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Grant agreement n°607405

Date: 2 March 2018

Lead Beneficiary: AUTH

Nature: Report

Dissemination level: PU

QA4ECV Report / Deliverable n° D3.10

Report on independent validation of

atmospheric reference data sets

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Work-package WP3 (QA of independent reference data and in-situ protocols) Deliverable Deliverable 3.10 – Draft version 1.0 Title Report on independent validation of atmospheric reference data

sets. Nature R Dissemination PU Lead Beneficiary AUTH Date 2 March 2018 Status Preliminary Authors F. Hendrick (IASB-BIRA), B. Dils (IASB-BIRA), B. Langerock (IASB-

BIRA), G. Pinardi (BIRA-IASB), M. Van Roozendael (IASB-BIRA), A. Seyler, F. Wittrock E. Peters (IUP-UB), A. Richter (IUP-UB), A. Piters (KNMI), T. Drosoglou (AUTH), A. Bais (AUTH), T. Wagner and S. Dönner (MPIC)

Editors A. Bais (AUTH) Reviewers Name (Affiliation), Name (Affiliation) Contact A. Bais, AUTH (abais@auth.gr) URL http://www.qa4ecv.eu/

This document has been produced in the context of the QA4ECV project (Quality Assurance for Essential

Climate Variables). The research leading to these results has received funding from the European Union's

Seventh Framework Programme (FP7 THEME [SPA.2013.1.1-03]) under grant agreement n° 607405. All

information in this document is provided "as is" and no guarantee or warranty is given that the information

is fit for any particular purpose. The user thereof uses the information at its sole risk and liability. For the

avoidance of all doubts, the European Commission has no liability in respect of this document, which is

merely representing the authors’ view.

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Executive Summary / Abstract

One of the main tasks of the QA4ECV WP3 is to build consistent historical record of independent reference data for the quality assessment of NO2, HCHO, and CO atmospheric ECV precursors. This task is divided into two parts: first, the harmonization of the retrievals of those species, and second, the validation of the retrieved data sets. Regarding the first step, the approaches adopted for harmonizing NO2, HCHO, and CO data sets are described in the Deliverable D3.8 (see http://www.qa4ecv.eu/sites/default/ files/QA4ECV_D3.8_v1.0_web.pdf). In the case of MAXDOAS NO2 and HCHO, harmonization is done through: (1) an intercomparison of NO2 and HCHO differential slant column densities (dSCDs) retrievals from a common set of measured spectra in order to assess the consistency of DOAS retrieval tools in use in the community and to derive recommendations for standard analysis settings, and (2) the development of generic look-up tables of differential air mass factors (dAMFs) generally applicable within the network to convert SCDs at 30° elevation into vertical column densities (VCDs). In the case of FTIR CO, since the retrieval strategy is already strongly harmonized across the different NDACC stations, the harmonization effort done within QA4ECV focused on the uncertainty budget which was strongly site dependent.

The present document describes the second step of the harmonization, i.e. the quality assessment of the ground-based MAXDOAS and FTIR reference data sets. In the first part of the report, the quality assessment of the NO2 and HCHO reference data sets is discussed:

An indirect validation of the harmonized QA4ECV data sets was performed by applying the QA4ECV retrieval approach to the measurements obtained during the CINDI-2 intercomparison campaign. The retrieved VCDs were compared with products from more advanced profiling methods.

The a priori profiles used in the dAMF-LUT developed for the QA4ECV NO2 VCD retrievals were validated with independent profiles obtained from NO2 sondes.

NO2 and SO2 VCDs were validated against near-surface observations from independent data sets.

In the second part of the report, the quality assessment of FTIR measurements of total column CO is presented. CO data sets currently undertaken by the Network for the Detection of Atmospheric Composition Change (NDACC) are compared with correlative aircraft profile measurements. In addition, Total Carbon Column Observing Network (TCCON) data sets are validated with CO measurement data from commercial aircrafts.

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Table of Contents

Table of Contents 1. Quality assessment of QA4ECV MAXDOAS NO2 and HCHO reference data sets ........... 6

1.1. CINDI-2-based HCHO and NO2 VCD quality assessment ......................................................... 6

1.1.1. Evaluation of CINDI-2 HCHO and NO2 SCD data .............................................................. 6

1.1.2. Evaluation of the QA4ECV SCD to VCD conversion approach ......................................... 9

1.2. Evaluation of the consistency between QA4ECV v1 and v2 MAXDOAS data sets ................ 12

1.3. Validation of the dAMF-LUT a priori profile shapes with NO2 sondes .................................. 13

1.3.1. Introduction ................................................................................................................... 13

1.3.2. NO2 sonde profiles ......................................................................................................... 13

1.3.3. QA4ECV dAMF-LUT profiles ........................................................................................... 14

1.3.4. Comparison results ........................................................................................................ 15

1.3.5. Conclusion and recommendation ................................................................................. 16

1.4. Verification and validation of MAX-DOAS measurements of NO2 and SO2 close to a shipping

lane 17

1.4.1. Introduction ................................................................................................................... 17

1.4.2. Comparison of NO2 mixing ratios retrieved in UV and visible fitting windows ............ 17

1.4.3. Comparison of MAX-DOAS and in-situ NO2 and SO2 mixing ratios ............................... 18

1.4.4. Comparison of diurnal and weekly cycles ..................................................................... 20

1.4.5. Summary and conclusions ............................................................................................. 21

1.5. Validation of NO2 VCDs based on surface concentration measurements: Application to the

QA4ECV Thessaloniki station ............................................................................................................. 21

1.5.1. Introduction ................................................................................................................... 21

1.5.2. MAX-DOAS NO2 and O4 retrievals ................................................................................. 21

1.5.3. In situ and long-path DOAS observations ...................................................................... 22

1.5.4. From NO2 slant column densities to near surface volume mixing ratios ..................... 22

1.5.5. From in situ and long-path observations near the surface to vertical column densities

24

1.5.6. Conclusions .................................................................................................................... 26

2. Quality assessment of QA4ECV FTIR CO reference data sets ...................................... 27

2.1. TCCON validation ................................................................................................................... 27

2.2. NDACC validation .................................................................................................................. 28

Acknowledgements ....................................................................................................................... 29

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3. Concluding remarks ................................................................................................. 30

References: ............................................................................................................................................ 31

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1. Quality assessment of QA4ECV MAXDOAS NO2 and HCHO reference data

sets

Given the fact that the common AMF-based MAXDOAS retrieval approach adopted in QA4ECV only provides VCDs, the quality assessment of the NO2 and HCHO reference data sets is made difficult due to the lack of corresponding correlative observations at the QA4ECV sites. Since all QA4ECV MAXDOAS groups participated to the CINDI-2 intercomparison campaign (see http://www.tropomi.eu/data-products/cindi-2) with similar or even the same instruments as those operated at the QA4ECV sites, it has been decided to apply the QA4ECV retrieval approach to the campaign measurements and assess the quality of the retrieved VCDs through comparison to retrieval results from more advanced profiling methods. This exercise should be therefore considered as an indirect validation of the harmonized QA4ECV data sets (so called v2 hereafter; v1 being the non-harmonised data sets; see Sect. 1.2). In addition, the QA4ECV dAMF-LUT a priori profile shapes have been validated with NO2 sondes measured by KNMI. Regarding the Thessaloniki station, two methodologies for the validation of NO2 VCDs using surface concentrations from in situ air pollution monitoring network and a long-path DOAS are also investigated. Moreover, validation of MAX-DOAS measurements of NO2 and SO2

performed by University of Bremen close to a shipping lane is presented. The CINDI-2-based quality assessment of the QA4ECV MAXDOAS harmonization approach is described in Sect. 1.1 while the consistency between v1 and v2 data sets is discussed in Sect. 1.2. The validation of dAMF-LUT a priori profiles is presented in Sect. 1.3. The validation of MAX-DOAS

NO2 and SO2 measurements from the University of Bremen and the NO2 VCDs at the Thessaloniki station are discussed in Sect. 1.4 and Sect. 1.5, respectively.

