by ~35 % when using EUCLID lightning data. The main sources of uncertainty when using ENGLN lightning data are the estimation of background NOx that is not produced by lightning and the time window before the TROPOMI overpass that is used to count the total number of lightning flashes contributing to fresh-produced LNOx. The main source of uncertainty when using EUCLID lightning data is the uncertainty in the detection efficiency of EUCLID. [ABSTRACT FROM AUTHOR]

We present the first global glyoxal (CHOCHO) tropospheric column product derived from the TROPOspheric Monitoring Instrument (TROPOMI) on board of the Sentinel-5 Precursor satellite. Atmospheric glyoxal results from the oxidation of other non-methane volatile organic compounds (NMVOCs) and from direct emissions caused by combustion processes. Therefore, this product is a useful indicator of VOC emissions. It is generated with an improved version of the BIRA-IASB scientific retrieval algorithm relying on the Differential Optical Absorption Spectroscopy (DOAS) approach. Among the algorithmic updates, the DOAS fit now includes corrections to mitigate the impact of spectral misfits caused by scene brightness inhomogeneity and strong NO2 absorption. The product comes along with a full error characterization, which allows providing random and systematic error estimates for every observation. Systematic errors are typically in the range of 1-3 × 1014 molec/cm2 (~30-70 % in emission regimes). Random errors are larger (> 6 × 1014 molec/cm2) but can be reduced by averaging observations in space and/or time. Benefiting from a high signal-to-noise ratio and a large number of small-size observations, TROPOMI provides glyoxal tropospheric column fields with an unprecedented level of details. Using the same retrieval algorithmic baseline, glyoxal column data sets are also generated from the Ozone Monitoring Instrument (OMI) on Aura and from the Global Ozone Monitoring Experiment-2 (GOME-2) on board of Metop-A and Metop-B. Those four data sets are intercompared over large-scale regions worldwide and show a high level of consistency. The satellite glyoxal columns are also compared to glyoxal columns retrieved from ground-based Multi-Axis (MAX-) DOAS instruments at nine stations in Asia and Europe. In general, the satellite and MAX-DOAS instruments provide consistent glyoxal columns both in terms of absolute values and variability. Correlation coefficients between TROPOMI and MAX-DOAS glyoxal columns range between 0.61 and 0.87. The correlation is only poorer at one mid-latitude station, where satellite data appears low biased during wintertime. The mean absolute glyoxal columns from satellite and MAX-DOAS generally agree well for low/moderate columns with differences less than 1 × 1014 molec/cm2. A larger bias is identified at two sites where the MAX-DOAS columns are very large. Despite this systematic bias, the consistency of the satellite and MAX-DOAS glyoxal seasonal variability is excellent. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the method for validation of random uncertainties in the remote sensing measurements based on evaluation of the structure function, i.e., root-mean-square differences as a function of increasing spatio-temporal separation of the measurements. The limit at the zero mismatch provides the experimental estimate of random noise in the data. At the same time, this method allows probing the natural variability of the measured parameter. As an illustration, we applied this method to the clear-sky total ozone measurements by TROPOMI/Sentinel-5P. We found that the random uncertainties reported by the TROPOMI inversion algorithm, which are in the range 1-2 DU, agree well with the experimental uncertainty estimated by the structure function. Our analysis of the structure function has shown the expected results on total ozone variability: it is significantly smaller in the tropics compared to mid-latitudes. At mid-latitudes, ozone variability is much larger in winter than in summer. The ozone structure function is anisotropic (being larger in latitudinal direction) at horizontal scales larger than 10-20 km. The structure function rapidly grows with the separation distance. At mid-latitudes in winter, the ozone values can differ by 5 % at separations 300-500 km. The discussed method is a powerful tool in experimental estimates of the random noise in data and studies of natural variability and it can be used in various applications. [ABSTRACT FROM AUTHOR]

