Glyoxal tropospheric column retrievals from TROPOMI – multi-satellite intercomparison and ground-based validation

Abstract. We present the first global glyoxal (CHOCHO) tropospheric column product derived from the TROPOspheric Monitoring Instrument (TROPOMI) on board 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 for providing random and systematic error estimates for every observation. Systematic errors are typically in the range of 1 ×1014–3 ×1014 molec. cm−2 (∼30 %–70 % in emission regimes) and originate mostly from a priori data uncertainties and spectral interferences with other absorbing species. The latter may be at the origin, at least partly, of an enhanced glyoxal signal over equatorial oceans, and further investigation is needed to mitigate them. Random errors are large (>6×1014 molec. cm−2) 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 detail. 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 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 DOAS (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 appear to be biased low during wintertime. The mean absolute glyoxal columns from satellite and MAX-DOAS generally agree well for low/moderate columns with differences of less than 1×1014 molec. cm−2. 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 high.

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Glyoxal tropospheric column retrievals from TROPOMI, multi-satellite intercomparison and ground-based validation

10.5194/amt-2021-158 ◽

2021 ◽

Author(s):

Christophe Lerot ◽

François Hendrick ◽

Michel Van Roozendael ◽

Leonardo M. A. Alvarado ◽

Andreas Richter ◽

...

Keyword(s):

Large Scale ◽

Optical Absorption Spectroscopy ◽

Retrieval Algorithm ◽

High Signal ◽

Data Sets ◽

Systematic Bias ◽

Monitoring Instrument ◽

Error Characterization ◽

Ozone Monitoring ◽

The Impact

Abstract. 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.

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

Atmospheric Measurement Techniques ◽

10.5194/amt-14-455-2021 ◽

2021 ◽

Vol 14 (1) ◽

pp. 455-479

Author(s):

Lok N. Lamsal ◽

Nickolay A. Krotkov ◽

Alexander Vasilkov ◽

Sergey Marchenko ◽

Wenhan Qin ◽

...

Keyword(s):

Nitrogen Dioxide ◽

Retrieval Algorithm ◽

Data Sets ◽

Ozone Monitoring Instrument ◽

Monitoring Instrument ◽

Standard Product ◽

Independent Observations ◽

Moderate Resolution ◽

Ozone Monitoring ◽

Moderate Resolution Imaging Spectroradiometer

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

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Retrieval algorithm for OClO from TROPOMI (TROPOspheric Monitoring Instrument) by differential optical absorption spectroscopy

Atmospheric Measurement Techniques ◽

10.5194/amt-14-7595-2021 ◽

2021 ◽

Vol 14 (12) ◽

pp. 7595-7625

Author(s):

Jānis Puķīte ◽

Christian Borger ◽

Steffen Dörner ◽

Myojeong Gu ◽

Udo Frieß ◽

...

Keyword(s):

Optical Absorption ◽

Absorption Spectroscopy ◽

Large Scale ◽

Order Term ◽

Differential Optical Absorption Spectroscopy ◽

Optical Absorption Spectroscopy ◽

Retrieval Algorithm ◽

Monitoring Instrument ◽

Innovation Project ◽

Improved Accuracy

Abstract. Here we present a new retrieval algorithm of the slant column densities (SCDs) of chlorine dioxide (OClO) by differential optical absorption spectroscopy (DOAS) from measurements performed by TROPOspheric Monitoring Instrument (TROPOMI) on board of Sentinel-5P satellite. To achieve a substantially improved accuracy, which is especially important for OClO observations, accounting for absorber and pseudo absorber structures in optical depth even of the order of 10−4 is important. Therefore, in comparison to existing retrievals, we include several additional fit parameters by accounting for spectral effects like the temperature dependency of the Ring effect and Ring absorption effects, a higher-order term for the OClO SCD dependency on wavelength and accounting for the BrO absorption. We investigate the performance of different retrieval settings by an error analysis with respect to random variations, large-scale systematic variations as a function of solar zenith angle and also more localized systematic variations by a novel application of an autocorrelation analysis. The retrieved TROPOMI OClO SCDs show a very good agreement with ground-based zenith sky measurements and are correlated well with preliminary data of the operational TROPOMI OClO retrieval algorithm currently being developed as part of ESA's Sentinel-5P+ Innovation project.

