TROPOMI ATBD of thetotal and troposphericNO2 data products

document number: S5P-KNMI-L2-0005-RP

authors: J.H.G.M. van Geffen, H.J. Eskes, K.F. Boersma and J.P. Veefkind

CI identification: CI-7430-ATBD

issue: 2.4.0

applicable to processor release : 2.4.0
date: 2022-07-11

status: released

TROPOMI ATBD tropospheric and total NO2
issue 2.4.0, 2022-07-11 – released

S5P-KNMI-L2-0005-RP
Page 2 of 88

Document approval record
This document was prepared by J.H.G.M. van Geffen, H.J. Eskes, K.F. Boersma and J.P. Veefkind. It waschecked by S. Beirle, A. Richter, B. Sanders.

TROPOMI ATBD tropospheric and total NO2
issue 2.4.0, 2022-07-11 – released

S5P-KNMI-L2-0005-RP
Page 3 of 88

Document change record
issue

date

item

comments

0.0.1

2012-08-17

All

Initial draft version

0.0.2

2012-09-12

All

Major reordering, adding text and references throughout

0.0.3

2012-09-26

6.1–6.2

Several small corrections and additions

0.1.0

2012-09-26—

First official release

0.2.0

2012-11-15

5.1–5.4

Large number of updates throughout the text after internal

6.1–6.2

reviewing by the TROPOMI Level-2 Working Group

0.2.1

2013-04-10

5, 6, 7

Document number corrected, reorganisation of Sect. 6; removal ofthe discussion on an alternative retrieval approach; various minor
corrections, updates and additions

0.2.2

2013-06-03

5, 6, 7

Major updates and further reorganisation of Sect. 5–7

8, 9, 10first versions of Sect. 8–10

0.3.0

2013-06-04—

Release for Level-2 Working Group review

0.3.1

2013-06-19

5.1–5.3

Correction and additions resulting from v0.3.0 internal Level-2

6.2–6.6

Working Group review, and other minor corrections and additions

7.1–7.2

7.3–7.5
8.4, 9.2
0.5.0

2013-06-20—

Release for external review

0.5.1

2013-09-05

5.1–5.2

First round of updates taking into account comments and6.1–6.6

suggestions of the reviewers of version 0.5.0

7.2, 7.5
0.5.2

2013-11-21

5.5

Further updates in view of the reviewers comments, and providing

6.2–6.7

more details regarding the fit procedure and the processing chain

7.1, 7.2
8.2, 8.3
0.9.0

2013-11-27—

Release to ESA and external reviewers

0.9.1

2014-03-17

4.

Instrument overview section now in separate document; section numbering of this document unchanged to maintain change record

0.9.2

2014-04-07

6.2

Some small typographic corrections and updates

0.10.02014-04-15—

Release to ESA

0.10.1

2014-09-19

6.1–6.6

Minor updates and corrections in text and tables; old Sects. 6.5 and6.7 combined; Sects. 6.6 updated; references updated.

7.1–7.4

Descriptions updated and tables of input and output data added.
Notation of variables improved or clarified in view of the IODD [RD1].

0.10.2

2014-09-25

6.6

Table added with overview user applications and data the users need

0.11.02014-10-02—

Release to ESA

0.11.1

2015-08-27

6.4.1

Update of AMF look-up table entries

6.4.4.1

Update text regarding using cloud fraction from NO2 spectral window

6.6, 7.4

Minor updates in product data set tables, incl. dataset units

0.13.02015-09-14—

Limited release to S5P Validation Team

0.13.1

2015-12-08

6.2, 7.5

Minor textual corrections

7.1

Correction of units of input datasets

0.14.02015-12-11—

Release to ESA

0.14.1

2016-01-21

6.4.2

Temperature correction equation updated

6.4.6

Description of de-striping implementation clarified

TROPOMI ATBD tropospheric and total NO2
issue 2.4.0, 2022-07-11 – released

S5P-KNMI-L2-0005-RP
Page 4 of 88

Document change record – continued
issue

date

item

comments

1.0.0

2016-02-05—

Public release1.0.1

2017-01-31

All

Corrections and additions in response to internal review comments

1.0.2

2017-06-13

A–D

Appendices added

1.0.3

2017-07-13

All

Finalising the corrections and additions

1.1.0

2017-08-16—

Updated public release for commissioning phase (E1)

1.1.1

2018-02-02

6.2

Formulation of the Ring term to match operational implementation

6.2

Improved description of the wavelength calibration

6.2.3

New section with text from main section to improve readability

6.4.6

Expanded description of a possible de-striping algorithm

7.4

Updated detailed product overview table to match output product6.2

Updated to match the operational implementation

C, D

Updated to match the operational implementation

E

Appendix on the qa_value definition added

1.1.2

2018-05-31

1.1.3

2018-06-08

5–9

Many textual corrections and improvements to match the operational
implementation and to incorporate results based on the TROPOMI
measurements from the commissioning phase1.2.0

2018-06-11—

Release for operational phase (E2)

1.2.1

2018-10-15—

Version with main text changes v1.1.0 to v1.2.0 marked for reviewers

1.2.2

2018-11-08—

Figures 1, 3, 5, 7, 27 redone using TROPOMI data

5.4

Text and Table updated

5.6

Text updated and Table 2 added

6.3.1

Text on updates in TM5-MP and Figure 8 added

6.4.6

Text on de-striping expanded and Figure 20 added

6.6

Improved treatment of snow/ice cases described and Figure 24 added

7.4

Tables 11 and 12 updated to include stripe amplitude

8.2

Text updated and superfluous figure removed

E

Updated qa_value for treatment of snow/ice cases (cf. Sect. 6.6)—

Further textual updates

processor version 1.0.0

1.3.0

2018-11-08—

Releaseprocessor version 1.2.0

1.3.1

2019-01-31

6.4.4.1

Text on treatment of cloud data improved; Figure 10 added

6.6

Product overview Table 5 and product usage Table 6 updated

7.4

Detailed product overview Table 11 updatedE

Updated qa_value for M trop /M geo threshold—

Further minor textual updates and corrections

1.4.0

2019-02-06—

Release

1.4.1

2019-04-15

6.2 & F

Added description of removal of outliers from the DOAS fit residual

processor version 1.3.0

C

Description on limiting cloud fraction to [0 : 1] addedE

Updated qa_value for solar_eclipse

1.4.2

2019-10-02—

Remarks and suggestions from A. Richter & S. Beirle addressed

1.4.3

2019-10-03

6.2

Section structure modified to improve readability

9Section on Validation restructured—

Further textual updates and corrections

1.5.0

2019-10-03—

Release to ESA for review

2.0.0

—

—

Not released: no processor update implemented

TROPOMI ATBD tropospheric and total NO2
issue 2.4.0, 2022-07-11 – released

S5P-KNMI-L2-0005-RP
Page 5 of 88

Document change record – continued
issue

date

item

comments

2.0.1

2020-02-19—

Textual updates in view of processor version changes

6.4.4

Usage of ECMWF snow/ice flag & impact of FRESCO update

2.0.2

2020-03-10—

Textual updates addressing remarks from ESA on v1.5.0

2.0.3

2021-01-21—

Minor updates and corrections & digital signatures (page 2) removed

2.0.4

2021-02-24

5.1

General NO2 description expanded

5.6

Version history expanded, including note on data change

6.4

Several subsections finetuned and expanded

6.4.4

Subsection on the FRESCO-wide update added

6.6

Product description and usage updated

7.1–4

Feasibility expanded and updated where needed

9.1–4

Vaidation discussion updated where needed

2.1.0

2021-02-24—

Release to ESA for review

2.1.1

2021-04-15

All

Minor updates after review and other textual improvements

6.4.4

Section on cloud data reorganised and somewhat extended

2.1.2

2021-04-20

9.1–4

Validation sections reorganised and shortend

2.1.3

2021-06-15—

Details finalised

2.2.0

2021-06-16—

Release2.2.1

2022-01-27

All

Minor updates and textual corrections

2.2.2

2022-06-08

6.4.4.2

Description of new TROPOMI DLER v1 surface albedo usage in the

6.4.5
2.4.0

2022-07-11—

processor versions 1.4.0 & 2.2.0

FRESCO cloud and NO2 data retrieval
Releaseprocessor versions 2.4.0

TROPOMI ATBD tropospheric and total NO2
issue 2.4.0, 2022-07-11 – released

S5P-KNMI-L2-0005-RP
Page 6 of 88

Contents
Document approval record . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2Document change record . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

8

1
1.1
1.2
1.3
1.4

Introduction to the document . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Purpose and objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Document overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9
9
9
9
9

2
2.1
2.2
2.3
2.4

Applicable and reference documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Applicable documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Standard documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Reference documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Electronic references . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10
10
10
10
11

3
3.1
3.2

Terms, definitions and abbreviated terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Terms and definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Acronyms and abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

13
13
13

4

TROPOMI instrument description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

14

5
5.1
5.2
5.3
5.4
5.5
5.6

Introduction to the TROPOMI NO2 data products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Nitrogen dioxide in troposphere and stratosphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
NO2 satellite retrieval heritage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Separating stratospheric and tropospheric NO2 with a data assimilation system . . . . . . . . . . . . .
NO2 data product requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
NO2 retrieval for TROPOMI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
NO2 data product: version history and access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15
15
17
19
20
20
21

6
6.1
6.2
6.2.1
6.2.2
6.2.3
6.2.4
6.2.5
6.3
6.3.1
6.4
6.4.1
6.4.2
6.4.3
6.4.4
6.4.4.1
6.4.4.2
6.4.4.3
6.4.4.4
6.4.5
6.4.5.1
6.4.5.2
6.4.6
6.4.7
6.4.8
6.4.9
6.4.106.5

Algorithm description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Overview of the NO2 retrieval algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Spectral fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Wavelength calibration & common wavelength grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Minimising the chi-squared merit function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Reference spectra. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
DOAS fit details for OMI and TROPOMI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Some notes regarding other DOAS implementations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Separation of stratospheric and tropospheric NO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Stratospheric chemistry in the TM5-MP model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Air-mass factor and vertical column calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Altitude dependent AMFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Temperature correction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Correction for cloud cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Cloud cover and cloud pressure data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The FRESCO-S cloud pressure & NO2 cloud fraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Cloud pressure: the FRESCO-wide update. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The O2 –O2 cloud pressure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Other cloud data products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Surface albedo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Processor versions v1.0.2 up to v2.3.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Processor version v2.4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Snow and ice cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Surface pressure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A-priori vertical NO2 profiles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Averaging kernels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
De-striping the NO2 data product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Processing chain elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

23
23
23
25
26
27
27
28
29
31
31
33
34
36
36
36
37
41
41
41
41
42
43
44
45
46
46
48

TROPOMI ATBD tropospheric and total NO2
issue 2.4.0, 2022-07-11 – released

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Page 7 of 88

6.5.1
6.5.2
6.6

Off-line (re)processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Near-real time processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The NO2 data product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

48
49
50

7
7.1
7.1.1
7.1.2
7.2
7.3
7.4

Input-Output file description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Required input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Inputs at the PDGS for spectral fitting and air-mass factor calculation . . . . . . . . . . . . . . . . . . . . . . . .
Inputs at the IDAF for the data assimilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Computational effort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Near-real time timeliness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
NO2 product description and size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

54
54
54
55
55
57
57

8
8.1
8.2
8.3
8.4

Error analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Slant column errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Errors in the stratospheric (slant) columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Errors in the tropospheric air-mass factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Total errors in the tropospheric NO2 columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

60
60
61
62
63

9
9.1
9.2
9.3
9.4

Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Routine validation & validation activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Algorithm testing and verification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Stratospheric NO2 validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Tropospheric NO2 validation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

66
66
66
66
67

10

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

69

A
A.1
A.2
A.2.1
A.3

Wavelength calibration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Description of the problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Non-linear model function and Jacobian. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Prior information for the optimal estimation fit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Application of the wavelength calibration in NO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

70
70
70
72
72

B

High-sampling interpolation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

73

C
C.1

Effective cloud fraction in the NO2 window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Adjusting albedo to respect physical limits to the cloud fraction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

74
75

D

Surface albedo correction using the snow/ice flag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

76

EData quality value: the qa_value flags . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

77

F

Spike removal in the DOAS fit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

78

GReferences. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

80

List of Tables
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

NO2 data product requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
NO2 processor version overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Settings of DOAS retrieval of NO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AMF LUT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Final NO2 vertical column data product. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Data product user applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Dynamic input data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Static input data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Computational effort: off-line processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Computational effort: NRT processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Data product list of main output file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Data product list of support output file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Estimate of AMF errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Tropospheric AMF uncertainty estimates from OMI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A-priori values for the wavelength fit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Look-up tables for reflectance calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Snow/ice flags . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Data quality value detemination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

20
21
26
35
51
52
55
56
56
56
58
59
63
65
72
75
76
77

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19

S5P-KNMI-L2-0005-RP
Page 8 of 88

NO2 process configuration for saturation and outliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

79

List of Figures
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31

Tropospheric NO2 for April 2018 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
COVID-19 lockdown impact on NO2 in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Stratosperic NO2 for 1 April 2018 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
NO2 data record UV/Vis satellite instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
DOAS fit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Reference spectra. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
NO2 forecast and analysis differences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
High-latitude improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Temperature correction factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Example of improvements in cloud treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Explanation of the FRESCO-wide approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FRESCO case study, 23 Jan 2019 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
TROPOMI vs OMI NO2 over China, Winter 2020/21. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
FRESCO cloud fraction change due to S5P DLER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Cloud fraction method comparison. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
S5P DLER at 758 nm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Change NO2 column due to S5P DLER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Albedo adjustment in version 2 compared to version 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Tropospheric NO2 difference from resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
De-striping example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Comparison of vertical columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Scheme of the TROPOMI processing system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Scheme of the TROPOMI processing system in NRT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Enhanced coverage over snow/ice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Error in slant column versus SNR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Comparison of slant column errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Tropospheric column and error estimates from TROPOMI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Tropospheric column comparisons OMI-TROPOMI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
High sampling interpolation on part of a solar observation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Examples of outliers in fit residual . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Examples of outliers and saturation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15
16
17
18
25
28
32
33
34
38
38
39
39
40
40
42
43
44
45
47
48
49
50
53
61
61
64
67
73
78
79

TROPOMI ATBD tropospheric and total NO2
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1

Introduction to the document

1.1

Identification

S5P-KNMI-L2-0005-RP
Page 9 of 88

This document, identified as S5P-KNMI-L2-0005-RP, is the Algorithm Theoretical Basis Document (ATBD)
for the TROPOMI total and tropospheric NO2 data products. It is part of a series of ATBDs describing the
TROPOMI Level-2 data products. The latest public release version of the ATBD is available via [ER1].
This ATBD describes NO2 processor version 2.4.0 and changes to the processor up to this version.
An overview of which NO2 processor version is used for processing which TROPOMI orbits is given in Table 2.
Additional documents related to the TROPOMI NO2 data products:• Product User Manual (PUM), identified as S5P-KNMI-L2-0021-MA, available via [ER2].
• Product ReadMe File (PRF), identified as S5P-MPC-KNMI-PRF-NO2, available via [ER3].
• Product User Manual (PUM) for the TM5 NO2 , SO2 and HCHO profile auxiliary support product, identified
as S5P-KNMI-L2-0035-MA, available via [ER2].
• Quarterly Validation Report (ROCVR), identified as S5P-MPC-IASB-ROCVR, available via [ER4].
S5P/TROPOMI product and algorithm documents are also available via [ER5].

1.2

Purpose and objective

The purpose of this document is to describe the theoretical basis and the implementation of the NO2 Level-2
algorithm for TROPOMI. The document is maintained during the development phase and the lifetime of thedata products. Updates and new versions will be issued in case of changes of the algorithm.

1.3

Document overview

Sections 2 and 3 list the applicable and reference documents and the terms and abbriviations specific forthis document; references to peer-reviewed papers and other scientific publications are listed in Appendix G.
Section 4 provides a reference to a general description of the TROPOMI instrument, which is common to all
ATBDs of the TROPOMI Level-2 data products. Section 5 provides an introduction to the NO2 data products,
their heritage, the set-up of their retrieval, the requirements of the products, and their availability. Section 6
gives an overview of the TROPOMI NO2 data processing system and important aspects of the various steps inthe processing. Section 7 lists some aspects regarding the feasibility of the NO2 data products, such as thecomputational effort and the auxiliary information needed for the processing. Section 8 deals with an error
analysis of the NO2 data product. Section 9 gives a brief overview of validation issues and possibilities, suchas campaigns and satellite intercomparions. Section 10 formulates some conclusion regarding the NO2 data
products.

1.4

Acknowledgements

The authors would like to thank the following people for useful discussions, information, reviews of earlier
versions of this document and other contributions: Andreas Hilboll, Andreas Richter, Angelika Dehn, Bram
Maasakkers, Bram Sanders, Deborah Stein – Zweers, Dominique Brunner, Huan Yu, Isabelle De Smedt, Jason
Williams Johan de Haan, Lidia Saavedra De Miguel, Maarten Sneep, Marina Zara, Mark ter Linden, Michel
Van Roozendael, Piet Stammes, Pieter Valks, Ronald van der A, Steffen Beirle, Thomas Wagner.

TROPOMI ATBD tropospheric and total NO2
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2

Applicable and reference documents

2.1

Applicable documents

S5P-KNMI-L2-0005-RP
Page 10 of 88[AD1] Level 2 Processor Development – Statement of Work.
source: ESA/ESTEC; ref: S5P-SW-ESA-GS-053; issue: 1.1; date: 2012-05-21.
[AD2] GMES Sentinel-5 Precursor – S5p System Requirement Document (SRD).
source: ESA/ESTEC; ref: S5p-RS-ESA-SY-0002; issue: 4.1; date: 2011-04-xx.

2.2Standard documents
There are no standard documents

2.3

Reference documents[RD1] Sentinel 5 precursor/TROPOMI KNMI and SRON level 2 Input Output Data Definition.
source: KNMI; ref: S5P-KNMI-L2-0009-SD; issue: 11.0.0; date: 2019-02-01.
[RD2] Terms, definitions and abbreviations for TROPOMI L01b data processor.
source: KNMI; ref: S5P-KNMI-L01B-0004-LI; issue: 3.0.0; date: 2013-11-08.
[RD3] Terms and symbols in the TROPOMI Algorithm Team.
source: KNMI; ref: S5P-KNMI-L2-0049-MA; issue: 2.0.0; date: 2016-05-17.
[RD4] TROPOMI ATBD of the UV aerosol index.
source: KNMI; ref: S5P-KNMI-L2-0008-RP; issue: 1.1.0; date: 2018-06-15.
[RD5] GMES Sentinels 4 and 5 Mission Requirements Document.
source: ESA/ESTEC; ref: EOP-SMA/1507/JL-dr; issue: 3; date: 2011-09-21.
[RD6] QA4ECV - Quality Assurance for Essential Climate Variables.
source: KNMI; ref: EU-project 607405, SPA.2013.1.1-03; date: November 2012.
[RD7] Science Requirements Document for TROPOMI. Volume I: Mission and Science Objectives andObservational Requirements.
source: KNMI, SRON; ref: RS-TROPOMI-KNMI-017; issue: 2.0.0; date: 2008-10-30.
[RD8] CAPACITY: Operational Atmospheric Chemistry Monitoring Missions – Final report and technical notesof the ESA study.
source: KNMI; ref: CAPACITY; date: Oct. 2005.
[RD9] CAMELOT: Observation Techniques and Mission Concepts for Atmospheric Chemistry – Final reportof the ESA study.
source: KNMI; ref: RP-CAM-KNMI-050; date: Nov. 2009.
[RD10] TRAQ: Performance Analysis and Requirements Consolidation – Final report of the ESA study.
source: KNMI; ref: RP-ONTRAQ-KNMI-051; date: Jan. 2010.
[RD11] Sentinel-5P Calibration and Validation Plan for the Operational Phase.
source: ESA; ref: ESA-EOPG-CSCOP-PL-0073; issue: 1; date: 2017-11-6.
[RD12] NO2 PGE Detailed Processing Model.
source: Space Sytems Finland; ref: TN-NO2-0200-SSF-001; issue: 1.2; date: 2010-04-21.
[RD13] Algorithm theoretical basis document for the TROPOMI L01b data processor.
source: KNMI; ref: S5P-KNMI-L01B-0009-SD; issue: 8.0.0; date: 2017-06-01.
[RD14] S5P/TROPOMI Static input for Level 2 processors.
source: KNMI/SRON/BIRA/DLR; ref: S5P-KNMI-L2CO-0004-SD; issue: 4.0.0; date: 2016-03-21.
[RD15] QA4ECV D4.2 - Recommendations on best practices for retrievals for Land and Atmosphere ECVs..
source: KNMI; ref: EU-project 607405, SPA.2013.1.1-03; date: April 2016.

TROPOMI ATBD tropospheric and total NO2
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S5P-KNMI-L2-0005-RP
Page 11 of 88[RD16] An improved temperature correction for OMI NO2 slant column densities from the 405-465 nm fitting
window.
source: KNMI; ref: TN-OMIE-KNMI-982; issue: 1.0; date: 2017-01-24.
[RD17] Cloud retrieval algorithm for GOME-2: FRESCO+.
source: EUMETSAT/KNMI; ref: EUM/CO/09/4600000655/RM; issue: 1.3; date: 2010-10-18.
[RD18] Sentinel-5 L2 Prototype Processor – Algorithm Theoretical Baseline Document for Cloud data product.
source: KNMI; ref: KNMI-ESA-S5L2PP-ATBD-005; issue: 3.1; date: 2019-05-02.
[RD19] S5P/TROPOMI ATBD Cloud Products.
source: DLR; ref: S5P-DLR-L2-ATBD-400I; issue: 2.2.0; date: 2020-06-15.
[RD20] TROPOMI ATBD of the directionally dependent surface Lambertian-equivalent reflectivity.
source: KNMI; ref: S5P-KNMI-L3-0301-RP; issue: 1.2.0; date: 2022-01-13.
[RD21] Dutch OMI NO2 (DOMINO) data product v2.0 – see URL https://www.temis.nl/
airpollution/no2.php.
source: KNMI; ref: OMI NO2 HE5 2.0 2011; date: 18 August 2011.
[RD22] Product user manual for the TM5 NO2 , SO2 and HCHO profile auxiliary support product.
source: KNMI; ref: S5P-KNMI-L2-0035-MA; issue: 1.0.0; date: 2021-02-04.
[RD23] Preparing elevation data for Sentinel 5 precursor.
source: KNMI; ref: S5P-KNMI-L2-0121-TN; issue: 2.0.0; date: 2015-09-11.
[RD24] S5P/TROPOMI Science Verification Report.
source: IUP; ref: S5P-IUP-L2-ScVR-RP; issue: 2.1; date: 2015-12-22.
[RD25] Wavelength calibration in the Sentinel-5 precursor Level 2 data processors.
source: KNMI; ref: S5P-KNMI-L2-0126-TN; issue: 1.0.0; date: 2015-09-11.
[RD26] Determine the effective cloud fraction for a specific wavelength.
source: KNMI; ref: S5P-KNMI-L2-0115-TN; issue: 2.0.0; date: 2019-04-10.

2.4Electronic references[ER1] TROPOMI level-2 product ATBD documents. URL https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-5p/.
[ER2] TROPOMI level-2 product PUM documents. URL https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-5p/.
[ER3] TROPOMI level-2 product PRF documents. URL https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-5p/.
[ER4] TROPOMI Quarterly Validation Report. URL http://mpc-vdaf.tropomi.eu/.
[ER5] Sentinel-5P TROPOMI Products and algorithms documentation. URL https://sentinel.esa.
int/web/sentinel/technical-guides/sentinel-5p/.
[ER6] TEMIS website: NO2 data product page. URL https://www.temis.nl/airpollution/no2.
php.
[ER7] QA4ECV website. URL http://www.qa4ecv.eu/.
[ER8] QA4ECV NO2 ECV precursor data. URL http://www.qa4ecv.eu/ecv/no2-pre.
[ER9] Sentinel-5P Pre-Operations Data Hub. URL https://s5phub.copernicus.eu/.
[ER10] TROPOMI website. URL http://www.tropomi.eu/.
[ER11] S5P/TROPOMI ISRF. URL http://www.tropomi.eu/data-products/isrf-dataset.
[ER12] Vandaele et al. NO2 cross sections. URL http://spectrolab.aeronomie.be/no2.htm.

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Page 12 of 88[ER13] TM5 website. URL http://www.projects.science.uu.nl/tm5/.
[ER14] S5P-PAL reprocessing: NO2 dataset. URL https://data-portal.s5p-pal.com/products/
no2.html.
[ER15] Q. L. Kleipool, M. R. Dobber, J. F. De Haan et al.; OMI Surface Reflectance Climatology (2010). URL
https://disc.gsfc.nasa.gov/datasets?page=1&source=AURA~OMI.
[ER16] TROPOMI surface LER & DLER database. URL https://www.temis.nl/surface/albedo/
tropomi_ler.php.
[ER17] M. J. Brodzik and J. S. Stewart; Near Real-Time SSM/I EASE–Grid Daily Global Ice Concentration and Snow Extent. Boulder, Colorado USA. NASA National Snow and Ice Data Center (2016);
10.5067/3KB2JPLFPK3R. Updated daily; URL http://nsidc.org/data/NISE.
[ER18] EUMETSAT Ocean & Sea Ice Satellite Application Facility. Updated daily; URL http://osisaf.
met.no/.
[ER19] J.J. Danielson and D.B. Gesch; Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010)(2011). URL http://topotools.cr.usgs.gov/gmted_viewer/.
[ER20] L. G. Tilstra, O. N. E. Tuinder, P. Wang et al.; Surface reflectivity climatologies from UV to NIR
determined from Earth observations by GOME-2 and SCIAMACHY (2017). URL https://temis.
nl/surface/gome2_ler.php.
[ER21] TROPOMI Mission Performance Centre. URL http://www.tropomi.eu/data-products/
mission-performance-centre.
[ER22] S5P Mission Performance Centre VDAF website. URL http://mpc-vdaf.tropomi.eu/.
[ER23] In-service Aircraft for a Global Observing System (IAGOS). URL https://www.iagos.org/.
[ER24] What are outliers in the data? URL https://www.itl.nist.gov/div898/handbook/prc/
section1/prc16.htm.
[ER25] Important z-scores. URL http://www.cs.uni.edu/~campbell/stat/normfact.html.
[ER26] The Standard Normal Distribution. URL http://sphweb.bumc.bu.edu/otlt/MPH-Modules/
BS/BS704_Probability/BS704_Probability9.html.

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Terms, definitions and abbreviated terms

Terms, definitions and abbreviated terms that are used in development program for the TROPOMI L0-1b dataprocessor are described in [RD2]. Terms, definitions and abbreviated terms that are used in development
program for the TROPOMI L2 data processors are described in [RD3]. Terms, definitions and abbreviatedterms that are specific for this document can be found below.

3.1

Terms and definitions

The most important symbols related to the data product described in this document – some of which are not in[RD3] – are the following; see also the data product overview list in Table 11.

M
Mcld
Mclr
M tropM stratNs
NstropNsstratNv
Nvtrop
NvstratNvsum

total air-mass factor
cloudy air-mass factor
clear-sky air-mass factor
tropospheric air-mass factor
stratospheric air-mass factor
total slant column density
tropospheric slant column density
stratospheric slant column density
total vertical column density
tropospheric vertical column density
stratospheric vertical column density
sum of tropospheric and stratospheric vertical column density

3.2

Acronyms and abbreviations

AAI

Absorbing Aerosol Index

ACE

Atmospheric Chemistry Experiment

AMF

Air-mass factor

CAMS

Copernicus Atmosphere Monitoring Service

CTM

Chemistry Transport Model

DAK

Doubling-Adding KNMI

DLER

Directionally dependent Lambertian equivalent reflectivity

DEM

Digital Elevation Map

DOAS

Differential Optical Absorption Spectroscopy

DOMINO

Dutch OMI NO2 data products of KNMI for OMI

ECMWFEuropean Centre for Medium-Range Weather Forecast

ENVISAT

Environmental Satellite

EOS-Aura

Earth Observing System (Chemistry & Climate Mission)

ERBS

Earth Radiation Budget Satellite

ERS

European Remote Sensing satellite

FRESCO

Fast Retrieval Scheme for Clouds from the Oxygen A band

GOME

Global Ozone Monitoring Experiment

HALOE

Halogen Occultation Experiment

IDAF-L2

Instrument Data Analysis Facility, Level 2 (at KNMI)

IPA

Independent pixel approximation

ISRF

Instrument Spectral Response Function (aka slit funtion)

LER

Lambertian equivalent reflectivity

LUT

Look-up table

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MAX-DOAS

Multi-axis DOAS

MERIS

Medium Resolution Imaging Spectrometer

MetOp

Meteorological Operational Satellite

MPC

S5P Mission Performance Centre

NISE

Near-real-time Ice and Snow Extent

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NRTnear-real time (i.e. processing within 3 hours of measurement)

OMI

Ozone Monitoring Instrument

OMNO2AOMI NO2 slant column data product (at NASA)OSIRIS

Optical Spectrograph and Infrared Imager System

OSISAF

Ocean & Sea Ice Satellite Application Facility

PANDORA

not an acronym; direct-sun UV-visible spectrometer

PDGS

Sentinel-5Precursor Payload Data Ground Segment (at DLR)

POAM

Polar Ozone and Aerosol Measurements

PRF

Product ReadMe File

PUM

Product User Manual

ROCVR

Routine Operations Consolidated Validation Report

QA4ECV

European "Quality Assurance for Essential Climate Variables" project

S5P

Sentinel-5 Precursor (satellite carrying TROPOMI)

SAGE

Stratospheric Gas and Aerosol Experiment

SAOZ

Systeme d’Analyse par Observations Zenithales instrument

SCIAMACHY

Scanning Imaging Absorption Spectrometer for Atmospheric Cartography

SME

Solar Mesosphere Explorer

SNR

Signal-to-Noise Ratio

SPOT

Système Pour l’Observation la Terre

STREAM

STRatospheric Estimation Algorithm from Mainz

TM4, TM5

Data assimilation / chemistry transport model (version 4 or 5)

TM4NO2ANO2 data products of KNMI for GOME, SCIAMACHY and GOME-2

TOATop-of-atmosphere

TROPOMI

Tropospheric Monitoring Instrument

UARS

Upper Atmosphere Research Satellite

VDAF

Validation Facility of the MPC

4

TROPOMI instrument description

A description of the TROPOMI instrument and performance, referred to from all ATBDs, can be found in [RD4].
See also the overview paper of Veefkind et al. [2012].

