The STRatospheric Estimation Algorithm from Mainz (STREAM):
estimating stratospheric  NO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; from nadir-viewing satellites by weighted convolution

Beirle, Steffen; Hörmann, Christoph; Jöckel, Patrick; Liu, Song; Vries, Marloes Penning de; Pozzer, Andrea; Sihler, Holger; Valks, Pieter; Wagner, Thomas

doi:10.5194/amt-9-2753-2016

Cited by 40 publications

(73 citation statements)

References 38 publications

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“…Note that these larger differences are reduced to ∼ 2 × 10 14 molec cm 2 in monthly averages (not shown). The found deviations are in agreement with the uncertainty estimates in Beirle et al (2016).…”

Section: Performance Of Streamsupporting

confidence: 90%

“…For NO 2 retrieval, the use of effective slit functions with an asymmetric Gaussian leads to similar results as using a preflight slit function. In addition, Beirle et al (2017) have proposed a slit function parameterisation using a super Gaussian, which is proved to quickly and robustly describe the slit function changes for satellite instrument OMI or TROPOMI. In the case of GOME-2, the super Gaussian obtains nearly identical results as the asymmetric Gaussian and is therefore not applied in here.…”

Section: Long-term Variationsmentioning

confidence: 99%

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An improved total and tropospheric NO2 column retrieval for GOME-2

et al. 2019

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Abstract. An improved algorithm for the retrieval of total and tropospheric nitrogen dioxide (NO2) columns from the Global Ozone Monitoring Experiment-2 (GOME-2) is presented. The refined retrieval will be implemented in a future version of the GOME Data Processor (GDP) as used by the EUMETSAT Satellite Application Facility on Atmospheric Composition and UV Radiation (AC-SAF). The first main improvement is the application of an extended 425–497 nm wavelength fitting window in the differential optical absorption spectroscopy (DOAS) retrieval of the NO2 slant column density, based on which initial total NO2 columns are computed using stratospheric air mass factors (AMFs). Updated absorption cross sections and a linear offset correction are used for the large fitting window. An improved slit function treatment is applied to compensate for both long-term and in-orbit drift of the GOME-2 slit function. Compared to the current operational (GDP 4.8) dataset, the use of these new features increases the NO2 columns by ∼1–3×1014 molec cm2 and reduces the slant column error by ∼24 %. In addition, the bias between GOME-2A and GOME-2B measurements is largely reduced by adopting a new level 1b data version in the DOAS retrieval. The retrieved NO2 slant columns show good consistency with the Quality Assurance for Essential Climate Variables (QA4ECV) retrieval with a good overall quality. Second, the STRatospheric Estimation Algorithm from Mainz (STREAM), which was originally developed for the TROPOspheric Monitoring Instrument (TROPOMI) instrument, was optimised for GOME-2 measurements to determine the stratospheric NO2 column density. Applied to synthetic GOME-2 data, the estimated stratospheric NO2 columns from STREAM shows good agreement with the a priori truth. An improved latitudinal correction is introduced in STREAM to reduce the biases over the subtropics. Applied to GOME-2 measurements, STREAM largely reduces the overestimation of stratospheric NO2 columns over polluted regions in the GDP 4.8 dataset. Third, the calculation of AMF applies an updated box-air-mass factor (box-AMF) look-up table (LUT) calculated using the latest version 2.7 of the Vector-LInearized Discrete Ordinate Radiative Transfer (VLIDORT) model with an increased number of reference points and vertical layers, a new GOME-2 surface albedo climatology, and improved a priori NO2 profiles obtained from the TM5-MP chemistry transport model. A large effect (mainly enhancement in summer and reduction in winter) on the retrieved tropospheric NO2 columns by more than 10 % is found over polluted regions. To evaluate the GOME-2 tropospheric NO2 columns, an end-to-end validation is performed using ground-based multiple-axis DOAS (MAXDOAS) measurements. The validation is illustrated for six stations covering urban, suburban, and background situations. Compared to the GDP 4.8 product, the new dataset presents improved agreement with the MAXDOAS measurements for all the stations.