Information about the height and loading of sulfur dioxide (SO2) plumes from volcanic eruptions is crucial for aviation safety and for assessing the effect of sulfate aerosols on climate. While SO2 layer height has been successfully retrieved from backscattered Earthshine ultraviolet (UV) radiances measured by the Ozone Monitoring Instrument (OMI), previously demonstrated techniques are computationally intensive and not suitable for near real-time applications. In this study, we introduce a new OMI algorithm for fast retrievals of effective volcanic SO2 layer height. We apply the Full Physics Inverse Learning Machine (FP_ILM) algorithm to OMI radiances in the spectral range of 310-330 nm. This approach consists of a training phase that utilizes extensive radiative transfer calculations to generate a large dataset of synthetic radiance spectra for geophysical parameters representing the OMI measurement conditions. The principal components of the spectra from this dataset in addition to a few geophysical parameters are used to train a neural network to solve the inverse problem and predict the SO2 layer height. This is followed by applying the trained inverse model to real OMI measurements to retrieve the effective SO2 plume heights. The algorithm has been tested on several major eruptions during the OMI data record. The results for the 2008 Kasatochi, 2014 Kelud, 2015 Calbuco, and 2019 Raikoke eruption cases are presented here and compared with volcanic plume heights estimated with other satellite sensors. For the most part, OMI-retrieved effective SO2 heights agree well with the lidar measurements of aerosol layer height from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and thermal infrared retrievals of SO2 heights from the infrared atmospheric sounding interferometer (IASI). The errors in OMI retrieved SO2 heights are estimated to be 1-1.5 km for plumes with relatively large SO2 signals (> 40 DU). The algorithm is very fast and retrieves plume height in less than 10 min for an entire OMI orbit. This approach offers a promising prospect of using physics-based machine learning applications to other instruments. [ABSTRACT FROM AUTHOR]

Ozone in the troposphere affects humans and ecosystems as a pollutant and as a greenhouse gas. Observing, understanding and modelling this dual role, as well as monitoring effects of international regulations on air quality and climate change, however, challenge measurement systems to operate at opposite ends of the spatio-temporal scale ladder. On board of the ESA/EU Copernicus Sentinel-5 Precursor (S5P) satellite launched in October 2017, TROPOspheric Monitoring Instrument (TROPOMI) aspires to take the next leap forward by measuring ozone and its precursors at unprecedented horizontal resolution until at least the mid 2020s. In this work, we assess the quality of TROPOMI's first release (V01.01.05-08) of tropical tropospheric ozone column data (TrOC). Derived with the Convective Cloud Differential (CCD) method, TROPOMI daily TrOC data represent the three-day moving mean ozone column between surface and 270 hpa under clear sky conditions gridded at 0.5° latitude by 1° longitude resolution. Comparisons to almost two years of co- located SHADOZ ozonesonde and satellite data (Aura OMI and MetOp-B GOME-2) conclude to TROPOMI biases between -0.1 and +2.3 DU (< +13 %) when averaged over the tropical belt. The field of the bias is essentially uniform in space (deviations < 1 DU) and stable in time at the 1.5-2.5 DU level. However, the record is still fairly short and continued monitoring will be key to clarify whether observed patterns and stability persist, alter behaviour or disappear. Biases are partially due to TROPOMI and the reference data records themselves, but they can also be linked to systematic effects of the non perfect co-locations. Random uncertainty due to co-location mismatch contributes considerably to the 2.6-4.6 DU (~14-23 %) statistical dispersion observed in the difference time series. We circumvent part of this problem by employing the triple co-location analysis technique and infer that TROPOMI single-measurement precision is better than 1.5-2.5 DU (~8-13 %), in line with uncertainty estimates reported in the data files. Hence, the TROPOMI precision is judged to be 20-25 % better than for its predecessors OMI and GOME- 2B, while sampling at four times better spatial resolution and almost twice better temporal resolution. Using TROPOMI tropospheric ozone columns at maximal resolution nevertheless requires consideration of correlated errors at small scales of up to 5 DU due to the inevitable interplay of satellite orbit and cloud coverage. Two particular types of sampling error are investigated and we suggest how these can be identified or remedied. Our study confirms that major known geophysical patterns and signals of the tropical tropospheric ozone field are imprinted in TROPOMI's two- year data record. These include the permanent zonal wave-one pattern, the pervasive annual and semiannual cycles, the high levels of ozone due to biomass burning around the Atlantic basin, and enhanced convective activity cycles associated with the Madden- Julian Oscillation over the Indo-Pacific warm pool. A quasi-periodic signal of 1-2 weeks and 3-5 DU amplitude in TrOC time series, especially at low latitudes, is reminiscent of Kelvin wave activity. TROPOMI's combination of higher precision and higher resolution reveal details of these patterns and the processes involved, at considerably smaller spatial and temporal scales and with more complete coverage than contemporary satellite sounders. If the accuracy of future TROPOMI data proves to remain stable with time, these hold great potential to be included in Climate Data Records, as well as serve as a travelling standard to interconnect the upcoming constellation of air quality satellites in geostationary and low Earth orbits. [ABSTRACT FROM AUTHOR]

