Nitrogen dioxide (
This study describes improvements in the TROPOMI
On average the
The Tropospheric Monitoring Instrument (TROPOMI), launched on 13 October 2017
aboard the polar-orbiting Sentinel-5 Precursor (S5P) satellite, provides measurements of atmospheric trace gases (such as
The reason to monitor
The TROPOMI
Apart from the operational product described in this paper, several groups
presented scientific TROPOMI
The first step in the
Validation with ground-based measurements and comparison with OMI
measurements (e.g.
An improved FRESCO cloud pressure retrieval (Sect.
This paper discusses updates in the TROPOMI
TROPOMI
The standard operational TROPOMI
The
The following is an overview of the
Note that near-real time (NRT) data are not considered here; validation of
both the offline (OFFL) and NRT data has shown that results of the two processing chains do not differ significantly
Overview of the diagnostic dataset (DDS) periods processed for evaluation of the updated
NA: not available.
TROPOMI
Individual ground pixels are 7.2 km (5.6 km as of 6 August 2019) in the
along-track and 3.6 km in the across-track directions in the middle of the swath. The full swath width is about 2600 km, with which TROPOMI achieves
global coverage each day, except for narrow strips between orbits of
about 0.5
In order to test the
To be able to evaluate the new tropospheric and stratospheric VCDs, the full DDS periods are passed through the TM5-MP data
assimilation system, starting from v1.x
DDS-3 also contains three periods of about 1 d, one of which (4 April 2019) overlaps with one of the DDS-2 periods and is therefore included in
Table
The
The pre-launch calibration results, used for most of the v1.0 level-1b
spectra, are described by
Saturation effects may occur in the detectors of band 4 (visible, e.g. used
for
In the updated irradiance product
In the time between the generation of DDS-2 and DDS-3 the calibration key
data (CKD) of the level-1b v2.0 spectra, including the irradiance degradation correction, were recalculated using fits over more data (for
DDS-2 up to May 2019, for DDS-3 up to February 2021; over the latter period the irradiance degradation was about 2.6 % in band 4 and less than 0.5 % in band 6).
This recalculation leads to minor differences for overlapping data periods
of DDS-2 and DDS-3: for band 4 both radiance and irradiance differ by less
than 0.1 %.
The impact on the
An additional radiance degradation correction and further improved flagging
for transients will be implemented; see Sect.
Configuration parameters in the
NA: not available.
OMI
Individual ground pixels are 13 km in the along-track and 24 km in the
across-track directions in the middle of the swath. The full swath width is about 2600 km, and with that OMI achieves global coverage each day. The swath is across-track divided into 60 ground pixels (rows), and their size increases towards the edges of the swath to
Comparisons of the magnitude of the TROPOMI and OMI
Since June 2007 a part of the OMI detector has suffered from a so-called row anomaly, which appears as a signal suppression in the level-1b radiance data
at all wavelengths
Due to this issue and the fact that the TROPOMI and OMI orbits do not exactly overlap, because they measure from slightly different altitudes, direct orbit-to-orbit comparisons are not possible. Instead, data comparisons in this paper are performed after conversion to a common longitude–latitude grid.
The first step in the data processing chain is the selection of the spectral
index range
Across-track nominal wavelengths of the level-1b radiance spectra of v2.0 for a selected number of spectral indices:
Spectral pixels flagged in the level-1b v1.0 data as suffering from saturation or transients (or other errors) are skipped from the measurement before the spectra are used in the data processing.
Level-1b v1.0 spectra have no flagging for spectral pixels suffering from
blooming (cf. Sect.
Since the
Map of the TROPOMI
Figure
Figure
Along-track averages over the latitude ranges [
The SCD depends strongly on the along-track and across-track variation in
the solar and viewing zenith angles. To ease evaluation of the SCD, consider what could be called the geometric column density (GCD), defined as
The
Relative differences in the DOAS retrieval results between the TROPOMI DDS and OFFL data, averaged over the
Relative and absolute differences in some DOAS retrieval results between the DDS and OFFL data averaged over the full DDS-2 and DDS-3 periods
(cf. Table
Figure
The main SCD retrieval results shown here are the SCD value and the
associated error following from the DOAS fit as well as the RMSE: the root-mean-square of the so-called fit residual, i.e. of the difference between the modelled and measured reflectance, which serves as a measure of the quality of the fit. Another such measure is the magnitude
The averages in Fig.