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Introduction to the TROPOMI NO2 data products

5.1

Nitrogen dioxide in troposphere and stratosphere

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Nitrogen dioxide (NO2 ) and nitrogen oxide (NO) – together usually referred to as nitrogen oxides (NOx =NO + NO2 ) – are important trace gases in the Earth’s atmosphere, present in both the troposphere and thestratosphere. They enter the atmosphere as a result of anthropogenic activities (notably fossil fuel combustion
and biomass burning) and natural processes (such as microbiological processes in soils, wildfires and lightning).
Approximately 95% of the NOx emissions is in the form of NO. During daytime, i.e. in the presence of sunlight,a photochemical cycle involving ozone (O3 ) converts NO into NO2 (and vice versa) on a timescale of minutes,
so that NO2 is a robust measure for concentrations of nitrogen oxides (Solomon [1999], Jacob [1999]).
In the troposphere NO2 plays a key role in air quality issues, as it directly affects human health [World
Health Organisation, 2003]. In addition nitrogen oxides are essential precursors for the formation of ozone
in the troposphere (e.g. Sillman et al. [1990]) and they influence concentrations of OH and thereby (shorten)
the lifetime of methane (CH4 ) (e.g. Fuglestvedt et al. [1999]). Although NO2 is a minor greenhouse gas initself, the indirect effects of NO2 on global climate change are probably larger, with a presumed net cooling
effect mostly driven by a growth in aerosol concentrations through nitrate formation from nitrogen oxides andenhanced levels of oxidants (e.g. Shindell et al. [2009]). Deposition of nitrogen is of great importance for
eutrification [Dentener et al., 2006], the response of the ecosystem to the addition of substances such as
nitrates and phosphates – negative environmental effects include the depletion of oxygen in the water, which
induces reductions in fish and other animal populations.
For typical levels of OH the lifetime of NOx in the lower troposphere is less than a day. For Riyadh, forexample, Beirle et al. [2011] find a lifetime of about 4.0 ± 0.4 hours, while at higher latitudes (e.g. Moscow) the
lifetime can be considerably longer, up to 8 hour in winter, because of a slower photochemistry in that season.
For Switzerland Schaub et al. [2007] report lifetimes of 3.6 ± 0.8 hours in summer and 13.1 ± (3.8) hours in
winter. With lifetimes in the troposphere of only a few hours, the NO2 will remain relatively close to its source,
making the NOx sources well detectable from space. As an example, Fig. 1 shows distinct hotspots of NO2
pollution over the highly industrialised and urbanised regions of London, Rotterdam and the Ruhr area in themonthly average tropospheric NO2 for April 2018 over Europe derived from TROPOMI data.
Since July 2018, with the first public release of the TROPOMI datasets including NO2 , the number of
TROPOMI users and publications has grown strongly. A review of these applications is beyond the scope of
this ATBD. Topics addressed range from changes in global-scale NO2 distributions and impacts on atmosphericFigure 1: Monthly average distribution of tropospheric NO2 columns for April 2018 over Europe based on
TROPOMI data, derived with processor version 1.2.0.

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Figure 2: Strong reduction of NO2 over India as a result of the COVID-19 lockdown in March-April 2020
(lower panel) compared to 2019 (top panel). Concentrations were reduced strongly in cities like Delhi, Mumbai,
Dhaka. In contrast, some of the coal-fired power plants continued the electricity production with only minor
reductions. Source: https://www.esa.int/, news story 24 April 2020.

chemistry, data assimilation applications, validation of regional air quality models and NOx emission inversion
studies. In particular the combination of the high spatial resolution, the large signal-to-noise ratio and daily
global coverage makes TROPOMI unique. This has been and will be further used for the analysis of emissions
and concentrations at the local scale, for individual cities, power plants, industrial complexes, road traffic andshipping lanes. The power of TROPOMI is nicely demonstrated by the observation of pollution plumes fromindividual ships [Georgoulias et al., 2020].
In 2020 the number of publications and attention in the media for TROPOMI NO2 observations has exploded.
As a result of the COVID-19 related lockdowns pollution levels have dropped dramatically as observed inreal-time by the TROPOMI instrument, largely consistent with surface observations [Gkatzelis et al., 2021].
This clearly demonstrates the value of real-time global monitoring of concentrations from space. Fig. 2 shows,as example, COVID-19 lockdown impact on NO2 in India.
In the stratosphere NO2 is involved in some photochemical reactions with ozone and thus affects the ozone
layer (Crutzen et al. [1970]; Seinfeld and Pandis [2006]). NO2 in the stratosphere originates mainly fromoxidation of N2 O in the middle stratosphere, which leads to NOx , which in turn acts as a catalyst for ozone
destruction (Crutzen et al. [1970]; Hendrick et al. [2012]). But NOx can also suppress ozone depletion by
converting reactive chlorine and hydrogen compounds into unreactive reservoir species (such as ClONO2 andHNO3 ; Murphy et al. [1993]).
Fig. 3 shows, as an example, the stratospheric NO2 distribution derived from TROPOMI measurements on
1 April 2018 at the 13:30 overpass local time. The image shows variability related to atmospheric transport anddiurnal variability in the stratosphere. In a study into the record ozone loss, triggered by enhanced NOx levels,in the exceptionally strong Arctic polar vortex in Spring 2011, Adams et al. [2013] showed the usefulness ofsuch data when investigating the anomalous dynamics and chemistry in the stratosphere. With its high spatial
resolution and signal-to-noise ratio, TROPOMI is clearly well-suited to help understand the stratospheric NO2content and its implications for the ozone distribution.

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Figure 3: Distribution of stratospheric NO2 on 1 April 2018 along the individual TROPOMI orbits, derived withprocessor version 1.2.0. The image shows that atmospheric dynamics creates variability in the stratosphericcolumns, mainly at mid-latitudes. Furthermore we can see the effect of the increase of NO2 in the stratosphere
during daytime leading to small jumps from one orbit to the next. Note that the colour scale range is different
from the range in Fig. 1.

From observed trends in N2 O emissions one would expect a trend in stratospheric NO2 with potential
implications for persistent ozone depletion well into the 21st century [Ravishankara et al., 2009]. There havebeen some reports of such trends in stratospheric NO2 , for instance from New Zealand [Liley et al., 2000] andnorthern Russia [Gruzdev and Elokhov, 2009]. On the other hand, Hendrick et al. [2012] report that changes
in the NOx partitioning in favour of NO may well conceal the effect of trends in N2 O. TROPOMI continues
the important record of stratospheric NO2 observations that started with GOME in 1995, and improves thedetectability of trends.
Over unpolluted regions most NO2 is located in the stratosphere (typically more than 90%). For polluted
regions 50–90% of the NO2 is located in the troposphere, depending on the degree of pollution. Over polluted
regions, most of the tropospheric NO2 is found in the planetary boundary layer, as has been shown among
others in campaigns using measurements made from aeroplanes, such as INTEX (e.g. Hains et al. [2010]). In
areas with strong convection, enhanced NO2 concentrations are observed at higher altitudes due to productionof NOx by lightning (e.g. Ott et al. [2010]; Allen et al. [2021]).
The important role of NO2 in both troposphere and stratosphere implies that it is not only important toknow the total column density of NO2 , but rather the tropospheric NO2 and stratospheric NO2 concentrations
separately. A proper separation between the two is therefore important, in particular for areas with low pollution,
where the stratospheric concentration forms a significant part of the total column.

5.2

NO2 satellite retrieval heritage

Tropospheric concentrations of NO2 are monitored all over the world by a variety of remote sensing instruments– ground-based, in-situ (balloon, aircraft) or satellite-based – each with its own specific advantages, and tosome extent still under development.
Stratospheric NO2 has been measured by a number of satellite instruments since the 1980s, such as
the spectrometer aboard SME (1981-1989; Mount et al. [1984]), SAGE-II/III (ERBS/Meteor-3M, 1984-2005;

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GOME
SCIAMACHY
OMI
GOME-2A
GOME-2B
S5P/TROPOMIGOME-2C
Sentinel-4
Sentinel-5
1997

2000

2003

2006

2009

2012

2015

2018

2021

2024

Figure 4: Overview of the European UV/Vis polar orbiting and geostationary backscatter satellite instruments
capable of retrieving tropospheric and stratospheric NO2 column data since the launch of GOME aboard
ERS-2, including missions to be launched in the near future.

Chu and McCornick [1986]), HALOE (UARS, 1991-2005; Gordley et al. [1996]), POAM (SPOT-3, 1993-1996;
Randall et al. [1998]), SCIAMACHY (ENVISAT, 2002–2012; Bovensmann et al. [1999], Sierk et al. [2006]),
OSIRIS (Odin, 2001–present; Llewellyn et al. [2004], Adams et al. [2016]), and ACE (SCISAT-1, 2003–present;
Bernath et al. [2005]).
Over the past 22 years tropospheric NO2 has been measured from UV/Vis backscatter satellite instruments
such as GOME (ERS-2, 1995–2011; Burrows et al. [1999]), SCIAMACHY (ENVISAT, 2002–2012; Bovensmann
et al. [1999]), OMI (EOS-Aura, 2004–present; Levelt et al. [2006]), the GOME-2 instruments [Munro etal., 2006] aboard MetOp-A (2007–present), MetOp-B (2012–present) and MetOp-C (2019–present), the OMPS
instrument [Yang et al., 2014] on the Suomi NPP platform (2011–present) and the NOAA-20 satellite (2017–present). TROPOMI (see [RD4]; Veefkind et al. [2012]) extends the records of these observations, and in turnwill be followed up by several forthcoming instruments including Sentinel 5 and the geostationary platforms
GEMS (Bak [2013], Kim [2020]; launched in 2020), TEMPO [Zoogman et al., 2017] and Sentinel 4 [Ingmann etal., 2012], [RD5]. Fig. 4 shows the timelines of the NO2 data records of some of these instruments. Note that
TROPOMI, OMI, the GOME-2 instruments and Sentinel-5 provide (near-)global coverage in one day, and that
Sentinel-4 is a geostationary instrument.
For the UV/Vis backscatter instruments that observe NO2 down into the troposphere, KNMI has operated –in close collaboration with BIRA-IASB, NASA and DLR – a real-time data processing system, the results ofwhich are freely available via the TEMIS website [ER6]. The data has been used for a variety of studies inareas like validation (see e.g. Boersma et al. [2009], Hains et al. [2010], Lamsal et al. [2010]), trends (seee.g. Van der A et al. [2008], Stavrakou et al. [2008], Dirksen et al. [2011], Castellanos and Boersma [2012],
DeRuyter et al. [2012]), and NOx emission and lifetime estimates (see e.g. Lin et al. [2010], Beirle et al. [2011],
Mijling and Van der A [2012], Wang et al. [2012]).
The DOMINO approach for OMI (and the similar approach called TM4NO2A for GOME, SCIAMACHY
and GOME-2) is based on a DOAS retrieval, a pre-calculated air-mass factor (AMF) look-up table and a data
assimilation / chemistry transport model for the separation of the stratospheric and tropospheric contributions
to the NO2 column (see Sect. 6 for details). The differences between the processing systems for the different
instruments are small and related to instrument issues, such as available spectral coverage and wavelength
calibration, other absorbing trace gases fitted along, and details of the cloud cover data retrieval.
The European Quality Assurance for Essential Climate Variables (QA4ECV) project ([RD6], [ER7], Boersma
et al. [2018]) has led to a homogeneous reprocessing dataset of NO2 for the sensors GOME, SCIAMACHY,OMI and GOME-2A. This project has investigated and improved all the individual steps/modules in the NO2
retrieval. The new NO2 datasets are available via the QA4ECV project website at [ER8]. This new release
replaces the DOMINO-v2 OMI NO2 dataset and TM4NO2A datasets for the other sensors. Due to IT equipment
issues the OMI/QA4ECV dataset ends on 29 March 2021; a follow-up dataset, based on new collection-4 OMI
data, with reprocession of the full mission, is currently being set up.
The TROPOMI NO2 processor includes many of the developments from the QA4ECV project, including improvements in the TM5-MP/DOMINO chemistry modelling-retrieval-assimilation approach, DOAS optimisations(cf. Van Geffen et al. [2020]) and air-mass factor lookup table. On top of that, several further improvements

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have been implemented, notably in the TM5-MP/DOMINO system and the output data file

5.3

Separating stratospheric and tropospheric NO2 with a data assimilation system

The NO2 processing system starts with a DOAS retrieval step that determines the NO2 slant column density,
which represents the total amount of NO2 along the line of sight, i.e. from sun via earth’s atmosphere tosatellite. To determine the tropospheric NO2 slant column density, the stratospheric NO2 slant column density is
subtracted from the total slant column, after which the tropospheric sub-column is converted to the troposphericvertical NO2 column.
Several approaches to estimate the stratospheric NO2 amount have been introduced in the past. The
TM5-MP/DOMINO approach uses information from a chemistry transport model by way of data assimilation tosimulate the instantaneous stratospheric NO2 distribution and to force consistency between the stratosphericNO2 column and the satellite measurement [Boersma et al., 2004]. Other methods applied elsewhere include
the following (in arbitrary order).
a) The wave analysis method uses subsets of satellite measurements over unpolluted areas to remove
known areas of pollution, i.e. areas with potentially large amounts of tropospheric NO2 , from a 24-hourcomposite of the satellite measured NO2 and expands the remainder with a planetary wave analysis
across the whole stratosphere, followed where necessary by a second step to mask pollution events(e.g. Bucsela et al. [2006]). This approach has been used between 2004 and 2012 for the OMI NO2
Standard Product (SP) of NASA/KNMI.
b) The reference sector method method uses a north-to-south region over the Pacific Ocean that is assumed to be free of tropospheric NO2 , as there are no (surface) sources of NO2 , so that all NO2
measured is assumed to be in the stratosphere (e.g. Richter and Burrows [2002], Martin et al. [2002]).
This stratospheric NO2 is then assumed to be valid in latitudinal bands for all longitudes. In some
implementaions this method is extended with a spatial filtering to include other relatively clean areas
across the world (e.g. Bucsela et al. [2006], Valks et al. [2011]).
c) Image processing techniques assume that the stratospheric NO2 shows only smooth and low-amplitudelatitudinal and longitudinal variations (e.g. Leue et al. [2001], Wenig et al. [2003]). This approach willprobably miss the finer details in the stratospheric NO2 distribution (as is the case for methods a and b
above). The next version of NASA’s OMI NO2 SP will use a similar approach [Bucsela et al., 2013].
d) Independent stratospheric NO2 data, such as collocated limb measurements (e.g. Beirle et al. [2010],
Hilboll et al. [2013b]) or data taken from a chemistry transport model (e.g. Hilboll et al. [2013a]), canbe subtracted from the total (slant) column measurements to find the tropospheric NO2 concentrations.
Unfortunately, limb collocated stratospheric measurements are not available for satellite retrievals fromthe GOME(-2), OMI, and TROPOMI sensors. Nevertheless this approach is potentially very useful forcomparison and validation studies. Possible cross-calibration problems between the stratospheric andthe total measurements would complicate the approach.
e) The STRatospheric Estimation Algorithm from Mainz (STREAM; Beirle et al. [2016]). The STREAM
approach is based on the total column measurements over clean, remote regions as well as overclouded scenes where the tropospheric column is effectively shielded. STREAM is a flexible and robustinterpolation algorithm and does not require input from chemical transport models. It was developed asa verification algorithm for the then upcoming satellite instrument TROPOMI, as a complement to theoperational stratospheric correction based on data assimilation. STREAM was successfully applied to
the UV/vis satellite instruments GOME 1/2, SCIAMACHY, and OMI. It overcomes some of the artifactsof previous algorithms, as it is capable of reproducing some of the gradients of stratospheric NO2 , e.g.,
related to the polar vortex, and reduces interpolation errors over continents.
f) The Standard Product 2 (SP2) includes a new stratospere-troposphere separation approach (Bucsela etal. [2013]). This approach has aspects in common with STREAM. It is based on the measurements only
and uses tropospheric pollution masking and subsequent interpolation over the masked areas.
These ways of treating the stratospheric NO2 field may not be accurate enough to capture the variabilityof the stratospheric NO2 in latitudinal and longitudinal direction, as well as in time. At the same time it is
not certain whether these methods do actually separate stratospheric NO2 : some of the NO2 interpreted as"stratospheric" may be in the (upper) troposphere.
Also the assimilation approach suffers from these uncertainties, but in a different way since actual meteorological fields are used to model the dynamical and chemical variability of NOx in the stratosphere and freetroposphere. The assimilation analyses the retrieved total slant column with a strong forcing to the observations

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Table 1: NO2 data product requirements for the TROPOMI NO2 data products, where accuraries are split in
the systematic and random components. The numbers are taken from [RD11]; see also the Product ReadMe
File (PRF; available via [ER3]).
NO2 data productVertical resolution

Bias

RandomStratospheric NO2
Tropospheric NO2Stratospheric column
Tropospheric column< 10%
25 − 50%

0.5×1015 molec/cm2
0.7×1015 molec/cm2

over clean regions (regions with small tropospheric column amounts). The data assimilation ensures thatthe model simulations of the stratospheric NO2 column agrees closely with the satellite measurements. The
modelled stratospheric NO2 (slant column) amount is subtracted from the full column observation to derive thetropospheric column.
The use of a data assimilation system to provide stratospheric NO2 concentrations has been shown toprovide realistic results, as indicated by validation studies. For example, Hendrick et al. [2012] found very
good agreement between satellite retrievals using data assimilation to estimate the stratospheric NO2 column(GOME, SCIAMACHY and GOME-2) and ground-based measurements at the station of Jungfraujoch.
The advantages of the use of stratospheric chemistry transport modelling in combination with data assimilation are:
• The system models the chemistry (diurnal cycle) and dynamics of the stratosphere based on meteorological analyses.
• Data assimilation provides a realistic error estimate of the stratospheric NO2 column [Dirksen et al., 2011].
• The height of the tropopause, obtained from the meteorological data, provides a point of separation of
the stratospheric from the tropospheric NO2 column.
• The result of the data assimilation is a comprehensive understanding of 3-D NO2 distributions that covers
the whole world, taking into account the spatial and temporal variability of the NO2 profiles.

5.4

NO2 data product requirements

S5P/TROPOMI mission requirements have been discussed in several documents, including the GMES
Sentinels-4, -5 and -5Precursor Mission Requirements Document [RD5] and the Science Requirements
Document for TROPOMI [RD7]. These requirements are based on the findings of the CAPACITY [RD8],CAMELOT [RD9] and TRAQ [RD10] studies. For the TROPOMI NO2 column data products the set of
requirements which are used as baseline in the routine validation work are the NO2 data product requirement
listed in Table 1; these are given in the "Sentinel-5P Calibration and Validation Plan for the Operational Phase"
document [RD11] and also given in the NO2 Product ReadMe File (PRF; available via [ER3])
The uncertainties stated in Table 1 include retrieval errors as well as instrument errors. Over polluted areas
retrieval errors will dominate the uncertainties; these relate to the presence of clouds and aerosols and to thesurface albedo. Over rural areas, with low NO2 concentrations, errors in tropospheric NO2 are mostly driven byrandom noise related to the instrument’s Signal-to-Noise Ratio (SNR), to estimates of the stratospheric NO2column, and to uncertainties in the NO2 profile.

5.5

NO2 retrieval for TROPOMIThe TROPOMI retrieval of total and tropospheric NO2 is based on the TM5-MP/DOMINO system (see Sect. 6.1),
thus extending the long-term record of NO2 data, produced using a reliable, well-established and well-described
processing system (see Boersma et al. [2004], Boersma et al. [2007] and Boersma et al. [2011]). In particular,
the inclusion of many of the retrieval developments of the QA4ECV project ([RD6], [ER7]) in the TROPOMI
NO2 retrieval will ensure a good continuity from the QA4ECV OMI and GOME-2 NO2 records to TROPOMI.
For the OMI NO2 retrieval a number of improvements are related to spectral fitting [Van Geffen et al., 2015]
and to the chemistry modelling, stratosphere-troposphere separation and the air-mass factor [Maasakkers etal., 2013]. The TROPOMI NO2 processing chain is described in Sect. 6.5.
In order to comply with the SI unit definitions, the TROPOMI NO2 data product file (described further in
Sect. 6.6) provides the trace gas columns in mol/m2 , rather then in the commonly used unit molec/cm2 . For
convenience sake, most of the text and figures of this document will remain in the latter unit; only the tables
listing the input (Sect. 7.1) and output (Sect. 7.4) dataset use the SI based units.

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Table 2: Overview of periods of operation of the operational NO2 processor versions in the near-real time
(NRTI) and the off-line (OFFL) data streams, as well as for officially reprocessed (RPRO) data; see also thelatest PRF [ER3]. Concerning NO2 , there is no difference between the different minor release versions v1.2.x,
and the same holds for the versions v1.3.x. The differences between version 1.2.2 and 1.3.2 are relatively
small and involve only a small fraction of the observations. Version v1.4.0 involves a major upgrade in theLevel-2 cloud product FRESCO used by the NO2 processor (viz. Sect. 6.4.4), leading to a significant increaseof the tropospheric NO2 over polluted scenes. Major release version v2.2.0 of the NO2 processor marks theswitch to v2.0.0 of the Level-1b spectral data as well as a significant change to the surface albedo treatment.
A full mission reprocessing is planned to take place in 2022. Note that on 6 August 2019, as of orbit 9388,
the nadir ground pixel dimensions reduced from approximately 7.0 × 3.5 km2 to approximately 5.5 × 3.5 km2
without a change in the processor.

5.6

Processor
versionATBD
versionData
streamIn operation from
orbit
date

In operation until
orbit
date

01.00.00

1.2.0

NRTI
OFFL

03745
03661

2018-07-04
2018-06-28

03946
03847

2018-07-18
2018-07-11

01.01.00

1.2.0

NRTI
OFFL

03947
03848

2018-07-18
2018-07-11

05333
05235

2018-10-24
2018-10-17

01.02.00

1.3.0

NRTI
OFFL

05336
05236

2018-10-24
2018-10-17

05929
05832

2018-12-05
2018-11-28

01.02.02

1.3.0

NRTI
OFFL
RPRO

05931
05833
02818

2018-12-05
2018-11-28
2018-04-30

07518
07424
05235

2019-03-27
2019-03-20
2018-10-17

01.03.00

1.4.0

NRTI
OFFL

07519
07425

2019-03-27
2019-03-20

07999
07906

2019-04-23
2019-04-30

01.03.01

1.4.0

NRTI
OFFL

08000
07907

2019-04-30
2019-04-23

08906
08814

2019-07-03
2019-06-26

01.03.02

1.4.0

NRTI
OFFL

08906
08815

2019-07-03
2019-06-26

16256
16212

2020-12-02
2020-11-29

01.04.00

2.2.0

NRTI
OFFL

16259
16213

2020-12-02
2020-11-29

19306
19257

2021-07-05
2021-07-01

02.02.00

2.2.0

NRTI
OFFL

19308
18258

2021-07-05
2021-07-01

21222
21187

2021-11-17
2021-11-14

02.03.01

2.2.0

NRTI
OFFL

21223
21188

2021-11-17
2021-11-14[ TBD ][ TBD ]

2022-07-....
2022-07-....

02.04.00

2.4.0

NRTI
OFFL
RPRO

[ TBD ][ TBD ]
02818

2022-07-....
2022-07-....
2018-04-30[ current version ][ current version ][ TBD ] 2022-07-...

NO2 data product: version history and access

The NO2 processing has started directly after "first light", providing data for initial checks and validations. The
near-real time (NRTI) data product is released from 4 July 2018 onwards; the reprocessed (RPRO) and off-line(OFFL) datasets contains data starting from 30 April 2018. TROPOMI NO2 data processed in near-real time(NRT) is available within 3 hours after measurement; this data stream uses a forecast of the TM5-MP data

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Page 22 of 88(see next sections). A few days later, the data is processed in off-line mode (OFFL), using TM5-MP analysis
data. Table 2 provides an overview of the operational TROPOMI NO2 data processor versions; see also thelatest NO2 PRF [ER3]. The table also lists for each processor version the version number of the ATBD that
describes the data product. Older ATBD versions are usually not publicly available, though ATBD v1.4.0 willremain available as long as v1.2.x and v1.3.x data will be available.
It is important to realise that a change of processor version may imply significant changes in NO2 and as
general rule different version cannot be used together for time-series and trend studies. The change fromversion 1.2.x to 1.3.x, however, was a relatively minor update [ER3], affecting only a minority of pixels, andthese versions have been combined in past studies. The update from version 1.3.x to 1.4.0 [Van Geffen etal., 2022] on 2 December 2020 has led to significant changes in tropospheric NO2 , in particular over scenes
with low but non-zero cloud fractions (cloud radiance fractions between 0.1 and 0.5) and high pollution levels
where NO2 tropospheric columns have increased. The updates from version 1.4.0 to 2.2.0 [Van Geffen etal., 2022] have mainly affected cloud-free scenes and lead to a further increase in tropospheric NO2 in polluted
regions. Furthermore, for snow/ice conditions and coastal areas the impact on tropospheric NO2 may be larger.
Datasets should therefore not be combined without carefully addressing these changes.
All data (near-real time, offline and reprocessed) is freely accessible via the Copernicus Open Access Data
Hub [ER9]. Note that NRT data is available for the last 30 or so days; older NRT data is removed from the Hub.
See the TROPOMI website [ER10] for more information on data availability and dissemination.

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6

Algorithm description

6.1

Overview of the NO2 retrieval algorithm

The TROPOMI NO2 processing system is based on the DOMINO and QA4ECV processing systems, withimprovements related to specific TROPOMI aspects and new scientific insights. The basis for the processing
is a retrieval-assimilation-modelling system which uses the 3-dimensional global TM5 chemistry transportmodel as an essential element. The retrieval consists of a three-step procedure, performed on each measured
Level-1b spectrum:
1. the retrieval of a total NO2 slant column density (Ns ) from the Level-1b radiance and irradiance spectra
measured by TROPOMI using a DOAS (Differential Optical Absorption Spectroscopy) method,
2. the separation of the Ns into a stratospheric (Nsstrat = Nvstrat ∗ M strat ) and a tropospheric (Nstrop ) part on thebasis of information coming from a data assimilation system, and3. the conversion of the tropospheric slant column density into a tropospheric vertical column density(Nvtrop = Nstrop /M trop ),where M trop and M strat are the tropospheric and stratospheric air-mass factor (AMFs), which are derived from alook-up table of altitude-dependent AMFs and actual, daily information on the vertical distribution of NO2 fromthe TM5-MP model on a 1◦ × 1◦ grid; the altitude-dependent AMF depends on the satellite geometry, terrain
height, cloud fraction and height and surface albedo.
The retrieval process is described in detail in the sections below.

6.2

Spectral fitting

The baseline method to determine NO2 total slant columns is DOAS (see Platt [1994], Platt and Stutz [2008]).
The DOAS fitting function for TROPOMI follows the current non-linear fitting approach for OMI (Boersma etal. [2011], Van Geffen et al. [2015], Van Geffen et al. [2020], [RD12]).
The reflectance spectrum observed by the satellite instrument, Rmeas (λ ), is the ratio of the radiance atthe top of the atmosphere, I(λ ), and the extraterrestrial solar irradiance, E0 (λ ) (where I also depends on theviewing geometry, but those arguments are left out for brevity):

Rmeas (λ ) =π I(λ )
µ0 E0 (λ )(1)where E0 and I are recorded at the same detector row and given on the same wavelength grid (see below), andµ0 = cos(θ0 ) is the cosine of the solar zenith angle. The E0 is measured once a day, when TROPOMI crosses
the terminator along a given orbit, and the TROPOMI data processing uses E0 measured closest in time to I .
Since in Eq. (1) the factor π/µ0 becomes very large at high solar zenith angle, the algorithm internally actuallyuses the sun-normalised radiance I(λ )/E0 (λ ); note that this ratio is sometimes also called reflectance.
In space-borne DOAS, Rmeas is related to the extinction of light by scattering and absorbing species along
the average photon path between sun and satellite instrument. The effective, integrated absorption of NO2
along the average photon path is represented by the total NO2 slant column density (Ns ). The DOAS spectral
fitting is performed for all satellite ground pixels with θ0 < 88◦ , so that there is no potential danger from thedivision by µ0 in Eq. (1). The FRESCO cloud data product (Sect. 6.4.4) uses this θ0 cut-off as well.
The DOAS spectral fitting attempts to find the optimal modelled reflectance spectrum, Rmod (λ ), by minimising the chi-squared merit function, i.e. the smallest possible differences between the observed and modelledreflectance spectrum:nλ 

χ2 = ∑

i=1

Rmeas (λi ) − Rmod (λi ) 2
∆Rmeas (λi )(2)with nλ the number of wavelengths in the fit window and ∆Rmeas (λi ) the noise on the reflectance, which
depends on the radiance and irradiance noise given in the Level-1b product:∆Rmeas (λi ) =1E0 (λi )

q

2
2
2
∆I(λi ) + ∆E0 (λi ) · Rmeas (λi )(3)i.e. on the signal-to-noise (SNR) of the measurements. For numerical reasons a maximum of 2500 is set to
the SNR on the reflectance and ∆Rmeas is adjusted upwards when this SNR limit is exceeded. In the NO2

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wavelength range the SNR is typically 1500, but it may be larger over very bright scenes (notably clouds),
potentially leading to saturation effects in the spectra, hence limiting the SNR does not pose a significant limiton the lowest SCD error estimates (the lower ∆Rmeas the lower the SCD error is). Radiance spectral pixels
flagged in the Level-1b data as bad or as suffering from saturation are filtered out before doing any further
processing step.
The magnitude of χ 2 is a measure for how good the fit is. Another measure for the goodness of the fit is
the so-called root-mean-square (RMS) error, which is defined as follows:s
RRMS =n2
1 λ
Rmeas (λi ) − Rmod (λi )∑nλ i=1(4)

where the difference Rmeas (λ ) − Rmod (λ ) is usually referred to as the residual of the fit.
The baseline model function for TROPOMI follows the approach for OMI and reads as follows:

h nk
i 
Iring (λ ) Rmod (λ ) = P(λ ) · exp − ∑ σk (λ ) · Ns,k · 1 + Cring
E0 (λ )
k=1

(5)

with σk (λ ) the absolute cross section and Ns,k the slant column amount of molecule k = 1, . . . , nk taken into
account in the fit (NO2 , O3 , etc.), Cring the Ring fitting coefficient and Iring (λ ) the synthetic Ring spectrum(generated from an Eref (λ ) reference irradiance) and E0 (λ ) the measured irradiance. The Ring spectrum
describes the differential spectral signature arising from inelastic Raman scattering of incoming sunlight by N2
and O2 molecules. The last term in Eq. (5) describes both the contribution of elastic scattering to the differential
absorption signatures (i.e. the 1), and the modification of these differential structures by inelastic scattering(the +Cring · Iring (λ )/E0 (λ ) term) to the reflectance spectrum. The sources of the reference spectra used are
discussed in Sect. 6.2.3.
In the modelled spectrum of Eq. (5) a polynomial of order n p with coefficients am :np

P(λ ) = ∑ am λ m

(6)

m=0

is introduced to account for spectrally smooth structures resulting from molecular (single and multiple) scatteringand absorption, aerosol scattering and absorption, and surface albedo effects. Because of the polynomial term,
only the highly structured differential absorption features contribute to the fit of the slant column densities. In
order to prevent the numerical value of the polynomial components in Eq. (6) to become very large or verysmall (for the 405 − 465 nm fit window, for example, usually n p = 5), the wavelengths are scaled to the range[−1 : +1] over the fit window in the processor.
Fig. 5 shows an example of a reflectance spectrum observed by TROPOMI on 4 July 2018 during orbit
03747, along with the modelled spectrum obtained from the DOAS fit using Eq. (5). The (almost cloud-free)ground pixels lies over the industrial area of Rotterdam (scanline 2012, row 323, θ0 = 27.82◦ , θ = 31.47◦ ).
trop
Fit results: Ns,NO2 = 2.73×1016 molec/cm2 , Nv,NO2 = 1.60×1016 molec/cm2 , Nv,NO= 1.52×1016 molec/cm2 ,2
RMS = 1.59×10−4 . The residual (shown in the bottom panel of Fig. 5) is of the order of 10−4 , corresponding
to an unexplained residual reflectance (which is about equal to the differential optical depth times the averagereflectrance in the fit window; Van Geffen et al. [2020]) of that magnitude.
In order to remove strong outliers in the DOAS fit residual (caused by, e.g., high-energy particles hitting the
CCD detector, variations in the dark current, or bad pixels not correctly flagged in the Level-1b data), a "spikeremoval" algorithm was implemented in v2.2.0 of the DOAS processor to detect outliers in the fit residual; after
removal of the spectral pixels of such outliers from the measured reflectance, the NO2 DOAS fit is redone toprovide the final fit parameters. See Appendix F and Van Geffen et al. [2022] for a description and examples ofthe spike removal.
Table 3 provides an overview of the operational DOAS fit settings used in the TROPOMI processor andthose used for some current and past UV/Vis backscatter satellite instruments: the fit window, the reference
spectra used in the fit (see Sect. 6.2.3) and the degree of the DOAS polynomial. Note that for the processing of
GOME(-1) data it was necessary to include a correction for the undersampling of the spectra, i.e. the fact thatthe spectral sampling is of the same order as the FWHM of the instrument slit function. For the instruments
listed in Table 3 this correction is not necessary: their spectral resolution, i.e. the FWHM of the slit function,
is 2–3 times as large as their spectral sampling. For TROPOMI, for example, the spectral sampling is about
0.2 nm and the FWHM is about 0.55 nm [RD4].

reflectance [x 10-2]

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22

20.0

21

19.5

20

19.0
426

428

430

432

434

425

435
wavelength [nm]

445

455

465

425

435
wavelength [nm]

445

455

465

19

18

17
405

measured reflectancemodelled reflectance415

residual [x 10-4]

6
4
2
0
-2
-4
-6
405

fit residual
415

Figure 5: The top panel shows an example of a reflectance spectrum (black solid line) obtained by TROPOMI
on 4 July 2018 during orbit 03747 and the spectrum modelled in the DOAS fit procedure (dashed red line); theinset shows an enlargement of a 10 nm wide part of the fit window. The bottom panel shows the residual of the
DOAS fit, i.e. the measured minus the modelled reflectance spectrum; note that the vertical scale is a factor of
100 smaller than the scale in the top panel.