Accurate knowledge of cloud properties is essential to the measurement of atmospheric composition from space. In this work we assess the quality of the cloud data derived from Copernicus Sentinel-5 Precursor (S5P) TROPOMI radiance measurements: cloud top height and cloud optical thickness (retrieved with the S5P OCRA/ROCINN_CAL algorithm), cloud height (S5P OCRA/ROCINN_CRB and S5P FRESCO) and radiometric cloud fraction (all three algorithms). The analysis combines: (i) the examination of cloud maps for artificial geographical patterns, (ii) the comparison to other satellite cloud data (MODIS, NPP-VIIRS and OMI O2-O2), and (iii) ground-based validation with respect to correlative observations (2018-04-30 to 2020-02-27) from the CLOUDNET network of ceilometers, lidars and radars. Peculiar geographical patterns were identified, and will be mitigated in future releases of the cloud data products. Zonal mean latitudinal variation of S5P cloud properties are similar to that of other satellite data. S5P OCRA/ROCINN_CAL agrees well with NPP VIIRS cloud top height and cloud optical thickness, and with CLOUDNET cloud top height, especially for the low (mostly liquid) clouds. For the high clouds, S5P OCRA/ROCINN_CAL cloud top height is below the cloud top height of VIIRS and of CLOUDNET, while its cloud optical thickness is higher than that of VIIRS. S5P OCRA/ROCINN_CRB and S5P FRESCO cloud height are well below the CLOUDNET cloud mean height for the low clouds, but match on an average better with the CLOUDNET cloud mean height for the higher clouds. As opposed to S5P OCRA/ROCINN_CRB and S5P FRESCO, S5P OCRA/ROCINN_CAL is well able to match the lowest CTH mode of the CLOUDNET observations. [ABSTRACT FROM AUTHOR]

TROPOMI near UV radiances are used as input to an inversion algorithm that simultaneously retrieves aerosol optical depth (AOD) and single scattering albedo (SSA) as well as the improved qualitative UV Aerosol Index (UVAI) that accurately accounts for the scattering effects of water clouds. We first present the TROPOMI aerosol algorithm (TropOMAER), an adaptation of the currently operational OMI near UV (OMAERUV & OMACA) inversion schemes, that take advantage of TROPOMI's unprecedented fine near UV spatial resolution and the availability of ancillary aerosol-related information to derive aerosol loading in cloud-free and above-cloud aerosols scenes. An evaluation analysis of TROPOMI retrieved AOD/SSA products using sun-photometer observations shows improved levels of agreement with respect to those obtained with the heritage coarser resolution sensor. We then use TropOMAER aerosol retrieval results to discuss the the US Northwest and British Columbia 2018 wildfire season, the 2019 biomass burning season in the Amazon Basin, and the unprecedented January 2020 fire season in Australia that injected huge amounts of carbonaceous aerosols in the stratosphere. [ABSTRACT FROM AUTHOR]