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Section: Performance Of Streamsupporting

confidence: 90%

Section: Long-term Variationsmentioning

confidence: 99%

An improved total and tropospheric NO2 column retrieval for GOME-2

et al. 2019

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“…It is also interesting to note in Table 5 that the intercepts are (2020) when comparing the OMI QA4ECV assimilated stratospheric columns (based on an approach similar to the one used in the OMI DOMINO algorithm) to ground-based zenith-sky data. This bias was reduced to about -0.01 x1015 molec/cm² when using the STREAM (Beirle et al, 2016) approach.…”

mentioning

confidence: 93%

Validation of tropospheric NO2 column measurements of GOME-2A and OMI using MAX-DOAS and direct sun network observations

Pinardi¹,

Roozendaël²,

Hendrick³

et al. 2020

Preprint

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Abstract. MAX-DOAS and direct sun NO2 vertical column network data are used to investigate the accuracy of tropospheric NO2 column measurements of the GOME-2 instrument on the MetOP-A satellite platform and the OMI instrument on Aura. The study is based on 23 MAX-DOAS and 16 direct sun instruments at stations distributed worldwide. A method to quantify and correct for horizontal dilution effects in heterogeneous NO2 field conditions is proposed. After systematic application of this correction to urban sites, satellite measurements are found to present smaller biases compared to ground-based reference data in almost all cases. We investigate the seasonal dependence of the validation results, as well as the impact of using different approaches to select satellite ground pixels in coincidence with ground-based data. In optimal comparison conditions (satellite pixels containing the station) the median bias between satellite tropospheric NO2 column measurements and the ensemble of MAX-DOAS and direct sun measurements is found to be significant and equal to −36 % for GOME-2A and −20 % for OMI. These biases are further reduced to −24 % and −8 % respectively, after application of the dilution correction. Comparisons with the QA4ECV satellite product for both GOME-2A and OMI is also performed, showing less scatter but also a slightly larger median tropospheric NO2 column bias with respect to the ensemble of MAX-DOAS and direct sun measurements.

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“…One procedure is via data assimilation in a chemistry transport model (CTM), which estimates the stratospheric NO 2 vertical column density (VCD). Alternative approaches estimate the stratospheric column directly from the satellite total column measurements over remote regions and above mid-altitude clouds, without input from CTMs (Bucsela et al, 2013;Beirle et al, 2016). The stratospheric NO 2 SCD is then subtracted from the total SCD yielding the tropospheric NO 2 SCD.…”

Section: Introductionmentioning

confidence: 99%

Improved slant column density retrieval of nitrogen dioxide and formaldehyde for OMI and GOME-2A from QA4ECV: intercomparison, uncertainty characterisation, and trends

