# ACTRIS PPP D5.1: Documentation on technical concepts and requirements for ACTRIS Observational Platforms

Ulla Wandinger, Arnoud Apituley, Thomas Blumenstock, Nicolas Bukowiecki, Jean-Pierre Cammas, Paul Connolly, Martine de Maziere, Bart Dils, Markus Fiebig, Evelyn Freney, et al.

► To cite this version:

Ulla Wandinger, Arnoud Apituley, Thomas Blumenstock, Nicolas Bukowiecki, Jean-Pierre Cammas, et al.. ACTRIS PPP D5.1: Documentation on technical concepts and requirements for ACTRIS Observational Platforms. TROPOS. 2018. hal-04618093

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# ACTRIS PPP D5.1: Documentation on technical concepts and requirements for ACTRIS Observational Platforms

Ulla Wandinger, Arnoud Apituley, Thomas Blumenstock, Nicolas Bukowiecki, Jean-Pierre Cammas, Paul Connolly, Martine de Maziere, Bart Dils, Markus Fiebig, Evelyn Freney, et al.

<figure><img src="image_1.png" /></figure>

To cite this version:

Ulla Wandinger, Arnoud Apituley, Thomas Blumenstock, Nicolas Bukowiecki, Jean-Pierre Cammas, et al.. ACTRIS PPP D5.1: Documentation on technical concepts and requirements for ACTRIS Observational Platforms. TROPOS. 2018. hal-04618093

HAL Id: hal-04618093

https://hal.science/hal-04618093v1

Submitted on 21 Jun 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Deliverable D5.1: Documentation on technical concepts and requirements for ACTRIS Observational Platforms

**Authors:** Ulla Wandinger, Arnoud Apituley, Thomas Blumenstock, Nicolas Bukowiecki, Jean-Pierre Cammas, Paul Connolly, Martine De Mazière, Bart Dils, Markus Fiebig, Evelyn Freney, Martin Gallagher, Sophie Godin-Beekmann, Philippe Goloub, Martin Gysel, Martial Haeffelin, Frank Hase, Markus Hermann, Hartmut Herrmann, Tuija Jokinen, Mika Komppula, Dagmar Kubistin, Bavo Langerock, Heikki Lihavainen, Nikos Mihalopoulos, Paolo Laj, Cathrine Lund Myhre, Emmanuel Mahieu, Stephan Mertes, Ottmar Möhler, Lucia Mona, Doina Nicolae, Ewan O'Connor, Mathias Palm, Gelsomina Pappalardo, Andrea Pazmino, Tuukka Petäjä, Sabine Philippin, Christian Plass-Duelmer, Bernhard Pospichal, Jean-Philippe Putaud, Stefan Reimann, Franz Rohrer, Herman Russchenberg, Stéphane Sauvage, Karine Sellegri, Rainer Steinbrecher, Frank Stratmann, Ralf Sussmann, Dominik van Pinxteren, Michel Van Roozendael, Corinne Vigouroux, Chris Walden, Robert Wegener, Alfred Wiedensohler

<table><tr><td>Work package no.</td><td>WP5</td></tr><tr><td>Deliverable no.</td><td>D5.1</td></tr><tr><td>Lead beneficiary</td><td>TROPOS</td></tr><tr><td>Deliverable type</td><td><input type="checkbox" disabled> R (Document, report)<br><input type="checkbox" disabled> DEC (Websites, patent fillings, videos, etc.)<br><input type="checkbox" disabled> OTHER: please specify</td></tr><tr><td>Dissemination level</td><td><input type="checkbox" disabled> PU (public)<br><input type="checkbox" disabled> CO (confidential, only for members of the Consortium, incl. Commission)</td></tr><tr><td>Estimated delivery date</td><td>Month 18</td></tr><tr><td>Actual delivery date</td><td>21/06/2018</td></tr><tr><td>Version</td><td>Final</td></tr><tr><td>Reviewed by</td><td>National ACTRIS communities</td></tr><tr><td>Accepted by</td><td>Sanna Sorvari</td></tr><tr><td>Comments</td><td></td></tr></table>
Deliverable D5.1: Documentation on technical concepts and requirements for ACTRIS Observational Platforms

Authors: Ulla Wandinger, Arnoud Apituley, Thomas Blumenstock, Nicolas Bukowiecki, Jean-Pierre Cammas, Paul Connolly, Martine De Mazière, Bart Dils, Markus Fiebig, Evelyn Freney, Martin Gallagher, Sophie Godin-Beekmann, Philippe Goloub, Martin Gysel, Martial Haeffelin, Frank Hase, Markus Hermann, Hartmut Herrmann, Tuija Jokinen, Mika Komppula, Dagmar Kubistin, Bavo Langerock, Heikki Lihavainen, Nikos Mihalopoulos, Paolo Laj, Cathrine Lund Myhre, Emmanuel Mahieu, Stephan Mertes, Ottmar Möhler, Lucia Mona, Doina Nicolae, Ewan O'Connor, Mathias Palm, Gelsomina Pappalardo, Andrea Pazmino, Tuukka Petäjä, Sabine Philippin, Christian Plass-Duelmer, Bernhard Pospichal, Jean-Philippe Putaud, Stefan Reimann, Franz Rohrer, Herman Russchenberg, Stéphane Sauvage, Karine Sellegri, Rainer Steinbrecher, Frank Stratmann, Ralf Sussmann, Dominik van Pinxteren, Michel Van Roozendael, Corinne Vigouroux, Chris Walden, Robert Wegener, Alfred Wiedensohler

<table><tr><td>Work package no.</td><td>WP5</td></tr><tr><td>Deliverable no.</td><td>D5.1</td></tr><tr><td>Lead beneficiary</td><td>TROPOS</td></tr><tr><td>Deliverable type</td><td><input type="checkbox" disabled> R (Document, report)<br><input type="checkbox" disabled> DEC (Websites, patent fillings, videos, etc.)<br><input type="checkbox" disabled> OTHER: please specify ....................................</td></tr><tr><td>Dissemination level</td><td><input type="checkbox" disabled> PU (public)<br><input type="checkbox" disabled> CO (confidential, only for members of the Consortium, incl. Commission)</td></tr><tr><td>Estimated delivery date</td><td>Month 18</td></tr><tr><td>Actual delivery date</td><td>21/06/2018</td></tr><tr><td>Version</td><td>Final</td></tr><tr><td>Reviewed by</td><td>National ACTRIS communities</td></tr><tr><td>Accepted by</td><td>Sanna Sorvari</td></tr><tr><td>Comments</td><td></td></tr></table>
Contents