We present a new total column water vapor (TCWV) retrieval algorithm in the visible blue spectral band for the Global Ozone Monitoring Experience 2 (GOME-2) instruments on board the EUMETSAT MetOp satellites. The blue band algorithm allows retrieval of water vapor from sensors which do not cover longer wavelengths, such as Ozone Monitoring Instrument (OMI) and the Copernicus atmospheric composition missions Sentinel-5 Precursor (S5P), Sentinel-4 (S4) and Sentinel-5 (S5). The blue band algorithm uses the differential optical absorption spectroscopic (DOAS) technique to retrieve water vapor slant columns. The measured water vapor slant columns are converted to vertical column using air mass factors (AMFs). The new algorithm has an iterative optimization module to dynamically find the optimal a priori water vapor profile. This makes it better suited for climate studies than usual satellite retrievals with static a priori or vertical profile information from chemistry transport model (CTM). The dynamic a priori algorithm makes use of the fact that the vertical distribution of water vapor is strongly correlated to the total column. The new algorithm is applied to GOME-2A and GOME-2B observations to retrieve TCWV. The data set is validated by comparing to the operational product retrieved in the red spectral band, sun-photometer and radiosonde measurements. Water vapor columns retrieved in the blue band are in good agreement with the other data sets, indicating that the new algorithm derives precise results, and can be used for the current and forthcoming Copernicus Sentinel missions S4 and S5. [ABSTRACT FROM AUTHOR]

In this study we compare the satellite-based GOME-type Total Ozone Essential Climate Variable (GTO-ECV) record, generated as part of the European Space Agency's Climate Change Initiative (ESA-CCI) ozone project, with the Adjusted total ozone product from the Modern Era Retrospective Analysis for Research and Applications version 2 (Adjusted-MERRA) reanalysis, produced at the National Aeronautics and Space Administration (NASA) Global Modeling and Assimilation Office (GMAO). Total ozone columns and associated standard deviations show a very good agreement in terms of both spatial and temporal patterns during their 23-year overlap period from July 1995 to December 2018. The mean difference between Adjusted-MERRA and GTO-ECV 5° × 5° monthly mean total ozone columns is −0.9 ± 1.5 %. A small discontinuity in the deviations is detected in October 2004 when data from the Ozone Monitoring Instrument (OMI) was ingested in the GTO-ECV data record. This induces a small overall negative drift in the differences for almost all latitude bands, which, however, does not exceed 1 % decade−1. The mean difference for the period prior to October 2004 is −0.5 ± 1.7 %, whereas the difference is −1.1 ± 1.2 % for the second period. The variability in the differences is considerably reduced in the later period due to significant increase in data coverage and sampling. In the tropical region the differences indicate a slight zonal variability with negative deviations over the Atlantic, Africa, and the Indian Ocean, and positive deviations over the Pacific. Ozone anomalies and the distribution of their statistical moments indicate a very high correlation among both data records as to the temporal and spatial structures. Furthermore, we evaluate the consistency of the datasets by means of an empirical orthogonal function (EOF) analysis. The interannual variability is assessed in the tropics, and both GTO-ECV and Adjusted-MERRA exhibit a remarkable agreement with respect to the derived patterns. The first four EOFs can be attributed to different modes of interannual climate variability, and correlations with the Quasi-Biennial Oscillation (QBO), the El Niño Southern Oscillation (ENSO) signal, and the solar cycle were found. [ABSTRACT FROM AUTHOR]

The retrieval of trace gas, cloud and aerosol measurements from ultraviolet, visible and near-infrared (UVN) sensors requires precise information on the surface properties that are traditionally obtained from Lambertian equivalent reflectivity (LER) climatologies. The main drawbacks of using such LER climatologies for new satellite missions are (a) climatologies are typically based on previous missions with a significant lower spatial resolution, (b) they usually do not fully take into account the satellite viewing dependencies characterized by the bidirectional reflectance distribution function (BRDF) effects, and (c) climatologies may differ considerably from the actual surface conditions especially under snow/ice situations. In this paper we present a novel algorithm for the retrieval of geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) from UVN sensors based on the full-physics inverse learning machine (FP_ILM) retrieval. The radiances are simulated using a radiative transfer model that takes into account the satellite viewing geometry and the inverse problem is solved using machine learning techniques to obtain the GE_LER from satellite measurements. The GE_LER retrieval is optimized for the trace gas retrievals using the DOAS algorithm and the large amount of data of the new atmospheric Sentinel satellite missions. The GE_LER can either be used directly for the computation of AMFs using the effective scene approximation or a global gapless geometry-dependent LER (G3_LER) daily map can be easily created from the GE_LER under clear-sky conditions for the computation of AMFs using the independent pixel approximation. The FP_ILM GE_LER algorithm is applied to measurements of TROPOMI launched in October 2017 on board the EU/ESA Sentinel-5 Precursor (S5P) mission. The TROPOMI GE_LER/G3_LER results are compared with climatological OMI LER data and the advantages of using GE_LER/G3_LER are demonstrated for the retrieval of total ozone from TROPOMI. [ABSTRACT FROM AUTHOR]