Figure
What stands out from comparing the two panels in Fig.
Maps of gridded data averaged over the spring 2019 VCD period (cf. Table
Averaging the SCD error changes in the overlapping 21 orbits of the 4 April 2019 test data shows a clear decrease of about 0.33
The RMSE is not directly affected by the reflectance noise, and the numbers given in Table
The SCD values themselves show an increase of 3 %–4 % for DDS-2 and about 2.5 % for DDS-3, while averaged over the 4 April 2019 test data the DDS-3 SCD values are 1.1 % lower than those of DDS-2. Again, the difference between DDS-2 and DDS-3 may be due to the small change in the irradiance degradation correction (reflectances have changed by less than 0.5 %) and/or to atmospheric circumstances.
In summary, the v2.1–v2.2 DDS data, compared to the v1.2–v1.3 OFFL data, show an improved DOAS fit quality, a reduced SCD error, and a small increase in the SCD values (Table
The SCD increase shows in Fig.
The data assimilation is set up in such a way that the total column is made consistent with the TROPOMI observations over regions with small levels of air pollution (oceans, remote land regions), basically by adjusting the stratospheric column because of the minor contribution of the troposphere in those locations. A uniform increase in the TROPOMI total column will therefore lead to a similar increase in the stratospheric vertical column, while the tropospheric columns will be hardly affected.
Relative and absolute differences in the stratospheric VCD
between the DDS and OFFL data averaged over the VCD periods (cf. Table
Table
Figure
Scatter plot of the TROPOMI v1.3 and v2.1 gridded
Figure
According to
The FRESCO
Cloud pressure frequency distribution from orbit 03707 on 1 July 2018, considering only ocean and land ground pixels that are free of snow/ice, for small
Frequency distribution of the cloud fraction in the
FRESCO
FRESCO-wide, used as of
The FRESCO-wide approach is also used for the cloud pressure in v2.1 (DDS-2) and v2.2 (DDS-3 and its public data release)
Inspection of the frequency distribution of the
The cloud fraction used in the AMF and VCD steps of the processing is not
taken from the FRESCO data but is directly calculated in the
The procedure to determine
It is important to have information on the presence of snow or ice in a
given satellite ground pixel, so that if necessary the climatological
surface albedo can be adjusted or the AMF calculation can switch from using
the cloud fraction and cloud pressure to the use of the effective scene
pressure and effective scene albedo (assuming
Snow/ice flag comparison for the ground pixels of orbits 03707 and 03708 of 1 July 2018 based on NISE (top panel) and ECMWF (bottom panel) snow/ice cover data. The NISE coding for the flags is used, except that “ocean” is coloured with orange (value 175) instead of red for its flag value 255 so as to clearly distinguish it from the flags 252 (mixed pixels at coastlines), 253 (suspect ice value) and 254 (error); the latter three do
not occur in the ECMWF data. Other flag meanings are 0: snow-free land, 1–100: percentage sea ice, 101: permanent ice, and 103: snow. The depicted area is longitude [
To this end the v1.2–v1.4 processing uses the daily snow/ice cover database from NISE
As of v2.1 the snow/ice information is taken from the daily ECMWF
meteorological data, which solves the issues with NISE, thus improving the
reliability of the
Figure
Another issue solved with the switch to the ECMWF snow/ice data is that the
NISE data over shallow water areas that may run dry during low tide can be
wrong. Over the western part of the Waddenzee in the Netherlands, for example, NISE gives on 1 January 2019 3 % sea ice, whereas this area cannot possibly have any sea-ice: the ECMWF data correctly identify pixels as ocean (flag value zero). Because of this corrected identification, the
The surface albedo in the
The cloud fraction,
Figure
Map of the TROPOMI
Comparison of the TROPOMI
Results of linear fit and correlation coefficients (
Scatter plot of the TROPOMI v1.2 and v2.1 gridded
As Fig.
Regional averages of the gridded
The impact of processor changes on the tropospheric VCD data is dominated by
the update of the FRESCO cloud retrieval, mentioned in
Sect.
The bottom row of Fig.
Figure
From Fig.
It should be noted here that the OMI/QA4ECV processing does not apply the
albedo adjustment discussed in Sect.