1.1. CINDI-2-based HCHO and NO2 VCD quality assessment

François Hendrick, Gaia Pinardi, and Michel Van Roozendael (BIRA-IASB)

The CINDI-2 intercomparison campaign successfully brought together a large community of research groups aiming at intercalibrating remote-sensing instruments used for the regular monitoring of atmospheric pollutants such as NO2, HCHO, O3 and aerosols. Over 50 instruments, including 36 (MAX)DOAS systems were operated side-by-side with the suite of ancillary instrumentation in Cabauw (The Netherlands) during 3 weeks in September 2016, with exceptionally favourable weather conditions and variable air quality scenarios. It was the first time that such a large number of MAXDOAS spectrometers were operated within a strict data acquisition framework, ensuring the use of common DOAS settings and a tight spatial and temporal synchronicity of the observations - an essential requirement for intercalibrating instruments measuring rapidly varying species like NO2 or HCHO.

The present QA4ECV assessment based on CINDI-2 data includes two steps: first, the quality of the CINDI-2 dSCDs from the different QA4ECV groups is evaluated based on regression plot analysis; then the QA4ECV harmonization approach is applied to these dSCDs and resulting VCDs are compared to retrievals performed using OEM (Optimal Estimation Method)-based profiling techniques.

1.1.1. Evaluation of CINDI-2 HCHO and NO2 SCD data

The post-campaign SCD evaluation focused on the systematic analysis of regression plots between each measurement and a reference value (see Figures 1 and 2), which was obtained through calculation of the median from all available measurements. A performance assessment matrix (see Figure 3) for all 36 instruments and 8 MAX-DOAS and 4 zenith-sky data products was generated by

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empirically deriving threshold values for 3 parameters, which are slope, intercept, and RMS from regression analyses against median reference values. A green, yellow, orange, or red light is given to instruments which fulfil 3, 2, 1 or 0 out of the 3 criteria, respectively. Groups which fulfil 2 or 1 criteria, but exceed the limit by more than a factor of 4 in at least one unfulfilled criterion (extreme deviation) are labelled in pink. About 7 instruments on a total of 36 were found to underperform significantly. As can be seen from Figures 1 and 2, a tight correlation was found for NO2 for most of the participating instruments while in the case of HCHO, larger discrepancy and dispersion with respect to median values were found. The latter result indicates that, due to the lower absorption signal of HCHO, some groups/instruments had difficulties to measure this species. Regarding the QA4ECV groups/instruments, they all performed well, except KNMI and AUTH for HCHO.

Figure 1: Regression analysis for NO2 DSCDs (UV) plotted against median values and for all elevation and azimuth angles. Instruments are identified with their affiliation and instrument ID number. R, S, I, RMS, and F values correspond to correlation coefficient, slope, intercept, regression RMS error, and fraction of theoretical total number of measurements (according to the acquisition protocol) included in the regression plots, respectively. Instruments corresponding to QA4ECV groups are bira-4, iup-18, mpic-28, auth-3, and knmi-21.

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Figure 2: Same as Figure 1 but for HCHO.

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Figure 3: Performance assessment matrix for all CINDI-2 MAX-DOAS and zenith-sky data products (daily noon reference analysis) and all participating instruments. A green, yellow, orange, or red light is given to instruments which fulfil 3, 2, 1 or 0 out of the 3 criteria, respectively. Groups which fulfil 2 or 1 criteria, but exceed the limit by more than a factor of 4 in at least one unfulfilled criterion (extreme deviation) are labelled in pink. In each data product category, groups are sorted by increasing values of the median DOAS fit RMS. A white cell indicates that no data is available. Instruments corresponding to QA4ECV groups are bira-4, iup-18, mpic-28, auth-3, and knmi-21.

1.1.2. Evaluation of the QA4ECV SCD to VCD conversion approach

In this quality assessment, the QA4ECV SCD to VCD conversion approach – which consists in dividing the 30°-90° elevation dSCDs of each scan by the corresponding differential AMFs extracted from LUTs (see Deliverable D3.8) - is applied to the NO2 and HCHO dSCDs measured by each QA4ECV group during the CINDI-2 campaign. As for the harmonized QA4ECV v2 data sets, the aerosol optical depth (AOD) and boundary layer height (BLH) values needed to extract appropriate AMFs are taken from climatologies coupled to the AMF extraction tool (see D3.8). The resulting VCDS are then compared to those obtained by integrating NO2 and HCHO vertical profiles retrieved by using the state-of-the-art profiling tools MMF and HEIPRO developed by BIRA-IASB and University of Heidelberg, respectively. Both algorithms are based on the Optimal Estimation Method (Rodgers, 2000) and have been found to perform very well during intercomparison exercises of profiling algorithms conducted in the framework of the CINDI-2 campaign and the ESA FRM4DOAS project (see http://frm4doas.aeronomie.be/). It should be noted that in contrast to the QA4ECV approach based on the 30° elevation dSCD only, profiling tools are applied for each scan to dSCDs measured at all elevation angles.

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Figure 4 shows the comparison results between NO2 VCDs retrieved by applying the QA4ECV AMF-based approach and the MMF and HEIPRO profiling tools to the NO2 dSCDs measured during the semi-blind phase of the campaign (12-28 September 2016) by the BIRA-IASB and University of Heidelberg instruments, respectively. The following 5 QA4ECV groups are involved in this comparison: BIRA-IASB, IUP-Bremen, MPIC, KNMI, and AUTH.

Figure 4: Comparison between NO2 VCDs retrieved by applying the QA4ECV AMF-based approach and the MMF and HEIPRO profiling tools on CINDI-2 NO2 dSCDs measured by the BIRA-IASB, IUP-Bremen, MPIC, KNMI, and AUTH groups. In the correlation plots, NO2 VCD is expressed in 10

16 molec/cm

2 and ‘Profiling NO2

VCD’ (x-axis) correspond to the averages of MMF and HEIPRO profiling results obtained using BIRA-IASB and University of Heidelberg NO2 dSCDs, respectively.

As can be seen, the consistency between the different groups in terms of difference between the QA4ECV AMF-based and profiling approaches is very good with slope values comprise in the 0.88-1.07 range and correlation coefficient ranging between 0.85 and 0.92. These results indicate that the mean systematic bias between QA4ECV AMF-based approach at 30° elevation and more advanced profiling methods is comprised between -12% and +7%, which is within the uncertainty associated to the AMF-based approach (~22%; see Deliverable D3.9).

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Corresponding comparison plots for HCHO are presented in Figure 5. Since KNMI does not provide HCHO data in QA4ECV due to the low performance of their instrument for measuring this species, comparison results are shown only for BIRA-IASB, IUP-Bremen, MPIC, and AUTH.

Figure 5: Similar as Figure 4 but for HCHO. In the case of AUTH, comparisons of QAECV data at two wavelength ranges (324.5-359 nm and 336.5-359 nm) are shown.