In October 2017, the Sentinel-5 Precursor (S5p) mission was launched, carrying the TROPOspheric Monitoring Instrument, TROPOMI, which provides a daily global coverage at a spatial resolution as high as 7km×3.5km and is expected to extend the European atmospheric composition record initiated with GOME/ERS-2 in 1995, bringing up significant new components to the scientific knowledge of atmospheric processes. Due to the ongoing need to understand and monitor the recovery of the ozone layer, as well as the evolution of tropospheric pollution, total ozone remains one of the leading species of interest during this mission. In this work the TROPOMI Near-Real Time, NRTI, and Offline, OFFL, total ozone column (TOC) products are presented and compared to daily ground-based quality-assured Brewer and Dobson TOC measurements deposited in the World Ozone and Ultraviolet Radiation Data Centre (WOUDC). Additional comparisons to individual Brewer measurements from the Canadian Brewer Network and the European Brewer Network (Eubrewnet) are performed. Furthermore, twilight zenith-sky measurements obtained with ZSL-DOAS (Zenith Scattered Light Differential Optical Absorption Spectroscopy) instruments, that form part of the SAOZ network (Système d'Analyse par Observation Zénitale), are used for the validation. The quality of the TROPOMI TOC data is evaluated in terms of the influence of location, solar zenith and viewing angles, season, effective temperature, surface albedo and clouds. For this purpose, globally distributed ground-based measurements have been utilized as the background truth. The overall statistical analysis of the global comparison shows that the mean bias and the mean standard deviation of the percentage difference between TROPOMI and ground-based TOC is within 0-1.5% and 2.5-4.5%, respectively. The mean bias that results from the comparisons is well within the S5p product requirements, while the mean standard deviation is very close to those limits, especially considering that the statistics shown here originate both from the satellite and the ground-based measurements. Additionally, the TROPOMI OFFL and NRTI products are evaluated against already known space-borne sensors, namely, the Ozone Mapping Profiler Suite on board the Suomi National Polar-orbiting Partnership (OMPS/Suomi-NPP), NASA v2 TOCs, and the Global Ozone Monitoring Experiment-2 (GOME-2) on board the MetopΑ (GOME-2/MetopΑ) and MetopB (GOME-2/MetopB) satellites. This analysis shows a very good agreement for both TROPOMI products to well established instruments, with the absolute differences in mean bias and mean standard deviation being below 0.7% and 1%, respectively. These results assure the scientific community of the good quality of the TROPOMI TOC products during its first year of operation and enhance the already high expectations that S5p TROPOMI will play a very significant role in the continuity of the ozone monitoring from space. [ABSTRACT FROM AUTHOR]

Precise knowledge of the location and height of the volcanic SO2 plumes is essential for accurate determination of SO2 emitted by volcanic eruptions for aviation control applications, but so far very time-consuming to retrieve from UV satellite data. The SO2 height is furthermore one of the most critical parameters that determine the impact on the climate. We have developed an extremely fast yet accurate SO2 layer height retrieval algorithm using the Full-Physics Inverse Learning Machine (FP&lowbar;ILM) algorithm, which, for the first time, is applied to TROPOMI aboard Sentinel-5 Precursor. In this work we demonstrate the ability of the FP&lowbar;ILM algorithm to retrieve layer heights in near-real time applications with an accuracy of better than 2 km for SO2 total columns larger than 20 DU and show SO2 layer height results for selected volcanic eruptions. [ABSTRACT FROM AUTHOR]