<table><tr><td>1</td><td>Introduction</td><td>3</td></tr><tr><td>2</td><td>Overview on ACTRIS Observational Strategy</td><td>4</td></tr><tr><td>3</td><td>General Principles for ACTRIS Observational Platforms</td><td>5</td></tr><tr><td>4</td><td>Technical concepts and requirements for ACTRIS Observational Platforms</td><td>7</td></tr><tr><td>4.1</td><td>Aerosol remote sensing</td><td>8</td></tr><tr><td>4.1.1</td><td>Introduction</td><td>8</td></tr><tr><td>4.1.2</td><td>Observational capabilities</td><td>8</td></tr><tr><td>4.1.3</td><td>Instrumentation and calibration</td><td>10</td></tr><tr><td>4.1.4</td><td>Operation</td><td>11</td></tr><tr><td>4.1.5</td><td>Data production and data products</td><td>13</td></tr><tr><td>4.1.6</td><td>Data delivery and quality control</td><td>14</td></tr><tr><td>4.2</td><td>Cloud remote sensing</td><td>16</td></tr><tr><td>4.2.1</td><td>Introduction</td><td>16</td></tr><tr><td>4.2.2</td><td>Observational capabilities</td><td>16</td></tr><tr><td>4.2.3</td><td>Instrumentation and calibration</td><td>20</td></tr><tr><td>4.2.4</td><td>Operation</td><td>24</td></tr><tr><td>4.2.5</td><td>Data production and data products</td><td>24</td></tr><tr><td>4.2.6</td><td>Data delivery and quality control</td><td>25</td></tr><tr><td>4.3</td><td>Reactive trace gases remote sensing</td><td>26</td></tr><tr><td>4.3.1</td><td>Introduction</td><td>26</td></tr><tr><td>4.3.2</td><td>Observational capabilities</td><td>27</td></tr><tr><td>4.3.3</td><td>Instrumentation and calibration</td><td>28</td></tr><tr><td>4.3.4</td><td>Operation</td><td>32</td></tr><tr><td>4.3.5</td><td>Data production and data products</td><td>33</td></tr><tr><td>4.3.6</td><td>Data delivery and quality control</td><td>35</td></tr><tr><td>4.4</td><td>Aerosol in situ measurements</td><td>36</td></tr><tr><td>4.4.1</td><td>Introduction</td><td>36</td></tr><tr><td>4.4.2</td><td>Observational capabilities</td><td>36</td></tr><tr><td>4.4.3</td><td>Instrumentation and calibration</td><td>37</td></tr><tr><td>4.4.4</td><td>Operation</td><td>41</td></tr></table>
# Contents