6.2.1

Wavelength calibration & common wavelength grid

Both the irradiance and radiance spectra are wavelength calibrated prior to the DOAS fit, using the same
wavelength calibration approach, in the [λb : λe ] wavelength window. Prior to NO2 data version v2.2.0 thiswindow equalled the NO2 fit window, with λb = 405nm and λe = 465nm. As of v2.2.0 the NO2 processor
includes the O2 –O2 slant column retrieval for cloud properties (see Sect. 6.4.4.3) and to accommodate this fit
the wavelength calibration windows is extended to λe = 495nm.
Using the subscripts ’nom’ and ’cal’ to denote nominal (i.e. from the Level-1b data product [RD13]) andcalibrated wavelengths, respectively, the calibrated irradiance and radiance to be used in Eq. (1) are then givenby:E0 ) = E (λ E0 + wE0 )E0 (λcal
0 nomsI(λcal ) = I(λnom + ws + wq (λnom − λ0 ))(7)where ws represents a wavelength shift and wq a wavelength stretch (wq > 0) or squeeze (wq < 0), with wq
defined w.r.t. the central wavelength of the fit window λ0 . Since in view of numerical stability, the wavelengths

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Table 3: Main settings of the operational DOAS retrieval of NO2 for TROPOMI, and for the current and previous
satellite instruments in the operational processing of KNMI, which converts the NO2 slant column data products
into tropospheric and stratospheric vertical column data. For OMI the settings used for the QA4ECV v1.1
processing ([RD6], [ER7]) are given; these are an extention of the settings used for the DOMINO v2 processing(see Sect. 6.2.4 for a brief discussion).
TROPOMIwavelength range [nm ]secondary trace gases
pseudo-absorbers
fitting method
degree of polynomial
polarisation correction
slant column processing
references

OMI

GOME-2SCIAMACHY(QA4ECV v1.1)(TM4NO2A v2.3)(TM4NO2A v2.3)

405 − 465
O3 , H2 Ovap ,O2 –O2 , H2 Oliq
Ring
non-linear5
no405 − 465
O3 , H2 Ovap ,O2 –O2 , H2 Oliq
Ring
non-linear5
no425 − 450
O3 , H2 Ovap ,O2 –O2Ring
linear3
no426.5 − 451.5
O3 , H2 Ovap ,O2 –O2Ring
linear2
yes

PDGS (@ DLR)—

NASA / KNMI

DLR / BIRA-IASB

BIRA-IASB[Boersma et al., 2011][Valks et al., 2011][Van Roozendael[Van Geffen et al., 2015][Liu et al., 2019]

et al., 2006]

are scaled to the range [−1 : +1] over the fit window, computationally λ0 = 0. Each wavelength calibration of
Eq. (7) comes with its own χw2 as a goodness-of-fit. Turning on the wq fit parameter in the calibration of theradiande of a given TROPOMI orbit resulted in a very small stretch with a precision larger than the stretch itself,
and the effect on the retrieval results is negligible [Van Geffen et al., 2020], and hence the wq fit parameter willremain turned off. Note that for the irradiance calibration we only consider a shift.
In order to avoid possible extrapolations, both these steps are performed on a wavelength range that is
1 nm wider than the fit window, i.e. the measured reflectance is formed on the common wavelength grid andthen cut to the fit window. Up to v1.4.0 of the NO2 processor the spectal pixel index selection is based on thefirst pixel after λb and the last before λa . As of v2.2.0 the selection is based on nearest neighbours, providing amore consistent pixel index selection along-track.
The wavelength calibration of Eq. (7) is performed on the irradiance at the start of the processing of a given
granule, and per radiance spectrum prior to forming the measured reflectance of Eq. (1). In order to form thisreflectance, both (calibrated) spectra I(λ ) and E0 (λ ) need to be given on the same wavelength grid. In our
approach E0 (λ ) is converted to the radiance wavelength grid by way of a high-sampling interpolation, taking
advantage of the fact that we have additional information from a high-resolution solar reference spectrum
Eref (λ ). Details of the wavelength calibration and the high-sampling interpolation implemented for TROPOMI
are given in Appendices A and B, respectively.
6.2.2

Minimising the chi-squared merit function

Slant column densities Ns,k , the Ring coefficent Cring , and the polynomial coefficients am are obtained froma minimisation of the χ 2 of Eq. (2), i.e. the differences between the observed and modelled reflectances. In
the initial TROPOMI NO2 DOAS, we implented a version of the OMI NO2 DOAS processor, called OMNO2A,
which uses a non-linear least squares fitting based on routines available in the SLATEC mathematical library[Vandevender and Haskell, 1982]. During the commissioning phase, however, we discovered that this implementation suffered from some issues (in particular the χ 2 and/or the slant column error estimates were scaled
incorrectly) that could not be solved due to inflexibility of the OMNO2A code. To solve this issue, we chose to
use the optimal estimation (OE) routine based on Rodgers [2000]) already available in the processor, since it
was implemented for the wavelength calibration; see Appendix A.
For the χ 2 -minimisation using the OE solver suitable a-priori values of the fit parameters were selected andthe a-priori errors are set very large, so as not to limit the solution of the fit, while for numerical stability reasonsa pre-whitening of the data is performed. (Whitening transforms a vector of random variables with a known
covariance matrix into a set of new variables whose covariance is the identity matrix, meaning that they areuncorrelated and each have variance 1; cf. Rodgers [2000], Ch. 2.)
A number of fitting diagnostics is provided by the fitting procedure. Estimated slant column and fitting
coefficient uncertainties are obtained from the covariance matrix of the standard errors, which is given as

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a standard output of the OE procedure. The SCD error estimates are scaled with the square-root of thenormalised χ 2 , where χ 2 is normalised by (nλ − D), with nλ the number of wavelengths in the fit window andD the degrees of freedom of the fit, which is almost equal to the number of fit parameters. All fitting coefficientsare provided in the NO2 output data file as diagnostic data.
6.2.3

Reference spectra

The selection of the reference spectra for the trace gas cross sections in Eq. (5) is driven by whether a species
shows substantial absorption in the wavelength range relevant for NO2 retrieval, and exploits the best available
sources prior to commissioning phase. Experience with OMI has shown that NO2 , ozone, water vapour, andRotational Raman Scattering (RRS), i.e. the inelastic part of the Rayleigh scattering (the so-called "Ring effect"),
are most relevant in the wavelength interval relevant to NO2 . Van Geffen et al. [2015] (cf. Sect. 6.2.4) showedthat including also absorption in liquid water and by the O2 –O2 collision complex improves the fit, hence these
are included for TROPOMI.
High-resolution laboratory measured absorption cross sections are convolved with the TROPOMI slit
function (or: instrument spectral response function, ISRF; available via [ER11]), and sampled at a resolution of
0.01 nm to create the necessary reference spectra in the data files used by the processor. Since the ISRF is(slightly) different for different detector rows, the convolved reference spectra are determined per detector row.
Given the relative smoothness of these convolved cross sections, interpolation to the radiance wavelength gridin Eq. (5) is performed by way of a 4th degree spline interpolation. The final set of reference spectra (see also
[RD14] and Fig. 6) is:• trace gas cross sections σk (λ ) in Eq. (5):
– NO2 from Vandaele et al. [1998] at 220 K; see [ER12]– O3 from Gorshelev et al. [2014] and Serdyuchenko et al. [2014] at 243 K– Water vapour (H2 Ovap ) based on HITRAN 2012 data
(see Van Geffen et al. [2015] and Sect. 4.1 of [RD14])– O2 –O2 from Thalman and Volkamer [2013] at 293 K– Liquid water (H2 Oliq ) from Pope and Frey [1997],
resampled at 0.01 nm with a cubic spline interpolation• an effective Ring spectrum Iring (λ ) following Chance and Spurr [1997](see Van Geffen et al. [2015] and Sect. 4.2 of [RD14])• a high-resolution solar reference spectrum Eref (λ ) from Chance and Kurucz [2010]
The inclusion of absorption by soil (as discussed by e.g. Richter et al. [2011]; Merlaud et al. [2012]) is notconsidered for TROPOMI, as its potential absorption signal lies well above 465 nm, the upper limit of the NO2
fit window. Also currently not being considered for inclusion in the fit is the vibrational Raman scattering inclear ocean waters (e.g. Vasilkov et al. [2002], Vountas et al. [2003]), as its potential effect on the fit is currently
poorly understood; cf. Sect. 6.2.5.
The temperature for the O3 , H2 Ovap and O2 –O2 cross section spectra is fixed. Variation of these crosssection temperatures has little effect on the fit residual in the retrieval of NO2 slant columns, since the shape ofthe differential NO2 cross section is in good approximation invariant of temperature. In the case of TROPOMI,
the baseline is to use an NO2 cross section that has been measured for 220 K.
Note that the amplitude of the differential cross section features has a significant temperature dependence
which is important to account for. The resulting NO2 slant column are corrected for deviations from 220 K atlater retrieval steps, as described in Sect. 6.4.2.
6.2.4

DOAS fit details for OMI and TROPOMI

Comparisons of OMI NO2 data from the DOMINO v2 processing system to independent data from other
instruments have shown that OMI slant NO2 columns are higher than columns derived from GOME-2 andSCIAMACHY (as first stated by N. Krotkov at the OMI Science Meeting in Sept. 2012), as well as columns
derived from ground-based measurements. Due to the separation between stratospheric and troposphericNO2 , which proceeds in the same way for the three satellite instruments, the high bias in the NO2 slant columns
is propagated to the stratospheric column [Belmonte Rivas et al., 2014].
Van Geffen et al. [2015] showed that improving the OMI wavelength calibration of the Level-1b spectra in
the OMNO2A data processing of the NO2 slant columns used by DOMINO v2 reduces both the total NO2 slant
column values and the RMS of the DOAS fit. Van Geffen et al. [2015] further showed that including both O2 –O2

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10

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2NO2 [x 1e-19 cm /molec]
2O3 [x 1e-22 cm /molec]
2
H2Ovap [x 1e-26 cm /molec]
5
O2-O2 [x 1e-47 cm /molec2]
H2Oliq [x 1e-3 m-1]
Ring [x 1e14 ph/s/nm/cm2]

6

4

2

0
405

415

425

435
wavelength [nm]

445

455

465

Figure 6: Absorption cross sections σk (λ ) for NO2 , O3 , water vapour, O2 –O2 and liquid water, as well as the
Ring spectrum Iring (λ ), the pseudo-absorber which accounts for the Ring effect, in Eq. (5) for the 405 − 465 nm
wavelength range used in the TROPOMI data processor. The reference spectra have been multiplied by thefactors given in the plot legend to make the spectral signatures visible in one plot.

and H2 Oliq (discussed by e.g. Richter et al. [2011], Lerot et al. [2010]) in the fit improves the OMI NO2 fit results
and ensures that fit coefficients for O3 and O2 –O2 have realistic values. Criteria for establishing what are thebest settings for the fit can be summarised as follows: (a) a low error on the NO2 slant column, (b) a low RMS
error value, (c) inclusion of secondary trace gases that clearly improve the fit, e.g. by removing specific featuresin the fit residual, (d) physically realistic values for the slant column values of these secondary trace gases.
The improvements described by Van Geffen et al. [2015] for OMNO2A have been used for the processingof OMI NO2 data within the QA4ECV-project ([RD6], [ER7]), which demonstrated that the different spectral
fitting approaches lead to consistent results, but with better precision for the QA4ECV spectral fitting algorithm[Zara et al., 2018]. The improvements are incorporated in the TROPOMI NO2 slant column processing. For
comparisons between TROPOMI and OMI slant column retrieval results, see Van Geffen et al. [2020].
6.2.5

Some notes regarding other DOAS implementations

Many implementations of DOAS deploy a linearised version of Eq. (5), with the Ring effect included as a
pseudo-absorber, giving the equation in terms of optical depth rather than in terms of reflectances:nk∗
ln[Rmod (λ )] = P∗ (λ ) − ∑ σk (λ ) · Ns,k − σring (λ ) ·Cring

(8)

k=1
∗ and the polynomial P∗ (λ ) are essentially different from C
where the Ring coefficient Cring
ring and P(λ ) in Eq. (5).
In this approach the Ring cross section σring (λ ) is constructed from the Ring radiance spectrum Iring (λ ) divided
by a reference solar spectrum minus a low-order polynomial (so that σring (λ ) varies around zero). The relation∗ is discussed briefly by Van Geffen et al. [2020].
between Cring and Cring
The linearisation leading to Eq. (8) allows then for the use of a linear least squares fitting routine, which iscomputationally faster than a non-linear solver needed when using Eq. (5). We feel, however, that the Ring
effect is physically described better by the non-linear approach of Eq. (5) and we therefore use that in the NO2
data processing for TROPOMI. Apart from dropping the physical description of the Ring effect, a disadvantage

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of the linearised approach is that error propagation is no longer straightforward, because taking the logarithmof the observed spectra implies that the error no longer has a Gaussian distribution.
Several DOAS applications include an intensity offset correction, a constant or linear in wavelength, toimprove the retrievals in some spectral ranges. The precise physical origin of such an intensity offset is notknown, but it is thought to be related to instrumental issues (e.g. incomplete removal of stray light or darkcurrent in Level-1b spectra) and/or atmospheric issues (e.g. incomplete removal of Ring spectrum structures,vibrational Raman scattering in clear ocean waters); see, for example, Platt and Stutz [2008], Richter et
al. [2011], Lampel et al. [2015], [RD15].
In Eq. (5) such an intensity offset correction would be represented by an additional term on the righthand side:

... +

noff
Soff
· ∑ cm λ mE0 (λ ) m=0(9)with fit parameters cm and Soff a suitable scaling factor which has the unit of the irradiance E0 ; in mostapplications noff = 0 or 1 in case an intensity offset is included. (Note that the NO2 slant column retrieval
algorithm of OMI (OMNO2A) is not able to handle such an intensity offset correction.)
The possibility of an intensity offset correction has been implemented in the TROPOMI NO2 slant column
processor, but this option is currently turned off as (i) it has not been fully tested yet, (ii) we would first like tounderstand the physical meaning and implications of such a correction term, and (iii) we need to investigate
whether it might be relevant for TROPOMI NO2 retrievals. For a first discussion see Van Geffen et al. [2020],
based on which an intensity offset correction will not be included in the regular TROPOMI NO2 , also because
instrumental effects such as straylight and dark current are corrected for in the spectral calibration in the Level
0-to-1b processor (Kleipool et al. [2018]; Ludewig et al. [2020]).

6.3

Separation of stratospheric and tropospheric NO2

The baseline method for the TROPOMI NO2 algorithm to separate the stratospheric and tropospheric contribution to the NO2 total slant columns is by data assimilation of slant columns in the TM5-MP CTM [Williams etal., 2017]. KNMI has considerable experience with this method [Dirksen et al., 2011], and in the absence of
collocated independent (e.g. limb) information on stratospheric NO2 , we consider this to be the most viable
method to distinguish stratospheric from tropospheric NO2 . This approach explicitly accounts for chemistry
and dynamics in the stratosphere.
The central idea of the data assimilation is to regularly update a CTM simulation of the three-dimensional,
coupled troposphere-stratosphere NO2 distribution with available measurement data in such a way that the
CTM simulation of the stratospheric NO2 column achieves close agreement with the TROPOMI slant columns
over areas known to have little or no tropospheric NO2 . The assimilation effectively relies on slant columns
observed over regions where the model predicts the NO2 column to be dominated by stratospheric NO2(e.g. over the remote oceans). For those regions and times, the modeled slant column, i.e. the inner product of
the observation operator H and the simulated vertical distribution ~x, is effectively forced to the observed state.
For regions and times where the model predicts large tropospheric contributions, the slant column is not a goodproxy for stratospheric NO2 , and the analysis adjustment is only very small. Because total reactive nitrogen(NOy ) is a well-conserved quantity in the stratosphere, with relatively small source and sink contributions, theinformation from the observations can be stored in the model over long time periods. The stratospheric wind
will transport the stratospheric analysis results from the oceans and remote regions to the polluted areas.
The assimilation scheme is based on the Kalman filter technique, with a prescribed parameterisation of thehorizontal correlations between forecast errors. The assimilation time step in the model is 30 minutes. A fullorbit of TROPOMI observations is analysed simultaneously by the Kalman filter. This is done in order to avoid
discontinuities in the analysis that may occur at the location where the orbit is divided. The mid-time of the orbit
is used to determine the model time step of the analysis. The analysed profile field ~xa is the 3D model fieldof NO2 including both troposphere and stratosphere, and is calculated from the forecast ~x f and the satellite
superobservations ~y by:~xa =~x f + PHT (HPHT + R)−1 (~y −~y f )(10)with matrix H the observation operator, P the forecast error covariance matrix, and R the combined observation
and representativeness error covariance (Eskes et al. [2003]; Dirksen et al. [2011]). The superobservations areconstructed by averaging the observations and averaging kernels over each 1◦ × 1◦ model grid cell [Boersma
et al., 2016]. The term PHT (HPHT + R)−1 determines the weight given to the observations depending on
the uncertainty of the observation versus the uncertainty of the model forecast. The departure (~y −~y f ) is theTROPOMI ATBD tropospheric and total NO2
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difference between observed and forecasted model column (observation minus forecast). The ratio between
analysis and forcast following from the Kalman equation is also applied to species that are chemically closely
related to NO2 in the stratosphere, i.e. NO, NO3 , N2 O5 and HNO4 [Dirksen et al., 2011].
A simplified modelling of the observation error is introduced [Dirksen et al., 2011], with a fixed small error
attached to the stratospheric part of the slant column (where the AMF is well known) and a large error attachedto the tropospheric contribution to the slant column (where the AMF is uncertain). These uncertainties (0.2 and6.0×1015 molec/cm2 for the stratosphere and troposphere, respectively; both numbers refer to the total slant
column divided by the geometric AMF, defined by Eq. (17)) are fixed and have been optimised with sensitivity
runs in such a way that the impact of major source regions on the analysis is minimal, and the forcing overclean regions is strong and consistent with observation-minus-forecast statistics.
The observation operator H is a combination of a horizontal interpolation and the application of the averaging
kernel [Eskes and Boersma, 2003], an nl -element vector that contains the sensitivity of TROPOMI to NO2
in each model layer. The scalar product of the observation operator vector and the model NO2 profile at thelocation of the individual TROPOMI observations yields the slant column that would be observed by TROPOMI.
To further speed up the analysis step by about a factor of 8, we assimilate only half of the superobservations,those grid cells with i + j = even, where i and j are the longitude and latitude indices of the TM5-MP grid (the"checkerboard" approach). ~y f is the model forecast of the superobservations, given by H~x f .
We use the TM5-MP CTM (Williams et al. [2017]; see also Huijnen et al. [2010a]; Huijnen et al. [2010b];[ER13]) for the assimilation of TROPOMI NO2 slant columns. This is a major improvement over the DOMINO v2
data assimilation systems operated at KNMI for GOME, SCIAMACHY, OMI, and GOME-2, which use an older
version of the TM CTM (TM4; e.g. Dentener et al. [2003]). The main advantage of the transition to TM5-MP is
the better spatial resolution (1◦ × 1◦ ), updated information on (NOx ) emissions, and an improved description of
relevant physical (photolysis rate constants) and chemical (reaction rate constants) processes in that model[Williams et al., 2017]. The assimilation system operates at a resolution of 1◦ × 1◦ (longitude × latitude), withnl sigma pressure layers up to 0.1 hPa in the vertical direction. TM5-MP uses forecast and analysed 3-hourly
meteorological fields from the European Centre for Medium Range Weather Forecast (ECMWF) operationalmodel. These fields include global distributions of wind, temperature, surface pressure, humidity, (liquid andice) water content, precipitation and surface parameters.
Once the TROPOMI slant columns have been assimilated, the integral from the layer above the tropopause
to the upper TM5-MP layer provides the stratospheric slant column that can be isolated from the total slant
column, giving the tropospheric slant column (cf. Sect. 6.4):

Nstrop = Ns − Nsstrat(11)

For the tropopause definition the WMO-1985 temperature gradient criterion is followed, but other definitions
would not lead to significantly different results (e.g. Bucsela et al. [2013]). NOx has a C-shape profile andthe air around the tropopause has only a small contribution to the total column. Compared to the QA4ECV
processing of OMI a new routine has been introduced for TROPOMI as of data version 1.2.0 which reduces
the fine-scale jumps in tropopause level.
The TM5-MP model provides the following information, necessary for the subsequent processing in thecalculation of the AMF (see Sect. 6.4) needed for the conversion of the tropospheric slant column to thetropospheric vertical column and the final NO2 data product (see Sect. 6.6):• the stratospheric slant and vertical columns: Nsstrat and Nvstrat• an estimate of the error on the stratospheric vertical column: ∆Nvstrat• the NO2 profile: vl,NO2 , with l the index for layer number 1, 2, . . . , nl – this is represented by ~x f in Eq. (10)• the temperature profile at the layers: TlTM5 , for l = 1, 2, . . . , nl
• the pressure level coefficients: ATM5
, BTM5, for l = 0, 1, . . . , nlllTM5
• the index of the pressure level of the tropopause: ltp• the surface elevation and pressure: zTM5
and pTM5, at the 1◦ × 1◦ model resolutionssNote that the model divides the atmosphere in nl layers. The pressure level coefficients determine the pressure
at the nl + 1 levels separating the layers: pl = ATM5
+ BTM5· ps , for l = 0, 1, . . . , nl , with ps the surface pressurellfor the given TROPOMI ground pixel. The pressure for the layer l , for which the concentration (volume mixing
TM5
ratio) vl,NO2 and the temperature TlTM5 are given, is then midway between the level pressures pTM5.
l−1 and pl
TM5
The layer with index ltp contains the tropopause.

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Stratospheric chemistry in the TM5-MP model

TM5-MP is primarily a tropospheric chemistry model [Williams et al., 2017]. NOx -Ox -HOy chemical processes
are implemented according to the Carbon Bond 05 (CB05) chemistry scheme, which includes non-methanehydrocarbons to account for loss by reactions with OH [Williams et al., 2017]. Because the chemistry version of
TM5-MP does not simulate N2 O, the actual source of NOx to the stratosphere, NOx is derived from simulated
HNO3 concentrations, which follow climatological HNO3 :O3 ratios observed by ODIN between 2 hPa and
60 hPa [Maasakkers et al., 2013] and the multi-sensor reanalysis of stratospheric O3 columns [Van der A etal., 2015] with climatological ozone profile shapes. In this way the model partly compensates for the biases
that occur due to the missing N2 O source globally, and the missing reactions involving halogens which areimportant in the polar vortex. During the QA4ECV project, the representation of stratospheric NOy in the model
has been improved by nudging ODIN HNO3:O3 ratios, leading to more realistic NO2 concentrations in thefree-running mode. These improvements are applied to TROPOMI as well.
Processes included in the TM5-MP tracer evolution are advection, convection, diffusion, photolysis anddeposition. Rapid changes in stratospheric NO2 due to e.g. sudden stratospheric warmings or changes in thevortex edge location are largely accounted for through the use of the ECMWF analysis. Solar proton events are
not included in the model, but the related biases are largely removed by the assimilation. NOx emissions are
based on the RETRO-REAS emission inventories for 2006. For more details, the reader is referred to Dirksen
et al. [2011].
For QA4ECV and for TROPOMI the original implementation of the stratospheric climatologies has been
improved by a better interpolation to the TM5-MP vertical levels and by adding an extra nudging to NOx
observations from HALOE in the upper stratosphere above 1 hPa which is not well constrained by HNO3
[Grooß and Russell, 2005].
The data assimilation provides a regular update of the TM5-MP simulation, with a time step of 30 minutes,of the NO2 distribution in the atmosphere on the basis of available observations: if NO2 slant columns areavailable with a measurement time within 15 minutes of the model time, the model field is updated, i.e. theforecast TM5-MP state is adjusted towards the observations. The stratospheric error estimate is based on"observation minus forecast" statistics (over relatively unpolluted areas) in the assimilation. Our experience
with NO2 data assimilation using GOME, SCIAMACHY, OMI, and GOME-2 in TM has shown that the model
chemistry responds smoothly to the updates forced by the satellite measurements.
Fig. 7 provides an example of the "observation minus forecast" (O–F) and the model forcing ("analysis
minus forcast", A–F) for TROPOMI data of 1 April 2018. The difference between the two panels of Fig. 7
illustrates the effect of the assimilation: considerable O–F differences, resulting mostly from (anthropogenic)
tropospheric NO2 sources, have only a minor influence on the analysis. On the other hand, synoptic-scale
structures in O–F persist in the A–F differences. That the A–F differences are much smaller (generally less than
±0.15×1015 molec/cm2 ) than the O–F differences (up to ±1.0×1015 molec/cm2 ) in particular over polluted
regions like China, Europe and the USA, demonstrates that most tropospheric contributions are effectively
discounted by the assimilation procedure.
As of TROPOMI NO2 product v1.2.0 several improvements have been included. The TM5-MP model
was upgraded to the latest version, including some bug fixes. In the TM5-MP model the photolysis for SZA> 85◦ was improved, impacting in particular the stratospheric NO2 columns at high latitudes. Furthermore
the assimilation of NO2 observations is now restricted to the ascending part of the orbit, which is especially
important during the spring-summer months (June-July). These changes have improved the retrieval for high
SZA and in the polar regions (see Fig. 8).