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An improved tropospheric NO2 column retrieval algorithm for the Ozone Monitoring Instrument

Atmospheric Measurement Techniques ◽

10.5194/amt-4-1905-2011 ◽

2011 ◽

Vol 4 (9) ◽

pp. 1905-1928 ◽

Cited By ~ 396

Author(s):

K. F. Boersma ◽

H. J. Eskes ◽

R. J. Dirksen ◽

R. J. van der A ◽

J. P. Veefkind ◽

...

Keyword(s):

High Resolution ◽

Surface Albedo ◽

Retrieval Algorithm ◽

Ozone Monitoring Instrument ◽

Air Mass ◽

Monitoring Instrument ◽

Tropospheric No2 ◽

Mass Factor ◽

Ozone Monitoring ◽

The Impact

Abstract. We present an improved tropospheric nitrogen dioxide column retrieval algorithm (DOMINO v2.0) for OMI based on better air mass factors (AMFs) and a correction for across-track stripes resulting from calibration errors in the OMI backscattered reflectances. Since October 2004, NO2 retrievals from the Ozone Monitoring Instrument (OMI), a UV/Vis nadir spectrometer onboard NASA's EOS-Aura satellite, have been used with success in several scientific studies focusing on air quality monitoring, detection of trends, and NOx emission estimates. Dedicated evaluations of previous DOMINO tropospheric NO2 retrievals indicated their good quality, but also suggested that the tropospheric columns were susceptible to high biases (by 0–40%), probably because of errors in the air mass factor calculations. Here we update the DOMINO air mass factor approach. We calculate a new look-up table (LUT) for altitude-dependent AMFs based on more realistic atmospheric profile parameters, and include more surface albedo and surface pressure reference points than before. We improve the sampling of the TM4 model, resulting in a priori NO2 profiles that are better mixed throughout the boundary layer. We evaluate the NO2 profiles simulated with the improved TM4 sampling as used in the AMF calculations and show that they are highly consistent with in situ NO2 measurements from aircraft during the INTEX-A and INTEX-B campaigns in 2004 and 2006. Our air mass factor calculations are further updated by the implementation of a high-resolution terrain height and a high-resolution surface albedo climatology based on OMI measurements. Together with a correction for across-track stripes, the overall impact of the improved terrain height and albedo descriptions is modest (<5%) on average over large polluted areas, but still causes significant changes locally. The main changes in the DOMINO v2.0 algorithm follow from the new LUT and the improved TM4 sampling that results in more NO2 simulated aloft, where sensitivity to NO2 is higher, and amount to reductions in tropospheric NO2 columns of up to 20% in winter, and 10% in summer over extended polluted areas. We investigate the impact of aerosols on the NO2 retrieval, and based on a comparison of concurrent retrievals of clouds from OMI and aerosols from MODIS Aqua, we find empirical evidence that OMI cloud retrievals are sensitive to the presence of scattering aerosols. It follows that an implicit correction for the effects of aerosols occurs through the aerosol-induced cloud parameters in DOMINO, and we show that such an empirical correction amounts to a 20 %AMF reduction in summer and ±10% changes in winter over the eastern United States.

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TROPOMI observations of total column water vapor

10.5194/egusphere-egu21-2673 ◽

2021 ◽

Author(s):

Ka Lok Chan ◽

Sander Slijkhuis ◽

Katerina Garane ◽

Pieter Valks ◽

Diego Loyola

Keyword(s):

Water Vapor ◽

Pearson Correlation ◽

A Priori ◽

Optical Absorption Spectroscopy ◽

Retrieval Algorithm ◽

Blue Band ◽

Monitoring Instrument ◽

Ozone Monitoring ◽

Typical Satellite ◽

Total Column

We present the total column water vapor (TCWV) retrieval for the TROPOspheric Monitoring Instrument (TROPOMI) observations in the blue band. The retrieval was first developed to retrieve TCWV from Global Ozone Monitoring Experience 2 (GOME-2). We have modified the settings of the retrieval to adapt it for TROPOMI observations. The TROPOMI TCWV retrieval algorithm consists of two major steps. The first step is the retrieval of water vapor slant columns by applying the differential optical absorption spectroscopy (DOAS) technique to TROPOMI observations in the blue band. The retrieved water vapor slant columns are then converted to vertical columns using air mass factors (AMFs). An iterative optimization has been developed to dynamically find the optimal a priori water vapor profile for AMF calculation. The dynamic search algorithm makes use of the fact that the vertical distribution of water vapor is strongly correlated to the total column amount. This makes the algorithm better suited for climate studies compared to typical satellite retrievals with static a priori or vertical profile information from the chemistry transport model (CTM). Details of the TCWV retrieval are presented. The TCWV retrieval algorithm is applied to TROPOMI observations. The results are validated by comparing to Ozone Monitoring Instrument (OMI), GOME-2 and Special Sensor Microwave Imager Sounder (SSMIS) satellite observations. TCWV derived from TROPOMI observations agree well with the other data sets with Pearson correlation coefficient (R) ranging from 0.94 to 0.99. The correlation is slight better during winter time of the northern hemisphere. Small discrepancies are found among TROPOMI, OMI, GOME-2 and SSMIS observations. The discrepancies are mainly due to differences in measurement time and cloud filtering. More detailed validation against ground based sun-photometer observations are presented separately in this session*.&#160;*see the respective abstract by Katerina Garane.