<table><tr><td>1</td><td>Introduction</td><td>3</td></tr><tr><td>2</td><td>Overview on ACTRIS Observational Strategy</td><td>4</td></tr><tr><td>3</td><td>General Principles for ACTRIS Observational Platforms</td><td>5</td></tr><tr><td>4</td><td>Technical concepts and requirements for ACTRIS Observational Platforms</td><td>7</td></tr><tr><td>4.1</td><td>Aerosol remote sensing</td><td>8</td></tr><tr><td>4.1.1</td><td>Introduction</td><td>8</td></tr><tr><td>4.1.2</td><td>Observational capabilities</td><td>8</td></tr><tr><td>4.1.3</td><td>Instrumentation and calibration</td><td>10</td></tr><tr><td>4.1.4</td><td>Operation</td><td>11</td></tr><tr><td>4.1.5</td><td>Data production and data products</td><td>13</td></tr><tr><td>4.1.6</td><td>Data delivery and quality control</td><td>14</td></tr><tr><td>4.2</td><td>Cloud remote sensing</td><td>16</td></tr><tr><td>4.2.1</td><td>Introduction</td><td>16</td></tr><tr><td>4.2.2</td><td>Observational capabilities</td><td>16</td></tr><tr><td>4.2.3</td><td>Instrumentation and calibration</td><td>20</td></tr><tr><td>4.2.4</td><td>Operation</td><td>24</td></tr><tr><td>4.2.5</td><td>Data production and data products</td><td>24</td></tr><tr><td>4.2.6</td><td>Data delivery and quality control</td><td>25</td></tr><tr><td>4.3</td><td>Reactive trace gases remote sensing</td><td>26</td></tr><tr><td>4.3.1</td><td>Introduction</td><td>26</td></tr><tr><td>4.3.2</td><td>Observational capabilities</td><td>27</td></tr><tr><td>4.3.3</td><td>Instrumentation and calibration</td><td>28</td></tr><tr><td>4.3.4</td><td>Operation</td><td>32</td></tr><tr><td>4.3.5</td><td>Data production and data products</td><td>33</td></tr><tr><td>4.3.6</td><td>Data delivery and quality control</td><td>35</td></tr><tr><td>4.4</td><td>Aerosol in situ measurements</td><td>36</td></tr><tr><td>4.4.1</td><td>Introduction</td><td>36</td></tr><tr><td>4.4.2</td><td>Observational capabilities</td><td>36</td></tr><tr><td>4.4.3</td><td>Instrumentation and calibration</td><td>37</td></tr><tr><td>4.4.4</td><td>Operation</td><td>41</td></tr></table>
<table><tr><td>4.4.5</td><td>Data production and data products</td><td>42</td></tr><tr><td>4.4.6</td><td>Data delivery and quality control</td><td>43</td></tr><tr><td>4.5</td><td>Cloud in situ measurements</td><td>44</td></tr><tr><td>4.5.1</td><td>Introduction</td><td>44</td></tr><tr><td>4.5.2</td><td>Observational capabilities</td><td>44</td></tr><tr><td>4.5.3</td><td>Instrumentation and calibration</td><td>45</td></tr><tr><td>4.5.4</td><td>Operation</td><td>49</td></tr><tr><td>4.5.5</td><td>Data production and data products</td><td>49</td></tr><tr><td>4.5.6</td><td>Data delivery and quality control</td><td>49</td></tr><tr><td>4.6</td><td>Reactive trace gases in situ measurements</td><td>51</td></tr><tr><td>4.6.1</td><td>Introduction</td><td>51</td></tr><tr><td>4.6.2</td><td>Observational capabilities</td><td>52</td></tr><tr><td>4.6.3</td><td>Instrumentation and calibration</td><td>54</td></tr><tr><td>4.6.4</td><td>Operation</td><td>56</td></tr><tr><td>4.6.5</td><td>Data production and data products</td><td>57</td></tr><tr><td>4.6.6</td><td>Data delivery and quality control</td><td>58</td></tr><tr><td>5</td><td>Organizational and strategic concepts for ACTRIS Observational Platforms</td><td>59</td></tr><tr><td>5.1</td><td>ACTRIS Observational Platforms focussing on synergy</td><td>59</td></tr><tr><td>5.1.1</td><td>Observational requirements</td><td>59</td></tr><tr><td>5.1.2</td><td>Geographical requirements</td><td>59</td></tr><tr><td>5.2</td><td>ACTRIS Observational Platforms focussing on regional coverage</td><td>60</td></tr><tr><td>5.2.1</td><td>Observational requirements</td><td>60</td></tr><tr><td>5.2.2</td><td>Geographical requirements</td><td>60</td></tr><tr><td>5.3</td><td>Access requirements</td><td>60</td></tr><tr><td>5.4</td><td>Recommendations for national strategies</td><td>61</td></tr><tr><td colspan="2">Annex A: ACTRIS Variables and Related Instruments</td><td>62</td></tr><tr><td colspan="2">Annex B: References</td><td>74</td></tr></table>
<table><tr><td>4.4.5</td><td>Data production and data products</td><td>42</td></tr><tr><td>4.4.6</td><td>Data delivery and quality control</td><td>43</td></tr><tr><td>4.5</td><td>Cloud in situ measurements</td><td>44</td></tr><tr><td>4.5.1</td><td>Introduction</td><td>44</td></tr><tr><td>4.5.2</td><td>Observational capabilities</td><td>44</td></tr><tr><td>4.5.3</td><td>Instrumentation and calibration</td><td>45</td></tr><tr><td>4.5.4</td><td>Operation</td><td>49</td></tr><tr><td>4.5.5</td><td>Data production and data products</td><td>49</td></tr><tr><td>4.5.6</td><td>Data delivery and quality control</td><td>49</td></tr><tr><td>4.6</td><td>Reactive trace gases in situ measurements</td><td>51</td></tr><tr><td>4.6.1</td><td>Introduction</td><td>51</td></tr><tr><td>4.6.2</td><td>Observational capabilities</td><td>52</td></tr><tr><td>4.6.3</td><td>Instrumentation and calibration</td><td>54</td></tr><tr><td>4.6.4</td><td>Operation</td><td>56</td></tr><tr><td>4.6.5</td><td>Data production and data products</td><td>57</td></tr><tr><td>4.6.6</td><td>Data delivery and quality control</td><td>58</td></tr><tr><td>5</td><td>Organizational and strategic concepts for ACTRIS Observational Platforms</td><td>59</td></tr><tr><td>5.1</td><td>ACTRIS Observational Platforms focussing on synergy</td><td>59</td></tr><tr><td>5.1.1</td><td>Observational requirements</td><td>59</td></tr><tr><td>5.1.2</td><td>Geographical requirements</td><td>59</td></tr><tr><td>5.2</td><td>ACTRIS Observational Platforms focussing on regional coverage</td><td>60</td></tr><tr><td>5.2.1</td><td>Observational requirements</td><td>60</td></tr><tr><td>5.2.2</td><td>Geographical requirements</td><td>60</td></tr><tr><td>5.3</td><td>Access requirements</td><td>60</td></tr><tr><td>5.4</td><td>Recommendations for national strategies</td><td>61</td></tr><tr><td colspan="2">Annex A: ACTRIS Variables and Related Instruments</td><td>62</td></tr><tr><td colspan="2">Annex B: References</td><td>74</td></tr></table>
# 1 Introduction

The functionality of ACTRIS is based on a large number of **National Facilities (NFs)** widely distributed over Europe and also located at selected sites outside of Europe. NFs are developed, managed, and operated by the **national Research-Performing Organizations (RPOs)**. Decisions on the implementation and operation of NFs and respective funding solely fall to the member states. The major task of the NFs is the acquisition and delivery of quality-controlled data. NFs can also provide physical access, which will be centrally managed by the **Service and Access Management Unit (SAMU)** within the **ACTRIS Head Office (HO)**.

National Facilities within ACTRIS consist of Observational and Exploratory Platforms. **Observational Platforms** are fixed ground-based stations that deliver long-term data based on a regular measurement schedule and common operation standards. **Exploratory Platforms** are atmospheric simulation chambers, laboratory platforms and mobile platforms that perform dedicated experiments and contribute data on atmospheric constituents, processes, events or regions by following common ACTRIS standards.

<figure><img src="image_2.png" /></figure>

This document aims at providing the description of technical concepts and requirements for ACTRIS Observational Platforms. It is complemented by the respective document on Exploratory Platforms (D5.2: *Documentation on technical concepts and requirements for ACTRIS Exploratory Platforms*). The concepts are outlined in sufficient detail to facilitate the substantiated estimation of all efforts and costs for implementation, long-term operation and decommissioning of different kinds of Observational Platforms. The related data can be found in the *ACTRIS Cost Book* (D3.1). With the description of the technical requirements the document also sets the basis for the future labelling of ACTRIS Observational Platforms. The labelling principles are discussed in D5.3 (*Documentation on ACTRIS National Facility labelling principles*).