To assess the impact of the processor changes on the
The validation approach is described in
Ground-based validation results are presented here in the commonly used
unit Pmolec cm
The ground-based validation of the stratospheric
Investigating results at individual ground stations
(Fig.
Similar to Fig.
The comparison of TROPOMI to MAX-DOAS tropospheric
Looking at the change in bias at each individual station
(Fig.
Similar to Fig.
Similar to the tropospheric column validation, the comparison of TROPOMI to
PGN total column
Figure
Looking at the bias per station (Fig.
In summary, ground-based validation of the updated DDS
For the tropospheric and total
Note that the two most polluted measurement sites, which show in the
tropospheric (Fig.
Processor version v2.2, used for DDS-3 and operational since 1 July 2021,
includes in the
The
Fixes have been included in data processor version v2.3, operational as of
14 November 2021, of minor bugs related to the output of some detailed data not
used by most data users (notably wavelength calibration parameters and
The improvements in the level-1b v2 spectra (cf. Sect.
With this update a new test dataset, DDS-4, was made in autumn 2021. For
Level-1b v2.1 will also include a further improvement of the flagging of transients. Analysis of the
As mentioned in Sect.
For this reason a dedicated TROPOMI surface albedo climatology has been
developed, based on TROPOMI measurements, which contains both a traditional
LER as well as a directionally dependent LER (DLER), similar to the one
developed recently from GOME-2 measurements by
The TROPOMI Small corrections in the wavelength assignment of the reflectance used in the DOAS slant column fit reduce the SCD error of ground pixels along some detector rows, without affecting other rows or the SCD values significantly. The introduction of an outlier removal improves the SCD retrieval quality for ground pixels suffering from charged particles hitting the detector (notably over the SAA) and those suffering from saturation and blooming effects (notably over bright clouds), without affecting other ground pixels. The use of improved level-1b v2.0 (ir)radiance spectra, with among others better handling of blooming and transients effects, improved (ir)radiance calibration, and improved irradiance degradation correction, in combination with the above two improvements, leads to (a) a reduction of the SCD error by about 2 %, (b) a reduction of the RMSE of the DOAS fit by about 7 %, and (c) an increase in the SCD values of about 3 %. The increase in the SCD values is fairly homogeneous and leads to an estimated increase in the stratospheric VCD by 2 %–4 % or 0.6–1.5 The use of the improved level-1b v2.0 leads (a) to a somewhat lower cloud pressure for ground pixels with small clouds fractions, which in turn leads to tropospheric VCDs for those ground pixels to be higher by some 5 %, and (b) to a small increase in the number of fully cloud-free ground pixels. Switching the source of the snow/ice flag from NISE to ECMWF improves the quality of the VCD data because of the higher spatial resolution of the ECMWF flag and its better handling of coastlines and shallow water cases. The climatological surface albedo reduction for cloud-free ground pixels with reflectances lower than expected, in combination with the use of improved level-1b v2.0 spectra, leads to tropospheric VCDs being higher by 10 %–15 % for cloud-free pixels.
The combined effect of all improvements on the vertical column data
necessarily includes the impact of an update of the FRESCO cloud retrieval
as of v1.4 since there is no DDS that covers v1.4 data.
On average the v2.x DDS data have tropospheric
Ground-based validation of the updated DDS
Part of the negative bias observed when comparing with ground-based
observations is probably due to the relatively coarse
(
Processor version 2.3, operational since 14 November 2021, contains only fixes of minor bugs not affecting the SCD or VCD data. Version 2.4, which is due for activation in the operational stream mid-2022 and which will be used for a full mission reprocessing later in 2022, contains further improvements: (a) level-1b v2.1 spectra with a radiance degradation correction and
improved transient flagging and (b) use of a dedicated TROPOMI DLER surface albedo climatology, which accounts for viewing angle dependencies, in both the cloud data and
Figure
Map of the TROPOMI
Table
Definition of the regions in Fig.