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An improved tropospheric NO2 column retrieval algorithm for the Ozone Monitoring Instrument

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-4-2329-2011 ◽

2011 ◽

Vol 4 (2) ◽

pp. 2329-2388 ◽

Cited By ~ 16

Author(s):

K. F. Boersma ◽

H. J. Eskes ◽

R. J. Dirksen ◽

R. J. van der A ◽

J. P. Veefkind ◽

...

Keyword(s):

High Resolution ◽

Surface Albedo ◽

Retrieval Algorithm ◽

Ozone Monitoring Instrument ◽

Air Mass ◽

Monitoring Instrument ◽

Tropospheric No2 ◽

Mass Factor ◽

Ozone Monitoring ◽

The Impact

Abstract. We present an improved tropospheric nitrogen dioxide column retrieval algorithm (DOMINO v2.0) for OMI based on better air mass factors (AMFs) and a correction for across-track stripes resulting from calibration errors in the OMI backscattered reflectances. Since October 2004, NO2 retrievals from the Ozone Monitoring Instrument (OMI), a UV/Vis nadir spectrometer onboard NASA's EOS-Aura satellite, have been used with success in several scientific studies focusing on air quality monitoring, detection of trends, and NOx emission estimates. Dedicated evaluations of previous DOMINO tropospheric NO2 retrievals indicated their good quality, but also suggested that the tropospheric columns were susceptible to high biases (by 0–40%), probably because of errors in the air mass factor calculations. Here we update the DOMINO air mass factor approach. We calculate a new look-up table (LUT) for altitude-dependent AMFs based on more realistic atmospheric profile parameters, and include more surface albedo and surface pressure reference points than before. We improve the sampling of the TM4 model, resulting in a priori NO2 profiles that are better mixed throughout the boundary layer. We evaluate the NO2 profiles simulated with the improved TM4 sampling as used in the AMF calculations and show that they are highly consistent with in situ NO2 measurements from aircraft during the INTEX-A and INTEX-B campaigns in 2004 and 2006. Our air mass factor calculations are further updated by the implementation of a high-resolution terrain height and a high-resolution surface albedo climatology based on OMI measurements. Together with a correction for across-track stripes, the overall impact of the improved terrain height and albedo descriptions is modest (<5%) on average over large polluted areas, but still causes significant changes locally. The main changes in the DOMINO v2.0 algorithm follow from the new LUT and the improved TM4 sampling that results in more NO2 simulated aloft, where sensitivity to NO2 is higher, and amount to reductions in tropospheric NO2 columns of up to 20% in winter, and 10% in summer over extended polluted areas. We investigate the impact of aerosols on the NO2 retrieval, and based on a comparison of concurrent retrievals of clouds from OMI and aerosols from MODIS Aqua, we find empirical evidence that OMI cloud retrievals are sensitive to the presence of scattering aerosols. It follows that an implicit correction for the effects of aerosols occurs through the aerosol-induced cloud parameters in DOMINO, and we show that such a correction amounts to a 20% AMF reduction in summer and ±10% changes in winter over the eastern US.

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Total column water vapour measurements from GOME-2 MetOp-A and MetOp-B

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-7-3021-2014 ◽

2014 ◽

Vol 7 (3) ◽

pp. 3021-3073 ◽

Cited By ~ 5

Author(s):

M. Grossi ◽

P. Valks ◽

D. Loyola ◽

B. Aberle ◽

S. Slijkhuis ◽

...