The document is structured as follows. After this introduction, in Chapter 2 an overview on the ACTRIS observational strategy is given, highlighting the scientific case, user needs and the heritage of established structures and strategies. Chapter 3 outlines the general principles for ACTRIS Observational Platforms. Chapter 4 provides a comprehensive description of the technical concepts and requirements for ACTRIS Observational Platforms in all necessary detail. Chapter 5 describes further organizational and strategic concepts and requirements with view on geographical distribution, co-location, synergies and access capabilities. Annex A summarizes the ACTRIS variables produced by the Observational Platforms and the related instrumentation in tabular form. References and the link to the ACTRIS glossary are provided in Annex B.
# 1 Introduction

The functionality of ACTRIS is based on a large number of **National Facilities (NFs)** widely distributed over Europe and also located at selected sites outside of Europe. NFs are developed, managed, and operated by the **national Research-Performing Organizations (RPOs)**. Decisions on the implementation and operation of NFs and respective funding solely fall to the member states. The major task of the NFs is the acquisition and delivery of quality-controlled data. NFs can also provide physical access, which will be centrally managed by the **Service and Access Management Unit (SAMU)** within the **ACTRIS Head Office (HO)**.

National Facilities within ACTRIS consist of Observational and Exploratory Platforms. **Observational Platforms** are fixed ground-based stations that deliver long-term data based on a regular measurement schedule and common operation standards. **Exploratory Platforms** are atmospheric simulation chambers, laboratory platforms and mobile platforms that perform dedicated experiments and contribute data on atmospheric constituents, processes, events or regions by following common ACTRIS standards.

<figure><img src="image_3.png" /></figure>

This document aims at providing the description of technical concepts and requirements for ACTRIS Observational Platforms. It is complemented by the respective document on Exploratory Platforms (D5.2: *Documentation on technical concepts and requirements for ACTRIS Exploratory Platforms*). The concepts are outlined in sufficient detail to facilitate the substantiated estimation of all efforts and costs for implementation, long-term operation and decommissioning of different kinds of Observational Platforms. The related data can be found in the *ACTRIS Cost Book* (D3.1). With the description of the technical requirements the document also sets the basis for the future labelling of ACTRIS Observational Platforms. The labelling principles are discussed in D5.3 (*Documentation on ACTRIS National Facility labelling principles*).

The document is structured as follows. After this introduction, in Chapter 2 an overview on the ACTRIS observational strategy is given, highlighting the scientific case, user needs and the heritage of established structures and strategies. Chapter 3 outlines the general principles for ACTRIS Observational Platforms. Chapter 4 provides a comprehensive description of the technical concepts and requirements for ACTRIS Observational Platforms in all necessary detail. Chapter 5 describes further organizational and strategic concepts and requirements with view on geographical distribution, co-location, synergies and access capabilities. Annex A summarizes the ACTRIS variables produced by the Observational Platforms and the related instrumentation in tabular form. References and the link to the ACTRIS glossary are provided in Annex B.
## 2 Overview on ACTRIS Observational Strategy

The goal of ACTRIS is to provide highly reliable information on the four-dimensional distribution of aerosols, clouds and reactive trace gases and on the processes that control their life-cycles, with the grand challenge to reduce uncertainties in future-climate predictions, increase knowledge on climate feedback mechanisms, evaluate air quality and related effects on health and ecosystems and address issues associated with climate-chemistry interaction. The atmospheric constituents investigated by ACTRIS comprise a multitude of species and types that exhibit a large variability in space and time due to the inhomogeneous distribution of natural and anthropogenic sources. The vast variety of complex formation, interaction, transport and removal processes result in relatively short constituent lifetimes of minutes to a few weeks. Therefore, adequate measurement strategies are required to meet the scientific user needs and to provide:

1. Representative long-term, standardized and quality-controlled data for the detection and understanding of trends in the changing atmosphere, the constraining of model predictions of future climate and air quality and the supervision of mitigation measures;

2. Dedicated high-resolved, high-precision and synergistic data for the investigation and understanding of atmospheric processes related to climate and climate feedback mechanisms, air chemistry and climate-chemistry interaction.

For this reason, ACTRIS Observational Platforms perform measurements of aerosols, clouds and reactive trace gases from the Earth surface throughout the troposphere up to the stratosphere by applying state-of-the-art remote-sensing and *in situ* measurement techniques under consideration of harmonized, standardized and quality-controlled instrumentation, operation procedures and data retrieval schemes. Observational sites of ACTRIS cover major geographical areas in Europe and, in addition, important global habitats in order to obtain data representing and contrasting scientifically relevant aspects, e.g., at urban and rural, polluted and clean, mountainous and flat, marine and continental or wet and dry sites or under polar, mid-latitude, sub-tropical, tropical and other climatic conditions. ACTRIS Observational Platforms deliver the data regularly to the ACTRIS DC, where they are accessible and where higher-level products are created and provided to the users. All data have standardized formats, are accompanied with the metadata, contain uncertainty and traceability information and undergo a strict quality control.

The ACTRIS observational strategy has been widely established already over more than two decades. The heritage evolves from European and global observational networks, which have been working on common standards for instrumentation, operation, quality assurance and quality control (QA/QC) and data products over many years. These networks are mainly based on the voluntary association of partners. Their developments have been supported by European infrastructure projects, national agencies, and international activities. ACTRIS incorporates ground-based remote-sensing sites of the European Aerosol Research Lidar Network (EARLINET), the European part of the global Aerosol Robotic Network (AERONET), the European cloud observation network (Cloudnet) and European parts of the global Network for the Detection of Atmospheric Composition Change (NDACC). The ACTRIS network for aerosol *in situ* observations has been established in the framework of the European projects CREATE (Construction, use and delivery of a European aerosol database) and EUSAAR (European Supersites for Atmospheric Aerosol
## 2 Overview on ACTRIS Observational Strategy

The goal of ACTRIS is to provide highly reliable information on the four-dimensional distribution of aerosols, clouds and reactive trace gases and on the processes that control their life-cycles, with the grand challenge to reduce uncertainties in future-climate predictions, increase knowledge on climate feedback mechanisms, evaluate air quality and related effects on health and ecosystems and address issues associated with climate-chemistry interaction. The atmospheric constituents investigated by ACTRIS comprise a multitude of species and types that exhibit a large variability in space and time due to the inhomogeneous distribution of natural and anthropogenic sources. The vast variety of complex formation, interaction, transport and removal processes result in relatively short constituent lifetimes of minutes to a few weeks. Therefore, adequate measurement strategies are required to meet the scientific user needs and to provide:

1. Representative long-term, standardized and quality-controlled data for the detection and understanding of trends in the changing atmosphere, the constraining of model predictions of future climate and air quality and the supervision of mitigation measures;

2. Dedicated high-resolved, high-precision and synergistic data for the investigation and understanding of atmospheric processes related to climate and climate feedback mechanisms, air chemistry and climate-chemistry interaction.