In this work we present evidence that quasi cyclical perturbations in total ozone (Quasi Biennial Oscillation (QBO), El Nino Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO)) can be used as independent proxies in validating Global Ozone Monitoring Experiment-2 aboard MetopA (GOME-2A) satellite total ozone data, using ground-based measurements, other satellite data and chemical transport model calculations. The analysis is performed in the frame of the validation strategy on longer time scales within the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), Satellite Application Facility on Atmospheric Composition Monitoring (AC SAF) project, and covers the period 2007-2016. In general, we find that GOME-2A total ozone data depict the QBO/ENSO/NAO natural fluctuations in concurrence with co-located Solar Backscatter Ultraviolet Radiometer (SBUV), GOME-type Total Ozone Essential Climate Variable (GTO-ECV) and ground-based (GB) observations. Total ozone from GOME-2A is well correlated with the QBO (highest correlation in the tropics of +0.8) in agreement with SBUV, GTO-ECV and GB data which also give the highest correlation in the tropics. The differences between deseazonalised GOME-2A and GB total ozone in the tropics are within ±1%. These differences were tested further as to their correlations with the QBO. The differences had practically no QBO signal, providing an independent test of the stability of the long-term variability of the satellite data. Correlations between GOME-2A total ozone and the Southern Oscillation Index (SOI) were studied over the tropical Pacific Ocean after removing seasonal and QBO related variability. Correlations between ozone and SOI are the order of +0.6, in consistency with SBUV and GB observations. Differences between GOME-2A and GB measurements at the station of Samoa (American Samoa; 14.25° S, 170.6° W) are within ±1.5%. We also studied the impact of NAO on total ozone in the northern mid-latitudes in winter. We find very good agreement between GOME-2A and GB observations over Canada and Europe as to their NAO-related variability, with mean differences reaching the ±1% levels. The agreement and small differences which were found between the independently produced total ozone data sets as to the influence of QBO, ENSO and NAO show the importance of these climatological proxies as additional tool for monitoring the long-term stability of satellite-ground truth biases. [ABSTRACT FROM AUTHOR]

The Global Ozone Monitoring Experiment (GOME) on-board the second European Remote Sensing satellite provided measurements of atmospheric constituents such as ozone or other trace gases for the 16 year period from 1995 to 2011. In this paper we present a detailed analysis of the long-term performance of the sensor and introduce the new homogenized and fully calibrated level 1 product which has been generated using the recently developed GOME Data Processor level-0-to-1b (GDP-L1) Version 5.1. By means of the various in-flight calibration parameters we monitor the behavior and stability of the instrument during the entire mission. Severe degradation of the optical components has led to a significant decrease in intensity in particular in channels 1 and 2 covering the spectral ranges of 240-316 nm and 311-405 nm, respectively. Thus, a soft correction based on using the sun as a stable calibration source is applied. Revision and optimization of other calibration algorithms such as the wavelength assignment, polarization correction, or dark current correction resulted in an improved and homogeneous level 1 product that can be regarded as the European satellite reference data for successor atmospheric composition sensors and that provides an excellent prerequisite for further exploitation of GOME measurements. [ABSTRACT FROM AUTHOR]