Keyword(s):

Water Vapour ◽

Optical Absorption Spectroscopy ◽

Retrieval Algorithm ◽

Data Sets ◽

Vertical Column ◽

Data Set ◽

Vertical Column Density ◽

Data Processor ◽

Climate Analysis ◽

Total Column

Abstract. The knowledge of the total column water vapour (TCWV) global distribution is fundamental for climate analysis and weather monitoring. In this work, we present the retrieval algorithm used to derive the operational TCWV from the GOME-2 sensors and perform an extensive inter-comparison and validation in order to estimate their absolute accuracy and long-term stability. We use the recently reprocessed data sets retrieved by the GOME-2 instruments aboard EUMETSAT's MetOp-A and MetOp-B satellites and generated by DLR in the framework of the O3M-SAF using the GOME Data Processor (GDP) version 4.7. The retrieval algorithm is based on a classical Differential Optical Absorption Spectroscopy (DOAS) method and combines H2O/O2 retrieval for the computation of the trace gas vertical column density. We introduce a further enhancement in the quality of the H2O column by optimizing the cloud screening and developing an empirical correction in order to eliminate the instrument scan angle dependencies. We evaluate the overall consistency between about 8 months measurements from the newer GOME-2 instrument on the MetOp-B platform with the GOME-2/MetOp-A data in the overlap period. Furthermore, we compare GOME-2 results with independent TCWV data from ECMWF and with SSMIS satellite measurements during the full period January 2007–August 2013 and we perform a validation against the combined SSM/I + MERIS satellite data set developed in the framework of the ESA DUE GlobVapour project. We find global mean biases as small as ± 0.03 g cm−2 between GOME-2A and all other data sets. The combined SSM/I-MERIS sample is typically drier than the GOME-2 retrievals (−0.005 g cm−2), while on average GOME-2 data overestimate the SSMIS measurements by only 0.028 g cm−2. However, the size of some of these biases are seasonally dependent. Monthly average differences can be as large as 0.1 g cm−2, based on the analysis against SSMIS measurements, but are not as evident in the validation with the ECMWF and the SSM/I + MERIS data. Studying two exemplary months, we estimate regional differences and identify a very good agreement between GOME-2 total columns and all three independent data sets, especially for land areas, although some discrepancies over ocean and over land areas with high humidity and a relatively large surface albedo are also present.

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The Midlatitude Continental Convective Clouds Experiment (MC3E) sounding network: operations, processing and analysis

Atmospheric Measurement Techniques ◽

10.5194/amt-8-421-2015 ◽

2015 ◽

Vol 8 (1) ◽

pp. 421-434 ◽

Cited By ~ 18

Author(s):

M. P. Jensen ◽

T. Toto ◽

D. Troyan ◽

P. E. Ciesielski ◽

D. Holdridge ◽

...

Keyword(s):

Large Scale ◽

Scale Model ◽

Data Sets ◽

Central Plains ◽

Data Set ◽

Convective Systems ◽

Convective Clouds ◽

Quality Checks ◽

Network Operations ◽

The Impact

Abstract. The Midlatitude Continental Convective Clouds Experiment (MC3E) took place during the spring of 2011 centered in north-central Oklahoma, USA. The main goal of this field campaign was to capture the dynamical and microphysical characteristics of precipitating convective systems in the US Central Plains. A major component of the campaign was a six-site radiosonde array designed to capture the large-scale variability of the atmospheric state with the intent of deriving model forcing data sets. Over the course of the 46-day MC3E campaign, a total of 1362 radiosondes were launched from the enhanced sonde network. This manuscript provides details on the instrumentation used as part of the sounding array, the data processing activities including quality checks and humidity bias corrections and an analysis of the impacts of bias correction and algorithm assumptions on the determination of convective levels and indices. It is found that corrections for known radiosonde humidity biases and assumptions regarding the characteristics of the surface convective parcel result in significant differences in the derived values of convective levels and indices in many soundings. In addition, the impact of including the humidity corrections and quality controls on the thermodynamic profiles that are used in the derivation of a large-scale model forcing data set are investigated. The results show a significant impact on the derived large-scale vertical velocity field illustrating the importance of addressing these humidity biases.

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Retrieving Forest Canopy Elements Clumping Index Using ICESat GLAS Lidar Data

Remote Sensing ◽

10.3390/rs13050948 ◽

2021 ◽

Vol 13 (5) ◽

pp. 948

Author(s):

Lei Cui ◽

Ziti Jiao ◽

Kaiguang Zhao ◽

Mei Sun ◽

Yadong Dong ◽

...