For this reason, ACTRIS Observational Platforms perform measurements of aerosols, clouds and reactive trace gases from the Earth surface throughout the troposphere up to the stratosphere by applying state-of-the-art remote-sensing and *in situ* measurement techniques under consideration of harmonized, standardized and quality-controlled instrumentation, operation procedures and data retrieval schemes. Observational sites of ACTRIS cover major geographical areas in Europe and, in addition, important global habitats in order to obtain data representing and contrasting scientifically relevant aspects, e.g., at urban and rural, polluted and clean, mountainous and flat, marine and continental or wet and dry sites or under polar, mid-latitude, sub-tropical, tropical and other climatic conditions. ACTRIS Observational Platforms deliver the data regularly to the ACTRIS DC, where they are accessible and where higher-level products are created and provided to the users. All data have standardized formats, are accompanied with the metadata, contain uncertainty and traceability information and undergo a strict quality control.

The ACTRIS observational strategy has been widely established already over more than two decades. The heritage evolves from European and global observational networks, which have been working on common standards for instrumentation, operation, quality assurance and quality control (QA/QC) and data products over many years. These networks are mainly based on the voluntary association of partners. Their developments have been supported by European infrastructure projects, national agencies, and international activities. ACTRIS incorporates ground-based remote-sensing sites of the European Aerosol Research Lidar Network (EARLINET), the European part of the global Aerosol Robotic Network (AERONET), the European cloud observation network (Cloudnet) and European parts of the global Network for the Detection of Atmospheric Composition Change (NDACC). The ACTRIS network for aerosol *in situ* observations has been established in the framework of the European projects CREATE (Construction, use and delivery of a European aerosol database) and EUSAAR (European Supersites for Atmospheric Aerosol
Research). The heritage builds on the European Monitoring and Evaluation Programme (EMEP) and the Global Atmospheric Watch (GAW) programme of the World Meteorological Organisation (WMO). Interactions with these programmes exist in the form of shared sites and regarding common standards for instrumentation, operation, QA/QC and data curation. ACTRIS standards have been implemented in EMEP and GAW networks on European and global level already in the past and ACTRIS will make a substantial European contribution to these networks in the future. The integration of *in situ* and remote-sensing observations started with the first ACTRIS infrastructure project in 2011. Since then the completion of ACTRIS observation capabilities for short-lived atmospheric species has been steadily pursued, e.g., by establishing standardized European-wide *in situ* observations of reactive trace gases.

One important task of the ACTRIS Implementation Phase is to transform the observational network structures into a consolidated, sustainable, long-term research infrastructure, at the same time maintaining and advancing the long-term efforts of standardization and QA/QC and the capacity to respond to changing scientific requirements. The latter issue is facilitated through the implementation of Topical Centres (TCs), in the form of Central Facilities (CFs), which define the standards, provide operation support and supervise the quality-assurance measures from instrumental setup to data delivery. The present document contributes to the ACTRIS implementation tasks by describing the technical concepts and requirements for the ACTRIS Observational Platforms, considering the heritage of previous developments as well as new aspects needed to improve and substantiate the capabilities of the distributed research infrastructure in the future.

## 3 General Principles for ACTRIS Observational Platforms

ACTRIS Observational Platforms are strategically located in diverse climatic regimes in Europe and outside of Europe. Operated or co-operated by ACTRIS partners, they deliver unique high-quality observational data to the scientific community through the ACTRIS Data Centre (DC) and provide key support to research projects and training activities for the atmospheric and climate science community. ACTRIS Observational Platforms are not necessarily organized at single locations, but can be composed of an ensemble of substations in a regional environment. ACTRIS Observational Platforms follow common general principles listed hereafter. The compliance will be proven through the ACTRIS labelling process (see D5.3).

### General Principles

1. ACTRIS Observational Platforms must support at least one of the **observational components**,

* aerosol remote sensing,

* cloud remote sensing,

* reactive trace gases remote sensing,

* aerosol *in situ* measurements,

* cloud *in situ* measurements,

* reactive trace gases *in situ* measurements,

by delivering respective high-quality data to the ACTRIS Data Centre, in close cooperation with the associated ACTRIS Topical Centres. The list of ACTRIS variables (to be either directly delivered from
Research). The heritage builds on the European Monitoring and Evaluation Programme (EMEP) and the Global Atmospheric Watch (GAW) programme of the World Meteorological Organisation (WMO). Interactions with these programmes exist in the form of shared sites and regarding common standards for instrumentation, operation, QA/QC and data curation. ACTRIS standards have been implemented in EMEP and GAW networks on European and global level already in the past and ACTRIS will make a substantial European contribution to these networks in the future. The integration of *in situ* and remote-sensing observations started with the first ACTRIS infrastructure project in 2011. Since then the completion of ACTRIS observation capabilities for short-lived atmospheric species has been steadily pursued, e.g., by establishing standardized European-wide *in situ* observations of reactive trace gases.

One important task of the ACTRIS Implementation Phase is to transform the observational network structures into a consolidated, sustainable, long-term research infrastructure, at the same time maintaining and advancing the long-term efforts of standardization and QA/QC and the capacity to respond to changing scientific requirements. The latter issue is facilitated through the implementation of Topical Centres (TCs), in the form of Central Facilities (CFs), which define the standards, provide operation support and supervise the quality-assurance measures from instrumental setup to data delivery. The present document contributes to the ACTRIS implementation tasks by describing the technical concepts and requirements for the ACTRIS Observational Platforms, considering the heritage of previous developments as well as new aspects needed to improve and substantiate the capabilities of the distributed research infrastructure in the future.