6.4

Air-mass factor and vertical column calculations

The TROPOMI NO2 algorithm uses as default pre-calculated air-mass factor look-up tables to convert the slant
columns into meaningful vertical columns [Lorente et al., 2017]. The AMF, denoted by the symbol M , is theratio of the slant column density of the absorbing trace gas along the (slant) optical path from sun to satellite,
and the vertical column density above the point at the surface area the satellite is viewing. The total verticalcolumn density then follows from the retrieved total slant column density:

Nv = Ns /M(12)

The AMF depends on the vertical profile shape of the trace gas and can be written as (Palmer et al. [2001];
Eskes and Boersma [2003]):M=∑l ml vl cl,∑l vl

ml ≡ δ Ns /δ vl(13)

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Figure 7: Observation-minus-forecast (O–F, top panel) and analysis-minus-forecast (A–F, bottom panel)differences in NO2 slant columns divided by the geometric AMF (Eq. (17)), for 1 April 2018 processed withversion 1.2.0. The observations are averaged to "superobservations" on the 1◦ × 1◦ grid of the TM5-MP
model. The model forecast is simulating the observations using the kernels and air-mass factors. The O–F
demonstrates clear differences (dark-blue and bright-red spots) between the model forcast and TROPOMI
concerning the fine-scale distribution of tropospheric pollution. The A–F plot shows that the assimilation hardly
changes the tropospheric distribution, but efficiently updates the stratospheric fields over the more unpolluted
regions like the oceans.

with ml the altitude-dependent AMFs or box AMF (see Sect. 6.4.1) that describe the vertically resolved
sensitivity to NO2 , vl the column density in layer l , and cl the temperature correction term discussed below (seeSect. 6.4.2) for layer l = 1, 2, . . . , nl [Boersma et al., 2004]. The altitude-dependent AMFs depend on retrieval(forward model) parameters, including the satellite viewing geometry, as well as surface albedo and surfacepressure, cloud fraction, and cloud pressure.
The TM5-MP assimilation of the total slant columns (Ns ) leads to an estimate for the stratospheric vertical

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Figure 8: NO2 tropospheric column retrievals for the descending part of TROPOMI orbit 3623, 25 June 2018,19h UTC, over Siberia. Version 1.0.2 is shown on the left, and version 1.2.0 on the right. Prominent unrealistic
positive biases are observed in v1.0.2 and v1.1.0 for the highest solar zenith angles on the left side of the orbit,
while v1.2.0 has much more realistic values close to zero with a tendency towards a weak negative bias.
profile shape with a stratospheric column amount (Nvstrat ) in close agreement to TROPOMI as shown in Fig. 7.
TM5
Summation over the layers above the tropopause level (l > ltp) to top-of-atmosphere (l = nl ) and multiplication
with the box AMF provides the stratospheric AMF, from which the stratospheric slant column (Nsstrat ) can thenbe calculated:

Nsstrat = Nvstrat ∗ M strat =nl∑

ml v l c l(14)

TM5 +1l=ltp

Note that there is a fundamental difference between Nv and Nvstrat . The total column Nv is a satellite-observed
quantity, related to the true profile shapes through the averaging kernel. In contrast, the stratospheric column
Nvstrat is a model quantity, the direct sum of the model layer subcolumns from the tropopause to the top of theatmosphere. A comparisons of Nvstrat with an other model or a profile measurement should therefore not makeuse of the averaging kernels!
Subtracting Nsstrat from the total slant column and using the tropospheric AMF, determined by adding up theTM5layers from the surface (l = 1) up to and including the tropopause level (l = ltp) in Eq. (13), then gives thetropospheric vertical column:

Nstrop = Ns − Nsstrat⇒

Nvtrop = Nstrop /M trop

(15)

Note that the total vertical column Nv in Eq. (12) is not the same as sum of the partial vertical columns:

Nvsum ≡ Nvtrop + Nvstrat 6= Nv(16)

Our best physical estimate of the NO2 vertical column at any given place is the sum Nvsum . Users who,
for example, wish to assimilate NO2 total columns should, however, use the total column Nv for this. The
total column Nv depends strongly on the modelled ratio of the stratospheric and tropospheric sub-columns,
a dependency which is partly removed in the summed product. For data assimilation use is made of theaveraging kernels, and in this way the resulting analyses are not dependent on the a-priori (including the ratioof the model tropospheric and stratospheric column).
In the absence of atmospheric scattering and in a plane-parallel atmosphere, a so-called geometric AMF,
denoted by M geo , can be defined by way of a simple function of the solar zenith angle θ0 and of the viewing
zenith angle θ :M geo =

1
1+
cos θ0 cos θ(17)This quantity is used in the criteria for the qa_value (see Appendix E) but not written to the output data
geo
product. The ratio Nv = Ns /M geo could be called the geometric column density, to distinguish it from thevertical column densities computed using AMFs that contain model information [Van Geffen et al., 2020].
6.4.1

Altitude dependent AMFs

The altitude-dependent AMFs, or vertical sensitivities, have been calculated with a radiative transfer modelby adding a small, optically thin amount of NO2 to the model atmosphere layer l for an atmosphere that is

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otherwise devoid of NO2 , and subsequently ratioing the excess NO2 slant column (simulated with a radiativetransfer model) to the vertical column added to that layer (ml = δ Ns /δ vl ) [Lorente et al., 2017]. The model
atmosphere does not include aerosols and describes the Earth’s surface as a Lambertian reflector.
As radiative transfer model we use the Doubling-Adding KNMI (DAK) radiative transfer model (De Haan
et al. [1987]; Stammes et al. [2001]), version 3.2, which has the possibility to include a pseudo-sphericity
correction. The radiative transfer calculations takes the sphericity of the atmosphere into account, with Rayleighscattering (including multiple scattering effects) and polarisation correction included (see Boersma et al. [2011]
and references therein); this includes a simple sphericity correction based on detailed comparisons between
DAK and McArtim as described in Lorente et al. [2017]. The DAK model atmosphere consists of a Lambertiansurface albedo, and an adjustable number of atmospheric layers. Atmospheric data are from the standard
AFGL midlatitude summer profile. We calculate the AMF at 437.5 nm, near the middle of the spectral fitting
window, for the corresponding TROPOMI NO2 slant column retrievals; this is a suitable choice for both thesmall (425 − 450 nm) and wide (405 − 465 nm) fit windows, as demonstrated in the QA4ECV-project ([RD6],[ER7], see document [RD15]).
The altitude-dependent AMFs are stored in a look-up table (LUT) as a function of solar zenith angle (θ0 ),viewing zenith angle (θ ), relative azimuth angle (φrel ), Lambertian surface albedo (As ), surface pressure ( ps ),and (mid-level) atmospheric pressure ( pl ). This 6-dimensional LUT is extended with more reference points
compared to earlier versions in order to respect the increase in variability of TROPOMI retrieval parameters(coarser OMI pixels have less variability in spatially smeared surface albedo and surface pressure values
than TROPOMI) and to minimise interpolation errors when looking up the appropriate altitude-dependent AMF.
Pixel-specific altitude-dependent AMFs are obtained by using the best estimates for forward model parameters
and a 6-D linear interpolation scheme.
Table 4 gives an overview of the reference points for the quantities that make up the 6 dimensions. The
dimensions for the LUT are chosen to balance sufficiently accurate 6-dimensional linear interpolation withcomputational efficiency and resource economy. For out-of-bounds values (there is a slight chance that thisoccurs for surface pressure or atmospheric pressure) we use the point nearest to the LUT reference point. In
the current OMI NO2 data product only ground pixels with θ0 < 80◦ (cos(θ0 ) = 0.174) are used in the conversion
to vertical columns. For TROPOMI and future OMI NO2 data products the slant to vertical column conversion
will not be limited in terms of θ0 ; in practice this means the range will be the same as for the FRESCO cloud
retrieval: θ0 < 88◦ (i.e. cos(θ0 ) = 0.035), hence the lower limit of cos(θ0 ) of 0.03 in Table 4. (For practicalimplementation purposes the LUT contains the altitude-dependent air-mass factor scaled with the geometric
AMF, vl /M geo , rather than vl directly.)
The TROPOMI qa_value (see Appendix E) indicates that observations with θ0 > 81.2 should not be used.
Experience has shown that observation-minus-forecast differences increase rapidly above this point.
6.4.2

Temperature correction

For the TROPOMI NO2 retrieval, a temperature correction is applied in the air-mass factor step (see Eq. (13)).
The NO2 cross-sections used in the DOAS retrieval, taken from Vandaele et al. [1998] [ER12], are valid forNO2 at a temperature of 220 K. The temperature at which the NO2 cross-section is evaluated does significantly

Figure 9: Temperature correction factors used in OMI NO2 algorithms from [RD16] (green line), Bucsela etal. [2013] (yellow line), and from Boersma et al. [2004] (red line) as a function of temperature. Source: [RD16].

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Table 4: Quantities and their reference points in the AMF look-up table to be used in the TROPOMI NO2 dataprocessing to convert the tropospheric slant column into the tropospheric vertical column. The lower limit ofcos(θ ) in the list is related to the maximum value of θ for TROPOMI, which is 72◦ (as for OMI).
Quantity

Number ofreference points

Values at
reference points

17

1.00, 0.95, 0.90, 0.80, 0.70, 0.60, 0.50, 0.45, 0.40,
0.35, 0.30, 0.25, 0.20, 0.15, 0.10, 0.05, 0.03

11

1.00, 0.95, 0.90, 0.80, 0.70, 0.60, 0.50, 0.45, 0.40,
0.35, 0.30

100◦ , 20◦ , 40◦ , 60◦ , 80◦ , 100◦ , 120◦ , 140◦ , 160◦ , 180◦26

0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09,
0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.25, 0.30, 0.35, 0.40,
0.50, 0.60, 0.70, 0.80, 0.90, 1.00

Surface pressure
ps [hPa ]141048, 1036, 1024, 1013, 978, 923, 840, 754, 667, 554,
455, 372, 281, 130

Atmospheric pressure
pl [hPa ]1741054.995, 1042.82, 1030.78, 1018.89, 1007.13, 995.51, 984.0309,
972.67, 961.45, 950.35, 939.39, 928.55, 917.84, 907.24, 896.71,
886.24, 875.88, 865.65, 855.54, 845.54, 835.67, 825.90, 816.26,
806.72, 797.12, 787.47, 777.93, 768.51, 759.21, 750.01, 740.93,
731.96, 723.09, 714.33, 705.65, 697.04, 688.54, 680.14, 671.85,
663.65, 655.56, 647.56, 639.66, 631.86, 624.07, 616.30, 608.62,
601.03, 593.54, 586.15, 578.85, 571.63, 564.51, 557.48, 550.44,
543.39, 536.43, 529.56, 522.77, 516.08, 509.47, 502.9492,
496.50, 490.14, 483.75, 477.32, 470.97, 464.71, 458.53, 452.44,446.42, 440.49, 434.63, 428.86, 423.12, 417.42, 411.80, 406.26,
400.79, 395.39, 390.07, 384.82, 379.64, 374.52, 369.43, 364.37,
359.37, 354.44, 349.57, 344.78, 340.05, 335.38, 330.78, 326.24,
321.70, 317.15, 312.66, 308.24, 303.89, 299.59, 295.35, 291.18,
287.06, 283.00, 261.31, 225.35, 193.41, 165.49, 141.03, 120.12,
102.68, 87.82, 75.12, 64.30, 55.08, 47.20, 40.535, 34.79, 29.86,
25.70, 22.14, 19.08, 16.46, 14.20, 12.30, 10.69, 9.29, 8.06, 6.70,
6.11, 5.37, 4.70, 4.10, 3.57, 3.12, 2.74, 2.41, 2.12, 1.87, 1.65,
1.46, 1.29, 1.141, 1.01, 0.89, 0.79, 0.69, 0.61, 0.54, 0.48, 0.42,
0.37, 0.33, 0.29, 0.23, 0.18, 0.13, 0.10, 0.07, 0.05, 0.04, 0.030,
0.020, 0.014, 0.0099, 0.0066, 0.004471, 0.002997, 0.002005,
0.001352, 0.0009193, 0.0006300, 0.0004387, 0.000307

Solar zenith anglecos(θ0 )Viewing zenith anglecos(θ )
Relative azimuth angle

180◦ − |φ − φ0 |Surface albedo

As

influence the fit: amplitudes of the differential NO2 absorption features decrease with increasing temperature,
while the overall shape of the differential cross-section is in good approximation independent of temperature.
To account for the temperature sensitivity, a correction factor has been determined for the difference
between the effective temperature of the NO2 (which is derived from the ECMWF temperature profile andthe modelled profiles in the data assimilation system) and the temperature of the cross-section, where thetemperature dependence is assumed to be linear. For layer l of the NO2 profile the correction factor cl is[RD16]:
cl = 1 − 0.00316(Tl − Tσ ) + 3.39×10−6 (Tl − Tσ )2(18)
with Tl and Tσ the temperature of the profile layer and cross-section, respectively. The function in Eq. (18) is

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an update w.r.t. the correction used for the OMI NO2 data in DOMINO v2 (Boersma et al. [2002], Boersma etal. [2004], Bucsela et al. [2013]) – see Fig. 9. Note that the temperature sensitivity given in the above equation
is determined for the default wavelength window 405 − 465 nm used for the fit; depending on the fit window andon TROPOMI’s spectral resolution details, the function may need to be adapted.
6.4.3

Correction for cloud cover

The AMF formulation accounts for cloud-contaminated pixels. Following Martin et al. [2002] and Boersma etal. [2002], the independent pixel approximation (IPA) is used to express the AMF as a linear combination of acloudy AMF (Mcld ) and a clear-sky AMF (Mclr ), both for the total column and the tropospheric column:

M = w Mcld + (1 − w) Mclr ,

trop
trop
M trop = w Mcld
+ (1 − w) Mclr

(19)with w the radiance weighted cloud fraction, which depends on the effective cloud fraction ( feff ):w=

feff Icl
feff Icl=
R
feff Icl + (1 − feff ) Icr(20)

where Icl is the radiance from the cloudy part of the pixel, Icr the radiance from the clear part of the pixel,
and R the total scene radiance. The NO2 column below the clouds, i.e. the TM5-MP NO2 profile integrated
from the surface to the cloud pressure level, is called the ghost column (Nvghost ). Both Icl and Icr depend on
the viewing geometry, the assumed (cloud) albedo, the surface pressure and the cloud pressure, following
from the FRESCO algorithm (Sect. 6.4.4). In the DOMINO v2 and TM4NO2A processing of data from OMI,
GOME-2 and their predecessors, these radiances were calculated following the analytical approach of Vermoteand Tanré [1992], using feff from the cloud retrieval process for the same instrument. For TROPOMI, thecloud (radiance) fraction is determined from the radiance in the NO2 fit window using LUTs, as detailed below(Sect. 6.4.4).
6.4.4

Cloud cover and cloud pressure data

The large spectral range covered by TROPOMI offers multiple wavelength windows where information on
the cloud height may be derived, including the O2 A-band [Wang et al., 2008], the O2 B-band [Desmons
et al., 2019] and the O2 –O2 absorption feature at 477 nm [Veefkind et al., 2016]. The OMI spectral range
does not cover the A- and B-band, and most retrievals make use of the O2 –O2 absorption. Because of thestrong absorption with enhanced sensitivity for high clouds, and long experience with cloud retrievals for the
SCIAMACHY and GOME-2 instruments, it was decided to use the O2 A-band as default for the cloud pressure
retrieval for TROPOMI, using the FRESCO algorithm. This choice is a major difference between OMI andTROPOMI and has a significant impact on the retrieved NO2 tropospheric columns.
The FRESCO+ algorithm (Wang et al. [2008]; [RD17]) retrieves cloud information from the O2 A-bandaround 758 nm: the cloud fraction and the cloud pressure, for all satellite ground pixels with solar zenith
angle θ0 < 88◦ . The surface albedo database that is used by the FRESCO+ algorithm is based on GOME-2
observations [Tilstra et al., 2017] at 758 and 772 nm; see Sect. 6.4.5.
Due to the high spectral resolution of TROPOMI compared to GOME-2, the FRESCO+ algorithm needed
to be re-written and the corresponding lookup tables have been generated once more. The result, called
FRESCO-S (short for FRESCO-Sentinel), is used for the TROPOMI NO2 product and will be used for other
Sentinels as well. The FRESCO-S algorithm was used up to processor version 1.3.x. As of version 1.4.0 afurther improvement, nicknamed FRESCO-wide and desribed in Sect. 6.4.4.2, was implemented.
The general name "FRESCO" is used below, unless a specific version is referred to. Note that FRESCO-S
is described in the Sentinel-5 ATBD [RD18], which is not yet publicly available, while the FRESCO-wide
approach is discussed in Sect. 6.4.4.2 below.
6.4.4.1

The FRESCO-S cloud pressure & NO2 cloud fraction

FRESCO does not provide the geometric cloud fraction but rather a radiometric equivalent cloud fraction:an effective cloud fraction, feff , that results in the same top-of-atmosphere (TOA) radiance as the real cloud,assuming an optically thick Lambertian cloud with a fixed albedo of Ac = 0.8 (which may be adapted in caseof very bright scenes) at the cloud pressure level, pc . This approach has proven to be useful for trace gas
retrievals; see Wang et al. [2008], who evaluated this for ozone and NO2 .

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Because of the large difference in wavelength between the O2 A-band and the NO2 retrieval window,
the cloud fraction retrieved by FRESCO in the O2 A-band may not be exactly representative for the cloud
fraction in the NO2 window, although Van Diedenhoven et al. [2007] found that cloud parameters retrieved
from UV and O2 A-band measurements showed good consistency for cloud fractions > 0.2; for mostly clear
skies, FRESCO provides somewhat higher cloud fractions than UV-based retrievals. For small cloud fractions
the O2 A-band retrieval becomes sensitive to errors in the surface albedo climatology used, especially overthe bright vegetation. In addition, a misalignment between ground pixel field-of-view of the VIS and NIR
bands [RD4], containing the NO2 retrieval window and the O2 A-band, respectively, exists for the TROPOMI
measurements.
For these reasons, the baseline option for the TROPOMI NO2 retrieval is to (i) use the cloud pressure pc
from FRESCO and (ii) retrieve the cloud fraction and cloud radiance fraction from the NO2 spectral window
itself at 440 nm. The latter is done by fitting the continuum reflectance at 440 nm to a simulated reflectanceconstructed with the independent pixel approximation and radiative transfer calculations for the clear-sky
and cloudy-sky part of the pixel, using the appropriate surface albedo in that spectral window, As,NOtwo , asforward model parameter. Here As,NOtwo is taken from the OMI albedo climatology (cf. Sect. 6.4.5) at 440 nm,
interpolated linearly in time, and using nearest neighbour sampling in latitude and longitude.
The continuum reflectance at 440 nm could be determined from the observed spectrum, averaged over asmall wavelength interval, but that may lead to unexpected values, e.g. in case of spikes in the measurement
or missing wavelength pixels. Instead, as of v1.4.0 [Van Geffen et al., 2022], the modelled reflectance ofEq. (5) is evaluated at λc,NO2 = 440 nm, without taking the absorbing trace gases into account: RTOA (λ ) =P(λ ) · (1 + Cring ), where the Cring term is included because Rayleigh scattering is a combination of elastic
Cabannes scattering and inelastic Raman scattering without the spectral structures of the latter. The trace
gas absorption is not taken into account here because the reflectance LUT used for the determination of thecloud fraction does not include trace gas absorption either. This reflectance-based approach to determine thecloud fraction in the NO2 window is very similar to FRESCO and explicitly accounts for Rayleigh scatteringand involves the calculation of LUTs with the TOA reflectance at 440 nm as a function of viewing geometry,surface/cloud albedo, and surface/cloud pressure. See Appendix C for details on the cloud fraction retrieval.
With processor version 1.3.0 some improvements were made in the FRESCO-S algorithm and in theway the FRESCO results are treated in the NO2 algorithm. Previously in FRESCO the surface albedo fromthe database was used without modifications other than snow or ice at the surface (see Sect. 6.4.5 andAppendix D). In the updated version the surface albedo is reduced to match the top-of-atmosphere reflectancein case the top of atmosphere reflectance is lower than expected for the climatological surface albedo (seeSect. 6.4.5 and App. C.1). Before the update these cases would lead to negative cloud fractions and unrealistic
cloud pressures. Another change in FRESCO is the treatment of very high cloud fractions. If the scene albedo
indicates an elevated scene height and a scene albedo higher than 0.8, the parameters from the scene retrieval
are used: the cloud fraction is set to 1, the cloud albedo is set to the scene albedo and the cloud pressure is
set to the scene pressure. This will prevent odd behaviour for scenes with cloud fraction feff > 1. Care has
been taken to not use this for snow scenes. Fig. 10 gives an example of the results of these improvements,
implemented for version 1.3.0, on the cloud pressure (top panels) and on the tropospheric NO2 column (lower
panels) .
6.4.4.2

Cloud pressure: the FRESCO-wide update

Early studies of the TROPOMI NO2 retrieval indicated that the cloud pressures produced by FRESCO-S
show a positive bias (of order 50 hPa) in cases where either low clouds are expected (e.g. low clouds andfog over ocean near the coast) or locations with thick aerosol layers, like often observed over Eastern China.
The NO2 retrieval is very sensitive to a positive bias in the cloud pressure in combination with cloud radiancefractions close to 0.5. This will result in large M values and underestimates of the tropospheric column.
Comparisons between TROPOMI and OMI-QA4ECV NO2 tropospheric columns have shown that NO2 data
versions 1.2.x and 1.3.x are substantially lower than OMI-QA4ECV, while the slant columns are in good
agreement. The main difference between these retrievals is the cloud pressure retrieval.
For processor version 1.4.0 an update of FRESCO-S was introduced [Van Geffen et al., 2022]. The mainchange is indicated in Fig. 11. The new scheme, called FRESCO-wide, makes use of the longer wavelength partof the O2 A-band. The wavelength ranges in FRESCO-wide are 758 − 759 nm, 760 − 761 nm and 765 − 770 nm.
The inclusion of the weaker absorption lines in the latter window mainly impacts the lower clouds, generally
increasing the height and decreasing the cloud pressure. In those cases the differences are typically in theorder of 50 hPa. For high clouds both FRESCO versions deliver very similar cloud heights on average.
Fig. 12 shows a worst-case example of wintertime China with a thick aerosol layer. The FRESCO-S andTROPOMI ATBD tropospheric and total NO2
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Figure 10: The retrieval of tropospheric NO2 over France on 24 February 2018, for processor version 1.2.0 orolder (left) and version 1.3.0 (right). The old FRESCO-S implementation was producing unrealistic low cloud
top pressures for negative cloud fractions, the blue spots in the top-left panel. These have been removed in
the new implementation (top-right). In the (relatively rare) case that the cloud fraction from the NO2 spectral
window is positive, but FRESCO-S (v1.2.0) retrieves a negative cloud fraction with unrealistic cloud pressures,
this was resulting in high, noisy NO2 spots around Paris (left panel, v1.2.0). The new treatment in FRESCO-S
results in more realistic cloud pressures, the spots disappear, and we observe a well-defined pollution plumefrom Paris transported by the wind from the north-east (right panel, v1.3.0). Note that the NO2 data has been
filtered for cloud-radiance fractions larger than 0.5 (white area in the lower panels).

Figure 11: The original FRESCO-S cloud retrieval (left panel) makes use of three narrow wavelength ranges
in the O2 A-band. The new approach (FRESCO-wide, right panel), introduced in version 1.4.0, extends thethird wavelength band and makes use of the weaker absorption lines.

NO2 spectral window derived cloud radiance fractions are consistent and are around 0.5 as a result of theenhanced scattering of sunlight by the aerosols. The FRESCO-S effective pressure, however, does not pick up
the aerosol scattering taking place at elevated levels, and the effective cloud pressure is close to or equal to thesurface pressure. FRESCO-wide does produce a uniform and elevated layer, with ps − pc values between 40
and 70 hPa. This level is in good agreement with roughly the middle of the aerosol layer as modelled by CAMS.
As shown in Fig. 13, the replacement of the FRESCO-S by FRESCO-wide in version 1.4.0 results in a large

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Figure 12: Surface pressure minus the original FRESCO-S cloud pressure (left panel) or the FRESCO-wide
cloud pressure (right panel) for the overpass over Eastern China on 23 January 2019. The VIIRS image
for the same day (centre panel) indicates a thick aerosol layer over the populated part of China, and the
TROPOMI-retrieved effective cloud-radiance fraction is close to 0.5.

Figure 13: Comparison of TROPOMI (vertical axis) and OMI-QA4ECV (horizontal axis) for Eastern China
for v1.3.x December/January 2019/2020 with FRESCO-S (top panels) and for v1.4.0 December/January
2020/2021 with FRESCO-wide (bottom panels). Both datasets have been time-averaged and gridded to acommon 0.8◦ × 0.4◦ grid. Individual grid boxes are shown in the plot. Slopes are 0.52 and 0.66 for v1.3.x, 0.98
and 1.11 for v1.4.0.

increase of tropospheric NO2 , especially over Eastern China in winter. Slopes in the plots are now close to
1, indicating a much better agreement between the OMI and TROPOMI results. Similar results are found forversion 2.x.x. In other regions like Europe or the USA we also see increases and better mean agreements withOMI-QA4ECV. In summer the differences are smaller than in winter.
In processor version 2.4.0 the NIR albedo LER datasets, based on GOME-2 observations up to v2.3.1,
was replaced by an albedo DLER datasets derived from the TROPOMI observations, see Sect. 6.4.5. Thisreplacement, which also accounts for the (viewing-angle dependent) directionality of the LER, leads tosignificant changes in the FRESCO-S cloud retrievals.
Fig. 14 shows the impact of the replacement of the GOME-2 LER by the TROPOMI DLER. Especially over

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Figure 14: FRESCO-wide cloud fraction for processor version 2.3.1 (left panel) and version 2.4.0 (middle
panel). The difference is shown in the right panel. Data is shown for orbit 21896 of 3 January 2022, passing
over South America.

Figure 15: Comparison between the TROPOMI cloud fraction retrieved in the NO2 fit window ( feff,NO2 ; x-axis)
and in the O2 A-band by the FRESCO algorithm ( feff ; y-axis) for the v2.3.1 S5P-PAL reprocessing data ([ER14]),which uses the OMI and GOME-2 LER (left panel) and the v2.4.0 test reprocessing data (named DDS6),
which uses the new TROPOMI DLER v1 (right panel). The comparison is shown for ground pixels over SouthAmerica (latitude range: [−30◦ : +5◦ ]) of orbit 20236 (8 Sep. 2021).

vegetation/forest we expect major scattering angle (viewing-angle) dependencies of the LER. This was notaccounted for in the GOME-2 LER used, leading to uniform high biases in the cloud fraction in especially theEast part of the TROPOMI swath. In the figure we observe an offset in the cloud fractions of around 0.2 − 0.3
over Brazil for v2.3.1. With the new TROPOMI viewing-angle dependent DLER we find also a realistic fractionof cloud-free footprints, as expected.
Fig 15 shows a comparios of the NO2 and the FRESCO cloud fractions over South America for both thev2.3.1 (left panel) and the v2.4.0 (right panel). In particular for low NO2 cloud fractions, the match between
these two is better for v2.4.0: in the v2.3.1 FRESCO gives substantial higher cloud fractions for the lower value
than in v2.4.0, representative in particular for the ground pixels in the East part of the TROPOMI swath.
For internal consistency of the NO2 column retrieval the cloud fractions derived in the O2 A-band and in
the NO2 fitting window should be similar. As shown above, in Fig. 15 this was not the case over high-albedo
vegetated scenes in processor versions up to v2.3.1. For instance, when the NO2 fitting window indicates asignificant cloud fraction, while the NIR has a cloud fraction close to zero, the cloud pressure is ill defined andwe expect a large error in the tropospheric column retrieval. With the new TROPOMI DLER in the NIR thissituation has improved substantially and cloud fractions have become much more equivalent.

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The changes in the cloud fraction and LER also impact the cloud pressure. On average we find an increaseof the FRESCO cloud pressure by about 15 hPa, but there are strong regional differences. The histogram (notshown) indicates that this is partly related to the disappearance of an unexpected maximum in the number of
FRESCO retrieved pressures around 550 hPa.
6.4.4.3

The O2 –O2 cloud pressure

The O2 –O2 cloud pressure retrieval used for the OMI NO2 retrievals [Veefkind et al., 2016] has been ported
to TROPOMI and is included in the operational software since version 2.2.0. The Level-2 NO2 product files
are extended with two separate groups for the cloud retrievals, one containing the FRESCO results takenfrom the seprate FRESCO support product files, and one containing the O2 –O2 retrieval datasets. The clouddata is derived from the O2 –O2 slant column density retrieved in the wavelength window 460 − 490 nm with a
DOAS approach similar to the NO2 retrieval (Sect. 6.2), with the model function formulated in Eq. (5) and using
cross-sections σk (λ ) for O2 –O2 , NO2 and ozone.
The air mass factors in version 2.2.0 are computed solely using the FRESCO-wide cloud pressures(Sect 6.4.4.2). For a later NO2 data release we will study the quality of both cloud products and rules will be
developed to use one or the other cloud product depending on under which circumstances they work best,
which will be then described in a future update of this ATBD.
6.4.4.4

Other cloud data products

Apart from the FRESCO and O2 –O2 support cloud products, TROPOMI cloud parameters are provided
by an algorithm developed at DLR ([RD19], available via [ER1]). Once the validity and reliability of this clouddata product is established, its cloud parameters will be tested in the NO2 processor and the results will becompared to the results found with FRESCO, O2 –O2 and other cloud data.
Cloud parameter retrieval similar to FRESCO in the O2 A-band [Wang et al., 2008] can also be done in the
the O2 B-band [Desmons et al., 2019], which has the advantage of a smaller albedo over vegetation. We plan
to also test whether these cloud products may be useful for the NO2 retrieval.
6.4.5
6.4.5.1

Surface albedo
Processor versions v1.0.2 up to v2.3.1

The baseline surface albedo climatology for TROPOMI NO2 retrievals for processor versions v1.0.2 up tov2.3.1 is the OMI database, aggregated to a grid of 0.5◦ × 0.5◦ ; see Kleipool et al. [2008], which describes a
climatology made from 3 years of OMI data. Meanwhile the climatology has been improved by using 5 years ofdata, based on the the same method [ER15]. This 5 years based climatology (version 3) has been used forthe DOMINO v2 and QA4ECV OMI NO2 retrievals, and is also used for the TROPOMI NO2 retrievals, wherethe "mode LER" is used. For the surface albedo in the NO2 window, As,NO2 , the 440 nm data is used. The
climatological value of the surface albedo are adapted in case the snow/ice flag (cf. Sect. 6.4.6) indicates there
may be substantial differences in albedo; see Appendix D for some details on this correction.
The OMI albedo climatology was chosen because of its spectral coverage in the NO2 fit region, its relatively
high spatial resolution, and the seamless transition between land and sea. The OMI albedo guarantees aconsistency between TROPOMI and OMI NO2 , since it is also used in the QA4ECV data product Boersma etal. [2018]. An additional advantage is that the OMI climatology has been derived from observations taken atsimilar local times and under similar viewing conditions as for the TROPOMI observations.
The Kleipool surface albedo climatology is based on OMI data, which does not cover the near-infrared
wavelengths in use by the FRESCO algorithm to derive cloud properties (Sect. 6.4.4). Instead, the surface
albedo database that is used by the FRESCO algorithm up to version 2.3.1 is based on GOME-2 observations[Tilstra et al., 2017] at 758 and 772 nm, given at 0.25◦ × 0.25◦ resolution. The relatively coarse spatial
resolution of GOME-2 measurements underlying the climatology and the fact that the overpass time of the OMI
measurements used for the Kleipool climatology is quite similar to the overpass times of TROPOMI (i.e. themeasurements are taken under similar viewing geometries), while the overpass time of GOME-2 is several
hours earlier, are in favour of our choice for the Kleipool surface albedo climatology for the NO2 retrieval, andfor our choice to determine the cloud fraction in the NO2 window.
Taking the anisotropic properties of the surface reflectance (so-called BRDF effects) into account was not
implemented in the TROPOMI NO2 retrieval algorithm up to version 2.3.1. Accounting for BRDF in OMI NO2
retrievals has a generally small effect (< 5%) with substantial effects only occurring at extreme viewing angles

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Figure 16: An example of the Sentinel-5P DLER computed for January (orbit 21896, 3 Jan. 2022) at 758 nm
in the near infrared, relevant for the FRESCO cloud retrieval. The central panel shows the TROPOMI DLER
used in v2.4.0, and is compared to the GOME-2 LER used in v2.3.1 and older versions of the NO2 retrieval.
The difference is shown in the right panel.

at high solar zenith angles [Zhou et al., 2010]. Developments in generating improved surface albedo data aredescribed for instance from the ADAM ([RD14], Sect. 6.1) and QA4ECV ([RD6], [ER7]) projects, and the recent
work by Vasilkov et al. [2017].
An alternative approach is to make use of the MODIS geometry-dependent surface Lambertian equivalent
reflectivity. This was used to generate version V4.0 of the NASA standard NO2 product from OMI [Lamsal etal., 2021]. Recently, it was shown by Loyola et al. [2020] how an effective geometry-dependent Lambertian
equivalent reflectivity may be derived from the TROPOMI data.
6.4.5.2

Processor version v2.4.0

A directional surface albedo climatology for GOME-2 was developed by Tilstra et al. [2021]. Following thesame approach, the author also developed a DLER based on TROPOMI Level-1b v1.0 measurements. This
v1.0 dataset, along with its ATBD [RD20], PUM and validation report is available from the TEMIS website at[ER16]. The directional LER, or DLER, is available for wavelengths in all bands, and can therefore be appliedconsistently to both the FRESCO cloud retrieval and the NO2 retrieval in the 405 − 465 nm window. NO2
processor version 2.4.0, which is operational as of mid-2022, will make use of this viewing-angle dependent
DLER product; this version will also be used for a full mission reprocessing.
This Sentinel-5P LER/DLER monthly product is based on TROPOMI v1 L1B data from 2018 onwards, has
21 selected wavelength bands in the range 328 − 2314 nm. The resolution of the dataset is 0.125◦ × 0.125◦
globally, which is a major improvement compared to previous LER products for OMI or GOME-2. The viewingangle dependence is parameterised using a third order polynomial, and the four coefficients, together with themean LER determine the DLER (see [RD20]).
An example of the implemented DLER is shown in Fig. 16. Most striking are the high albedo values at theEast side over the Amazon region in Brazil. This East-West asymmetry reflects a real property of the vegetationand is missing in the GOME-2 LER. As shown in Fig. 14 above, these higher albedo values remove the biases
in the cloud fraction which are systematically observed in FRESCO at the East side of the swath over forest
areas. Due to the small pixel size and short revisit time of TROPOMI we can see that over Canada and the
USA the albedo map is less influenced by snow at the surface. The big improvement in spatial resolution is
also clearly demonstrated by these maps, for instance over mountain ranges.
The impact of replacing the OMI and GOME-2 LER by the TROPOMI DLER is shown in Fig. 17. For
September 2020 we find relatively minor differences over Europe and the US. More major changes are foundover the rain forests in Brazil and Central Africa, as well as in the South of China. These are regions where thedirectionality of the LER is large in the NIR spectral range. Especially in Brazil we observe major increases in
the column up to 1015 molec/cm2 related to changes in the FRESCO cloud pressure. Note that changes inalbedo are partly compensated for in the NO2 retrieval though the cloud (fraction) retrieval and by applying
albedo adjustments (see below), which may explain the smaller impacts over Europe and the US.