As can be seen from Figure 5, the 30°elevation-based QA4ECV VCDs are noisier than in the case of NO2 and the level of noise strongly depends on the instrument performance: research-grade instruments using low-temperature-stabilized detector (BIRA-IASB and IUP-Bremen) show a much lower level of noise in their QA4ECV VCDs due to their significantly higher signal-to-noise ratio compared to the two other instruments. Regarding the comparison with the reference data set, correlation plots indicate that BIRA-IASB, IUP-Bremen, and MPIC agree well with the profiling VCDs while AUTH shows much larger bias and lower correlation coefficient. However, as illustrated in the last row of Figure 5, AUTH performs significantly better when using the larger (less noisy) wavelength range 324.5-359nm instead of the standard 336.5-359 nm one. Given these results, it is

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recommended to use the Thessaloniki HCHO VCD data available in the QA4ECV MAXDOAS database with caution and preferably only those corresponding to the large fitting window.

1.2. Evaluation of the consistency between QA4ECV v1 and v2 MAXDOAS data sets

François Hendrick, Gaia Pinardi, and Michel Van Roozendael (BIRA-IASB)

Two versions of MAXDOAS NO2 and HCHO have been created for QA4ECV. The first version (v1) consists of vertical profiles and/or columns retrieved by each group by applying their own settings for spectral analysis and own method/algorithm for SCD to profile/VCD conversion. A summary of those methods is presented in Table 1.

Participant Method Stations

AUTH Geom. approx. Thessaloniki

BIRA-IASB OEM-based profiling Uccle, Xianghe, Bujumbura

IUP-Bremen Geom. approx. Bremen, Athens, Nairobi

MPIC Geom. approx. Mainz

KNMI Parametrised approach De Bilt

Table 1: Retrieval methods used for the SCD to profile/VCD conversion for QA4ECV v1 data sets.

The second version (v2) is the harmonized version of the data sets generated by imposing both DOAS settings and method for the conversion of SCD into VCD (AMF LUT approach applied on 30° elevation DSCDs).

In this Section, the consistency between v1 and v2 NO2 and HCHO data sets is evaluated through regression analysis of v2 versus v1. A summary of the correlation coefficient, slope, and intercept values can be found in Figure 6.

Figure 6: Regression analysis results of v2 versus v1 HCHO (left) and NO2 (right) MAXDOAS data sets. Correlation coefficient R, slope, and intercept values for HCHO at Thessaloniki station (0.10, 0.13, and 1.15, respectively) are outside the limits of the y-axis.

As can be seen, the impact of the harmonisation is less marked for NO2 than for HCHO, with correlation coefficient, slope, and intercept values on overall closer than 1, 1, and 0, respectively. As

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already discussed in Sect. 1.1.2, this feature could be explained by the fact that, compared to NO2, the DOAS analysis of HCHO is made more difficult and more sensitive to settings changes and to instrument performance due to the lower absorption signal of this species. Therefore, the changes between v1 and v2 with respect to the DOAS settings could affect more strongly the resulting HCHO dSCDs than NO2 dSCDS.

As verification purpose, the v1 and v2 NO2 and HCHO VCDs have been also compared to the QA4ECV OMI products (see Figure 7). Although this test cannot be considered as a validation exercise, it indicates that the agreement between OMI and MAXDOAS data sets is on overall better for v2 than v1, especially in terms of slope and intercept. The consistency between the QA4ECV stations regarding the agreement between OMI and MAXDOAS data is also improved with version v2.

Figure 7: Regression analysis results of QA4ECV OMI HCHO and NO2 VCDs versus MAXDOAS v1 (black symbols) and v2 (colored symbols) data sets.

1.3. Validation of the dAMF-LUT a priori profile shapes with NO2 sondes

Ankie J.M. Piters (KNMI)

1.3.1. Introduction

The dAMFs used for the calculation of VCDs have strong dependence on the assumed (exponential) profile shape. The difference between using a block profile and an exponentially decreasing profile results in 5-8% differences in the dAMFs at 30° elevation, see QA4ECV D3.9. Here, we compare the NO2 profile shapes as measured by NO2 sondes, to the ones used for deriving the QA4ECV dAMF Look Up Tables (LUT), and asses the error on the resulting VCDs.

1.3.2. NO2 sonde profiles

The NO2 sonde, developed at KNMI, measures in-situ NO2 with 5m vertical resolution. The instrument is described in Sluis et al. (2010) and Zweers et al. (2018). The most important systematic uncertainty that influences the shape of the profile is an ozone-dependent bias, which is most dominantly influencing the profile in the free troposphere where we usually do not expect much NO2. In this study we derive the O3-induced bias using ozone sonde measurements, with the assumption that the amount of NO2 is negligible in the upper troposphere (around 7km). Another

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systematic uncertainty arises from the pre-launch calibration with in-situ surface monitors. An error in this in-situ monitor will translate in an error on the scaling factor for the whole NO2 profile. The shape of the profile is however not affected by such an error.

Over the years, we collected 52 NO2 sonde profiles up to 10 km or higher (Fig. 8). They were usually launched as part of a measurement campaign, and most of them in the morning (90% before 13 UT, see Fig. 9). This might affect the statistics of which kind of profile shapes are typically observed. There is a large variety in individual profile shapes, but we can distinguish a few basic shapes: block profiles with a sharp cut-off at the top of the boundary layer, profiles with a strong contribution above the boundary layer, profiles with several layers or plumes, and smooth profiles with no structure.

Figure 8. NO2 profiles (n=52) measured with the NO2 sonde between 2009 and 2016 in Cabauw, De Bilt, and Romenia. In this figure the values are averaged over 100m bins. The profiles are normalised so that the NO2 column below 7km is the same for every profile.

Figure 9. Distribution of NO2 sonde launches as a function of year (left) and time of the day (right).

1.3.3. QA4ECV dAMF-LUT profiles

For the QA4ECV data set, dAMFs are extracted from a LUT generated by BIRA-IASB (see QA4ECV D3.8). The assumed profile shapes for this LUT are exponentially decreasing. The scaling height (SH) is taken equal to a climatological boundary layer height (BLH), based on ECMWF data, and depends on latitude, longitude, month, and time of day.

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1.3.4. Comparison results

For each time and place of an NO2 sonde profile, we generated the QA4ECV exponential profile, based on the QA4ECV LUT extraction tool (see QA4ECV D3.8), and compared them. Figure 10 shows one example for the profile on 25 September 2016. We calculated the dAMFs using both profiles, for three different azimuth viewing directions, with the DAK radiative transfer model, developed at KNMI. The aerosol profile and optical depth used in this calculation are extracted from the QA4ECV extraction tool. For illustration the BLH is calculated for which the dAMFs of the NO2 sonde profile and the exponentially decreasing profile would be equal (dotted line in Fig. 10). If the NO2 sonde profile does not resemble an exponential profile, this 'effective' BLH will depend on relative azimuth.

Figure 10. NO2 sonde profile (blue line) of 25 September 2016, during the CINDI-2 campaign. The orange line is the smoothed sonde profile used in the calculation of the dAMF. The green solid line shows the exponential profile that would be used for the calculation of the QA4ECV dAMF (SH=0.83). The green dotted line is an exponential profile with SH=1.54, which would result in a comparable dAMF as when the NO2 sonde profile would have been used for relative azimuth 90.