This paper assesses the quality of IASI/Metop-A (IASI-A) and IASI/Metop-B (IASI-B) ozone (O3) products (total and partial O3 columns) retrieved with the Fast Optimal Retrievals on Layers for IASI Ozone (FORLI-O3) v20151001 software for nine years (2008-2017) through an extensive inter-comparison and validation exercise using independent observations (satellite, ground-based and ozonesonde). IASI-A and IASI-B Total O3 Columns (TOCs) are generally consistent, with a global mean difference less than 0.3 % for both day- and nighttime measurements, IASI-A being slightly higher than IASI-B. A global difference less than 2.4 % is found for the tropospheric (TROPO) O3 column product (IASI-A being lower than IASI-B), which is partly due to a temporary issue related to IASI-A viewing angle in 2015. Our validation shows that IASI-A and IASI-B TOCs are consistent with GOME-2, Dobson, Brewer and SAOZ retrieved ones, with global mean differences in the range 0.1-2 % depending on the instruments. The IASI-A and ground-based TOC comparison for the period 2008-July 2017 shows good long-term stability (negative trends within 3 % decade-1). The comparison results between IASI-A and IASI-B against smoothed ozonesonde partial O3 columns vary in altitude and latitude, with maximum standard deviation for the 300-150 hPa column (20-40 %) due to strong ozone variability and a priori uncertainty. The worst agreement with the ozonesondes and with UV-vis retrieved TOC [satellite and ground] is found at the southern high latitudes. Compared to ozonesonde data, IASI-A and IASI-B O3 products overestimate the O3 abundance in the stratosphere (up to 20 % for the 150-25 hPa column) and underestimates the O3 abundance in the troposphere (within 10 % for the mid-latitudes and ~ 18 % for the tropics). Based on the period 2011-2016, non-significant drift is found for the northern hemispheric tropospheric columns while a small drift prevails for the period before 2011. [ABSTRACT FROM AUTHOR]

On board of the Copernicus Sentinel-5 Precursor (S5P) platform, the TROPOspheric Monitoring Instrument (TROPOMI) is a double channel nadir-viewing grating spectrometer measuring solar back-scattered earthshine radiances in the ultraviolet, visible, near-infrared and shortwave infrared with global daily coverage. In the ultraviolet range, its spectral resolution and radiometric performance are equivalent to those of its predecessor OMI, but its horizontal resolution at true nadir is improved by an order of magnitude. This paper introduces the formaldehyde (HCHO) tropospheric vertical column retrieval algorithm implemented in the S5P operational processor, and comprehensively describes its various retrieval steps. Furthermore, algorithmic improvements developed in the framework of the EU FP7-project QA4ECV are described for future updates of the processor. Detailed error estimates are discussed in the light of Copernicus user requirements and needs for validation are highlighted. Finally, verification results based on the application of the algorithm to OMI measurements are presented, demonstrating the performances expected for TROPOMI. [ABSTRACT FROM AUTHOR]

The GOME-type Total Ozone Essential Climate Variable (GTO-ECV) is a Level-3 data record, which combines individual sensor products into one single cohesive record covering the 22 year period from 1995 to 2017, generated in the frame of the European Space Agency's Climate Change Initiative Phase-II. It is based on Level-2 total ozone data produced by the GODFIT v4 algorithm as applied to the GOME/ERS-2, OMI/Aura, SCIAMACHY/Envisat and GOME-2/MetopA and /MetopB observations. In this paper we examine whether GTO-ECV meets the specific requirements set by the international climate-chemistry modelling community for decadal stability, long-term and short term accuracy. In the following, we present the validation of the 2017 release of the Climate Research Data Package Total Ozone Column (CRDP TOC), both at Level-2 and Level-3. The individual Level-2 data sets show excellent inter-sensor consistency with mean differences generally within 0.5 % at moderate latitudes (±50°), whereas the Level-3 data sets show mean differences with respect to the OMI reference data record that span between -0.2 ± 0.9 % (for GOME-2B) and 1.0 ± 1.4 % (for SCIAMACHY). Very similar findings are reported for the Level-2 validation against independent ground-based TOC observations reported by Brewer, Dobson and SAOZ instruments; the mean bias between GODFIT v4 satellite TOC and ground instrument is well within 1.0 ± 1.0 % for all sensors, the drift per decade spans between -0.5 % to 1.0 ± 1.0 % depending on the sensor, and the peak-to-peak seasonality of the differences ranges between ~ 1 % for GOME and OMI, to ~ 2 % for SCIAMACHY. For the Level-3 validation, as a first step the aim was to show that the Level-3 CRDP produces consistent findings as the Level-2 individual sensor comparisons. We show an excellent agreement with 0.5 to 2 % peak-to-peak amplitude for the monthly mean difference time series and a negligible drift per decade in the Northern Hemisphere differences at -0.11 ± 0.10 % per decade for Dobson and +0.22 ± 0.08 % per decade for Brewer collocations. The exceptional quality of the Level-3 GTO-ECV v3 TOC record temporal stability well satisfies the requirements for the total ozone measurement decadal stability of between 1-3 % and the short term and long-term accuracy requirements of 2 % and 3 % respectively, showing an excellent inter-sensor consistency both in the Level-2 GODFIT v4 as well as in the Level-3 GTO-ECV v3 datasets and thus can be used for longer term analysis of the ozone layer, such as decadal trend studies, chemistry-climate model evaluation and data assimilation applications. [ABSTRACT FROM AUTHOR]