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Figure 17: The difference in tropospheric NO2 column between the test dataset (named DDS6) for processor
version 2.4.0, which uses the TROPOMI DLER database, and the S5P-PAL reprocessed datasets [ER14]
based on version 2.3.1, which uses the OMI LER in the NO2 fit window and the GOME-2 LER for the FRESCO
cloud retrieval in the NIR. Note that 1015 molec/cm2 corresponds to about 16 µ mol/m2 . Data is gridded andaveraged for September 2020.

The availability of the TROPOMI version 2 Level-1b radiance/irradiance product with degradation correction
will be an important next step to generate an improved DLER surface albedo database. This use of the Level-1b
v2 product (available since summer 2021) is important especially for the shorter wavelengths. A version-2 fullmission reprocessing of TROPOMI is expected to become available in the second half of 2022.
One more major change in processor version 2.x.y compared to version 1.x.y is in the albedo treatment.
As mentioned above, the cloud fraction and cloud radiance fraction are retrieved from the NO2 fit window. In
version 1.x the cloud fractions are clipped to the [0, 1] interval before the AMF is computed. As of version
2.x the albedo is adjusted if the cloud retrieval would give a cloud fraction outside the [0, 1] interval to ensureradiative closure [Van Geffen et al., 2022].
The process is the following: when the reflectivity is lower than expected based on the surface albedo
climatology (the OMI LER) then the albedo is adjusted to match the observations, see App. C.1. The adjusted
albedo is written to the Level-22 output file, replacing the climatology. This albedo is subsequently used tocompute the (tropospheric) AMF. Because the albedo is lowered by this process, the AMF is lower and theapproach leads to increased tropospheric NO2 columns.
The same approach is also applied for very bright scenes, but now the cloud albedo is adjusted. In thecloud fraction retrieval the cloud albedo is fixed to 0.8. When the reflectivity is large than expected for a fully
clouded scene, the cloud albedo is increased to match the observation. The new cloud albedo is reported in
the Level-2 NO2 file.
Fig. 18 shows the impact of this procedure for one overpass over Europe. Note that pixels with a cloud
cover > 0 are not affected (white parts in the figure). A lowering of the albedo is observed over most of thecloud-free pixels (white areas correspond largely to cloud fractions > 0). A lowering of the albedo by 1% may
already have a significant impact on the retrieved tropospheric NO2 column.
6.4.6

Snow and ice cover

Substantial errors are introduced if the real albedo differs considerably from what is expected from the albedo
climatology, for example in the case of the sudden snowfall or ice cover. Correcting the surface albedo from

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Figure 18: Difference in the surface albedo in the NO2 fit window between v1.2.2 and v2.1.0 for orbit 3704 on
1 July 2018 over Europe. Blue colors indicate smaller albedo values in v2.1.0. The white parts correspondlargely with retrieved cloud fractions > 0. The red spots over the Alps correspond to snow- or cloud-covered
pixels where the albedo was increased.

the climatology (which contains a climatological snow cover) using knowledge of actual snow/ice cover will
therefore improve the final data product, in terms of the retrieval itself and for flagging such cases. For the
As,NO2 this correction follows the approach included in the OMI cloud data product OMCLDO2 [Veefkind etal., 2016] to adapt the surface albedo in the O2 –O2 fit window (i.e. at 471 nm); Appendix D provides somedetails on this correction of the surface albedo reported in the Level-2 file.
The baseline for snow/ice cover information as of processor version v2.2.0 is the daily data provided by the
ECMWF [Van Geffen et al., 2022]; up to v1.4.0 the NISE snow/ice data [ER17] was used (see also [RD1]). A
feature of the NISE data is that the flag is set to 252 when the scene contains both land and water ("mixed pixels
at coastlines"), which occurs frequently. In these cases there is no information on snow or ice, and (for highlatitudes) the qa_value is set to a small value to reflect this additional uncertainty. The snow/ice informationfrom ECMWF does not have this coastline problem. In addition, the spatial resolution of the ECMWF datais higher than that of NISE. The ECMWF snow data [DeRosnay et al., 2015] is derived from synoptic data
and from the Interactive Multi-sensor Snow and Ice Mapping System (IMS); Cooper et al. [2018] show thatIMS has better agreement with in situ observations over North America and that NISE misses a significant
number of snow-covered pixels. An alternative for the snow/ice cover might come from OSISAF [ER18], but
implementation is not straightforward as this are currently separate products for land and for sea.
The FRESCO algorithm (Sect. 6.4.4) provides two sets of data (Wang et al. [2008]; [RD17]): (i) the effective
cloud fraction feff and cloud pressure pc using a cloud albedo Ac = 0.8 (this cloud albedo may adapted by
FRESCO over bright scenes), and (ii) the scene albedo Asc and the scene pressure psc assuming a cloud
fraction feff = 0.0. With the snow/ice flag the NO2 processing will select which of these two sets is used forthe determination of the AMFs and subsequent vertical NO2 columns. When the snow/ice flag indicates that
there is more than a 1% snow/ice coverage, the retrieval will move to scene mode by setting the cloud radiancefraction Eq. (20) equal to 1.0. The Asc and psc from FRESCO are then used to determine the effective albedo
and pressure of this (ficticious) cloud. Which mode is used can be found via the selection criteria of the
qa_value definition, listed in Appendix E; see also the NO2 Product User Manual (PUM; available via [ER2]).
6.4.7

Surface pressure

The (altitude-dependent) AMFs in Eq. (13) depend on the surface pressure, ps . For the TROPOMI NO2 retrieval
this information is obtained from the TM5-MP model (1◦ × 1◦ ) driven by ECMWF meteorological data. Because

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Figure 19: Tropospheric NO2 from OMI retrieved with TM5-MP at a resolution of 1◦ × 1◦ minus retrieved with
TM5-MP at a resolution of 3◦ × 2◦ for 20–30 October 2004 over Europe.

the TM5-MP information is representative for spatially coarse pressures, the TM5-MP results are correctedbased on the method described in Zhou et al. [2009] and Boersma et al. [2011]. This correction computes
a new surface pressure based on the difference between the corresponding spatially coarse terrain height
and the actual, pixel-averaged terrain height based on a 3-km resolution digital elevation map [Maasakkers etal., 2013].
6.4.8

A-priori vertical NO2 profiles

A CTM is considered to be the best source of information for a-priori NO2 vertical profiles. The baseline for
the TROPOMI NO2 retrieval algorithm is to use TM5-MP [Williams et al., 2017] vertical NO2 profile shape
simulated at a 1◦ × 1◦ (longitude × latitude) spatial resolution for nl = 34 layers covering troposphere andstratosphere. In future updates this layer choice may be further optimised. The a-priori profile shapes arecalculated at the centre of the TROPOMI ground pixel via weighted linear interpolation based on the fournearest neighbour TM5-MP cell centres. Using TM5-MP instead of TM4 constitutes a significant improvement
in itself: TM5 v3 is a benchmarked recent model version (Huijnen et al. [2010a]; Huijnen et al. [2010b]; [ER13];
Williams et al. [2017]), with more up-to-date NOx emissions (from the MACCity inventory), chemistry, andongoing improvements of ship, soil and lightning NOx emission descriptions.
Using TM5-MP with a global 1◦ × 1◦ resolution is an important improvement over previous global satellite
NO2 retrievals that used vertical profile shape computed at spatial resolutions of 2◦ ×2.5◦ or 3◦ ×2◦ (e.g. Lamsal
et al. [2010], Boersma et al. [2011]). Obviously, there are spatial gradients in NO2 concentrations over scales
smaller than a degree, but a resolution of 1◦ × 1◦ should capture the most relevant gradients much better thana resolution of 3◦ × 2◦ . Using higher resolution models in combination with the TROPOMI averaging kernels
will in effect further improve the spatial resolution in the a-priori NO2 fields for advanced users interested inregionally focused investigations (e.g. Huijnen et al. [2010b]).
The effect of the improved spatial resolution is illustrated by Fig. 19, which shows the difference between
averaged tropospheric NO2 columns from the OMI sensor from 20–30 October 2004 retrieved with TM5 at
3◦ × 2◦ and at 1◦ × 1◦ . The retrieval with the higher resolution profile shape leads to more pronounced contrasts
between the sources of pollution and background (ventilated) pollution. To better capture the sources of air
pollution is an important target of the TROPOMI mission.

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Averaging kernels

For each ground pixel, the TROPOMI data product provides the corresponding total NO2 column averaging
kernel. The averaging kernel for DOAS retrievals is equal to the altitude-dependent AMF ratioed by the total
air-mass factor [Eskes and Boersma, 2003]. Furthermore, the height-dependent air-mass factors include aterm that corrects for the difference between the temperature of the cross section used in the DOAS fit and theactual temperature in a given layer [RD16] (cf. Sect. 6.4.2).
The averaging kernel as provided in the file is linked to the total column NO2 product. The tropospheric
averaging kernel is obtained by scaling the total-column kernel by M/M trop (see [RD21]) and setting all elements
TM5of the kernel to zero above the tropopause layer, i.e. for l > ltp. This step is explained in the Product User
Manual (PUM; available via [ER2]) and has to be implemented by the user. Note that the stratospheric NO2column reported in the product is derived from the model after assimilation of the TROPOMI measurements.
Therefore this quantity does not have a corresponding averaging kernel.
Using the averaging kernel is important for data users who wish to minimise the discrepancies between theassumptions in the TROPOMI retrieval and their application of interest, for example for validation, data assimilation, or comparison to a model (e.g. Silver et al. [2013]; Boersma et al. [2016]). In particular, comparisons thatmake use of the averaging kernel are no longer depending on the a-priori TM5-MP profile shape [Eskes andBoersma, 2003].
The averaging kernel should be used in validation exercises, model evaluations, and assimilation or inversion
attempts with TROPOMI NO2 columns whenever possible (i.e. whenever independent profile information isavailable). The recipe for using the averaging kernel A for the purpose of obtaining a model estimate of thetropospheric NO2 column (Nvtrop ) that can be compared to TROPOMI is as follows:nl

Nvtrop = A~xm = ∑ Al Sl xm,l

(21)

l=1

where Sl are the components at the l -th vertical layer of an operator that executes a mass-conserving verticalinterpolation, followed by a conversion to sub-columns (molec/cm2 ) in case the model vertical distribution xm,lis not given in those units.
Alternatively, the kernels may be used to replace the global TM5-MP a-priori profile used in the retrieval
by an alternative modelled NO2 profile shape, e.g. from a high-resolution regional chemistry-transport model(Griffin et al. [2019], Lin et al. [2014]). The recipe for this replacement is provided in the PUM [ER2].
6.4.10De-striping the NO2 data product

The OMI measurements show across-track biases (stripes) in NO2 resulting from viewing zenith angle
dependent calibration errors in the OMI backscatter reflectances. For the DOMINO v2 NO2 data product,
Boersma et al. [2011] developed an empirical post-hoc de-striping correction based on the daily mean acrosstrack dependency of the NO2 slant columns. This correction is applied in the final step of the NO2 processing,i.e. after the conversion to vertical columns. A new de-striping approach was developed within the QA4ECV
project, which is now fully integrated in the retrieval approach, avoiding the extra post-processing step.
Given that TROPOMI is measuring with a CCD detector similar to the one used by OMI, calibration related
across-track biases are likely also present in the TROPOMI NO2 data. For this reason an option has been
included in the Level-2 processor that allows for a de-striping correction on the NO2 slant column data, similar
to the approach implemented for QA4ECV.
Because the striping amplitude was found to be much smaller than for OMI, the stripe correction was
switched off in the first release of the TROPOMI NO2 product, v1.0.2 and v1.1.0, of July 2018. In the first
update to v1.2.0 (24 October 2018) it was however decided to turn on the de-striping to remove small but
systematic across-track features and further improve the product quality in this way.
The de-striping is determined from on orbits over the Pacific Ocean (longitude between 150◦ W − 180◦ W),
in order to avoid interference by tropospheric pollution hotspots. Observations are averaged over a 30◦ latitude
range in the tropical belt, which moves north-south with the seasons.
A slant column stripe amplitude is determined for each viewing angle. The row-dependent slant column
diffdifference Ns,NO
is defined as the difference between the measured total slant columns (Ns,NO2 ) and total slant
2
modcolumns derived from the model profiles (Ns,NO) and the averaging kernels (A), averaged along-track over the
2
◦
str
diff
diff
30 latitude range. The stripe amplitude (Ns,NO2 ) is then computed as Ns,NO– mean(Ns,NO), where the mean22
is taken over the across-track rows. The slant columns are stripe corrected by subtracting this stripe amplitude
corr
str
from the individual slant column observations: Ns,NO= Ns,NO2 − Ns,NO. The NO2 data product file contains
22
str
Ns,NO2 and Ns,NO2 , so that a user of the slant column data can/must apply the stripe correction.

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50

48

46

44

42
0

50

100

150
200
250
300
across track row index number

350

400

450

Figure 20: Comparison of the total column (i.e. stratosphere plus troposphere), averaged over the tropical
Pacific Ocean on 15 July 2018 (orbit 03747) as a function of row index. The red curve is the v1.1.0 results
without de-striping, the blue curve is the v1.2.0 result with the de-striping (the stripe correction is averaged overa week), and the black curve shows a 50-row running mean of the blue curve. See the text for a discussion

In order to retain only features which are slowly varying over time, and in order to reduce the sensitivity tofeatures observed during a single overpass, the stripe correction factors are averaged over a time period of
7 days, or about 7 Pacific orbits.
The slant column stripe amplitudes, one for each viewing angle, are stored in three places:• For off-line (OFFL) processing, in a separate daily data file which contains the stripe amplitudes
determined on the previous day. This file is read during every restart of the TM5-MP/DOMINO system
in order to initialise the stripe correction. These files are written during the TM5-MP/DOMINO run after
every update of the stripe amplitudes, when processing a Pacific orbit.
• The stripe amplitudes are written to the NO2 Level-2 datafiles.
• The stripe amplitudes are written to the TM5-MP output files, which are used by the near-real time (NRT)NO2 Level-2 processor.
Fig. 20 shows an example for TROPOMI of the impact of the de-striping of the total NO2 column (i.e. stratosphere plus troposphere), averaged over the tropical Pacific Ocean on 15 July 2018 (orbit 03747) as a functionof the row index. The red curve is the v1.1.0 result without de-striping, the blue curve is the v1.2.0 result withthe de-striping (the stripe correction is averaged over a week), and the black shows a 50-row running meanof the blue curve. The red curve shows single-row spikes, as well as correlated structures, such as the highvalues around row 200 and the low values around rows 40, 320 or 420. These correlated features are partly
removed with the update of the Level 0-to-1b data to v2.0.0, in use as of NO2 v2.2.0, but part of the structures
remain unexplained. The plot shows that the stripe filtering removes the major part of both the high and low
frequency variability. Note that the amplitude of the structures in the red curve is small, generally within 5%of the column over the clean Pacific Ocean. Also note that we expect an increase of the total column in thestratosphere from left to right, as indicated by the black curve, due to the diurnal cycle of stratospheric NOxchemistry.
Fig. 21 shows a direct comparison of the NO2 retrieval – in the form of the "geometric" vertical columns,
defined as Ns /M geo – of OMI and TROPOMI as reported by the DOAS fits on actual observations from both
instruments. The figure demonstrates that TROPOMI and OMI retrieve roughly the same absolute retrieved
NO2 slant columns, but that TROPOMI has a much smaller across-track variation ("stripiness"). A more detailed
comparison of TROPOMI and OMI slant column retrieval results is presented by Van Geffen et al. [2020].

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38

2.2
2.1
34
2.0
1.9
30

1.8
1.7
*) row-anomaly rows removed

-30
0
30
across-track viewing zenith angle [degrees]

2

1.6

26
-60

15

2.4

molec/cm ]2.5

TROPOMI orbit 01852* OMI orbit 72356
OMI orbit 03213

NO2 SCD / AMFgeo [x 10

2

NO2 SCD / AMFgeo [µmol/m ]42

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60

Figure 21: NO2 "geometric" vertical column densities, defined as Ns /M geo , for the Pacific Ocean orbit on 20
Feb. 2018 of TROPOMI are compared to those for the almost overlapping OMI orbit (with rows affected by therow anomaly removed) and to those of a similar OMI orbit from 20 Feb. 2005. Same data as in Fig. 26.

6.5

Processing chain elements

6.5.1

Off-line (re)processing

The off-line (re)processing of the TROPOMI NO2 retrieval algorithm, schematically displayed in Fig. 22, takes
place at two locations (for more details, see [RD1]):(1) The first step of the NO2 processing system, illustrated in the top left part of Fig. 22, the DOAS retrieval,ingests the Level-1b spectra and is running in the PDGS TROPOMI processing system at DLR. Also
performed in the PDGS, in a separate processing chain (not shown), is the FRESCO cloud retrieval(Sect. 6.4.4), needed by several Level-2 data products. The processor uses the slant column and cloud
cover data to assemble a "backup" NO2 vertical column product, based on the TM5-MP NO2 verticalprofile forecast produced for the NRT processing at the observation date (cf. Sect. 6.5.2).
(2) The NO2 backup data product is then transferred to the IDAF at KNMI, where once a day the data of allorbits is ingested in the data assimilation / chemistry transport model TM5-MP, as illustrated in the rightpart of Fig. 22, to compute the off-line NO2 product using the CTM model profiles computed with thelatest ECMWF meteorological fields.
(3) The off-line or nominal NO2 data product is then transferred back to the PDGS (bottom left part of
Fig. 22), where it is made available for the users.
The motivation for this set-up is to take full advantage of the available processing elements at DLR and
KNMI, and at the same time keep the number of data transfers limited. DLR will operate in the PDGS a suite ofprocessors geared to handling large amounts of TROPOMI spectra, including the processing of NO2 column
data from TROPOMI spectra. The IDAF at KNMI hosts a complete data assimilation system based on the
TM5-MP model, and has considerable experience in both the off-line and on-line retrieval of NO2 from theGOME, SCIAMACHY, OMI, and GOME-2 instruments. The essential inputs for the processing of TROPOMI
NO2 data are (1) the Level-1b spectra measured by TROPOMI at the PDGS, and (2) the ECMWF meteo dataat the IDAF.
As illustrated by Fig. 22, the data assimilation system not only provides vertical profiles for the processingof NO2 data, but also for other TROPOMI data products: formaldehyde (HCHO) and sulphur dioxide (SO2 ).
Unlike NO2 , HCHO and SO2 are not assimilated in the TM5-MP model: their profiles are output of the TM5-MP
model, based on the chemistry involving these species.

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DLR

NO2 proﬁleshape
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HCHO &
SO2 proﬁles
(NRT)

L1B

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IDAF
KNMI

Once per day

One day of orbits

Once per TBD

Once per orbit

Data product

Process

Aux. data

Distribution

In system
data transfer

Long distancedata transfer

ECMWF
Meteo

RestartﬁleNO2_SC

NO2___

NO2_SC
NO2___(backup)

NO2_SC
NO2___(backup)Time

NO2 proﬁleshape,HCHO &
SO2 proﬁles(oﬄine)
UPAS
SO2
HCHO

NO2___
NO2___
NO2___
NO2___(nominal)

End-userpickuppoint

NO2 proﬁleshape,HCHO &
SO2 proﬁles(oﬄine)Dataassim.

NO2___

RestartﬁleNO2___
NO2___
NO2___(nominal)
RestartﬁleLong term archive

Figure 22: Schematic representation of the TROPOMI processing of tropospheric NO2 data from a Level-1b
spectrum received in the PDGS in the off-line mode. The dotted line marks the division of the processing
locations: the Payload Data Ground Segment (PDGS) at DLR on the left and the Instrument Data Analysis
Facility (IDAF-L2) at KNMI on the right. (Source of the figure: [RD1].)

6.5.2

Near-real time processing

The NRT processing of TROPOMI NO2 is based on the same principles as the off-line processing, described in
Sect. 6.5.1. The main difference between the NRT processing, depicted in Fig. 23, and the off-line processing(Fig. 22) is the timing of the data assimilation step and the use of ECMWF meteorological forecasts rather thananalysed ECMWF meteorological fields. For the NRT processing of TROPOMI data, the TM5-MP model is run
once per day in the IDAF at KNMI, and ingests the NO2 slant columns from the orbits that have been observed
thus far. Based on the assimilated "state" of day i, the TM5-MP model provides a forecast of the NO2 vertical
distribution for days i + 1 until i + 4. This information is then transferred to the PDGS, as illustrated in Fig. 23,
for the NO2 NRT data product.
This procedure ensures that as soon as new TROPOMI measurements are available in NRT, all necessary
information from the TM5-MP model is ready to be processed in the PDGS to provide an NO2 vertical column
data product, without the need for a (time consuming) model run first. With NO2 profile data available in a
5-day forecast, an interruption of the data stream from the IDAF system is not an immediate problem for the
NRT processing system. In case the interruption lasts longer than 5 days, the PDGS processing system will
use the latest available NO2 profile as a fall-back to be able to continue providing NO2 data in NRT. As fall-backthe latest available NO2 profile is used, rather than NO2 profile data from a climatology, because a switch toclimatology data would constitute an evident discontinuity in the NO2 data.
In the NRT processing, the TM5-MP data assimilation run is started just after midnight, as soon as the
ECMWF meteo data has arrived. In that run, the system incorporates all the NO2 slant column data that has
been processed since the previous data assimilation run (from 24 hours before). Since the (ECMWF) forecast
is provided up to 5 days ahead, the NRT processing is capable of providing tropospheric NO2 data, even after
a period of missing data. Previous analysis [Boersma et al., 2007] has shown that the forecast is accurate

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NO2 proﬁleshape
forecast,
HCHO &
SO2 proﬁles

L1B

NO2 proﬁleshape
forecast,
HCHO &
SO2 proﬁles

UPAS
SO2
HCHO

NO2___

Once per day

Once per orbit

Data product

Process

Aux. data

Distribution

In system
data transfer

Long distancedata transfer

One day of orbits

NO2___
NO2___

ECMWF
Meteo

NO2___

Time(day)

NO2___

NO2___

PDGS
DLR

IDAF
KNMI

End-userpickuppoint
NO2 proﬁleshape
forecast,
HCHO &
SO2 proﬁlesRestartﬁleData
assim.
forecast

NO2 proﬁleshape
forecast,
HCHO &
SO2 proﬁlesRestartﬁleNO2___
UPAS
SO2
HCHO

Figure 23: Schematic representation of the TROPOMI processing of tropospheric NO2 data from a Level-1b
spectrum received in the PDGS in the NRT mode. The dotted line marks the division of the processing
locations: the Payload Data Ground Segment (PDGS) at DLR on the left and the Instrument Data Analysis
Facility (IDAF-L2) at KNMI on the right. (Source of the figure: [RD1].)

enough to provide reliable NO2 tropospheric columns for a few days ahead. The differences between theoff-line and NRT NO2 product are found to be small.

6.6

The NO2 data product

The NO2 vertical column data product contains the data sets listed in Table 5. The main product is thetropospheric NO2 column, but the file also contains all intermediate steps such as the results from the DOAS
retrieval, output from the data assimilation, cloud information, input database information, flags, uncertainties
and the AMF calculation results. The attributes in the file provide full traceability of the data product (including
information on processor version, settings, inputs).
Table 6 provides a list of seven main classes of possible TROPOMI NO2 data usage, and lists the data setsthat these users will need for their applications. For notes on applying the averaging kernel, see Sect. 6.4.9.
More information on the content and usage of the data product can be found in the NO2 Product User Manual(PUM; available via [ER2]).
In order to comply with the SI unit definitions, the TROPOMI NO2 data product file gives trace gas
concentrations in mol/m2 , rather than in the commonly used unit molec/cm2 . The following multiplication factors– also provided as attributes to the data sets – enabling the user to easily make the conversions, if needed:• The multiplication factor to convert mol/m2 to molec/cm2 is 6.02214×1019 .
• The multiplication factor to convert mol/m2 to DU is 2241.15.
• The O2 –O2 concentration is given in mol2 /m5 ; the multiplication factor to convert this to the commenlyused unit molec2 /cm5 is 3.62662×1037 .

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Table 5: Overview of data sets for each ground pixel in the final NO2 data product assembled for dissemination
via the TROPOMI website, the Sentinel-5P Core Service. Where relevant, the precision of a data set is provided
as well. Data sets marked with ∗ are not part of the official Level-2 data product, but will be provided in a
separate support data file. A more detailed overview can be found in Tables 11 and 12.
origin of data set

for each ground pixel

symbols

Level-1b spectrum

measurement time
ground pixel centre and corner coordinatesviewing geometry datatϑgeo , δgeo
θ0 , θ , φ0 , φ

Databases

surface albedo in the NO2 window
surface albedo used for the cloud retrieval
surface elevation and pressure

As,NO2As
zs , psCloud retrieval

cloud fraction and cloud pressure FRESCO
scene pressure and scene albedo FRESCO
cloud fraction in the NO2 window
cloud radiance fraction in the NO2 window

feff , pc
psc , Asc
feff,NO2wNO2

DOAS retrieval

NO2 slant column
slant columns of secondary trace gases

Ns,NO2
Ns,O3 , Ns,H2 Ovap , Ns,. . .
Cring
am [m = 0, 1, ..., n p ]cm [m = 0, 1, ..., noff ]nλD
RRMS , χ 2
wE0
s , ws , wq

Ring effect coefficientpolynomial coefficients
intensity offset coefficients
number of spectral points
degrees of freedom of the fit
RMS error and χ 2 of the fit
wavelength calibration coefficients
Data assimilation &AMF calculation

Flags

NO2 slant column stripe amplitude
NO2 ghost column
tropospheric AMF
stratospheric and total AMF
averaging kernel
TM5 tropopause layer index
TM5 pressure level coefficients∗ NO2 profile for stratosphere and troposphere∗ TM5 temperature profile∗ TM5 surface elevation and pressure

Nvtrop
NvstratNv ≡ Ns /M
Nvsum ≡ Nvtrop + Nvstrat
str
Ns,NO
2
Nvghosttrop
trop
M trop , Mclr
, Mcld
strat
M ,M
A
TM5
ltp
, BTM5ATM5llvl,NO2
TlTM5
zTM5, pTM5ssquality assurance value (qa_value)
processing quality flags
absorbing aerosol index
snow/ice flag and land/water classification

fQA
—
—
—

NO2 tropospheric vertical column
NO2 stratospheric vertical column
NO2 total vertical columns

The output for each ground pixel is accompanied by two flags indicating the status of the results of theprocessing and the retrieval. The "quality assurance value" (qa_value or fQA ) is a continuous variable,
ranging from 0 (no output) to 1 (all is well). Warnings that occur during processing or results of the processing
can be reasons to decrease the flag value. The qa_value is the main flag for data usage:• qa_value > 0.75.
For most users this is the recommended pixel filter. This removes clouds (cloud radiance fraction > 0.5),

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Table 6: Overview of different user applications of NO2 data and the data sets from the TROPOMI NO2 data
product the users will need. In addition all users may need pixel related data, such as measurement time,
geolocation, viewing geometry, etc., as well as the processing and data quality flags.
user application

data sets needed#1

Tropospheric chemistry / air quality model evaluationand data assimilation
Validation with tropospheric NO2 profile measurements(aircraft, balloon, MAX-DOAS)

Nvtrop , ∆Nvtrop,kernel
M trop , M , A †TM5
ATM5, BTM5
, ltp
, ps
l
l
fQA

#2

Tropospheric column comparisons, e.g. with otherNO2 column retrievals

Nvtrop , ∆Nvtrop
fQA#3Stratospheric chemistry model evaluation and data
assimilation
Validation with stratospheric NO2 profile measurements(limb/occultation satellite observations)

Nvstrat , ∆Nvstrat ‡TM5
ATM5, BTM5
, ltp
, ps
l
l
fQA

#4

Stratospheric column comparisons, e.g. withground-based remote sensors

Nvstrat , ∆Nvstrat
fQA#5Whole atmosphere (troposphere + stratosphere) data
assimilation systems

Nv , ∆Nvkernel , A §TM5
ATM5, BTM5
, ltp
, ps
l
l
fQA

#6

Whole atmosphere (troposphere + stratosphere) comparisonswith ground-based remote sensing (e.g. Pandora)

Nvsum , ∆Nvsum
fQA#7

Visualisation of the NO2 product, as well as
generation of Level-3 gridded and time averaged NO2 fields

Nvtrop , Nvstrat , Nvsum
fQA

§

§

† The tropospheric kernel Atrop is derived from the total kernel A and the air-mass factors M and M trop .
‡ The stratospheric column is a model (data assimilation analysis) quantity and therefore does not have an averaging
TM5
kernel. The tropopause pressure can be computed using ltp
and the hybrid level specification.