Many NO2 sonde profiles look very different from exponentially decreasing profiles. Figure 11 shows the worst three cases (left, errors over 100% for relative azimuth=0) and the best three cases (right, all errors less or equal to 3%). The figure suggests that profiles with strong peaks or elevated layers in the lowest 2-3 km typically cause larger errors in the dAMFs.

Figure 11. The three worst cases (left) and the three best cases (right) regarding the calculated error in the dAMF due to the deviation of the 'true' profile (solid lines) from the modelled profile (dashed lines). Relative errors in % are given for relative azimuth angles 0, 90 and 180 respectively. The inlays are zoomed versions of the lowest part of the profiles, below 2 km.

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The error in the dAMFs, resulting from the use of an exponentially decreasing profile can be up to 30% for relative azimuth angles 90 and 180, and to more than 100% when looking in the direction of the sun (see Fig. 12). The median of the differences is +4% for all relative azimuth angles.

Figure 12. Distribution of the relative error in the QA4ECV dAMF resulting from the use of an exponentially decreasing profile shape for three different relative azimuth angles.

There is a relation between the error in the AMF and the error in the effective NO2 layer height. This can be seen in Figure 13, where the relative error is plotted as a function of the ratio between the layer height of the NO2 sonde profile, defined as the height below which 75% of the NO2 resides, and the scaling height used in theQA4ECV exponential profile. The correlation coefficient is 0.70.

Figure 13. Relative error in dAMF as a function of the ratio between the effective NO2 layer height and the Scaling Height used in the QA4ECV LUT exponential profiles. Relative azimuth angle is 180 degrees.

1.3.5. Conclusion and recommendation

The actual NO2 profile shape can be very different from the assumed exponentially decreasing shape used for the derivation of dAMFs for the QA4ECV data set. For a sample of 52 NO2 sondes we find that the error in the dAMf resulting from this difference is on average 4%, but it can be up to 30% for relative azimuth angles of 90 and 180 degrees and over 100% for viewing directions close to the sun. The sample used in this study is typically based on morning profiles, with 90% launched before 13 UT. The AODs are typically between 02 and 0.3. The large errors at relative azimuth of 0 will be less for smaller AOD.

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Based on this study, it is recommended to increase the uncertainties reported for the QA4ECV MAXDOAS tropospheric NO2 columns (see Deliverable D3.9). This study suggests that the exponential profile assumption in the QA4ECV dAMFs results in an average error on the tropical NO2 columns of -4%, with a standard deviation of 7% for relative azimuth angles of 90 and 180 degrees and 40% for relative azimuth angles close to zero.

1.4. Verification and validation of MAX-DOAS measurements of NO2 and SO2 close

to a shipping lane

André Seyler, Folkard Wittrock, Enno Peters and Andreas Richter (IUP-Bremen)

1.4.1. Introduction

University of Bremen has operated a MAX-DOAS instrument on the radar tower on the island of Neuwerk in the German Bight for several years. The station was located close to a major shipping lane directed to and from the port of Hamburg, one of the three largest ports of Europe. Measurements were performed at several azimuthal angles pointing towards different parts of the shipping lane. The station on the tower was also equipped with in-situ instrumentation including NO2 and SO2 observations. Both MAX-DOAS and in-situ measurements were performed at 60 m altitude.

More than two years of data from this station were analysed for shipping signals, and this data set can be used to evaluate the consistency and usefulness of MAX-DOAS observations of boundary layer trace gas concentrations. As the focus of the observations was on ship emissions, and the best sensitivity is obtained for measurements at very low elevation angles, mixing ratios were computed from the slant columns taken at 0.5° elevation by division through the light path length as computed from the O4 absorption. Details on the instrument and data analysis can be found in Seyler et al. (2017).

Using the MAX-DOAS data from two wavelength regions (UV and visible) in combination with the in-situ data, three consistency tests can be performed on the data:

1. Comparison of NO2 results obtained in the UV and in the visible

2. Comparison of MAX-DOAS and in-situ derived mixing ratios

3. Comparison of MAX-DOAS and in-situ derived weekly and diurnal variations in mixing ratios

Results from the three comparisons are presented in the next sections.

1.4.2. Comparison of NO2 mixing ratios retrieved in UV and visible fitting windows

The MAX-DOAS instrument operated in Neuwerk had two channels, one for the UV and one for the visible. Therefore, two fitting windows could be used for the retrieval of NO2, 338–370 nm and 425–497nm, respectively. As Rayleigh scattering increases rapidly at shorter wavelengths, the mean light path in the UV is shorter than in the visible, and for a homogeneous NO2 field, larger NO2 slant columns are expected in the visible fitting window. As shown in Figure 4, this is indeed the case, the ratio between the two slant columns being approximately 1.3. Apart from this expected factor, the consistency of the two data sets is very good (correlation 0.983) with small scatter. It is however interesting to note that there is a tail towards clearly larger ratios.

The mean light path can be determined from the O4 slant columns retrieved in the two windows. Dividing the slant columns by this length provides mixing ratios which should be independent of wavelength. As also shown in Figure 4, this is the case, and the two independent NO2 mixing ratio products agree very well (slope 0.98, correlation 0.984).

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While the good agreement between the two NO2 mixing ratio products does not prove that the values as such are correct, it shows the very good internal consistency of mixing ratios determined from MAX-DOAS measurements with this method.

Figure 14: (a) Scatter plot: NO2 slant column density retrieved in the visible vs. UV spectral ranges measured in all azimuth angles at 0.5° elevation for solar zenith angles smaller than 75°. The parameters derived from the linear fit by orthogonal distance regression (Deming regression) are also shown. (b) Histogram of the ratio of the two NO2 slant column densities (visible = UV). Panel (c) is the same as panel (a) but for volume mixing ratios. (d) Histogram of the ratio of the two NO2 volume mixing ratios (visible = UV).

1.4.3. Comparison of MAX-DOAS and in-situ NO2 and SO2 mixing ratios

When comparing the NO2 mixing ratios determined with the MAX-DOAS and the in-situ instrument for a single day (Error! Reference source not found.), three observations can be made: a) the in-situ mixing ratios are much higher than those determined by MAX-DOAS, b) the in-situ peaks are broader than the MAX-DOAS observed peaks and c) the in-situ observations appear to lag behind the MAX-DOAS measurements by several minutes, sometimes even more.

All these observations can be understood from the measurement geometry of the MAX-DOAS instrument and the location of the in-situ instrument close to the MAX-DOAS. The distance between the station and the main shipping lane was about 6-7km. A typical light path length of 15 km was determined for the visible MAX-DOAS measurements on good measurement days, reaching well beyond the shipping lane. It is therefore clear, that the assumption of a homogeneous NO2 mixing between instrument and the last scattering point is not correct. On the contrary, it is to be expected that a plume from a ship will be rather localised within the light path, and dividing the observed NO2 signal by the total light path length will lead to a systematic underestimation of the mixing ratio within the plume. How much of the light path is affected by the plume from an individual ship depends on the wind direction and the direction of travelling of the ship relative to the light path. This explains the difference in the magnitude of the mixing ratios reported from the two measurement systems.

Whether or not the in-situ instruments will measure the enhanced NO2 and SO2 values from a ship detected by the MAX-DOAS instrument, depends on wind direction. If the wind direction is such, that the air is transported from the ship to the radar tower, the signal will be observed but with a delay depending on wind speed and distance of the ship. During the transport, the ship plume will broaden

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as it evolves. This explains the delay between the two measurements as well as the broader peak seen by the ins-situ observations.