## 3 General Principles for ACTRIS Observational Platforms

ACTRIS Observational Platforms are strategically located in diverse climatic regimes in Europe and outside of Europe. Operated or co-operated by ACTRIS partners, they deliver unique high-quality observational data to the scientific community through the ACTRIS Data Centre (DC) and provide key support to research projects and training activities for the atmospheric and climate science community. ACTRIS Observational Platforms are not necessarily organized at single locations, but can be composed of an ensemble of substations in a regional environment. ACTRIS Observational Platforms follow common general principles listed hereafter. The compliance will be proven through the ACTRIS labelling process (see D5.3).

### General Principles

1. ACTRIS Observational Platforms must support at least one of the **observational components**,

* aerosol remote sensing,

* cloud remote sensing,

* reactive trace gases remote sensing,

* aerosol *in situ* measurements,

* cloud *in situ* measurements,

* reactive trace gases *in situ* measurements,

by delivering respective high-quality data to the ACTRIS Data Centre, in close cooperation with the associated ACTRIS Topical Centres. The list of ACTRIS variables (to be either directly delivered from
the NF or produced within the Data Centre from delivered data) is provided in Annex A of this document, together with the related ACTRIS instrumentation.

2. ACTRIS Observational Platforms are committed to long-term operation for the detection of trends and variability.

3. ACTRIS Observational Platforms are operated by personnel with identified expertise in running the relevant ACTRIS instrumentation. Support for training of personnel will be provided by the Topical Centres.

4. The instrumentation operated at ACTRIS Observational Platforms follows the ACTRIS recommendations (see Chapter 4 and Annex A) and is approved by the Topical Centres. Any deviation from ACTRIS instrumental standards must be documented and approved by the associated Topical Centre.

5. Measurement methodologies and procedures for operating the ACTRIS instruments comply with the standards of calibration, operation and quality assurance defined and recommended by the Topical Centres. Any deviation from ACTRIS standards must be documented and approved by the associated Topical Centre.

6. Data from measurements performed at the ACTRIS Observational Platforms are made available to users through the ACTRIS Data Centre. Data are transferred to the ACTRIS Data Centre following the procedures, formats and timelines described in Chapter 4 and in the Data Management Plan (D4.2). Whenever applicable, data should be transferred in real time (RRT, within 3 hours after acquisition) or near real-time (NRT, within 3 days after acquisition).

7. ACTRIS Observational Platforms may provide physical access for users if respective capacity and expertise is proven (see Chapter 5 and D6.3 for specific requirements).

8. ACTRIS Observational Platforms follow the specific technical requirements outlined in Chapter 4. Consequently, ACTRIS Observational Platforms aim at operating an optimum setup of instruments to produce a comprehensive set of ACTRIS variables belonging to one or more of the ACTRIS observational components. In addition, they may deliver individual data belonging to other ACTRIS observational components, in agreement with the related Topical Centres and the Data Centre.
the NF or produced within the Data Centre from delivered data) is provided in Annex A of this document, together with the related ACTRIS instrumentation.

2. ACTRIS Observational Platforms are committed to long-term operation for the detection of trends and variability.

3. ACTRIS Observational Platforms are operated by personnel with identified expertise in running the relevant ACTRIS instrumentation. Support for training of personnel will be provided by the Topical Centres.

4. The instrumentation operated at ACTRIS Observational Platforms follows the ACTRIS recommendations (see Chapter 4 and Annex A) and is approved by the Topical Centres. Any deviation from ACTRIS instrumental standards must be documented and approved by the associated Topical Centre.

5. Measurement methodologies and procedures for operating the ACTRIS instruments comply with the standards of calibration, operation and quality assurance defined and recommended by the Topical Centres. Any deviation from ACTRIS standards must be documented and approved by the associated Topical Centre.

6. Data from measurements performed at the ACTRIS Observational Platforms are made available to users through the ACTRIS Data Centre. Data are transferred to the ACTRIS Data Centre following the procedures, formats and timelines described in Chapter 4 and in the Data Management Plan (D4.2). Whenever applicable, data should be transferred in real time (RRT, within 3 hours after acquisition) or near real-time (NRT, within 3 days after acquisition).

7. ACTRIS Observational Platforms may provide physical access for users if respective capacity and expertise is proven (see Chapter 5 and D6.3 for specific requirements).

8. ACTRIS Observational Platforms follow the specific technical requirements outlined in Chapter 4. Consequently, ACTRIS Observational Platforms aim at operating an optimum setup of instruments to produce a comprehensive set of ACTRIS variables belonging to one or more of the ACTRIS observational components. In addition, they may deliver individual data belonging to other ACTRIS observational components, in agreement with the related Topical Centres and the Data Centre.
## 4 Technical concepts and requirements for ACTRIS Observational Platforms

In the following, a comprehensive description of the technical concepts and requirements for ACTRIS Observational Platforms, including instrumentation, calibration, operation, data evaluation, data products, data bases, data delivery and the related QA/QC procedures is provided. For each observational component, the respective section describes the entire chain of technical requirements from the instrumentation to data delivery and respective quality control. In the beginning of each section an overview is given, containing recommendations for an optimum setup, minimum technical requirements and suggestions for complementary added-value observations and auxiliary measurements.

The **optimum setup** is considered to be the most suitable configuration of instruments and operation procedures to satisfy the demands of a large user community and to produce a comprehensive set of ACTRIS variables belonging to the respective ACTRIS observational component. **Minimum technical requirements** describe specifications and procedures that are at least necessary to deliver a valuable set of quality-assured variables to the ACTRIS Data Centre. **Complementary added-value observations** may help in the scientific use and interpretation of data from an ACTRIS observational component. Added-value observations are not necessarily part of ACTRIS and thus do not always follow ACTRIS QA/QC procedures. They may however be subject of integration into ACTRIS in the future or follow the standards of other research infrastructures or international bodies. **Auxiliary measurements** cover fundamental parameters (e.g., meteorological data) needed to process or evaluate ACTRIS data and follow established international standards. If adequate, added-value observations and auxiliary measurements are shared among the observational components at the same NF.