§ Note that the total NOsum≡ Nvtrop + Nvstrat
2 vertical column Nv ≡ Ns /M is not the same as the sum Nv

scenes covered by snow/ice, errors and problematic retrievals. Default choice for user applications 1, 2,
5, 6, and 7 (viz. Table 6).

• qa_value > 0.50.
This adds the good quality retrievals over clouds and over scenes covered by snow/ice. Errors andproblematic retrievals are still filtered out. In particular this may be useful for assimilation and modelcomparison studies. Default choice for user applications 3 and 4, and may be considered for user
applications 1 and 5 (viz. Table 6).
The determination of the qa_value is described in detail in Appendix E. The qa_value indicates whether thefootprint is cloud covered or not, and whether there is snow or ice on the surface. It is set to 0 if anywhere in
the processing an error occured, as indicated by the processing_quality_flags. Warnings related to
the South Atlantic Anomaly, sun glint, or missing non-critical input data lower the qa_value. The qa_value
depends on the solar zenith angle, tropospheric air-mass factor and quality of the DOAS fit, and filters unrealistic
albedo values.
As of TROPOMI NO2 product version 1.2 the rather strict filtering of snow/ice covered scenes wasabandoned and retrievals receive a qa_value > 0.75 when the scene pressure from the cloud retrieval isclose to the surface pressure. This significantly enhances the coverage over high latitudes, as shown in
Figure 24.
The "processing quality flags" (processing_quality_flags) contains the individual event that led toprocessing failure, and/or a precise record of the warnings that occurred during processing. The definitions andTROPOMI ATBD tropospheric and total NO2
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Figure 24: In TROPOMI NO2 product version 1.2 (operational since 24 October 2018) the filtering over snow
and ice is relaxed, greatly improving the coverage. Left panel: monthly mean NO2 concentration for April 2018,
v1.2; right panel: same but for v1.0.

usage of these two flags is harmonised between the Level-2 data products of TROPOMI and is documented in
the NO2 Product User Manual (PUM; available via [ER2]).
The NO2 data product provides the Absorbing Aerosol Index (AAI; the ATBD of the AAI is available via[ER1] and [ER5]) and a snow/ice flag (see Sect. 6.4.6) as additional information for the NO2 data users, bothin the off-line and the NRT processing mode. The AAI is not yet used in the flags discussed above, but thismay be added in an upcoming update.
The data product consists of two files: one with the main NO2 retrieval results and a separate TM5-MP
model data file with vertical information on atmospheric NO2 , SO2 and HCHO profile and temperature at the1◦ × 1◦ grid of TM5-MP on a half-hourly basis. The additional model datafile (described in [RD22] and available
via [ER5]), is large and is not relevant for most NO2 data users, but for some advanced users the model profiles
have shown to be useful.

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7

Input-Output file description

7.1

Required input

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The processing of TROPOMI NO2 data poses different demands for different retrieval steps, as described in
Sect. 6.5. The dynamic and static input data needed in the PDGS for the off-line and NRT processing of theNO2 data product are listed below and summarised in Tables 7 and 8, respectively. Table 7 also mentions thefall-back in the processing of a given ground pixel in case the dynamical data is not available.
The FRESCO/KNMI cloud product is a TROPOMI Level-2 support product, provided by KNMI software
running in the PDGS as separate process with its own Level-2 data files. As of NO2 v2.2.0 an additional cloud
product, based on the KNMI O2 –O2 cloud algorithm developed for OMI, is produced during the processing forNO2 (cf. Sect. 6.4.4). Both the FRESCO and the O2 –O2 cloud data are given in separate data groups in theNO2 product files. The NO2 v2.2.0 data product uses the FRESCO cloud product. For a later release we will
study the quality of both cloud products and develope rules to use one or the other cloud product depending
on under which circumstances they work best. The S5P/DLR cloud product is optional; the actual use of thisproduct will be investigated.
For the snow/ice cover data, NISE [ER17] and ECMWF assimilated data are requested, at least one isrequired, with daily updates near the polar region, less frequent updates closer to the equator. (See also thegeneral TROPOMI documents [RD14] and [RD1].)
7.1.1

Inputs at the PDGS for spectral fitting and air-mass factor calculation

In the PDGS at DLR the following input is required, making a distinction between: (a) static (constant) input
data and dynamic input data, which changes every orbit, and (b) data needed for the spectral fitting, andinformation needed in the subsequent processing step. After the DOAS NO2 retrieval, the PDGS assembles
the NO2 vertical column data product, using information from the data assimilation system from the IDAF at
KNMI for further processing, as llustrated in Fig. 22.
Spectral fitting input data for the DOAS fit• Dynamic input:– Level-1b Earthshine and Solar spectra• Static input:– Reference spectra (convolved with the TROPOMI slit function; [ER11]) for
NO2 , O3 , H2 Ovap , O2 –O2 , H2 Oliq , Ring effect, irradiance
Note that since the TROPOMI slit function differs for each of the viewing directions, i.e. for each of the detectorrows, there is one set of reference spectra for each viewing direction.
NO2 data product input data

• Dynamic input:– NO2 slant column density & errors from the DOAS fit– NO2 profile shape from the TM5-MP data assimilation system– Temperature and pressure profile, orography and tropopause level from TM5-MP / ECMWF– Geolocation data (incl. pixel corner coordinates)– Viewing geometry– Effective cloud fraction and cloud pressure– Scene albedo and scene pressure– Snow and ice cover data
– Surface pressure data
• Static input:– Pixel-average representative (interpolated) surface albedo at
440 nm (representative for the NO2 window)– Pixel-average representative (interpolated) terrain heightfrom a digital elevation map, including a land/water classification– Altitude-dependent AMF look-up table– Cloud fraction and cloud radiance fraction look-up table

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Table 7: Overview of the dynamic input data needed for both the off-line and the NRT NO2 data processing in
the PDGS. The table does not list the meteorological input needed by the data assimilation system in the IDAF.
See Sect. 7.1 for further remarks.
pre-process

backup if

name/data

symbolunit

sourceneeds

not available

comments

S5P Level-1b EarthI(λ )mol/s/m2 /nm/sr

S5P Level-1b productper pixel

no retrieval—

radiance VIS band
S5P Level-1b Solar
irradiance VIS band

E0 (λ )

mol/s/m2 /nm

S5P Level-1b productper pixel

use previous—

NO2 profile

vl,NO2

mixing ratio

TM5-MP model

per pixel

latest available †N/A

NRT
off-line

KNMI cloud products
FRESCO &O2 –O2 ‡1Pa
1

S5P Level-2 support
product—

no VCD product

—

1
Pa
1

S5P Level-2 cloud—no VCD productoptional

productsnow/ice cover flag

feff
pc
Ac
Asc
psc
feff
pc
—

Pa
—

product
ECMWF

per pixel

previous day
NISE [ER17]#

surface pressure
aerosol absorbing index

ps
AAI

m
1

ECMWF
S5P Level-2 AAI
354/388 nm pair

per pixel—

from DEM (Table 8)
set AAI fill value
set AAI fill value§

S5P/DLR cloudNRT
off-line† Latest available forecast NO

2 profile for that day.

‡ The FRESCO and O –O
2

2 cloud product data are both included in the NO2 data files in data sub-groups of DETAILED_RESULTS; thecloud product selected from these two for a given ground pixels is copied to the data group INPUT_DATA.
If the ECMWF value for the day is not available, the value previous day is used; if that value is unavailable,
the snow/ice flag from NISE is used, with ultimate fall-back to a climatological value.
§ When determining the surface pressure from the DEM surface altitude, ECMWF data (if available) is used correct the DEM data.
#

7.1.2

Inputs at the IDAF for the data assimilation

In the IDAF at KNMI the NO2 slant column data received from the PDGS is used in the data assimilation system
to determine the NO2 profile shape needed for the conversion of the NO2 slant columns from the DOAS fit into
the stratospheric and tropospheric NO2 columns at the PDGS. For this step the following input is required forthe data assimilation system.

Data assimilation input data

• Dynamic input:– ECMWF meteorological fields (pressure, temperature, wind, . . . )– The "NO2 data product input data" listed above– TM5-MP start field from the previous day– (In case de-striping is turned on:) Destriping coefficients from the previous day• Static input:– TM5-MP static input data: NOx (and other) emission inventories, climatologies, . . .

7.2

Computational effort

Table 9 contains an overview of the processing time needed for the NO2 product; for one pixel the spectral
fitting and AMF calculation takes about 0.006 seconds (excluding overhead). The code was developed at KNMI,
mainly in C++, and was transferred to and tested at DLR.
Compared to OMI, TROPOMI has about 10 times more observations, which implies a factor 10 extra
computing time for the DOAS NO2 SCD retreival; the inclusion of the O2 –O2 cloud data retrieval as of NO2
processor v2.2.0 increased the processing time by a factor of about 2.7. Especially the TM5-MP processing of
TROPOMI was taking quite some CPU time. In April-May 2018 the AMF retrieval loop inside TM5-MP was

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Table 8: Overview of the static input data needed for both the off-line and the NRT NO2 data processing in
the PDGS. The reference spectra convolved with the TROPOMI slit function (see column 5) are given each of
the detector rows. The table does not list the input needed by the data assimilation system in the IDAF. See
Sect. 7.1 for further remarks.
pre-process
name/data

symbolunit

sourceneeds

comments

absorption cross sections
NO2
O3

σNO2 (λ )σO3 (λ )m2 /mol
m2 /mol

Vandaele et al. [1998]
Gorshelev et al. [2014] &convolution
convolution——

σO2 –O2 (λ )σH2 Ovap (λ )σH2 Oliq (λ )Iring (λ )Eref (λ )———zsAs,NO2

m5 /mol2
m2 /mol

Serdyuchenko et al. [2014]
Thalman and Volkamer [2013]
HITRAN 2012 data

convolution
convolution—

1/m

Pope and Frey [1997]

convolution—

mol/s/m2 /nm

Chance and Spurr [1997]

convolution†

mol/s/m2 /nm——

Chance and Kurucz [2010]
KNMI
KNMI

convolution
—
—

—
—
—

—
m
1

KNMI
GMTED2010 [ER19]
Kleipool et al. [2008]; [ER15]—
per pixel
per pixel‡

Tilstra et al. [2017]; [ER20]

per pixel

O2 –O2H2 Ovap
H2 Oliq
Ring reference spectrum
irradiance reference spectrum
retrieval input settings
air-mass factor lookup table
cloud fraction lookup table
digital elevation map (DEM)
surface albedo database

†

§

#

† Created e.g. as in Van Geffen et al. [2015]; see also [RD14].
‡ For the cloud fraction retrieval in the NO

2 fit window and for the cloud radiance fraction.
Climatological value may be adjusted based on the dynamical snow/ice flag; cf. Sect. 6.4.5 & App. D.
§ The elevation data is constructed as an average over a 10-km circular region (with standard deviation and extrema),
which is representative for the TROPOMI pixel, sampled per 3 km [RD23].
#

Table 9: Approximate computational effort for the off-line TROPOMI NO2 processing for processing groundpixels after the along-track pixel size reduction on 6 Aug. 2019 and including the O2 –O2 cloud retrieval. Any
delays introduced by the different processing steps having to wait for data to be available are not included.
Time needed for processing
one TROPOMI orbit

Time needed for processing
one day of TROPOMI data

Spectral fitting & O2 –O2 clouds & AMF
Data transfer DLR → KNMI
AMF/assimilation/modelling (TM5-MP)
Data transfer KNMI → DLR

27 min (9 cores)< 1 min4 mins (20 cores)< 1 min

6.5 hours (9 cores)< 15 min1 hour (20 cores)< 15 minTotal processing time

35 min

8 hours

Table 10: Estimate of the computational effort for the near-real time TROPOMI NO2 processing for processing
ground pixels after the along-track pixel size reduction on 6 Aug. 2019 and including the O2 –O2 cloud retrieval.
Any delays introduced by the different processing steps having to wait for data to be available are not included.
Time needed for processing
one TROPOMI orbit in NRTSpectral fitting & O2 –O2 clouds & AMF
Data transfer DLR → KNMI
Data assimilation with TM5-MP
Data transfer KNMI → DLR

27 min (16 cores)< 1 min
N/A< 15 min (once a day)Total processing time

45 minparallelised over observations. Together with other optimisations this has resulted in a speed-up of a factor 5,
to 1h processing time on 20 processors for one day of TROPOMI retrievals.
The assimilation of 15 million observations per day does not lead to a slowdown of the analysis step

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compared to OMI. The TROPOMI pixels are binned to so-called superobservations at 1◦ × 1◦ (Sect. 6.3).
The number of TROPOMI superobservations is comparable to the number for OMI (before the row anomaly
occurred). The number of superobservations to be assimilated is thinned out by a factor 2 (checkerboard
approach) to further reduce the computational burden.

7.3

Near-real time timeliness

For the NRT Level-2 data to be available within the required 3 hours after measurement, it is required that theprocessing of Level-2 data does not take more than about 40 minutes per orbit. Table 10 shows that the actual
processing time is about 27 minutes (using 16 cores) and is therefore within the NRT constraints.
The data assimilation run is done at KNMI once a day (just after midnight) to provide a forecast of the NO2
profile for the coming 5 days, based on assimilation of TROPOMI slant columns observed over the previous
day. These forecast runs have recently been accelerated and takes ∼3h for one forecast. Note that the NRT
chain does not need to wait for this, as mentioned in Sect. 6.5.2.

7.4

NO2 product description and size

The TROPOMI NO2 data output product consists of the retrieved tropospheric and stratospheric NO2 columns,
along with error estimates and the (total) averaging kernel. A general overview of the data product contents isgiven in Sect. 6.6 and Table 5. Table 11 provides a more detailed overview of the data sets, their unit, type, etc.
in the main output data product.
As of NO2 v2.2.0 the processor includes the O2 –O2 cloud retrieval; see Sect. 6.4.4.3. With this processorchange two data sub-groups of DETAILED_RESULTS are introduced to hold the FRESCO and the O2 –O2
cloud & scene data, named FRESCO and O22CLD, respectively. The cloud data selected from these two for agiven NO2 ground pixels are copied to the data group INPUT_DATA. For readability Table 11 lists the cloud &scene data fields only once.
Given the number of data per ground pixel listed in Table 11, the Level-2 NO2 output file of one orbit has thefollowing size: about 340 MB for v1.2.x and v1.3.x prior to the along-track pixel size reduction on 6 Aug. 2019,
and after that about 440 MB for v1.3.x and v1.4.0, while v2.2.0 is about 580 MB including the FRESCO andO2 –O2 data variables. Before (after) the pixel size reduction, an orbit has about 1.5 (1.9) million observations.
The averaging kernel describes how the retrieved NO2 columns relate to the true NO2 profile shape [Eskes
and Boersma, 2003]. The averaging kernel should be used in validation exercises, model evaluations, andassimilation or inverse modelling attempts with TROPOMI NO2 data (cf. Sect 6.4.9). The output productalso contains the necessary information (surface pressure and TM5-MP sigma coordinates) to construct thepressure grid to which the averaging kernel values correspond.
For advanced users, a separate support file is made available that contains the temperature and NO2 (SO2 ,HCHO) vertical profile. This data is given at the TM5-MP grid resolution of 1◦ × 1◦ , rather than on TROPOMI
pixel basis, on a half-hourly basis for one day per file (i.e. 48 time steps); each file is about 1.6GB). The
temperature and NO2 profiles are not included in the standard Level-2 product, because most users will notneed these and because vertical profiles will drastically increase the size of the TROPOMI Level-2 retrieval
files. Table 12 provides an overview of the data sets in the support output data product; a Product User Manual(PUM) for this TM5 NO2 , SO2 and HCHO profile auxiliary support product, identified as S5P-KNMI-L2-0035-MA[RD22], is available via [ER2].

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Table 11: Overview of the data sets, their units, types and sizes, in the main data output product file, listed
alphabetically; cf. Table 5. All quantities followed by a ∗ in the "symbol" column consist of the value and theassociated precision (for these the number of data per pixel is doubled in the 6th column); for the verticalcolumn densities the precisions are listed explicitly to clearly show the different types of precisions. See
Sect. 7.4 for some remarks on the data from the cloud retrieval. In the last column ’PV’ denotes the processor
version when this variable was introduced. The data sets in the support data file are listed in Table 12.

name/data

symbolunit

description

type

data
per pixel

comments

aerosol absorbing index
air-mass factor

—

1
1
1

L2 AAI 354/388 nm wavel. pairtropospheric AMF
clear-sky tropospheric AMF

float
float
float

1
1
1

added as flag—
since PV 1.3.0

1
1

cloudy tropospheric AMF
stratospheric AMF

float
float

1
1

since PV 1.3.0
—

1
1
1

total AMF—
χ 2 of the NO2 DOAS fit

float
float
float

1

—
†

1
1
1

used in the cloud retrieval
from the cloud retrieval
for the NO2 VCD

float
float
float

Pa
1
1

from the cloud retrieval
for the NO2 VCDof the slant column fit

float
float
float

mol/m2

total NO2 SCD

float

m

H2 Oliq coeff. in NO2 window

float

nl
1
1
1
1
1
1
1
1 ×2
1 ×2

2

H2 Ovap SCD in NO2 window

float

1 ×2

—

mol2 /m5

O2 –O2 SCD in NO2 window

float

1 ×2

—

mol/m2O3 SCD in NO2 window

float

1 ×2

—

1

Ring coeff. in NO2 window

float

1 ×2

—

mol/m2NO2 column below the clouds
VIS pixel – latitude
VIS pixel – longitude

float
float
float

1
5
5

‡

1
1
1

across-track pixel index
in the NO2 DOAS fit
surface type classification

int
float
int

1

—
cf. Eq. (9)—s
1

VIS pixelin the NO2 fit window

float
int

1
1
1

from the DOAS fit
in the NO2 DOAS fit
—

int
float
int

1
1
m

quality assurance value
RMS error of the NO2 DOAS fit
altitude of the satellite

float
float
float

◦

1

latitude sub satellite point
longitude sub satellite point
relative offset in orbit

float
float
float

1
1
Pa

along-track pixel index
from the cloud retrieval
from the cloud retrieval

int
float
float

1
mol/m2

snow/ice case flagging
NO2 SCD stripe amplitude

int
float

1

for the cloud retrieval

float

1
m
Pa

for cloud fraction NO2 window
VIS pixel
VIS pixel

float
float
float

Pa
1
1

—
—
—

float
float
int

averaging kernel
chi-squared
cloud albedo
cloud fraction
cloud fraction NO2
cloud pressure
cloud radiance fraction
degrees of freedom
DOAS fit results

M trop
trop
Mclr
trop
Mcld
M strat
M
A
χ2
Ac
feff
feff,NO2
pc
wNO2
D
Ns,NO2 ∗Ns,H2 Oliq ∗Ns,H2 Ovap

ghost column
ground pixel coordinates
ground pixel index
intensity off. coefficientsland/water classification
measurement timenumber of wavelengths
number of iterations
polynomial coefficientsprocessing quality flags
qa valueroot-mean-square error
satellite coordinates

scanline index
scene albedo
scene pressuresnow-ice flag
stripe amplitude
surface albedo
surface albedo NO2
surface elevation
surface pressure
TM5 pressure level
coefficients
TM5 tropopause∗

Ns,O2 –O2 ∗Ns,O3 ∗
Cring ∗
Nvghost
δgeo
ϑgeo
—
cm ∗
—
t
nλ
ni
am ∗
—
fQA
RRMS
zsat
δsat
ϑsat
ϕsat
—
Asc ∗
psc ∗
—
str
Ns,NO
2
As
As,NO2
zs ∗
ps
ATM5
l
BTM5
l
TM5
ltp

mol/m

◦
◦

◦

cf. Eq. (2)cf. Sect. 6.4.4—
in NO2 fit window—
in NO2 fit window———

centre, 4 corners
centre, 4 corners

noff + 1 ×2
12
1
1(n p + 1) ×2
1
1
1
1
1
1
1
1
1 ×2
1 ×2
1
0
1
1
1 ×2
1
0
0
1

—
#

—
cf. Eq. (6) §cf. Sect. 6.6
cf. Sect. 6.6 & E
cf. Eq. (4)
—
—
—
—
—
—
—
—
cf. Sect. 6.4.10 ⊗—cf. Sect. 6.4.4
—
—
¶
¶

—

layer index
Table continues on next page

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Table 11: — continued.

name/data

symbolunit

description

type

data
per pixel

comments

vertical column density

trop
Nv,NO
2
trop
∆Nv,NO2trop,kernel∆Nv,NO
2
strat
Nv,NO
2
strat
∆Nv,NO
2
Nv,NO2∆Nv,NO2
kernel∆Nv,NO
2
sum
Nv,NO2sum∆Nv,NO
2
θ0
φ0
θ
φ
ws ∗
wq ∗
χw2
∗
wE0
s
E0
(χw )2

mol/m2

tropospheric NO2 VCD

float

1

—

mol/m2

id. precision, kernel not applied

float

1

cf. Sect. 8.4

mol/m2

id. precision, kernel applied

float

1

cf. Sect. 8.4

mol/m2stratospheric NO2 VCD

float

1

—

mol/m2

id. precision

float

1

—

mol/m2

total NO2 VCD

float

1

mol/m2

id. precision, kernel not applied

float

1

mol/m2

id. precision, kernel applied

float

1

mol/m2summed NO2 VCD

float

1

mol/m2

id. precision

float

1

◦

solar zenith angle

float

1

◦

solar azimuth angle
viewing zenith angle

float
float

1
1

nm
1

viewing azimuth angle
wavelength shiftwavelength stretch

float
float
float

1
1 ×2
1 ×2

1
nm
1

χ 2 of the calibration
wavelength shiftχ 2 of the calibration

float
float
float

1
0 ×2
0

≡ Ns /M
cf. Sect. 8.4
cf. Sect. 8.4≡ Nvtrop + Nvstrat—
at surface
at surface
at surface
at surface
cf. Eq. (7)cf. Eq. (7)cf. Eq. (7)cf. Eq. (7) ⊗cf. Eq. (7) ⊗viewing geometry data

wavelength calibration
radiance
wavelength calibration
irradiance◦◦† The number of TM5-MP layers is n = 34.
l‡ The NO ghost column is the TM5-MP NO

2
2 profile integrated from the surface to the cloud pressure level.
The actual number of wavelengths nλ used in the fit (cf. Eq. (2)), i.e. after removal of, for example, bad pixels within the fit window.
§ The degree of the DOAS polynomial is n = 5p¶ One set of n + 1 (see note †) TM5-MP pressure level coefficients per data granule.
l⊗ One set per detector row.
#

Table 12: Overview of the data set units, types and sizes in the support output product file; this file is also
used to store the profiles of HCHO and SO2 , delivered along with the NO2 profile by the TM5-MP model (see[RD22], available via [ER5]). The data is provided on the TM5-MP grid resolution of 1◦ × 1◦ on a half-hourly
basis, rather than on TROPOMI pixel basis. The data sets in the main data file are listed in Table 11.

name/data

symbolunit

description

type

HCHO profile

vl,HCHOvl,NO2vl,SO2
TlTM5
ATM5
l
BTM5
l
zTM5
s
pTM5
s
TM5
ltp
str
Ns,NO
2
—
d

1

volume mixing ratio

float

1
1
K

volume mixing ratio
volume mixing ratio
—

float
float
float

Pa
1
m

—
—
—

float
float
float

Pa
1

—
—

float
int

mol/m2NO2 SCD stripe amplitude

float

1
days

year, month, day, hour, min, sec
no. of days since 1 Jan. 1950

int
float

NO2 profileSO2 profile
TM5 temperature profileTM5 pressure level coefficients
TM5 surface elevation
TM5 surface pressure
TM5 tropopause layer index
stripe amplitude
date & time
time

† The number of TM5 layers is n = 34.
l¶ One set of n + 1 (see note †) TM5-MP pressure level coefficients per data file.
l‡ This data set is provided via a separate static TROPOMI digital elevation map file.
§ One set per data file.
⊗ One set per detector row.

data pergrid cell

nl
nl
nl
nl
0
0
1
1
1
0
0
0

comments
†
†
†
†
¶
¶
‡

—
—
cf. Sect. 6.4.10 ⊗§§TROPOMI ATBD tropospheric and total NO2
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Error analysis

The TROPOMI NO2 retrieval algorithm generates stratospheric and tropospheric vertical column densities for
all pixels. Since assumptions differ considerably for stratospheric and tropospheric retrievals, the error budget
for each case is treated separately below.
The overall error for the retrieved tropospheric columns is determined through propagation of the threemain error sources: (a) measurement noise and spectral fitting affecting the slant columns, (b) errors related tothe separation of stratospheric and tropospheric NO2 , and (c) systematic errors due to uncertainties in model
parameters such as clouds, surface albedo, and a-priori profile shape, affecting the tropospheric air-massfactor. For the stratospheric NO2 column, the errors are driven by slant column errors, errors in the estimate of
the stratospheric contribution to the slant column, and stratospheric AMF (observation operator) errors.
For NO2 , the overall error budget thus consists of several different error source terms. Errors in the slant
columns are driven in part by instrumental noise (random errors), and in part by necessary choices on thephysical model and reference spectra used (systematic errors). Errors in the AMF are mostly systematic(e.g. assumptions on albedo) but will also have random contributions (e.g. from observed cloud parameters, or
sampling / interpolation errors). It is thus not possible to make a clear distinction between these error types inthe total error reported in the TROPOMI NO2 data product. This implies that by averaging TROPOMI pixels
over time or over a larger area, the random part of the overall error can be largely eliminated, but systematiceffects may still persist in averaged retrievals.
Experience with errors in OMI NO2 over polluted regions, largely stemming from theoretical error analysis
and practical validation studies, indicates that overall errors on the order of 25% for individual troposphericNO2 column retrievals may be expected. Validation studies show that the systematic part of this error is on theorder of 10-15% (e.g. Hains et al. [2010]; Irie et al. [2012]; Ma et al. [2013]). For stratospheric NO2 columns,
the errors are considerably smaller and depend mostly on the absolute accuracy of the slant columns, andon the separation of the stratospheric and tropospheric contributions. The stratospheric NO2 column error is
expected to have errors on the order of 5-10% (e.g. Hendrick et al. [2012]) or 0.15 − 0.2×1015 molec/cm2 .

8.1

Slant column errors

Instrument noise is the main source of errors in the spectral fitting of TROPOMI Level-1b spectra. The
radiometric signal-to-noise ratios (SNR) of TROPOMI in the 400 − 500 nm range turns out to be 1400 − 1500
for a individual Level-1b spectra [RD4]. Experience with OMI spectral fitting in the 405 − 465 nm spectral
domain showed that the uncertainty in OMI NO2 slant column densities of about 0.75×1015 molec/cm2 in 2005
(when the SNR of OMI was 900 − 1000) to about 0.90×1015 molec/cm2 in 2015 (Boersma et al. [2007], Zara etal. [2018]). Van Geffen et al. [2020] show a comparison between TROPOMI and OMI slant column errors andstatistical uncertainties. Other, potentially systematic, errors include inaccuracies in the NO2 cross-section
spectrum (Vandaele et al. [1998]; [ER12]), in other reference spectra, notably in the Ring spectrum, and in thetemperature dependence of the NO2 cross section, but these have been shown to be of little concern for theslant column errors [Boersma et al., 2002].
Fig. 25 shows as function of the SNR an estimate of the uncertainty of the retrieved slant column density
determined by a DOAS fit in the wavelength window 405 − 465 nm with polynomial degree 5. Spectra were
simulated with a radiative transfer code using an atmosphere with two NO2 profiles, taken from the CAMELOT
study [RD9], with the same profile shape in the stratosphere:(a) European background profile, simulated with a total vertical column Nv = 2.5×1015 molec/cm2(b) European polluted profile, simulated with a total vertical column Nv = 7.5×1015 molec/cm2
The simulations are performed with surface albedo As = 0.05, no clouds, solar zenith angle θ0 = 50◦ , andlooking down in nadir. The legend of Fig. 25 gives the total slant column Ns in 1015 molec/cm2 . The retrieved
Ns varies very little with the SNR: about 3×1012 molec/cm2 between SNR = 700 and 1100. For profile (a) the
retrieved Ns is within 5% of the inital Ns and for profile (b) it is within 3%. Given this, a good accuracy of the
DOAS fits can be expected, with uncertainties in the range of 10 − 15% for background NO2 cases and 5 − 10%for polluted cases.
Fig. 26 shows a direct comparison of the NO2 slant column error of OMI and TROPOMI as reported by
the DOAS fits on actual observations from both instruments. The figure demonstrates that TROPOMI has an
SCD error of about 8 − 10 µ mol/m2 , or 0.5 − 0.6×1015 molec/cm2 . The OMI noise level in 2005 is about 40%
higher than what is observed with TROPOMI. This is in agreement with the theoretical dependence on SNR
described above. Some further comparisons are reported by Van Geffen et al. [2020].
During the TROPOMI commissioning phase it became clear that over bright scenes, e.g. high thick clouds inthe tropics, the measurements at the visible wavelengths may become saturated. One very sensitive parameter

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background profile (SCD=5.05)
polluted profile (SCD=9.49)

0.90
NO2 SCD error [x 1e15 molec/cm2]

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0.85
8.4%

0.80
0.75

14.4%

0.70
0.65

6.7%

0.60

11.6%

0.55
0.50
700

750

800

850
900
950
signal-to-noise ratio

1000

1050

1100

Figure 25: DOAS retrieval slant column uncertainty estimate [in 1015 molec/cm2 ] as function of the SNR for
two NO2 profiles. The plot legend gives the retrieved slant column in 1015 molec/cm2 . At SNR equal 800 and1000 the relative slant column uncertainty is indicated. For further details see the text.