Figure 15: MAX-DOAS and in situ NO2 volume mixing ratio, AIS and wind data on 9 July 2014: (a) MAX-DOAS (visible) and in situ NO2 VMRs. (b) Vertical bars indicate that a ship is in the line of sight of the instrument. Solid bars: ship moves from left to right (west to east); dashed bars: vice versa; colours represent ship length. (c) Wind speed and direction measured on Scharhörn (HPA).

Figure 16: Comparison of MAX-DOAS (UV) and in situ daily mean VMRs of NO2 (a) during summer 2014 and SO2 (b) during summer 2013. Shaded areas show the standard deviation for each daily mean value.

In time periods where there are no enhanced NO2 values from a ship, the measurements from the in-situ and the MAX-DOAS instrument agree very well. These “background” observations are characterised by a more homogeneous NO2 distribution, and under these conditions, the MAX-DOAS measurements provide representative mixing ratios for the air around the station.

In Figure 16, the evolution of the NO2 and SO2 mixing ratios as observed from the two instruments is shown. As already illustrated for July 9, 2014, the NO2 from the in-situ instrument is consistently higher than the MAX-DOAS NO2. However, the temporal evolution of the two data sets is similar, and in particular in summer, very good correlation is found between the two data sets (0.86 for visible and 0.91 for UV) for daily values as shown in Figure 17. The fact that the UV measurements show slightly better correlation in spite of the lower signal to noise ratio is probably linked to the shorter

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mean light path which makes the data more comparable to the in-situ observations at the radar tower.

Figure 17: Scatter plot of (a) NO2 VMR and (b) SO2 VMR from MAX-DOAS vs. in situ. For NO2, daily means from summer 2014 are shown, and for SO2, daily means from summer 2013 are shown. For the MAX-DOAS instrument, to get a better statistic, all measurements in all azimuth viewing directions have been averaged. For the in situ instrument, the mean of all measurements during the daily MAX-DOAS measurement periods (sunrise till sunset) has been taken. The linear fits were calculated with orthogonal distance regression (Deming regression); parameters are shown in the figures.

Also shown in Figure 16 and Figure 17 are the measurements of SO2. Again, very good agreement in the temporal evolution as well as a high correlation (0.92) of daily values is found. Compared to NO2, the ratio between in-situ and MAX-DOAS measurements is 1.18, much closer to 1. The main reasons for the difference are a) the shorter light path in the UV fitting window (307.5–317.5 nm) which reduces averaging and b) the smaller contribution of shipping SO2 to the overall SO2 signal which reduces the effect of spatial inhomogeneity.

1.4.4. Comparison of diurnal and weekly cycles

Figure 18: Left: Average diurnal cycle of MAX-DOAS (UV and visible) and in situ NO2 volume mixing ratios for all measurements (solid lines) and for a subset of measurements with wind from the open North Sea (dashed lines). For a better visual comparability, the in situ values are scaled by a factor of 0.4. Right: Same as left but for the weekly cycle.

An additional test of the quality of the MAX-DOAS observations can be obtained by comparing the diurnal and weekly evolution of the NO2 mixing ratios determined by the two measurement methods. As it is expected that both show quite different behaviour depending on whether the wind is coming from the shipping lane or from land, the complete data set as well as the shipping influenced data have been evaluated separately. In Figure 18, the results are shown for both the diurnal and the weekly cycle. MAX-DOAS measurements are available only during daylight; for the weekly cycle, only daytime in-situ measurements were included. For better visual representation, the

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in-situ data have been scaled using the factor of 0.4 which was determined from the correlation of the two data sets. As can be seen from the figure, there is excellent agreement in both the diurnal and the seasonal evolution of the NO2 mixing ratios from both systems. The agreement is slightly better for the UV observations because of the shorter mean light path. In particular, the MAX-DOAS measurements are able to pick up the difference in behaviour between air masses coming from shore and those influenced mainly by shipping emissions. While air from land shows a clear weekly cycle, this is not the case for shipping emissions. Similarly, the diurnal cycle is much more pronounced for air masses coming from land. Both results reflect the more or less continuous operation of ships, which is in contrast to the more variable operation of sources on shore.

1.4.5. Summary and conclusions

Several years of MAX-DOAS measurements of NO2 and SO2 mixing ratios at the island of Neuwerk have been compared to co-located in-situ observations. The results show that under background conditions, excellent agreement is found between the results from the two measurement systems. In cases where emission plumes from individual ships are observed, the agreement is less good because of the characteristics of the MAX-DOAS measurements which average over a long light path which passes through both background and plume affected air masses. Taking this systematic difference into account, very good agreement is found for the temporal evolution, diurnal variation and weekly cycle of NO2 when comparing MAX-DOAS and in-situ measurements.

1.5. Validation of NO2 VCDs based on surface concentration measurements:

Application to the QA4ECV Thessaloniki station

Theano Drosoglou and Alkiviadis F. Bais (AUTH)

1.5.1. Introduction

Several studies have inter-compared NO2 measurements performed by MAX-DOAS with long-path DOAS or in-situ observations (e.g. Wagner et al., 2011). The trace gas volume mixing ratios (VMRs) near the surface can be obtained from MAX-DOAS measurements using appropriate inversion algorithms for the profile retrieval. Nevertheless, a few studies have applied parameterization methods, such as the one introduced by Sinreich et al. (2013). In the following, we compare MAX-DOAS NO2 measurements performed at the Laboratory of Atmospheric Physics (LAP) in Thessaloniki, Greece, with independent data sets from a co-located long-path DOAS as well as in situ products from the local air quality monitoring network. For the comparisons, two different methodologies are used. The first one is based on the studies of Sinreich et al. (2013) and Wang et al. (2014). The second methodology is based on NO2 profiles derived by air quality modeling and boundary layer height (BLH) data, assuming that the NO2 content observed by the MAX-DOAS is well within the boundary layer.

1.5.2. MAX-DOAS NO2 and O4 retrievals

For the comparisons presented here, MAX-DOAS data obtained in Thessaloniki for the years 2014 and 2015 were used. The differential slant column densities (dSCDs) have been produced by spectra analysis using the QDOAS software version 2.111 (http://uv-vis.aeronomie.be/software/). For the first comparison approach investigated, both NO2 and O4 dSCDs have been derived in the spectral window 338-370 nm. For the second methodology, the NO2 VCDs retrieved within the QA4ECV project are validated.

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1.5.3. In situ and long-path DOAS observations

A multi-gas long-path air quality monitoring system (SANOA) was deployed and had been operating at the campus of the Aristotle University of Thessaloniki (AUTH) for about 1.5 year, from mid-February 2013 to mid-July 2014. The SANOA receiver was installed at LAP, at a short distance from the MAX-DOAS instrument, while its projector was installed at the roof of the building of meteorology, at a distance of about 312m from LAP. The analyzed NO2 mixing ratios have been collected and averaged at 1 hour intervals.

In addition, in situ NO2 observations from the official air pollution monitoring network operated by the Region of Central Macedonia (RCM) are available online (http://www.ypeka.gr/). Hourly mean NO2 mixing ratios have been collected for the years 2013-2016.