In addition to the general principles discussed in Chapter 3, ACTRIS Observational Platforms are expected to fulfil technical concepts and requirements described in the following in order to receive an ACTRIS label. If a different concept than the one described here is used, it has at least to comply with all basic ACTRIS standards and requirements, has to be documented and has to be agreed with the responsible Topical Centre. The technical concepts are subject to innovation and scientific advancement. Thus, the Topical Centres will regularly revise the respective standards and requirements during the lifetime of ACTRIS and help implementing new concepts at the NFs.
## 4 Technical concepts and requirements for ACTRIS Observational Platforms

In the following, a comprehensive description of the technical concepts and requirements for ACTRIS Observational Platforms, including instrumentation, calibration, operation, data evaluation, data products, data bases, data delivery and the related QA/QC procedures is provided. For each observational component, the respective section describes the entire chain of technical requirements from the instrumentation to data delivery and respective quality control. In the beginning of each section an overview is given, containing recommendations for an optimum setup, minimum technical requirements and suggestions for complementary added-value observations and auxiliary measurements.

The **optimum setup** is considered to be the most suitable configuration of instruments and operation procedures to satisfy the demands of a large user community and to produce a comprehensive set of ACTRIS variables belonging to the respective ACTRIS observational component. **Minimum technical requirements** describe specifications and procedures that are at least necessary to deliver a valuable set of quality-assured variables to the ACTRIS Data Centre. **Complementary added-value observations** may help in the scientific use and interpretation of data from an ACTRIS observational component. Added-value observations are not necessarily part of ACTRIS and thus do not always follow ACTRIS QA/QC procedures. They may however be subject of integration into ACTRIS in the future or follow the standards of other research infrastructures or international bodies. **Auxiliary measurements** cover fundamental parameters (e.g., meteorological data) needed to process or evaluate ACTRIS data and follow established international standards. If adequate, added-value observations and auxiliary measurements are shared among the observational components at the same NF.

In addition to the general principles discussed in Chapter 3, ACTRIS Observational Platforms are expected to fulfil technical concepts and requirements described in the following in order to receive an ACTRIS label. If a different concept than the one described here is used, it has at least to comply with all basic ACTRIS standards and requirements, has to be documented and has to be agreed with the responsible Topical Centre. The technical concepts are subject to innovation and scientific advancement. Thus, the Topical Centres will regularly revise the respective standards and requirements during the lifetime of ACTRIS and help implementing new concepts at the NFs.
# 4.1 Aerosol remote sensing

## 4.1.1 Introduction

Atmospheric aerosols are highly variable in space and time as well as in their physical and chemical composition, and thus in their optical and radiative properties. The lifetime of particles near the Earth surface ranges from hours to a few days, whereas lofted aerosol layers may be present in the free troposphere over days to weeks and can remain in the stratosphere even over years. Trans-border and inter-continental aerosol transport of, e.g., desert dust, volcanic ash, anthropogenic pollution or biomass-burning smoke, has to be taken into account in the estimation of regional and global direct and indirect radiative effects and climate forcing, but also in the investigation of local and regional air pollution and respective health effects. Therefore, remote-sensing observations in ACTRIS aim at the four-dimensional observation of characteristic aerosol properties at regional, continental and global scales.

This section describes the technical concepts and requirements for ACTRIS aerosol remote-sensing observations. The remote-sensing equipment consists of active and passive instruments, which are operated following principles developed over a long time in the major European and global networks EARLINET (Pappalardo et al. 2006, 2014) and AERONET (Holben et al. 1998, 2001), respectively.

ACTRIS **high-power aerosol lidars** provide quantitative measurements of **aerosol extinction and backscattering profiles**, and thus the **extinction-to-backscatter or lidar ratio**, throughout the troposphere and lower stratosphere with high spatial and temporal resolution. **Spectral and polarization information (extinction-related and backscatter-related Ångström exponents and particle linear depolarization ratio)** is retrieved in order to classify **aerosol types** and to determine natural and anthropogenic contributions to the aerosol load. ACTRIS variables also include information on **geometrical boundaries of aerosol layers** and **layer-mean optical properties**.

**Automatic sun/sky and moon photometers** measure the columnar spectral aerosol optical depth and sky radiance, from which information on **particle microphysical and radiative properties**, e.g., **particle size distribution, absorption, complex refractive index, spherical/non spherical fraction, phase matrix**, is retrieved. The synergistic use of photometer and lidar observations, partly inherited from the AERONET inversion philosophy (Dubovik et al. 2000, 2011, 2014), aims at the retrieval of higher-level variables such as **fine and coarse particle mass concentration** and **spectral extinction, backscatter and absorption profiles**, all consistent with columnar aerosol optical and microphysical properties.

## 4.1.2 Observational capabilities

### 4.1.2.1 Optimum setup

The optimum setup of an ACTRIS aerosol remote-sensing station consists of a **three-wavelength Raman or high-spectral-resolution (HSR) lidar with polarization discrimination capability** and an **automatic sun/sky and moon photometer** (optionally also polarized) according to ACTRIS/AERONET standards, both operating continuously. Level 0 data of the instruments shall be uploaded automatically to the respective units of the DC (lidar) or TC (photometer) for processing in RRT or NRT, with the aim to provide products for assimilation purposes, fast model evaluation and immediate information of the public about special
# 4.1 Aerosol remote sensing

## 4.1.1 Introduction

Atmospheric aerosols are highly variable in space and time as well as in their physical and chemical composition, and thus in their optical and radiative properties. The lifetime of particles near the Earth surface ranges from hours to a few days, whereas lofted aerosol layers may be present in the free troposphere over days to weeks and can remain in the stratosphere even over years. Trans-border and inter-continental aerosol transport of, e.g., desert dust, volcanic ash, anthropogenic pollution or biomass-burning smoke, has to be taken into account in the estimation of regional and global direct and indirect radiative effects and climate forcing, but also in the investigation of local and regional air pollution and respective health effects. Therefore, remote-sensing observations in ACTRIS aim at the four-dimensional observation of characteristic aerosol properties at regional, continental and global scales.

This section describes the technical concepts and requirements for ACTRIS aerosol remote-sensing observations. The remote-sensing equipment consists of active and passive instruments, which are operated following principles developed over a long time in the major European and global networks EARLINET (Pappalardo et al. 2006, 2014) and AERONET (Holben et al. 1998, 2001), respectively.