et al. 2018

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Abstract. Nitrogen dioxide (NO2) and formaldehyde (HCHO) column data from satellite instruments are used for air quality and climate studies. Both NO2 and HCHO have been identified as precursors to the ozone (O3) and aerosol essential climate variables, and it is essential to quantify and characterise their uncertainties. Here we present an intercomparison of NO2 and HCHO slant column density (SCD) retrievals from four different research groups (BIRA-IASB, IUP Bremen, and KNMI as part of the Quality Assurance for Essential Climate Variables (QA4ECV) project consortium, and NASA) and from the OMI and GOME-2A instruments. Our evaluation is motivated by recent improvements in differential optical absorption spectroscopy (DOAS) fitting techniques and by the desire to provide a fully traceable uncertainty budget for the climate data record generated within QA4ECV. The improved NO2 and HCHO SCD values are in close agreement but with substantial differences in the reported uncertainties between groups and instruments. To check the DOAS uncertainties, we use an independent estimate based on the spatial variability of the SCDs within a remote region. For NO2, we find the smallest uncertainties from the new QA4ECV retrieval (0.8  ×  1015 molec. cm−2 for both instruments over their mission lifetimes). Relative to earlier approaches, the QA4ECV NO2 retrieval shows better agreement between DOAS and statistical uncertainty estimates, suggesting that the improved QA4ECV NO2 retrieval has reduced but not altogether eliminated systematic errors in the fitting approach. For HCHO, we reach similar conclusions (QA4ECV uncertainties of 8–12  ×  1015 molec. cm−2), but the closeness between the DOAS and statistical uncertainty estimates suggests that HCHO uncertainties are indeed dominated by random noise from the satellite's level 1 data. We find that SCD uncertainties are smallest for high top-of-atmosphere reflectance levels with high measurement signal-to-noise ratios. From 2005 to 2015, OMI NO2 SCD uncertainties increase by 1–2 % year−1, which is related to detector degradation and stripes, but OMI HCHO SCD uncertainties are remarkably stable (increase <  1 % year−1) and this is related to the use of Earth radiance reference spectra which reduces stripes. For GOME-2A, NO2 and HCHO SCD uncertainties increased by 7–9 and 11–15 % year−1 respectively up until September 2009, when heating of the instrument markedly reduced further throughput loss, stabilising the degradation of SCD uncertainty to <  3 % year−1 for 2009–2015. Our work suggests that the NO2 SCD uncertainty largely consists of a random component ( ∼  65 % of the total uncertainty) as a result of the propagation of measurement noise but also of a substantial systematic component ( ∼  35 % of the total uncertainty) mainly from stripe effects. Averaging over multiple pixels in space and/or time can significantly reduce the SCD uncertainties. This suggests that trend detection in OMI, GOME-2 NO2, and HCHO time series is not limited by the spectral fitting but rather by the adequacy of assumptions on the atmospheric state in the later air mass factor (AMF) calculation step.

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The STRatospheric Estimation Algorithm from Mainz (STREAM): estimating stratospheric NO<sub>2</sub> from nadir-viewing satellites by weighted convolution

Cited by 40 publications

References 38 publications

An improved total and tropospheric NO<sub>2</sub> column retrieval for GOME-2

An improved total and tropospheric NO<sub>2</sub> column retrieval for GOME-2

Validation of tropospheric NO<sub>2</sub> column measurements of GOME-2A and OMI using MAX-DOAS and direct sun network observations

Improved slant column density retrieval of nitrogen dioxide and formaldehyde for OMI and GOME-2A from QA4ECV: intercomparison, uncertainty characterisation, and trends

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The STRatospheric Estimation Algorithm from Mainz (STREAM): estimating stratospheric NO&lt;sub&gt;2&lt;/sub&gt; from nadir-viewing satellites by weighted convolution

Cited by 40 publications

References 38 publications

An improved total and tropospheric NO&lt;sub&gt;2&lt;/sub&gt; column retrieval for GOME-2

An improved total and tropospheric NO&lt;sub&gt;2&lt;/sub&gt; column retrieval for GOME-2

Validation of tropospheric NO&lt;sub&gt;2&lt;/sub&gt; column measurements of GOME-2A and OMI using MAX-DOAS and direct sun network observations

Improved slant column density retrieval of nitrogen dioxide and formaldehyde for OMI and GOME-2A from QA4ECV: intercomparison, uncertainty characterisation, and trends

Contact Info

Product

Resources

About

The STRatospheric Estimation Algorithm from Mainz (STREAM): estimating stratospheric NO<sub>2</sub> from nadir-viewing satellites by weighted convolution

An improved total and tropospheric NO<sub>2</sub> column retrieval for GOME-2

An improved total and tropospheric NO<sub>2</sub> column retrieval for GOME-2

Validation of tropospheric NO<sub>2</sub> column measurements of GOME-2A and OMI using MAX-DOAS and direct sun network observations