Standard TROPOMI
JvG conducted the research described in this paper and is responsible for the text. HE is responsible for the AMF and VCD steps and the final data product. SC, GP, TV, and JCL carried out the global validation analysis. MS and MtL implemented and tested the retrieval code in the TROPOMI processor. AL is leader of the TROPOMI level-1b team. KFB is involved in the final
At least one of the (co-)authors is a member of the editorial board of
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Part of the reported work was carried out in the framework of the Copernicus Sentinel-5 Precursor Mission Performance Centre (S5P MPC), contracted by the European Space Agency (ESA/ESRIN, contract no. 4000117151/16/I-LG) and supported by the Belgian Federal Science Policy Office (BELSPO), the Royal Belgian Institute for Space Aeronomy (BIRA-IASB), the Netherlands Space Office (NSO), and the German Aerospace Centre (DLR). The authors are grateful to ESA/ESRIN for supporting the ESA Validation Data Centre (EVDC) established at NILU and for running the Fiducial Reference Measurements (FRM) programme and in particular the FRM4DOAS and Pandonia projects.
Part of this work was carried out in the framework of the S5P Validation Team (S5PVT) AO projects NIDFORVAL (ID #28607, PI Gaia Pinardi, BIRA-IASB) and CESAR (ID #28596, PI Arnoud Apituley, KNMI). Steven Compernolle, Gaia Pinardi and Tijl Verhoelst at BIRA-IASB acknowledge national funding from BELSPO and ESA through the ProDEx projects TROVA-E2 (PEA 4000116692). The authors express special thanks to Ann Marie Fjæraa, José Granville, Sander Niemeijer, and Olivier Rasson for post-processing of the network and satellite data and for their dedication to the S5P/TROPOMI operational validation.
Andrea Pazmiño, Ariane Bazureau, Florence Goutail, Jean-Pierre Pommereau are acknowledged for the fast delivery of ZSL-DOAS SAOZ data with the LATMOS Real-Time processing facility, and the PIs and staff at stations from LATMOS/CNRS and NILU for operating SAOZ instruments. The SAOZ network received funding from the French Institut National des Sciences de l'Univers (INSU) of the Centre National de la Recherche Scientifique (CNRS), Centre National d'Etudes Spatiales (CNES) and Institut polaire fraçais Paul Emile Victor (IPEV).
The MAX-DOAS data used in this publication were obtained from John Burrows, Michel Grutter, Hitoshi Irie, Yugo Kanaya, Ankie Piters, Michel Van Roozendael, Vinayak Sinha, Thomas Wagner. Part of the MAX-DOAS data used here are available at the Network for the Detection of Atmospheric Composition Change (NDACC). Fast delivery of MAX-DOAS data tailored to the S5P validation was organized through the S5PVT AO project NIDFORVAL. IUP-Bremen ground-based measurements are funded by DLR-Bonn received through project 50EE1709A. We thank the IISER Mohali atmospheric chemistry facility for supporting the MAX-DOAS measurements at Mohali, India. KNMI ground-based measurements in De Bilt and Cabauw are partly supported by the Ruisdael Observatory project, Dutch Research Council (NWO) contract 184.034.015, by the Netherlands Space Office (NSO) for Sentinel-5p/TROPOMI validation, and by ESA via the EU CAMS-27 project.
We thank the PIs, support staff and funding for establishing and maintaining Pandora instruments at the 27 sites of the PGN used in this investigation, from institutes AEMET, CCNY, CITEDEF, ECCC, EPA, ESA, GA, INOE, LUFTBLICK, NASA.GSFC, NOAA.ESRL, PMOD.WRC, UAF, UAH, UC.BERKELEY, UNAM, UNIST and VCU. The PGN is a bilateral project supported with funding from NASA and ESA.
The authors would further like to thank the following people: Erwin Loots and Emiel van der Plas on level-1b issues, Gijsbert Tilstra on surface albedo issues, Ping Wang on FRESCO cloud retrieval, and Piet Stammes and Jos de Laat on general retrieval issues.
Sentinel-5 Precursor is an ESA mission on behalf of the European Commission (EC). The TROPOMI payload is a joint development by ESA and the Netherlands Space Office (NSO). The Sentinel-5 Precursor ground-segment development has been funded by ESA and with national contributions from the Netherlands, Germany, and Belgium. This work contains modified Copernicus Sentinel-5P TROPOMI data (2018–2021), processed in the operational framework or locally at KNMI, with post-processing for validation purposes performed by BIRA-IASB.
This paper was edited by Lok Lamsal and reviewed by three anonymous referees.