1.5.4. From NO2 slant column densities to near surface volume mixing ratios

Methodology

For the calculation of near-surface NO2 mixing ratios from MAX-DOAS dSCDs, the effective light path length was estimated by the O4 dSCDs using the equation:

𝐿 =𝑑𝑆𝐶𝐷𝑂4𝑐𝑂4

( 1 )

The O4 concentration near the surface was assumed equal to 2.84×1037 molecules2 cm-6. The effective path length can be then used to convert the NO2 dSCDs to VMRs:

𝑐𝑁𝑂2 =𝑑𝑆𝐶𝐷𝑁𝑂2

𝐿 ( 2 )

Sequential observations of NO2 and O4 at 2° and 3° elevation angles were used, only if the dSCDs at the two different directions agree within 10%. The agreement of O4 dSCDs of the lower angles ensures that the scattering occurred at comparable distances, while the agreement of NO2 dSCDs ensures that the scattering occurred in the NO2 layer, which can be assumed a homogeneous near-surface layer (Sinreich et al., 2013). Measurements performed with solar zenith angle (SZA) >70° and relative to the sun azimuth angle (RAA) <50° were excluded due to the large uncertainties introduced for those observation geometries according to Wang et al., 2014. Both O4 and NO2 dSCDs were derived at the same wavelength region and, thus, the effective path length estimated from O4 observations could be applied to NO2 measurements without the aid of a wavelength correction factor.

The application this simple retrieval approach introduces systematic errors if the NO2 profile shape is different from the O4 profile, which is usually the case since NO2 emissions occur mainly near the surface. In order to account for the different vertical distributions of O4 and NO2, correction factors (cf) have been calculated by simulations performed using the uvspec model. Based on the work of Wang et al., 2014, homogeneous trace gas layers with heights 0.5, 1 and 2 km were assumed for the simulations, while the aerosol vertical distribution was scaled for different AOD values in the range 0-2 and BLH (0.5, 0.8, 1.3 and 1.8 km). The aerosol content within the BLH was assumed to be 60% of the total AOD, while the other 40% in the free troposphere, based on co-located lidar climatology (Siomos et al., 2018, ACPD in review). The correction factors were calculated using equation (1) and the following formula:

𝑐𝑓 =𝑑𝐴𝑀𝐹𝑂4×𝑉𝐶𝐷𝑂4

𝐿×𝑐𝑂4 ( 3 )

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The O4 VCD was assumed equal to 1.3×1043 molecules2 cm-5 from the US standard atmosphere used as RTM input. A 2nd order polynomial fitted to the modeled correction factors as a function of O4 dAMF was used for the calculation of the cf value of each measurement.

Results

The MAX-DOAS derived VMRs tend to underestimate the near-surface VMRs, due to the different profile shapes of O4 and NO2. With the correction factors these deviations are reduced. However, differences from the near-surface VMR data sets are still observed (Figures 19 and 20).

Figure 19: Scatter plot of the MAX-DOAS derived VMRs versus the in situ (left panel) and long-path DOAS (right panel) observations. For the comparison with the SANOA hourly averages for the period mid-February 2013 to mid-July 2014 are presented, while for the comparison with in situ the hourly data set covers the years 2013-2016. The parameters of the regression line, as well as the 95% confidence and prediction intervals are presented on the plots.

By plotting the MAX-DOAS VMRs versus the near-surface data sets, a significant scattering of the data pairs can be clearly seen. The calculated correlation coefficients (R) are equal to 0.56 and 0.62 for the comparison with in situ and SANOA measurements, respectively. The R value is larger when the MAX-DOAS data are compared with the long-path DOAS VMRs. However, the estimated R from the comparison with in situ VMRs is more significant statistically due to the much larger number of data pairs. The 95% confidence intervals (CI) of the R value are estimated to be about 0.52 to 0.59 and 0.48 to 0.73 for in situ and long-path DOAS, respectively. The negative intercept (-1.9) of the linear regression in case of long-path DOAS indicates that the near-surface measured VMRs are in general larger than the MAX-DOAS derived VMRs for relatively low NO2 concentrations. In contrast, the positive intercept in case of in situ indicates higher VMRs from MAX-DOAS compared to the in situ observations. The much larger slope value in case of MAX-DOAS versus SANOA linear regression, almost double, indicates better agreement. However, the slope of the fit between MAX-DOAS and in situ observations is more significant statistically presenting a standard error of 0.019, about ten times lower.

A general underestimation of the VMRs from the MAX-DOAS instrument is obvious also from the frequency histograms of Figure 20. The differences of the NO2 concentrations between the MAX-DOAS and near-surface VMRs for both in situ and long-path DOAS are centered around -5 ppb, while a tail of a few positive differences is observed. The average difference of the MAX-DOAS VMRs is about -7.52 ± 18.78 ppb and -1.5 ± 13.42 ppb compared to in situ and long-path DOAS, respectively.

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Figure 20: Histogram of the deviations of the MAX-DOAS derived VMRs from the in situ (left panel) and long-path DOAS (right panel) observations. The data sets are the same as in Figure 19.

Diurnal averages of the data sets used in this study are presented in Figure 21. MAX-DOAS diurnals have been calculated for both comparison data pairs and are shown separately. The agreement with the near-surface data is very good quantitatively and in terms of the diurnal variations, although the latter are not significant. The better agreement of the VMRs derived from the MAX-DOAS measurements with the long-path observations is obvious also in this figure. The standard deviations of the hourly averages are quite large, with those of the MAX-DOAS reaching values up to about 30 ppb.

Figure 21: Diurnals of the MAX-DOAS derived VMRs and the near-surface VMRs from in situ (left panel) and long-path DOAS (right panel) observations. The color-shaded areas represent the 1σ standard deviations of the hourly averages. The data sets used for the diurnals are the ones presented in Figure 19.

1.5.5. From in situ and long-path observations near the surface to vertical column

densities

Methodology

The near-surface concentrations from the independent datasets expressed in ppm were converted in tropospheric vertical columns (molecules cm-2) in order to be compared with the MAX-DOAS data. The conversions were based on the following methodology: The SANOA and in situ mixing ratio

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values near the surface were used to scale the average vertical NO2 profile derived from air quality simulations with the CAMx-WRF modeling tool, in order to derive a simulated NO2 profile for each near-surface measurement. These scaled profiles were integrated up to the boundary layer height (BLH), assuming that the bulk of the NO2 resides well within the atmospheric boundary layer. The CAMx-WRF simulations cover the period from November 2014 to May 2015. It should be noted here that the BLH data were selected from the ERA Interim reanalysis dataset which was downloaded from the European Centre for Medium – Range Weather Forecast, ECMWF, in spatial resolution of 0.125° × 0.125° (~12.5 km) and temporal resolution of 3 h.

For the comparison, only MAX-DOAS NO2 tropospheric columns derived at an elevation angle of 15° were used, in order to eliminate the uncertainty introduced in AMFs of lower elevation angles by AOD loading. Moreover, observations corresponding to SZA>80° have been filtered out. Hourly averages have been calculated and compared with the tropospheric VCDs estimated by the in situ data.

Results

The MAX-DOAS tropospheric ΝΟ2 columns are plotted versus those estimated from in situ and long-path DOAS observations in Figure 22. Similarly to the comparison results in section 1.5.4 the long-path DOAS derived columns are in better agreement with the MAX-DOAS measurements, although the significance of the statistics is higher in case of in situ due to the larger number of data pairs. The R value is 0.55 and 0.64 for in situ and long-path DOAS, respectively. These values are comparable to the ones presented in section 1.5.4 indicating significant scattering of the data points. The 95% confidence intervals (CI) of the R value are estimated to be about 0.54 to 0.57 for in situ and 0.54 to 0.72 for long-path DOAS. The estimated slope and intercept from the linear regression fitting of the MAX-DOAS versus in situ data are 0.71 and 2.5×1015 molecules/cm2, respectively. In case of long-path DOAS, the higher than unity slope value indicates that the MAX-DOAS columns are in general larger than those derived from near-surface observations for relatively high NO2 concentrations. Probably, in such cases the MAX-DOAS observations capture better the high NO2 loadings from the city center compared to the long-path measurement which detect only the locally concentrated NO2 within ~300km in the university campus.