TROPOMI orbit 01852* OMI orbit 72356
OMI orbit 03213
across-track averages

1.4
2

2

1.2

15

18
1.0
14

0.8

10

0.6

*) row-anomaly rows removed

0.4

6
-60

-40
-20
0
20
40
across-track viewing zenith angle [degrees]

NO2 SCD error [x 10

NO2 SCD error [µmol/m ]22molec/cm ]26

60

Figure 26: NO2 slant column error estimates for the Pacific Ocean orbit on 20 Feb. 2018 of TROPOMI arecompared to those for the almost overlapping OMI orbit (with rows affected by the row anomaly removed) andto those of a similar OMI orbit from 20 Feb. 2005. Data of scanlines with nadir latitude in the range [−20◦ :+20◦ ]is averaged along-track; OMI data is processed within the QA4ECV project [RD6], [ER7].

to detect saturation is the NO2 slant column uncertainty, and therefore this is used as one of the quality criteria(qa_value, see Appendix E): when the slant column uncertainty exceeds 33 µ mol/m2 , or 2×1015 molec/cm2 ,the pixel is flagged as bad quality.

8.2

Errors in the stratospheric (slant) columns

Data assimilation of TROPOMI NO2 slant columns in TM5-MP provides the estimate of the stratosphericcontribution to the NO2 slant columns. The accuracy of these estimates is largely determined by the accuracyof the slant columns, as the TM5-MP stratospheric NO2 distributions are scaled to become consistent with theTROPOMI ATBD tropospheric and total NO2
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retrieved slant columns. Random error estimates are derived from the assimilation approach: a considerable
advantage of the assimilation scheme is that it provides a statistical estimate of the uncertainties in thestratospheric (slant) columns through the standard deviation of the differences between the TM5-MP model
analysis and forecast stratospheric NO2 ("A–F"). Generally, the uncertainty for the stratospheric NO2 columns isof the order of 0.1 − 0.2×1015 molec/cm2 , similar to OMI [Dirksen et al., 2011]. This similarity with OMI is partly
the result of using superobservations, which reduces the random contribution to the errors in the stratosphericslant column estimates. Fig. 7, bottom panel, shows the average A–F difference for 1 April 2018 in the data
assimilation system based on TM5-MP. The A–F differences are on average 0.15×1015 molec/cm2 , and O–F
over unpolluted scenes are about 0.2×1015 molec/cm2 . The latter is used as estimate of the uncertainties ofthe stratospheric NO2 columns.
Forward (radiative transfer) model calculations are important for, but contribute little to errors in theassimilation procedure. The observation operator H (see Eq. (10)) is proportional to the averaging kernel[Eskes and Boersma, 2003], the vector that contains the vertical sensitivity of TROPOMI to NO2 in eachlayer. The scalar product of the observation operator vector and the TM5-MP NO2 profile at the location of
the individual TROPOMI observations yields the slant column that would be observed by TROPOMI given
the modeled profile. Stratospheric radiative transfer calculations around 435 nm are relatively straightforward
compared to those for the troposphere, where multiple scattering occurs, and the effects of clouds and aerosols
interact with the vertical distribution of NO2 . The main forward model parameter influencing errors in thestratospheric estimate is the a-priori stratospheric NO2 profile shape (and associated temperature correction),but sensitivity tests suggest that uncertainties in the exact shape of this profile are of little influence to theoverall error of the stratospheric NO2 column.
One potential source of error is the sphericity correction in the radiative transfer model. These errors are
negligible for most viewing geometries, but need to be considered for far off-nadir viewing angles and high solar
zenith angles. Lorente et al. [2017] investigated the differences between stratospheric NO2 AMFs calculatedwith a model simulating radiative transfer for an atmosphere spherical for incoming, single-scattered, andmultiple-scattered light (McArtim), and a model with an atmosphere that is spherical for incoming light, butplane-parallel for scattered sunlight. When solar and viewing zenith angles are both large, the DAK model
overestimates the stratospheric AMFs by 5-10%. For TROPOMI, we therefore use an AMF LUT that is basedon DAK radiative transfer simulations, but whose values for extreme viewing geometries have been madeconsistent with the McArtim simulations. This is the same AMF LUT that is being used in the QA4ECV retrievalsof NO2 from OMI and GOME-2A ([RD6], [ER7]).

8.3

Errors in the tropospheric air-mass factors

The tropospheric AMF is calculated with a forward model (here version 3.2 of the DAK radiative transfer model)
and depends on the a-priori assumed profile shape and forward model parameters (cloud fraction, cloudpressure, surface albedo, surface pressure and aerosol properties). The AMF also depends on the solar zenith,viewing zenith and relative azimuth angles, but the measurement geometry is known with high accuracy andtherefore does not contribute significantly to the AMF errors. The forward model itself is assumed to represent
the physics of the measurement accurately, so that forward model errors can be characterised in terms ofmodel parameters only.
The most important AMF errors are cloud fraction, surface albedo, and a-priori profile shape. Cloud parameters are obtained from TROPOMI observations, and these have random as well as systematic components.
Surface albedo and NO2 profile shape are obtained from a-priori assumptions (i.e. a pre-calculated climatology
and CTM simulations, respectively), and much depends on the accuracy of these assumptions that are different
for different retrieval situations (e.g. season, surface type etc.). Because the retrieved cloud fraction depend on
similar (if not the same) surface albedo assumptions as the NO2 air-mass factors, errors will be dampened tosome extent [Boersma et al., 2004].
In Table 13 the most probable uncertainties of the forward model parameters to provide a cautious error
prediction for TROPOMI NO2 AMFs are listed. For this the theoretical error propagation framework used inBoersma et al. [2004] is followed. This approach takes into account the sensitivity of the AMF to uncertainties
around the actual value of a particular forward model parameter (e.g. the AMF is much more sensitive to albedo
errors for dark surfaces than for brighter surfaces).
Aerosol-related errors are intimately coupled to cloud parameter errors. The O2 A-band cloud algorithm
currently does not correct for the presence of aerosols, so that an effective cloud fraction and cloud pressure
are retrieved; the same holds for the cloud fraction in the NO2 window, which is computed in the samemanner as the O2 A-band cloud fraction (Sect. 6.4.4). It is a matter of ongoing research whether or not theTROPOMI ATBD tropospheric and total NO2
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Table 13: Estimate of the contributions to the error in the AMF due to individual error sources (’BL’ stands for
Boundary Layer.) The estimated AMF errors are considered to be representative of ’typical’ retrieval scenarios
over regions of interest, i.e. with substantial NO2 pollution for mostly clear-sky situations, and non-extreme
boundary conditions for surface albedo and pressure. Note that the uncertainties can be substantially lager for
specific condition, e.g. for very small albedo and large SZA.
Error type

Estimated error

Corresponding AMF error

Cloud fraction
Cloud pressure
Surface albedo
Surface pressure
A-priori NO2 profile shape±0.025±50 hPa±0.015±20 hPa
BL height & mixing schemes &free troposphere & emissions±[0 − 25]%

±10%±[0 − 10]%
±10%±[0 − 5]%±20%

A-priori NOx emissions
Aerosol-related errors±[0 − 10]%±[0 − 10]%±[15 − 25]%

Overall error

disentanglement of aerosol and cloud effects will improve the quality of the AMFs (Leitão et al. [2010]; Boersma
et al. [2011]; Lin et al. [2014]).
The results in Table 13 provide a general estimate of overall retrieval uncertainties that may be expected
for TROPOMI NO2 data under polluted conditions. In these conditions, AMF uncertainties contribute most to
the retrieval uncertainties. But error analysis for individual retrievals show considerable variability on theseestimates [Boersma et al., 2004]. For instance, regions with a low surface albedo are very sensitive to albedo
uncertainties, and this can be reflected in AMF errors of more than 50%. For TROPOMI NO2 a full error
propagation that takes these sensitivities into account are provided, and as well as a unique error estimate forevery pixel.
Table 13 shows the settings used for TROPOMI retrieval reprocessing. Compared to the OMI-QA4ECV
product we have increased the a-priori tropospheric profile shape error to 20%. A motivation for this is
the increased resolution of TROPOMI which leads to an increased variability in profile shapes. Also theuncertainties in free tropospheric NO2 lead to additional errors, and the OMI estimate may be too optimistic.
Note that the aerosol-related errors and emission errors are not explicitly accounted for in the error estimate.
The emission-related errors are implicitly included in the profile shape error. The estimate of the profile shapeerror comes from a comparison of the TM5-MP derived air-mass factors and air-mass factors computed with
the CAMS regional ensemble forecasts over Europe. Typical differences of the order of 20% are found over thepolluted areas.

8.4

Total errors in the tropospheric NO2 columns

The overall error in the TROPOMI tropospheric NO2 columns is driven by error propagation of the error terms
discussed before, i.e. (1) slant column errors, (2) errors associated with the separation of the stratospheric andtropospheric contributions to the slant column, and (3) tropospheric air-mass factor errors.
The overall error variance for each pixel is written as in Boersma et al. [2004]:

ε

2


=

σ (Ns )
M trop2

σ (Nsstrat )+M trop2(Ns − Nsstrat ) · σ (M trop )+(M trop )2


2
(22)

with σ (Ns ) the slant column error, σ (Nsstrat ) the stratospheric slant column error and σ (M trop ) the estimatederror in the tropospheric air-mass factor (±25%). The total error depends on details in the retrieval andtherefore differs from one pixel to the next. For small tropospheric excess slant columns, the overall retrieval
uncertainty is dominated by the random errors in spectral fitting, whereas for large tropospheric slant columns,
the retrieval uncertainty is dominated by air-mass factor uncertainties (the last term in Eq. (22)).
Fig. 27 shows the absolute and relative error in the tropospheric NO2 column retrieved for clear-sky scenes
from TROPOMI data on 17 April 2018. We see that over the oceans and the remote continental regions, theoverall tropospheric retrieval uncertainty is dominated by errors in the spectral fitting and the stratosphericcolumn estimate and is typically more than 100% (indicated by purple colours in the bottom panel of Fig. 27).

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Figure 27: TROPOMI tropospheric NO2 vertical column values (top panel; in 1015 molec/cm2 ), the corresponding absolute error estimate (middle panel; in 1015 molec/cm2 ; note that the scale range is reduced by afactor 4), and the relative error (bottom panel; in %) for 17 April 2018. Large relative errors are seen mostly overareas with small NO2 column values: oceans and remote continental regions. These errors reflect uncertainties
in the slant and stratospheric column. Over the very polluted hotspots typical errors are in the 25 − 40% range,
reflecting uncertainties in the air-mass factor.

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Table 14: Relative tropospheric NO2 vertical column per pixel uncertainty due to the tropospheric AMF
uncertainty only. Estimates based on QA4ECV OMI NO2 data for selected regions for the year 2005, takenfrom Boersma et al. [2018].
RegionChina
USAEuropeJohannesburg

Average
AMF uncertainty
17 - 22 %17 - 27 %18 - 26 %15 - 20 %box size ranges
longitude
latitude110 : 140
−100 : −75−10 : 15
26 : 30

35 : 45
35 : 45
40 : 55−28 : −24

For larger columns over continental areas, the relative uncertainty in the retrieved column reduces to 15 − 50%,and is dominated by the uncertainty in the tropospheric air-mass factor. Retrieval results are generally best forregions with strong NO2 sources and/or high surface albedos.
Based on the instrumental performance for TROPOMI, and our experience with OMI tropospheric NO2
retrievals (see Fig. 27 and Table 14), the overall error budget for individual TROPOMI tropospheric NO2
retrievals can tentatively be approximated as ε = 0.5×1015 molec/cm2 + [0.2 to 0.50] · Nvtrop . This is a more
complete and realistic error statement than the requirements from [RD5] (ε = 1.3×1015 molec/cm2 + 0.1 · Nvtrop
for a horizontal resolution of 5 − 20 km; cf. Table 1).
The error components can be split in two classes: input parameter plus DOAS related uncertainties (cloud,albedo, aerosol, stratosphere, slant column) and a-priori related uncertainties (profile shape). In Rodgers
optimal estimation formalism [Rodgers, 2000] the latter may be called the smoothing error. It depends on theuse of the data which uncertainty should be used. When the NO2 vertical columns are used without knowledge
trop,kernelof the NO2 profiles, then the uncertainty, ∆Nv,NO, is the sum of input parameter, DOAS and smoothing.
2
When profile information is available (e.g. when comparisons with models are performed) and the kernels are
trop
used, the uncertainty, ∆Nv,NO, is the sum of input parameter and DOAS only, without the smoothing error
2contribution. Both uncertainty estimates for the tropospheric vertical column are made available in the product:
one for applications with the kernel, one for applications without.
The individual components of the total uncertainty of the tropospheric column are available in the code andprovided in the NO2 data files of the QA4ECV project ([RD6], [ER7]). In the current TROPOMI NO2 processor(mid-2017) only the total error is made available in the data product. In the next upgrade we may consider to
add the tropospheric column error components due to the slant column uncertainties, stratospheric estimate
and the air-mass factor, and contributions of this AMF uncertainty due to cloud fraction, cloud pressure, albedo
and profile shape uncertainties.

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Validation

9.1

Routine validation & validation activities

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The routine validation of TROPOMI is organised through the S5P Mission Performance Centre (MPC), see[ER21]. Since November 2018 the MPC generates routine validation results for the TROPOMI Level-2 productsin the form of up-to-date validation results and consolidated quarterly validation reports (ROCVR, available via[ER4]). These validation activities are coordinated through the MPC VDAF website [ER22]. Further validationis performed by the S5P Validation Team (S5PVT) members.
The MPC validation activities include comparisons with MAX-DOAS and PANDORA observations (toevaluate the tropospheric column), SAOZ observations (to evaluate the stratospheric column), and satellite
observations (comparisons with OMI in particular); see, for example, Griffin et al. [2019], Ialongo et al. [2020],
Van Geffen et al. [2020], Verhoelst et al. [2021], Zhao et al. [2020], Van Geffen et al. [2022]. On top of theroutine MPC activities, the TROPOMI data has been and will be compared to any campaign data organised by
Europe and partners outside Europe.

9.2

Algorithm testing and verification

Algorithm testing and verification by the S5PVT before launch provided confidence in the retrieval algorithms,
including forward and inverse models, based on simulations, and comparisons between different techniquesand software programs, as described in [RD24]. That activity also included reviews and updates of the
TROPOMI NO2 ATBD.
Verification covers a wide range of activities, including:• Testing of all the individual input datasets (albedo, cloud parameters, surface properties, snow and icedata) and comparisons with alternative input datasets and measurements when available.
• Testing of the air mass factor calculations by comparing with alternative radiative transfer calculations[Lorente et al., 2017].
• Comparisons with alternative retrieval approaches, such as the scientific retrievals performed by theuniversity of Bremen, or approaches that start from the operational TROPOMI data and aim to improve
the air-mass factors (Liu et al. [2020]; Griffin et al. [2019]).
• Study the impact of alternative (high-resolution) a-priori profile shapes from for instance regional air
quality modelling systems (Laughner et al. [2019]; Marecal et al. [2015]; Liu et al. [2020]; Griffin etal. [2019]).

9.3

Stratospheric NO2 validation

For stratospheric NO2 columns, correlative (column and profile) measurements are needed in regions that are
representative for a complete zonal band, and hence need to be relatively unpolluted. The currently operational
NDACC ZSL-DOAS/SAOZ instruments are a key dataset to evaluate the stratospheric column (e.g. Verhoelst
et al. [2021], Dirksen et al. [2011] and the S5P MPC validation reports [ER4]). Additional routine information
comes from the Pandonia Global Network (PGN) remote and/or high altitude stations.
In our view a priority of the stratospheric validation efforts should be a better characterisation of the spatialand seasonal variations of the vertical profile of stratospheric NO2 . These profile shapes are an essential
input to the data assimilation system in use for the separation between tropospheric and stratospheric NO2columns from the TROPOMI measurements. Useful sources of stratospheric NO2 profile data are satellite
instruments that measure in limb view, SCIAMACHY (Beirle et al. [2010], Hilboll et al. [2013b]), HIRDLS andMLS [Belmonte et al., 2014], OSIRIS [Adams et al., 2016]. Note that there are difficulties in using these fordirect validation as they are often only sparsely validated themselves.
Stratospheric NO2 measurements near the Arctic vortex in late winter and early spring would be useful
to better test the capability of the data assimilation scheme (and other stratosphere-troposphere separation
schemes) in capturing the influence of stratospheric air masses low in NOx on stratospheric NO2 at lower
latitudes. Such excursions are known to occur and may lead to systematic errors in the separation scheme(e.g. Dirksen et al. [2011]; Bucsela et al. [2013]). Independent measurements may provide further important
information on how to improve these issues in the future.

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Figure 28: Tropospheric NO2 vertical column values retrieved from TROPOMI observations (top panel) on 23
February 2018 (unit µ mol/m2 ), compared with the corresponding OMI NO2 tropospheric column observations
for the same day (lower panel) (unit 1015 molec/cm2 ). The scales have been chosen to allow a quantitative
comparison. Note that the OMI data has been stripe corrected, while no stripe correction was applied to
TROPOMI.

9.4

Tropospheric NO2 validation

Information on tropospheric NO2 concentrations – with the NO2 in the planetary boundary layer and/or in thefree troposphere – comes from in-situ instruments (at the ground, in masts, on aircraft or on low-flying balloons)and from remote-sensing instruments at the ground, on balloons or aircraft.
For the validation of TROPOMI tropospheric NO2 columns, correlative (column and profile) measurements
are needed in the highly populated polluted regions at mid-latitudes, and also in regions with natural sources ofnitrogen oxides, e.g. from biomass burning, microbial soil activity and lightning. The expanding networks of
MAX-DOAS and PANDORA (PGN) instruments are used as backbone for tropospheric column validation in the
MPC VDAF validation reports ([ER4]; Verhoelst et al. [2021]).
Aircraft remote sensing tropospheric column observations with high spatial resolution mapping instruments
is also a very valuable source of validation data for TROPOMI, (e.g. Nowlan et al. [2016], Judd et al. [2020],
Tack et al. [2021]). In this way the impact of the fine-scale horizontal variability in NO2 can be quantified.
The local overpass time of OMI and TROPOMI are nearly identical. This implies that a direct comparisonof OMI and TROPOMI for (nearly) overlapping orbits is a key element of the TROPOMI validation and isalso reported in the MPC VDAF validation reports [ER4]. An early example of this is shown in Fig. 28. The
figure demonstrates a good quantitative agreement between OMI and TROPOMI measurements, but also
demonstrates the good agreement between the OMI-QA4ECV and TROPOMI retrieval software.
Important for the validation as well as for the data assimilation system in use for the separation between
tropospheric and stratospheric NO2 columns from the TROPOMI measurements is a good understandingof the vertical profile of the tropospheric NO2 . The best source of information on vertical profiles of NO2 isTROPOMI ATBD tropospheric and total NO2
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still from incidental aircraft campaigns. Apart from this, routine profile measurements with aircraft, such asprovided by a recent extension of the IAGOS programme [ER23] are very valuable. Alternatively, experimental
NO2 profiles from (tethered) balloon sondes and measurement towers, will provide valuable information on thevertical distribution of NO2 .
Since tropospheric retrievals depend on the concept of the air-mass factor, which has to rely on a-priori
information, it is important to also validate the inputs and assumptions that go into the air-mass factor calculation.
This mostly concerns cloud parameters – cloud fraction and cloud pressure – that should be well characterised.
Another critical issue, about which very little is known as yet, is the effect of the presence of aerosols on the
NO2 retrieval. Collocated information on the aerosol profile – e.g. coming from the TROPOMI Aerosol Layer
Height data product – could be useful for this. There is also a need for correlative surface albedo data toinvestigate the accuracy of the surface albedo climatology.
Despite the routine validation activities, based on a large number of measurement instruments and sites,
the quantification of the biases in the TROPOMI retrievals remains a difficult task. This difficulty is related
to several factors, e.g. the uncertainty of the independent surface remote sensing NO2 data used for thevalidation, large differences in the sensitivity profiles between e.g. MAXDOAS and TROPOMI, and the issueof representativity of the local ground-based and in-situ measurements w.r.t. the finite-sized satellite groundpixels (of order 5 × 5 km2 ), given the large fine-scale variability of NO2 close to the surface.

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Conclusion

We have presented the baseline approach for the retrieval of the operational tropospheric and stratosphericNO2 column products from the TROPOMI sensor. The NO2 data are deliverd both as an off-line product for theNO2 data record and as a near-real time product, with the NO2 data delivered within 3 hours after observation.
The TROPOMI NO2 data products pose an improvement over previous NO2 data sets, particularly in theirunprecedented spatial resolution (approximately 7.0 × 3.5 km2 or, since the along-track pixel size reduction on
6 Aug. 2019, 5.5 × 3.5 km2 at nadir), high signal-to-noise and daily global coverage, but also in the separationof the stratospheric and tropospheric contributions of the retrieved slant columns, and in the calculation of the
air-mass factors used to convert slant to total columns.
The backbone of the retrieval system is the TM5-MP chemistry transport model, that is operated at a
global resolution of 1◦ × 1◦ . The assimilation of NO2 slant columns in TM5-MP ensures that the modelledstratospheric state becomes consistent with the TROPOMI slant columns over regions with small tropospheric
NO2 amounts. The information from the data assimilation system is used to separate the slant column into
its stratospheric and tropospheric components and to provide the a-priori NO2 vertical profile required by the
air-mass factor calculation.
For each TROPOMI pixel an air-mass factor (AMF) is calculated, using altitude-dependent AMFs from alook-up table calculated with the DAK radiative transfer model, in combination with the vertical distributionof NO2 provided by the TM5-MP chemistry transport model (in assimilation mode) at a spatial resolution of1◦ × 1◦ . The AMF calculation uses local surface albedos from the OMI surface reflectance climatology that isbased on 5 years of OMI measurements. It accounts for cloud scattering using information on effective cloud
fraction and cloud pressure retrieved for every TROPOMI pixel from the reflectance at the AMF wavelengthand from the FRESCO retrieval algorithm, respectively.
Several additional algorithm improvements w.r.t. the OMI / DOMINO v2 processing have been implemented,
such as the inclusion of additional reference spectra in the DOAS spectral fit to improve the accuracy of theretrieved NO2 slant columns, major updates to the data assimililation / chemistry transport model used todetermine the vertical column densities, and a more careful quality filtering of the measurements, reflected in
the qa_value. Part of these improvements were developed during the European QA4ECV project. Residuals
resulting from tests with the TROPOMI prototype fitting algorithm on OMI spectra suggest a need to include
absorption by liquid water, in any case over cloud-free ocean scenes without substantial oceanic chlorophyll.
Revisiting the OMI spectra also re-emphasised the importance of an appropriate spectral calibration thatis representative for the complete fitting window. Using TM5-MP at a spatial resolution of 1◦ × 1◦ (insteadof a lower spatial resolution) has been shown to provide more accurate estimates of the NO2 profiles. The
conversion of the slant to vertical columns has been improved by using an air-mass factor look-up table withmore nodes, in order to reduces interpolation errors.
The TROPOMI NO2 processing chain enables us to provide a realistic error budget. The retrieval error
is dominated by the spectral fitting error over oceans and regions with low tropospheric NO2 amounts. Overthe polluted regions, air-mass factor errors contribute substantially to the overall error, which can be generally
approximated as 0.5×1015 molec/cm2 + 25% for an individual pixel.
Besides a complete error analysis, the TROPOMI data product also provides the averaging kernel, which
describes the sensitivity of TROPOMI to NO2 in each model layer, for every pixel. The averaging kernel is
especially relevant for data users who wish to minimise the discrepancies between the assumptions in the
TROPOMI retrieval and their own application of interest, e.g. for data assimilation, validation, or comparison
studies.
TROPOMI’s high spatial resolution enables monitoring NO2 columns with an unprecedented accuracy, bothin the troposphere and the stratosphere. From these measurements we learn more about the distribution ofNO2 , its sources and sinks, its transport through the atmosphere, its role in stratospheric and troposphericchemistry, as well as in climate issues, notably through the important role that nitrogen oxides play in theformation of secondary pollutants ozone and aerosol. The early-afternoon NO2 data record, which started with
OMI, is extended by TROPOMI, alongside the mid-morning measurements of the GOME-2 instruments, andSentinel-5 in the near future, thus providing essential information on the diurnal cycle of NO2 . Over the past
20-odd years various UV/Vis backscatter instruments have been used to monitor NO2 on a global scale. The
operational TROPOMI NO2 data processing is consistent with the NO2 retrieval record generated at KNMI,
and will continue and improve that record.

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Wavelength calibration

The S5P/TROPOMI radiance and irradiance spectra in the Level-1b input data are not wavelength calibrated,because most Level-2 processors perform a wavelength calibration on the fitting window specific to thealgorithm. If the calibrations had been done in the Level 0-to-1b processor, they would have been done for thewhole spectral band, and this may or may not have met the science requirements for the wavelength calibration
for the Level-2 trace gas retrievals. For the wavelength calibration of the radiances an atmosphere model isneeded, especially at the shorter wavelengths where ozone absorption is significant, but also the Ring effectmodifies the radiance spectra in ways that have to be taken into account when calibrating the wavelength. In
the unlikely case the wavelength calibration of radiance or irradiance fails, the retrieval will be performed using
the respective nominal wavelenth grids.
For the calibration of a complete band or a complete detector, the calibration is split up in micro-windows,
and a polynomial is drawn through the micro-windows to cover the whole band. When fitting for a specificretrieval window, a single fit covering the retrieval window is more appropriate. The model function that is used
for the radiance wavelength calibration is a modified version of a DOAS fit. Sections A.1 and A.2 describe
the generic wavelength fit used in most retrieval algorithms for S5P/TROPOMI, in section A.3 the actual
application to NO2 retrieval is discussed. For the irradiance calibration the same procedure is used, except that
atmosphere related effects should be disabled, specifically the Ring effect should not be included in this fit.
The polynomial order N is set to 1 for the irradiance fit.

A.1

Description of the problem

The S5P/TROPOMI Level-1b radiance spectra have a nominal wavelength scale (λnom ), but this wavelength
grid is not corrected for inhomogeneous slit illumination [RD13, section 28]. The measurements are also
not temperature corrected, but because the instrument itself is temperature stabilized it is expected that thiseffect can be ignored. The Level-2 processors must correct the nominal wavelength scale of the radiancemeasurements for inhomogeneous slit illumination due to the presence of clouds in the field of view.
One would like to follow the calibration of the irradiance spectra, for a short wavelength interval. The range
λfit = [λ− , λ+ ] is the approximate range on which to do the wavelength calibration. To avoid non-linearities thiswavelength range is tailored to the specific Level-2 algorithm. For each detector row the nominal wavelength
λnom is adjusted with a wavelength offset (or: shift) ws and a wavelength stretch wq to find the calibrated
wavelength λcal :λcal = λnom + ws + wq 2

λnom − λ0
λ+ − λ−

+···

(23)

with λ0 the center of the fit window, λ− the beginning of the fit window and λ+ the end of the fit window. In thethird term the factor 2 is used to ensure that the wavelength factor of the stretch lies in the range [−1 : +1].
The higher order terms in Eq. (23) are ignored, even fitting wq is optional.

A.2

Non-linear model function and Jacobian

The model function in the fit is similar to a non-linear DOAS equation. Instead of fitting the reflectance R, we
fit the radiance I directly, bringing the (model) irradiance Emod to the other side of the equation. The model
function M is given by:

M (λnom ; a0 , . . . , aN ,Cring , ws , wq , Ns,0 , . . . , Ns,M ) =∗PN (λ ) · exp

!

nk



∑ −Ns,k σk (λcal ) · Emod (λcal ) +Cring Iring (λcal )

(24)

k=0

with λcal the calibrated wavelength as given by the first three terms in Eq. (23),NPN (λ ∗ ) = ∑ a j (λ ∗ ) j ,k=0

λ∗ ≡ 2

λnom − λ0
λ+ − λ−(25)a polynomial of order N , Emod the reference irradiance spectrum, and Iring the Ring spectrum; both Emod andIring are convolved with the instrument slit function (or: instrument spectral response function; ISRF; available
via [ER11]). The spectra σk (k = 0, . . . , M ) are optional absorption spectra that have a relevant impact on theradiance, for instance the O3 absorption cross section. These additional reference spectra have also been

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convolved with the ISRF, but note that the DOAS assumption still applies: this merit function is not applicableto line absorbers such as H2 Ovap , CH4 , CO or O2 , and will fail at wavelengths below ∼ 320 nm because theprofile shape of O3 is relevant at those wavelengths. The order of the polynomial is 1 ≤ N ≤ 5, depending on
the length of the fit window.
The wavelength calibration fit adjusts the parameters a0 , . . . , aN ,Cring , ws , wq , Ns,0 , . . . , Ns,M to minimize χ 2 :
1 m−1 Ii − M (a0 , . . . , aN ,Cring , ws , wq , Ns,0 , . . . , Ns,M ) 2
χ =∑m − n i=0
∆Ii
2

(26)

with Ii the measured radiance at detector pixel index i, ∆Ii the precision of this radiance, and m the number ofspectral points between λ− and λ+ . The number of degrees of freedom is m minus the number of fit parameters:n = N +1+M+1+3(27)

The additional 3 here is when fitting Cring , ws and wq ; if Cring and/or wq are not fitted, the number of degrees of
freedom increases.
To minimize the number of function calls in the optimisation routine derivatives with respect to the fit
parameters as a Jacobian matrix need to be supplied, with i the detector pixel index: The components of theJacobian are given by Eqs. (28–32) below.

∂ Mi= (λi∗ ) j · exp
∂aj

!

M



!

M

∑ −Ns,k σk (λcal,i ) · Iring (λcal,i )

(29)

k=0

!

M

∑ −Ns,k σk (λcal,i ) ×
k=0

(

M

dσk
− ∑ Ns,kdλ λ =λcal,ik=0!