This paper presents the operational cloud retrieval algorithms for the TROPOspheric Monitoring Instrument (TROPOMI) on board the European Space Agency Sentinel-5 Precursor (S5P) mission scheduled for launch in 2017. Two algorithms working in tandem are used for retrieving cloud properties: OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks). OCRA retrieves the cloud fraction using TROPOMI measurements in the UV/VIS spectral regions and ROCINN retrieves the cloud top height (pressure) and optical thickness (albedo) using TROPOMI measurements in and around the oxygen A-band in the NIR. Cloud parameters from TROPOMI/S5P will be used not only for enhancing the accuracy of trace gas retrievals, but also for extending the satellite data record of cloud information derived from oxygen A-band measurements, a record initiated with GOME/ERS-2 over twenty years ago. Use of the oxygen A-band generates complementary cloud information (especially for low clouds), as compared to traditional thermal infrared sensors (as used in most meteorological satellites) that are less sensitive to low clouds due to reduced thermal contrast. The OCRA and ROCINN algorithms are integrated in the S5P operational processor UPAS (Universal Processor for UV/VIS/NIR Atmospheric Spectrometers), and we present here UPAS cloud results using OMI and GOME-2 measurements. In addition, we examine anticipated challenges for the TROPOMI/S5P cloud retrieval algorithms and we discuss the future validation needs for OCRA and ROCINN. [ABSTRACT FROM AUTHOR]

This paper describes an approach for cloud parameter retrieval (radiometric cloud-fraction estimation) using the polarization measurements of the Global Ozone Monitoring Experiment-2 (GOME-2) onboard the MetOp-A/B satellites. The core component of the Optical Cloud Recognition Algorithm (OCRA) is the calculation of monthly cloud-free reflectances for a global grid (resolution of 0.2° in longitude and 0.2° in latitude) to derive radiometric cloud fractions. These cloud fractions will serve as a priori information for the retrieval of cloud-top height (CTH), cloud-top pressure (CTP), cloud-top albedo (CTA) and cloud optical thickness (COT) with the Retrieval Of Cloud Information using Neural Networks (ROCINN) algorithm. This approach is already being implemented operationally for the GOME/ERS-2 and SCIAMACHY/ENVISAT sensors and here we present version 3.0 of the OCRA algorithm applied to the GOME-2 sensors. Based on more than five years of GOME-2A data (April 2008 to June 2013), reflectances are calculated for ≈35 000 orbits. For each measurement a degradation correction as well as a viewing-angle-dependent and latitudedependent correction is applied. In addition, an empirical correction scheme is introduced in order to remove the effect of oceanic sun glint. A comparison of the GOME-2A/B OCRA cloud fractions with colocated AVHRR (Advanced Very High Resolution Radiometer) geometrical cloud fractions shows a general good agreement with a mean difference of -0.15±0.20. From an operational point of view, an advantage of the OCRA algorithm is its very fast computational time and its straightforward transferability to similar sensors like OMI (Ozone Monitoring Instrument), TROPOMI (TROPOspheric Monitoring Instrument) on Sentinel 5 Precursor, as well as Sentinel 4 and Sentinel 5. In conclusion, it is shown that a robust, accurate and fast radiometric cloud-fraction estimation for GOME-2 can be achieved with OCRA using polarization measurement devices (PMDs). [ABSTRACT FROM AUTHOR]