Figure 22: Scatter plot of the MAX-DOAS NO2 columns versus those derived from in situ (left panel) and long-path DOAS (right panel) observations. For the comparison with the SANOA hourly averages for the period mid-February 2013 to mid-July 2014 are presented, while for the comparison with in situ the hourly data set covers the years 2013-2016. The parameters of the regression line, as well as the 95% confidence and prediction intervals are presented on the plots.

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The frequency histograms of the differences between the MAX-DOAS measured tropospheric NO2 and the columns estimated by near-surface observations are presented in Figure 23. The deviations from the in situ data are centered on zero, while those from SANOA estimations are centered around -2.5×1015 molecules/cm2. The average difference of the MAX-DOAS NO2 from in situ and long-path DOAS estimations is about 0.22 ± 5.68×1015 and 1.21 ± 6.55×1015 molecules/cm2, respectively.

Figure 23: Histogram of the deviations of the MAX-DOAS NO2 from the in situ (left panel) and long-path DOAS (right panel) observations. The data sets are the same as in Figure 22.

Diurnal averages of the columnar data pairs are presented in Figure 24. MAX-DOAS diurnals have been calculated again for both comparison data pairs and are shown separately. The agreement with the near-surface derived data is clearly good. Interestingly, there is better agreement compared to the diurnals of VMRs derived from the MAX-DOAS and in situ measurements in section 1.5.4

Figure 24: Diurnals of the MAX-DOAS tropospheric NO2 and the columns estimated from near-surface VMRs measured by in situ (left panel) and long-path DOAS (right panel) observations. The color-shaded areas represent the 1σ standard deviations of the hourly averages. The data sets used for the diurnals are the ones presented in Figure 22.

1.5.6. Conclusions

MAX-DOAS measurement of tropospheric NO2 have been compared with near-surface observations obtained by a long-path DOAS and an in situ network. In order to compare the different products two

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separate methodologies have been applied. In the first method, the MAX-DOAS columns were converted into VMRs by the estimation of the effective light path of photons through the atmosphere. In the second method the near-surface data were converted into integrated vertical columns using BLH reanalysis data and a simulated by air quality model vertical profile. The comparisons in both cases are sufficiently good, although the scattering of the data pairs is quite significant. Interestingly, the MAX-DOAS and in situ observations compare better when using the second methodology.

The differences between the MAX-DOAS and near-surface observations can be attributed to the different retrieval methods of the trace gas concentrations and mainly to the different vertical and horizontal distances within which the measurements are sensitive. The MAX-DOAS derived NO2 represent concentrations extended vertically up to about a few hundred meters, depending on the elevation viewing angle and the time of day, while the long-path DOAS and in situ observations are representative of the NO2 close to the surface. In addition, the the MAX-DOAS data sets represent the integrated concentration over an effective light path of several kilometers (a mean of ~10km was estimated for the data presented here), while the long-path DOAS measurements represent the average NO2 loading within a distance of 312m. In case of in situ measurements the observed NO2 values are dependent on the air sample inserted in the instrument and, thus, representative of the very local NO2 levels. Probably, this explains also the better agreement between MAX-DOAS and long-path DOAS.

2. Quality assessment of QA4ECV FTIR CO reference data sets

Bart Dils and Bavo Langerock (BIRA-IASB)

The CO reference dataset used within QA4ECV are derived from two distinct sources, namely TCCON (Total Carbon Column Observing Network) and NDACC (Network for the Detection of Atmospheric Composition Change). While both use solar-absorption FTIR measurements, they fundamentally differ in the spectroscopic region used and methodology employed. TCCON measures CO2, CO, N2O, CH4, H2O, HDO and HF in the near-infrared and uses the GFit algorithm to scale a fixed a priori shape so as to best confirm to the observed spectra. It also simultaneously measures O2 to derive the total column dry air mole fraction of CO, typically labelled XCO. All stations within TCCON adhere to the very same retrieval and analysis protocol. NDACC measurements (covering a much wider range of species) are performed in the mid and far infrared, using optimal estimation techniques which allows (within boundaries) variation of the profile shape itself. Therefore apart from the total column it can provide limited information on the vertical distribution of gases. Given that the network originated from a more research oriented objective, it is less harmonized compared to TCCON (which was set up with the distinct purpose of satellite validation) although constant efforts are made by the community to come to a uniform retrieval strategy for the wide range of species observed. Within QA4ECV further efforts to harmonize the retrieval (and uncertainty calculation) strategy have been completed. The uncertainty on the spectroscopy and the estimation of the uncertainty on the apriori state of the atmosphere (temperature, pressure, water vapor, …) and other important input parameters for the optimal estimation retrieval of the target gas are now fixed and consistent for all participating sites.

2.1. TCCON validation

TCCON itself is extensively compared with dedicated aircraft profile measurements. It was shown that all involved stations exhibited the same bias towards the aircraft measurements (see Wunch 2010). TCCON thus uses these comparisons to apply a correction factor to the entire network essentially bringing it in line with the WMO standard. For XCO the measurement precision varies

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somewhat from site to site but is generally below 1% under good measurement conditions. The correction factor derived from aircraft and balloon measurements is 1.0672±0.02, which implies an accuracy (1 sigma) of 1.9%.

2.2. NDACC validation

No such dedicated aircraft validation campaigns have been set up to validate NDACC (QA4ECV) CO profiles. However, the In-service Aircraft for a Global Observing System (IAGOS) provides in situ CO measurement data from commercial aircrafts. These measurements are performed using an improved infrared correlation instrument, with a 30 s time resolution (corresponding to a travel distance of 7.5 km at cruise altitude) and a precision of ±[5 ppbv + 5%] (see Nédélec et al. (2003)). To compare these vertical profiles (obtained during ascent and decent of the aircraft from/towards the airport) with the NDACC profiles, we collocate measurements that are taken within half a day of one another. The profiles are then interpolated onto the NDACC grid and smoothed using the NDACC averaging kernels according to Rodgers and Conner (2003). The IAGOS profile of course does not cover the entire altitude range, therefore missing values in the IAGOS profile a taken from the NDACC profile. See Figure 25 for an example.

Figure 25: CO vertical profile obtained from NDACC (red) and IAGOS (blue). The black line represents the IAGOS profile after interpolation on the NDACC grid and smoothing using the averaging kernel.

Unfortunately the limited (in terms of spatial and temporal coverage) IAGOS and QA4ECV time series and the absence of nearby IAGOS serviced airports make for a very limited comparison pool. Probably the best case are the FTIR measurements made at the University of Toronto which were matched with the Toronto airport profiles. Figure 26 shows the average bias (black solid line), the 95% confidence band on the mean (dashed black line) as well as the standard deviation of the bias (red dashed line). As one can see, the NDACC profile seems to somewhat underestimate the values below 1.5 km (up to -0.02 ppm), while overestimating those at higher altitudes (up to ~0.015 ppm). One has to note (from the standard deviation) that the difference in bias from one day to another is considerable. Also of note is that the FTIR profiles have limited degrees of freedom. Thus in essence one should look at partial columns which obtain a DOF≥1 instead of the high resolution profile itself. To this end we calculated the lowest possible partial column which reaches a DOF ≥ 1. The bias time series between the thus derived IAGOS and QA4ECV partial columns is listed in Figure 27. For Toronto we see an average difference between the partial column mixing rations =0.2 ppb + 0.5 ppb (95% confidence interval on mean), which is much lower than the 5 ppbv precision of the instrument onboard the aircraft (keeping in mind that one pertains to a partial column and the other to an in situ measurement).