· Emod (λcal,i ) +Cring Iring (λcal,i )+∂ Mi= PN (λi∗ ) · exp
∂ wq

(28)

k=0

∂ Mi
= PN (λi∗ ) · exp∂Cring∂ Mi= PN (λi∗ ) · exp∂ ws

∑ −Ns,k σk (λcal,i ) · Emod (λcal,i ) +Cring Iring (λcal,i )dIringdEmod+Cringdλ λ =λcal,idλ λ =λcal,i

!)
(30)

!

M

∑ −Ns,k σk (λcal,i ) ×
k=0

(

M

−∑
k=0

!

dσk
Ns,k λi∗dλ· Emod (λcal,i ) +Cring Iring (λcal,i )λ =λcal,i+∂ Mi= −PN (λi∗ ) · σk (λcal,i ) · exp∂ Ns,kM

dλ!)dIring+Cring λi∗

dEmod
λi∗
λ =λcal,idλ(31)
λ =λcal,i!
∑ −Ns,k σk (λcal,i ) · Emod (λcal,i ) +Cring Iring (λcal,i )

(32)

k=0

The reference spectra Emod (λ ), Iring (λ ) and σk (λ ) are pre-convolved with the ISRF. During the fitting 4th
degree splines are used to represent these spectra. An interesting feature is that a spline of the derivative withrespect to the independent variable can be calculated from the parameters of the original spline (given that thederivatives are w.r.t. the wavelength, the resulting spline for these derivaties is 3rd degree).
These equation can be solved with various optimization routines, for instance Levenberg-Marquardt orGauss-Newton, with or without constraints or regularization methods. After thorough testing the optimal
estimation method as implemented in DISAMAR, which is based on Rodgers [2000] and uses an unmodified
Gauss-Newton to find the state vector for the next iteration, was selected for the S5P/TROPOMI wavelength
calibration. For this usage, the a-priori error estimates are set very large (see Sect A.2.1), so that these do notlimit the solution, and a pre-whitening of the data is performed to improve numerical stability.

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Prior information for the optimal estimation fit

Optimal estimation needs prior information for the regularisation process during the fitting procedure, botha starting value and a covariance value. For input only the diagonal elements of the covariance matrix are
specified, on output a full posteriori error covariance matrix is available. The polynomial coefficients are notimportant, the values and variance were estimated from a large number of retrievals. The Ring coefficient
was taken from the same data set. The value for ws is taken from the spacing of the nominal grid. A 1-σ errorof a third of the spacing of the wavelength grid seems reasonable: σprior (ws ) = ∆λ /3. This value will mostly
prevent fitting a shift ws that is larger than half of the grid spacing, which basically means the wavelength is notknown at all. The prior value for wq is 0 (zero), i.e. no stretch or squeeze. The range depends on the size ofthe fitting window, a consequence of the use of λ ∗ , as defined in Eq. (25). The current value is a deliberate
overestimation. The slant column of O3 is typically 0.18 mol/m2 (about 600 DU); other trace gases are notincluded. An overview of the prior information used for S5P/TROPOMI is given in Table 15.
Table 15: A-priori values and a-priori error for the optimal estimation wavelength fit for S5P/TROPOMI. The
ozone slant column is expressed in mol/m2 ; the other quantities are dimensionless.
Names
Prior
Covariance
Optional

A.3

a0
1
(1)2
no

a1
−0.5
(0.5)2noa2,...,N
0.01(0.1)2yesCring
6×10−2(6×10−2 )2
yes

ws
0
(∆λ /3)2nowq
0(0.1)2
yes

Ns,O3
0.25
(0.18)2
yes

Application of the wavelength calibration in NO2

For the retrieval of NO2 the Ns,O3 is not fitted, as O3 shows little structure and is a weak absorber in band 4,
where the slant columns of NO2 (window 405 − 465 nm) and O2 –O2 (window 460 − 490 nm) are fitted.
Testing with OMI [RD25] has shown that there is no significant amount of stretch in the wavelength of thespectra of that instrument in the 405 − 465 nm range and given the similarities of the OMI and S5P/TROPOMI
detectors, no significant stretch was expected for S5P/TROPOMI. This has been confirmed using retrieval
results for a S5P/TROPOMI orbit, which resulted in a very small stretch with a precision larger than the stretch
itself, and a negligible effect on the retrieval results [Van Geffen et al., 2020], and hence the wq fit parameter
will remain turned off.
The order of the polynomial in Eq. (25) is set to 2 and the Ring effect is included in the fit. The a-priori errorof ws is set to 0.07 nm.

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High-sampling interpolation

After the wavelength calibration of the radiance spectrum, discussed in Appendix A, the irradiance and radianceobservations need to be brought to the same wavelength grid in order to be able to compute the reflectancein Eq. (1). Because of the geometry of the solar observations, these measurements are shifted with respect
to the radiance observations due to the Doppler shift caused by the motion of the satellite relative to the sun.
Given that the irradiance spectrum is known better than the radiance spectrum, the irradiance spectrum isshifted to the radiance grid and the radiance observations are left without modification:

E0 (λi,earth ) =Ehigh (λi,earth )E0 (λi,solar )Ehigh (λi,solar )(33)

with E0 the observed irradiance, Ehigh a high resolution solar reference spectrum, convolved with the instrument
spectral response function, λi,earth the wavelength of the earth radiance spectrum for pixel i, and λi,solar the
wavelength of the solar irradiance spectrum for pixel i. The index i is synchronized between the radiance andirradiance observations, such that they refer to the same physical pixel on the detector. On Ehigh 4th degree
spline interpolation is used to find the value at the indicated wavelengths. The input data for the splines havesufficient spectral resolution to allow for this.
Fig. 29 shows the procedure graphically. Panel (d) shows the effect of spline interpolation on the irradiancedata to find the values at the earth radiance wavelength grid. Errors are small but systematic. Note that theseerrors appear directly in the reflectance data. The reflectance in Eq. (1) is then be calculated a at the radiancewavelenth grid: Rmeas (λi,earth ) = π I(λi,earth )/µ0 E0 (λi,earth ).

(a)

(b)
1.02

8.0
7.5

1.01

7.0

Ratio

6.5

1.00

6.0
5.5
5.0
4.5

0.99
Reference
Irradiance
Radiance gridRatio (ref @ λearth/ref @ λsolar
Ratio at observations

4.0
484.0 484.5 485.0 485.5 486.0 486.5 487.0 487.5 488.0

485

486

487

(d)

8.0

6.0
5.5
5.0

Irradiance
On radiance grid (Irradiance × ratio)

4.0
484.0 484.5 485.0 485.5 486.0 486.5 487.0 487.5 488.0
Wavelength [nm]

7.5

0.1

7.0
6.5

0.0

6.0
5.5

0.1

4.5
0.2
484

488
8.5

0.2
Error of spline [%]Irradiance [10−6 mol s−1 nm−1 m−2 sr−1 ]

6.5

484

(c)

0.98

Spine interpolation on measured irradiance

Irradiance [10−6 mol s−1 nm−1 m−2 sr−1 ]

8.5

5.0

SplineReference
Error [%] (left)

485

4.5
486
Wavelength [nm]

487

4.0
488

Figure 29: High sampling interpolation on part of a solar observation. (a) The red dots show the actual
observation (taken from GOME-2A). The blue vertical lines indicate the wavelength grid of the radianceobservation. The solid line shows a high resolution solar reference spectrum that has been convolved
with the instrument spectral response function of the instrument (in this case GOME-2A). (b) The ratio
Ehigh (λi,earth ) /Ehigh (λi,solar ).
(c) In red E0 (λi,solar ), in blue E0 (λi,earth ).
(d) The solid line is the solar
reference spectrum. The dash-dotted line is a high resolution irradiance spectrum created by spline interpolation
directly on the observed irradiances, brought to the same average level of the window shown here to ease
comparisons. The black dots indicate the error in % that are caused by using spline interpolation directly on
the irradiance observations. Clear artifacts are caused by this, especially because noise on the observations
becomes correlated between nearby points in the spectrum.

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Effective cloud fraction in the NO2 window

The cloud radiance fraction, wNO2 (Sect 6.4.3), and the effective cloud fraction, feff,NO2 (Sect. 6.4.4), in theNO2 fit window, can be computed from a look-up table (LUT) with the top-of-atmosphere (TOA) reflectanceat λc,NO2 = 440 nm as a function of viewing geometry, surface & cloud albedo, and surface & cloud pressure,based on the continuum reflectance at 440 nm of the measurement. The continuum reflectance at 440 nm
could be determined from the observed spectrum, averaged over a small wavelength interval, but that may
lead to unexpected values, e.g. in case of spikes in the measurement. Instead, we have opted for using themodelled reflectance of Eq. (5) evaluated at 440 nm. The approach is very similar to FRESCO [RD18] andexplicitly accounts for Rayleigh scattering. The following description is adapted from [RD26].
The LUT assumes that the measured reflectance at TOA is defined as (cf. Eq. (1)):

RTOA (λ ) =π I(λ )
µ0 E0 (λ )(34)

In the independent pixel approximation the cloud faction, fc , for a given wavelength is given by:

fc =RTOA − Rs
Rc − Rs(35)

and the cloud radiance fraction, the fraction of the total radiation that comes from the clouds, is given by:

wNO2 =

fc Rc
fc Rc
=
RTOA
fc Rc + (1 − fc ) Rs

(36)

where Rs and Rc are the reflectances at surface and cloud, respectively. These are computed from a limited
LUT, based on Chandrasekhar (Chandrasekhar et al. [1950], Sect. 72). For bounding surface ’b’, i.e. either
surface (’s’) or cloud (’c’):Rb (λ , Ab (λ )) = R0 (λ ) +Ab (λ ) T (λ )1 − Ab (λ ) s(λ )(37)where:Rb (λ , Ab (λ ))=The reflectance of the combined atmosphere-surface system related to thelight coming from the boundary ’b’, i.e. either surface (’s’) or cloud (’c’).

R0 (λ )Ab (λ )T (λ )===The reflectance of the atmosphere if the surface is perfectly black: Ab = 0.

s(λ )

=

The spherical albedo of the atmosphere for illumination at its lower boundary; 1/[1 − Ab (λ )s(λ )] is the sum of a geometrical series accounting for thereflections between the atmosphere and the surface.

The albedo at the bounding surface, either cloud (Ac ) or surface (As ).
The transmittance of the atmosphere, a measure for the probability thatphotons travel through the atmosphere, are reflected by a surface with unit
albedo, and travel back to the sensor (reflections by the atmosphere backtowards the surface are ignored here).

The transmittance of the atmosphere T (λ ) is a product of two terms depending on the viewing and solar
zenith angles:
T (λ ) = t(λ ; µ)t(λ ; µ0 )(38)
where µ = cos(θ ) and µ0 = cos(θ0 ) and:1τ(λ )2µ 0 T0 (λ ; µ, µ 0 )dµ 0
)+
µ
0

Z

t(λ ; µ) = exp(−(39)

In Eq. (39) we assume a plane parallel atmosphere; for a spherical shell atmosphere the factor 1/µ in
exp(−τ/µ) has to be replaced by a different expression.
The TOA reflectance related to the light coming from the boundary ’b’, i.e. either surface (’s’) or cloud (’c’), is afunction of solar and viewing geometries and surface properties: Rb (λ , Ab (λ )) = Rb (λ ; θ0 , θ , φ − φ0 ; pb , Ab (λ )),where pb is the pressure at the boundary ’b’. In addition extra dependencies may be needed to account for
absorbing species, in particular at shorter wavelengths where absorption by ozone (O3 ) is significant. A more

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Table 16: Look-up tables and dimensions for reflectance calculations; no trace gas column entries included.
Reflectance of the black surface

R0
λ
µ0
µ
φ − φ0pb

For all wavelengths where a cloud fraction must be computed [1, . . . , n]
For µ0 = [0.0012141231 : 1.0], i.e. θ0 = [89.93◦ : 0◦ ], in 42 steps of 2 − 5◦
For µ = [0.0012141231 : 1.0], i.e. θ = [89.93◦ : 0◦ ], in 42 steps of 2 − 5◦
Dependency stores in three Fourier terms
Pressure of the bounding surface (cloud or surface) for pb = [1075 hPa : 95 hPa] in 68
steps *
Transmittance of the atmosphere

T
λ
µ0
µ
pb

For all wavelengths where a cloud fraction must be computed [1, . . . , n]
For µ0 = [0.0012141231 : 1.0], i.e. θ0 = [89.93◦ : 0◦ ], in 42 steps of 2 − 5◦
For µ = [0.0012141231 : 1.0], i.e. θ = [89.93◦ : 0◦ ], in 42 steps of 2 − 5◦
Pressure of the bounding surface (cloud or surface) for pb = [1075 hPa : 95 hPa] in 68
steps *
Spherical albedo of the atmospheresλpb

For all wavelengths where a cloud fraction must be computed [1, . . . , n]
Pressure of the bounding surface (cloud or surface) for pb = [1075 hPa : 95 hPa] in 68
steps **) Through a fixed scale height pb is linked to the elevationof the bounding surface: zb = [−55 m : 16250 m].

detailed study is needed to determine if O3 is needed for the cloud fraction, but for NO2 we estimate that
ignoring O3 absorption leads to an error of 0.01 − 0.02 in the cloud fraction. Raman scattering is ignored here.
The terms used in Eq. (37) have the same or less dependencies: R0 (λ ) = R0 (λ ; θ0 , θ , φ − φ0 ; pb ), but
crucially not on Ab (λ ). Further: T (λ ) = T (λ ; θ0 , θ ; pb ) and s(λ ) = s(λ ; pb ). The dependency of Rb (λ ) andR0 (λ ) on φ − φ0 can be expressed as a Fourier sum, in case of a Rayleigh atmosphere with three terms. All
in all this gives a small set of LUTs for R0 (λ ), T (λ ) and s(λ ); see the overview in Table 16. For use in the
NO2 retrieval, the set of LUTs has been computed using DAK at λc,NO2 = 440 nm, the wavelength used for the
air-mass factor calculations.
From these LUTs we can calculate the reflectance of the cloudy part of the pixel, Rc , using the cloudpressure, pc , and cloud albedo, Ac , from the cloud product. And the reflectance of the cloud-free part of the
pixel, Rs , using the surface pressure, ps , from meteorology or a fixed scale height and the surface elevation, zs ,and the surface albedo, As , from a climatology. Note that either ps or zs can be used as entry to the LUT: theyare "linked" through the fixed scale height.

C.1

Adjusting albedo to respect physical limits to the cloud fraction

In order to limit the cloud fraction to the range [0, 1], the albedo of the boundary is adjusted to ensure radiative
closure [Van Geffen et al., 2022]. From Eq. (35) it is clear that a negative cloud fraction results when Rs > RTOA .
Rewriting Eq. (37) to set Rs = RTOA provides an adjusted value for As :As (λ ) =RTOA (λ ) − R0 (λ , ps )T (λ , ps ) + s(λ , ps ) [RTOA (λ ) − R0 (λ , ps )](40)

In a similar fashion it is clear from Eq. (35) that a cloud fraction larger than 1 results when RTOA > Rc . Rewriting
Eq. (37) to set Rc = RTOA provides an adjusted value for Ac :Ac (λ ) =RTOA (λ ) − R0 (λ , pc )T (λ , pc ) + s(λ , pc ) [RTOA (λ ) − R0 (λ , pc )](41)

Note that in the FRESCO cloud retrieval (Sect. 6.4.4) the surface albedo is adjusted ignoring Rayleigh scattering,which simplifies Eq. (40) to As (λ ) = RTOA (λ ), and Eq. (41) to Ac (λ ) = RTOA (λ ).

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Surface albedo correction using the snow/ice flag

The retrieval process uses the surface albedo in the NO2 fit window, As,NO2 , as one of the input parameters forthe air-mass factor and vertical column calculations (Sect. 6.4). This surface albedo is taken from a surface
albedo climatology. For TROPOMI that is the 440 nm data from the 5-year average OMI database [Kleipool etal., 2008]; see Sect. 6.4.5. Substantial errors are introduced in the retrieval results if the real surface albedo,
As , differs considerably from what is expected, for example in the case of the sudden snowfall or ice cover.
Correcting the surface albedo from the climatology, Aclim , using knowledge of actual snow/ice cover (Sect. 6.4.6)
will therefore improve the final data product, in terms of the retrieval itself and for flagging such cases. For the
As,NO2 this correction follows the approach included in the OMI cloud data product OMCLDO2 [Veefkind etal., 2016] to adapt the surface albedo in the O2 –O2 fit window (i.e. at 471 nm).
The basis for the correction are the snow/ice flag values, sampled at the ground pixel centre coordinate, as
used in the processing (Sect. 6.4.6). Table 17 provides an overview of these flags, where an asterisk marks
snow/ice flag values that lead to an adjustment of the surface albedo in case a certain threshold is exceeded.
Table 17: Overview of the snow/ice flag values fNISE used by both the NISE and ECMWF snow/ice data sets,
where nrs. 252, 253 and 254 do not occur in the ECMWF data. Flag values marked with an asterisk may leadto adjustment of the climatological surface albedo as described in the text.

fNISE
000 *
001-100 *
101
103 *
252
253
254
255 *

meaningsnow-free landsea ice concentration (percent)permanent ice
dry & wet snow
mixed pixels at coastlines
suspect ice valueerror value
ocean

remarkland-ocean or snow/ice-ocean boundaries
considered to represent an error

The rules for modifying the climatological surface albedo Aclim are as follows:• In case of snow-free land or open ocean (flags 0 and 255) adjust As if the difference between Aclim anddefault value Adef = 0.04 is larger than a given threshold Athrs = 0.1, where the albedo is decreased only
if Aclim > Asnow = 0.6.
if ( (Aclim − Adef ) > Athrs & Aclim > Asnow ) then As = Adef

else As = Aclim• In case of dry or wet snow (flag fNISE = 103) adjust As if the difference between Aclim and Asnow = 0.6 islarger than a given threshold Athrs = 0.1.
if ( (Aclim − Asnow ) > Athrs ) then As = Asnow

else As = Aclim• In case of a non-zero sea ice concentration (flags fNISE = 1 − 100) adjust As if the difference between
Aclim and a default value Adef = 0.4 is larger than a given threshold Athrs = 0.1.
if ( |Aclim − Adef | > Athrs ) then As = Adef

else

As = Aclim

where Adef depends on the month of the year M and on which hemisphere H the ice is found:

Adef = (1.0 − 0.01 · fNISE ) ∗ 0.065 + 0.01 · fNISE · Aice (M, H)
with Aice (M, H) following from the LUT used to determine the OMI surface albedo database [Kleipool etal., 2008]:

Aice (M, ’north’) = 0.70, 0.73, 0.76, 0.80, 0.84, 0.78, 0.61, 0.61, 0.62, 0.68, 0.67, 0.71
Aice (M, ’south’) = 0.53, 0.50, 0.44, 0.60, 0.61, 0.64, 0.68, 0.76, 0.80, 0.83, 0.78, 0.66

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Data quality value: the qa_value flags

To make the use of the TROPOMI data products easier, a so-called qa_value (where ’qa’ stands for ’quality
assurance’) is assigned to each ground pixel. The qa_value is intended to serve as an easy filter of theobservations (dividing the dataset in useful versus not useful observations), depending on how the data isused.
The data files have for each ground pixel the so-called processing_quality_flags, which provides
the user information on processing issues, such as errors that were encountered in the processing, as well
as a number of warnings. Some of these warnings have been included in the qa_value. The meaning of
the processing_quality_flags values is detailed in Appendix A of the NO2 Product User Manual (PUM;
available via [ER2]).
The following differentiation of the qa_value, fQA , for usage of the NO2 data product has been made:0.75 ≤ fQA ≤ 1.00

The ground pixel is recommended for all applications, including column comparisons, visualisation, trends, monthly/seasonal averages. The data is restricted to cloud-free observations (cloud radiance fraction < 0.5), and snow-ice
free observations.

0.50 ≤ fQA < 0.75

The ground pixel is recommended for use in data assimilation and comparisons against models or vertical profile observations, given that the averaging
kernel is used to specify the sensitivity profile in cloudy situations; this includes
good quality retrievals over clouds and snow/ice.

0 < fQA < 0.50
fQA = 0

The ground pixel is not recommended for use due to serious retrieval issues.
A processing error occurred so that the ground pixel cannot be used at all, or
the solar zenith angle exceeds the limit set in the data assimilation

The determination of the qa_value is done as follows. Starting from the initial value fQA = 1, fQA is multiplied
i of each of the criteria i listed in Table 18 that have been met (i.e. if criterion i isby the modification factor fQA
i = 1).
not met then fQA
Table 18: Overview of the selection criteria for the qa_value, fQA , for the version v2.2.0 (viz. Table 2);previous versions may have different settings. Some quantities have a minimum or maximum value; thesevalues are configuration parameters in the processing. In this table fNISE stands for the snow/ice flags listed
in Table 17 and used by both the NISE and ECMWF snow/ice data sets. And fAAI represents the aerosol
index 354/388 nm pair, which is passed on to the NO2 data product file as added flag. Warning flags inprocessing_quality_flags not used for fQA are not listed.
i
0
1
2
3
4
5
6
7
8
9
10
11

criterion
if fatal error encountered according to processing_quality_flags
if south_atlantic_anomaly_warning set in processing_quality_flags
if sun_glint_warning set in processing_quality_flags
if pixel_level_input_data_missing_warning set in processing_quality_flags
if interpolation_warning set in processing_quality_flags
if solar_eclipse set in geolocation_flags

θ0 > θ0max,1 = 81.2◦
θ0 > θ0max,2 = 84.5◦
trop = 0.1
M trop /M geo < Mminmax
∆Ns > (∆Ns )= 33.0×10−6 mol/m2 (= 2×1015 molec/cm2 )max
fNISE < fNISE = 1 * or fNISE = 252 or fNISE = 255 then
As,NO2 > Amax
= 0.3
swNO2 > wmax
=
0.5
NO2
else-if ( fNISE 6= 253 and fNISE 6= 254 )if ( fNISE > 80 and fNISE < 104 and psc > 0.96 ps † )
if
if
if
if
if

i
fQA
0.00
0.95
0.93
0.90
0.90
0.20
0.30
0.10
0.45
0.15

[no snow or ice]

0.20
0.74

[snow/ice case]12[cloud-free snow/ice] 0.88
13
else
[cloudy snow/ice] 0.73
4 Pa
14
if psc < pmin=
3.0×100.25
sc
15 else[snow/ice error] 0.00
max = 1.0×1010
16fAAI > fAAI
[for future use] 0.40*) Note that this criterion means that the system switches to the scene mode if there is 1% or more snow/ice.
†) In NO2 data versions prior to v1.4.0 this threashold was 0.98 · ps .

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Spike removal in the DOAS fit

In version 2.2.0 of the TROPOMI NO2 processor a "spike removal" was implemented into the DOAS fit(Sect. 6.2), to remove strong outliers in the fit residual. Such outliers may be caused by, e.g., high-energy
particles hitting the CCD detector (so-called transients), variations in the dark current, or bad pixels not correctly
flagged in the Level-1b data. After removal of an outlier from the measured reflectance, the NO2 DOAS fit isredone. To avoid ending up in a cycle, the new fit residual is not checked again for outliers.
To detect outliers the so-called box-plot method used by Veefkind et al. [2016] for the OMI O2 –O2 cloudalgorithm is implemented. This method [ER24] determines lower and upper values based on the first and third
quartiles, Q1 and Q3 , i.e. the 25th and 75th percentile of a distribution (the second quartile, Q2 , is the median).
For data with a Gaussian distribution Q1 = −0.67 and Q3 = +0.67, which means that for normally distributed
data, one-half of the data is within 2/3-rd of a standard deviation unit of the mean [ER25].
If a certain value is larger than Q3 + Q f · Q3−1 or lower than Q1 − Q f · Q3−1 , with Q3−1 = Q3 − Q1 theinter-quartile range and Q f a suitable multiplication factor, it is termed an outlier. If Q f is set too low, valid
observations are unjustly removed from the fit and the NO2 SCD error will be underestimated.
For the so-called inner fences, defined by Q f = 1.5 [ER24] and used by Veefkind et al. [2016], 0.74%
of normally distributed data is termed an outlier [ER25]. This means that on average for each TROPOMI
ground pixel 2 or 3 wavelength pixels will be designated as outlier, if the NO2 fit residual on the 304 or 305
wavelength pixels within the NO2 fit window would be normally distributed. In reality the fit residual is notnormally distributed but drops off less steep, hence Q f = 1.5 is clearly too low a factor to use, as this would
inevitably lead to the removal of valid observations.
For the so-called outer fences, defined by Q f = 3.0 [ER24], about 0.002% of normally distributed data istermed an outlier [ER26], or in terms of the NO2 fit window about 1/100-th of a wavelength pixel (i.e. roughly
one wavelength pixel per 100 ground pixels), so that the chance that valid observations are removed is small.
Based on this evaluation, the TROPOMI NO2 processor is configured to use Q f = 3.0.
Figure 30 shows two examples of outlier detection. The fit residual in the left panel has one modest
outlier, at a level of Q f = 3.51. Removing this one spike, the NO2 SCD changes from 14.9445×1015 ± 0.6752×
1015 molec/cm2 to 14.8626×1015 ± 0.6599×1015 molec/cm2 (2.4680 ± 0.1096×10−4 mol/m2 ), while the RMS
error decreases by 3.7% from 5.0822×10−4 to 4.8928×10−4 . The right panel shows the effect on the fit
residual of the impact of a high-energy partical in the South Atlantic Anomaly, which appears to be so massivethat it affects two neighbouring wavelength pixels as well, while the three ground pixels to the east of thisone also have outliers at the same wavelength pixels, showing that the high-energy partical hit the detector
under a slant angle. The main outlier has a level of Q f = 69.04, while the two neighbouring pixels havelevels of Q f = 18.64 and Q f = 14.28. For this pixel removing these three wavelength pixels changes theNO2 SCD from an unrealistically low value with large error (4.8324 ± 4.5284×1015 molec/cm2 ) to a more
realistic 6.8079 ± 0.6510×1015 molec/cm2 (1.1305 ± 0.1081×10−4 mol/m2 ), while the RMS error decreases
from 33.4750×10−4 to 4.4143×10−4 .

NO2 SCD fit residual [x 10-3]

3

60

orbit 02818
lon = -171.468
lat = 57.307

2

1

0

-1

-2

orbit 03665
lon = -63.738
lat = -29.502

fit residual
outer fences: Qf = 3.0
identified spike points

50
NO2 SCD fit residual [x 10-3]

fit residual
quartiles
inner fences: Qf = 1.5
outer fences: Qf = 3.0
identified spike point4

without spike removal
after spike removal

40
2
30
0
20

-2
405

10

415

425

435

445

455

465

0
405

415

425

435
445
Wavelength [nm]

455

465

405

415

425

435
445
Wavelength [nm]

455

465

Figure 30: Examples of outliers in the TROPOMI NO2 fit residual. Left panel: fit residual for a ground pixel
over the northern Pacific Ocean, where the quartiles and the inner and outer fences are indicated by dashedlines. Right panel: fit residual for a ground pixel over Brazil, where the impact of a high-energy partical causedby the South Atlantic Anomaly created a massive spike, involving three wavelength pixels; the inset shows azoom-in of the residual before and after spike removal. For more details see the text.

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14-3

NO2 SCD fit residual [x 10 ]orbit 03665
lon = -71.158
lat = 31.997

without spike removal
initial spike points (12)after spike removal
outer fences after spike removal
remaining spike points (3)

16

12
10
8
6
4
2
0
-2
405

415

425

435
445
Wavelength [nm]

455

465

Figure 31: Examples of outliers in the TROPOMI NO2 SCD retrieval using Level-1b version 1 data of orbit
03658 (28 June 2018) over the Americas. Left panel: white spots mark ground pixels with 2 or more outliers,
where the South Atlantic Anomaly is clearly visible. Middle panel: idem, but 8 or more outliers. Right panel:
fit residual for a fully clouded ground pixel, where two wavelength pixels are flagged as saturated and thus
removed from the fit (at 450.8 and 451.0 nm), the spike removal then finds 12 outliers and after removal ofthese there are 3 outliers left (these are not removed, because there is no second round in the spike removal);
due to the spike removal the RMS error decreases from 21.173×10−4 to 5.613×10−4 .

Most ground pixels with outliers have one or two outliers, even those over the South Atlantic Anomaly, where
high-energy particle impacts on the CCD detector occur more often. Ground pixels suffering from saturation
effects, however, may have a much larger number of outliers. Due to the high SNR of TROPOMI, bright scenes,
notably over clouds in the tropics, the CCD may be over-exposed. As a result of this a number of wavelength
pixels may get saturated and the electronic effects of this may spread to neighbouring wavelength pixels andto grond pixels in neighbouring rows, an effect called "blooming". Saturation effects occur most in the dector
parts of band 4 (400 − 496 nm) and in band 6 (720 − 785 nm). In band 4 saturation is most prominent at highwavelengths but may spread far down into the NO2 fit window.
Version 1 Level-1b data contains flags on wavelength pixels affected by saturation (such wavelength pixels
are removed from the spectrum before the DOAS fit), but the criteria for this flagging are set rather conservative
and there is no flagging of blooming. This left a number of wavelength pixels unflagged but affected, leading
to multiple strong outliers (up to more than 50 outliers were observed in some ground pixels). Even after the
spike removal, the new fit may show outliers and these fit results cannot always be trusted.
Version 2 Level-1b data has a better flagging for saturation and flagging for blooming, drastically reducing
the number of outliers and spectra repaired by the spike removal give in most cases reliable results. (Flagging
for saturation and blooming uses the same Level-1b error code.)
Figure 31 shows the location of ground pixels with outliers detected in an orbit over the Americas, i.e. including part of the South Atlantic Anomaly, with some high and bright cloud complexes over the eastern part of
the USA and the western part of the Atlantic Ocean; one of the pixels in the latter region is used to give an
example of the fit residual with and without spike removal in Fig. 31.
The treatment of saturation and outliers on wavelength pixels in the NO2 SCD retrieval is governed byconfiguration parameters, listed in Table 19: the settings for NO2 -v2.2 are made for use with version-2 Level-1b
data in mind; prior NO2 processor versions accepted very few saturation flags.

Table 19: Configuration parameters in the NO2 processing related to saturation in the Level-1b spectra andoutliers in the NO2 retrieval residual; NO2 data version coverages are listed in Table 2.
configuration parameter
The maximum fraction of the radiance spectrum that is allowed
to be flagged as saturated before the ground pixel is skipped
The maximum number of outliers that is allowed to be in a
spectrum before the ground pixel is skipped

NO2 -v1.x
L1B-v1.0

NO2 -v2.2
L1B-v2.0

0.01

0.25

N/A

10

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