Keyword(s):

Large Scale ◽

Forest Canopy ◽

Remote Sensing Data ◽

Alternative Methods ◽

High Signal ◽

Laser Altimeter ◽

Waveform Data ◽

Forest Park ◽

Clumping Index ◽

The Impact

Clumping index (CI) is a canopy structural variable important for modeling the terrestrial biosphere, but its retrieval from remote sensing data remains one of the least reliable. The majority of regional or global CI products available so far were generated from multiangle optical reflectance data. However, these reflectance-based estimates have well-known limitations, such as the mere use of a linear relationship between the normalized difference hotspot and darkspot (NDHD) and CI, uncertainties in bidirectional reflectance distribution function (BRDF) models used to calculate the NDHD, and coarse spatial resolutions (e.g., hundreds of meters to several kilometers). To remedy these limitations and develop alternative methods for large-scale CI mapping, here we explored the use of spaceborne lidar—the Geoscience Laser Altimeter System (GLAS)—and proposed a semi-physical algorithm to estimate CI at the footprint level. Our algorithm was formulated to leverage the full vertical canopy profile information of the GLAS full-waveform data; it converted raw waveforms to forest canopy gap distributions and gap fractions of random canopies, which was used to estimate CI based on the radiative transfer theory and a revised Beer–Lambert model. We tested our algorithm over two areas in China—the Saihanba National Forest Park and Heilongjiang Province—and assessed its relative accuracies against field-measured CI and MODIS CI products. We found that reliable estimation of CI was possible only for GLAS waveforms with high signal-to-noise ratios (e.g., >65) and at gentle slopes (e.g., <12°). Our GLAS-based CI estimates for high-quality waveforms compared well to field-based CI (i.e., R2 = 0.72, RMSE = 0.07, and bias = 0.02), but they showed less correlation to MODIS CI (e.g., R2 = 0.26, RMSE = 0.12, and bias = 0.04). The difference highlights the impact of the scale effect in conducting comparisons of products with huge differences resolution. Overall, our analyses represent the first attempt to use spaceborne lidar to retrieve high-resolution forest CI and our algorithm holds promise for mapping CI globally.

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Retrieval algorithm for OClO from TROPOMI by Differential Optical Absorption Spectroscopy

10.5194/amt-2021-178 ◽

2021 ◽

Author(s):

Jānis Puķīte ◽

Christian Borger ◽

Steffen Dörner ◽

Myojeong Gu ◽

Udo Frieß ◽

...

Keyword(s):

Optical Absorption ◽

Absorption Spectroscopy ◽

Chlorine Dioxide ◽

Large Scale ◽

Order Term ◽

Differential Optical Absorption Spectroscopy ◽

Optical Absorption Spectroscopy ◽

Retrieval Algorithm ◽

Polar Regions ◽

Spectrally Resolved

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI) is a UV-VIS-NIR-SWIR instrument on board of Sentinel-5P satellite developed for monitoring the Earth’s atmosphere. It was launched on 13 October 2017 in a near polar orbit. It measures spectrally resolved earthshine radiances at an unprecedented spatial resolution of around 3.5 x 7.2 km² (3.5 x 5.6 km² starting from 6 Aug 2019) (near nadir) with a total swath width of ~ 2600 km on the Earth's surface providing daily global coverage. From the measured spectra high resolved trace gas distributions can be retrieved by means of differential optical absorption spectroscopy (DOAS). Chlorine dioxide (OClO) is a by-product of the ozone depleting halogen chemistry in the stratosphere. Although being rapidly photolysed at low solar zenith angles (SZAs) it plays an important role as an indicator of the chlorine activation in polar regions during polar winter and spring at twilight conditions because of the nearly linear dependence of its formation to chlorine oxide (ClO). Here we present a new retrieval algorithm of the slant column densities (SCDs) of chlorine dioxide (OClO) by DOAS. To achieve a substantially improved accuracy, which is especially important for OClO observations, accounting for absorber and pseudo absorber structures in optical depth even of the order of 10−4 is important. Therefore in comparison to existing retrievals, we include several additional fit parameters accounting for spectral effects like the temperature dependency of the Ring effect and Ring absorption effects, higher order term for the OClO SCD dependency on wavelength and account for the BrO absorption. We investigate the performance of different retrieval settings by an error analysis with respect to random variations, large scale systematic variations as function of solar zenith angle and also more localised systematic variations by a novel application of an autocorrelation analysis. The retrieved TROPOMI OClO SCDs show a very good agreement with ground based zenith sky measurements and are correlated well with preliminary data of the opeartional TROPOMI OClO retrieval algorithm currently being developed as part of ESA's S5p+I project.

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Glyoxal tropospheric column retrievals from TROPOMI – multi-satellite intercomparison and ground-based validation