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Figure 26: Vertical profile of the average difference between FTIR and (smoothed) IAGOS aircraft profiles (black solid line) measured at Toronto. The dashed lines correspond with the standard deviation (red) and 95% confidence interval on the mean (black)

Figure 27: Difference in (tropospheric) partial column CO concentrations (in ppm) at Toronto

None of the other FTIR sites featured such a close-proximity IAGOS serviced airport, so we unfortunately either had to stretch the spatial collocation or temporal collocation criteria too far to be of any scientific use. However no strong anomalies were observed even when relaxing the criteria considerably.

A second source of comparison is the comparison between NDACC CO and TCCON CO. These comparisons have been performed within the NORS project (Petri et al., 2013) at 4 different stations (Bremen, Izana, Jungfraujoch and St. Denis (Reunion)). They found that while the overall agreement between the total column values was good, it was not perfect (R values above 0.92 and bias values smaller than 0.013E18 molec/cm2). NDACC particularly shows higher variability in its CO values. This is assumed to originate from its retrieval of the lower troposphere (where most of the concentration variability takes place) where NDACC is more sensitive to this variability in its profile retrieval approach compared to the more constrained profile scaling method employed by TCCON.

Acknowledgements

MOZAIC/CARIBIC/IAGOS data were created with support from the European Commission, national agencies in Germany (BMBF), France (MESR), and the UK (NERC), and the IAGOS member institutions (http://www.iagos.org/partners). The participating airlines (Lufthansa, Air France, Austrian, China Airlines, Iberia, Cathay Pacific, Air Namibia, Sabena) supported IAGOS by carrying the measurement equipment free of charge since 1994. The data are available at http://www.iagos.fr thanks to additional support from AERIS.

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3. Concluding remarks

A first database of harmonized MAXDOAS NO2 and HCHO vertical column data has been created within the QA4ECV project, based on the use of common DOAS analysis settings and by applying an AMF LUT approach for the conversion of the 30° elevation DSCDs into VCDs at a selection of 9 stations. Due to the lack of correlative vertical column observations, the validation of the QA4ECV harmonization method has mainly consisted in (1) the application of this method on SCDs measured during the CINDI-2 MAXDOAS intercomparison campaign held in Cabauw (The Netherlands) in September 2016, and (2) the comparison of the resulting VCDs to those obtained using more advanced OEM-based profiling algorithms. Since all the groups operating MAXDOAS systems at QA4ECV stations also participated to this campaign, this exercise can be considered as an indirect validation of the harmonized MAXDOAS QA4ECV data sets.

In the case of NO2, a good consistency is found between QA4ECV and OEM-based VCDs with mean bias between -12%and +7% and correlation coefficients in the 0.85-0.92 range. Regarding HCHO, larger discrepancies are obtained, more likely related to the fact that, due to its lower absorption signal, the DOAS analysis of this species is made more difficult and more sensitive to settings changes and to instrument performance. Regression analyses between QA4ECV MAXDOAS v2 (harmonised) and v1 (non-harmonised) data sets, as well as between v2 and QA4ECV OMI data products also showed that the harmonisation has a stronger impact on HCHO than NO2 VCDs and that the consistency between the stations, e.g. in terms of agreement with OMI, is improved when using harmonised MAXDOAS data sets. The main outcome of these validation tests is that all the QA4ECV MAXDOAS HCHO and NO2 v2 data sets are recommended for satellite validation purpose, except the HCHO VCDs in the 336.5-359 nm wavelength range at the Thessaloniki station.

In addition, the NO2 a priori profiles used for the generation of QA4ECV dAMF LUTs have been validated against NO2 sondes performed by KNMI. An average difference of 4% is found, when using the actual NO2 profile instead of the exponentially decreasing assumed in QA4ECV retrievals. The corresponding mean error on the retrieved tropospheric NO2 columns is 4%. Higher errors can be observed at specific viewing directions; 7% for relative azimuth or 90° and 180° and up to 40% when pointing close to the sun. Moreover, independent attempts have been made by a couple of institutes, namely IUP-Bremen and AUTH, for the validation of their own data sets using near-surface NO2 observations. More specifically, NO2 and SO2 mixing ratios derived from measurements performed by IUP-Bremen MAXDOAS system at the island of Neuwerk have been compared with co-located in-situ data. The results imply excellent agreement under background conditions and a less good agreement when emission plumes from individual ships are observed. AUTH has applied two different methodologies for the comparison of MAXDOAS derived NO2 with co-located in situ and long-path DOAS data sets. Both methods indicate better agreement between MAXDOAS and long-path DOAS time series compared to in situ measuremnts. In general, the differences observed between the near-surface and MAXDOAS data can be attributed to the different retrieval methods and mainly to the different vertical and horizontal distances within which the measurements are sensitive. In any case, these comparisons with the independent data sets can be assumed only as indirect validation of the MAXDOAS reference data sets created within the QA4ECV project.

The QA4ECV MAXDOAS harmonisation exercise was a first attempt to develop a harmonised database of MAXDOAS data products. Although this exercise led to a reasonably good consistency between the stations, especially for NO2, it showed some limitations: e.g. only VCDs are provided, recommendations and settings were applied on a best effort basis, difficulty to harmonise the DOAS fit of species with low absorption signal like HCHO. Only the development of a systematic level 1 (radiance spectra) to level 2 (vertical columns and profiles) centralised processing service using state-of-the-art community algorithms and instrument operation guidelines would allow to overcome these issues and to reach the highest level of consistency/homogeneity between the stations. Such a

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service is currently being developed within the ESA project FRM4DOAS (see http://frm4doas.aeronomie.be/).

Regarding the total CO column, two FTIR reference data sets have been created within the QA4ECV project; each derived from a different observation network, namely TCCON and NDACC. The TCCON data set has been extensively validated with dedicated aircraft profile measurements. From the comparisons, a correction factor of 1.0672±0.02 has been estimated, indicating an accuracy of 1.9%. The NDACC QA4ECV data set has been compared with IAGOS in situ CO observations performed by commercial aircrafts. Due to the limited coverage of IAGOS and NDACC time series, in some cases the spatial or temporal collocation criteria had to be considerably relaxed. However, this has not affected the comparisons significantly. In the best case of FTIR observations from the University of Toronto, which were matched with the Toronto airport profiles, an average difference between the partial column mixing rations of 0.2 ppb is found. This value is much lower than the precision of the instrument onboard the aircraft (5ppbv). The agreement between NDACC and TCCON total CO columns is very good, revealing R values above 0.92 and bias less than 0.013E18 molec/cm2. In general, NDACC observations are characterized by higher variability compared to TCCON CO data, probably due to the higher sensitivity of NDACC retrievals to the lower troposphere.

References:

Rodgers, C. D., Inverse Methods for Atmospheric Sounding, Theory and Practice. World Scientific Publishing, Singapore-NewJersey-London-Hong Kong, 2000.

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