ACTRIS **high-power aerosol lidars** provide quantitative measurements of **aerosol extinction and backscattering profiles**, and thus the **extinction-to-backscatter or lidar ratio**, throughout the troposphere and lower stratosphere with high spatial and temporal resolution. **Spectral and polarization information (extinction-related and backscatter-related Ångström exponents and particle linear depolarization ratio)** is retrieved in order to classify **aerosol types** and to determine natural and anthropogenic contributions to the aerosol load. ACTRIS variables also include information on **geometrical boundaries of aerosol layers** and **layer-mean optical properties**.

**Automatic sun/sky and moon photometers** measure the columnar spectral aerosol optical depth and sky radiance, from which information on **particle microphysical and radiative properties**, e.g., **particle size distribution, absorption, complex refractive index, spherical/non spherical fraction, phase matrix**, is retrieved. The synergistic use of photometer and lidar observations, partly inherited from the AERONET inversion philosophy (Dubovik et al. 2000, 2011, 2014), aims at the retrieval of higher-level variables such as **fine and coarse particle mass concentration** and **spectral extinction, backscatter and absorption profiles**, all consistent with columnar aerosol optical and microphysical properties.

## 4.1.2 Observational capabilities

### 4.1.2.1 Optimum setup

The optimum setup of an ACTRIS aerosol remote-sensing station consists of a **three-wavelength Raman or high-spectral-resolution (HSR) lidar with polarization discrimination capability** and an **automatic sun/sky and moon photometer** (optionally also polarized) according to ACTRIS/AERONET standards, both operating continuously. Level 0 data of the instruments shall be uploaded automatically to the respective units of the DC (lidar) or TC (photometer) for processing in RRT or NRT, with the aim to provide products for assimilation purposes, fast model evaluation and immediate information of the public about special
events and hazardous situations. All higher-level ACTRIS variables will be produced and provided by the DC/TC units, including respective QA/QC. A list of variables is provided in Annex A.1 and the products are further described below. Calibration and quality assurance tools for photometer and lidar instruments have to be applied following the guidelines of the Centre for Aerosol Remote Sensing and the requirements outlined below.

### 4.1.2.2 Minimum requirements

The minimum setup of an ACTRIS aerosol remote-sensing station consists of a **one-wavelength Raman or HSR lidar with polarization discrimination capability** and a **sun/sky photometer**. The photometer has to be set up for automatic operation following the ACTRIS/AERONET standards. Photometer Level 0 data have to be uploaded to the TC in NRT. The lidar must be operated following the measurement schedule for climatological observations and shall in addition contribute to the observation of special events and satellite validation activities. Lidar data have to be uploaded to the DC unit for central processing in NRT in order to provide immediate public Level 1 images (quicklooks). All higher-level lidar and photometer products are centrally produced. Calibration and quality assurance tools for photometer and lidar have to be applied following the guidelines of the Centre for Aerosol Remote Sensing and the requirements described below.

### 4.1.2.3 Complementary added-value observations

In order to support aerosol process studies, **Raman lidar** measurements of **water vapour** and **temperature** are helpful, e.g., to study hygroscopic growth of particles. For this purpose, the high-power aerosol lidar can be equipped with additional measurement channels. **Microwave radiometer** can be used in synergy to improve the profiling capabilities for water-vapour content and relative humidity.

Complementary observations of the **vertical wind velocity** with **Doppler lidar** and/or **radar wind profilers** are useful to investigate aerosol vertical exchange in the planetary boundary layer and can also contribute to aerosol-cloud-interaction studies.

A continuously operating **low-power lidar** (ceilometer) is useful for stations that do not operate the high-power lidar continuously in order to provide information on atmospheric conditions, e.g., presence of low-level clouds prohibiting useful aerosol observation or indication of special events such as dust or smoke plumes.

**Brewer and UVVIS spectrometers** can extend the **aerosol optical depth** measurements towards the UV spectral range and provide a link to observations of **ozone** and **other trace gases**.

Collocated **surface radiation measurements** following the standards of the Baseline Surface Radiation Network (BSRN) help in aerosol-radiation closure studies and trend analysis and facilitate satellite validation activities.

### 4.1.2.4 Auxiliary measurements

Vertical profiles of **meteorological parameters** (temperature, pressure, relative humidity) are required in the retrievals for calculation of Rayleigh scattering profiles. They can be taken from the output of a high-
events and hazardous situations. All higher-level ACTRIS variables will be produced and provided by the DC/TC units, including respective QA/QC. A list of variables is provided in Annex A.1 and the products are further described below. Calibration and quality assurance tools for photometer and lidar instruments have to be applied following the guidelines of the Centre for Aerosol Remote Sensing and the requirements outlined below.

### 4.1.2.2 Minimum requirements

The minimum setup of an ACTRIS aerosol remote-sensing station consists of a **one-wavelength Raman or HSR lidar with polarization discrimination capability** and a **sun/sky photometer**. The photometer has to be set up for automatic operation following the ACTRIS/AERONET standards. Photometer Level 0 data have to be uploaded to the TC in NRT. The lidar must be operated following the measurement schedule for climatological observations and shall in addition contribute to the observation of special events and satellite validation activities. Lidar data have to be uploaded to the DC unit for central processing in NRT in order to provide immediate public Level 1 images (quicklooks). All higher-level lidar and photometer products are centrally produced. Calibration and quality assurance tools for photometer and lidar have to be applied following the guidelines of the Centre for Aerosol Remote Sensing and the requirements described below.

### 4.1.2.3 Complementary added-value observations

In order to support aerosol process studies, **Raman lidar** measurements of **water vapour** and **temperature** are helpful, e.g., to study hygroscopic growth of particles. For this purpose, the high-power aerosol lidar can be equipped with additional measurement channels. **Microwave radiometer** can be used in synergy to improve the profiling capabilities for water-vapour content and relative humidity.

Complementary observations of the **vertical wind velocity** with **Doppler lidar** and/or **radar wind profilers** are useful to investigate aerosol vertical exchange in the planetary boundary layer and can also contribute to aerosol-cloud-interaction studies.

A continuously operating **low-power lidar** (ceilometer) is useful for stations that do not operate the high-power lidar continuously in order to provide information on atmospheric conditions, e.g., presence of low-level clouds prohibiting useful aerosol observation or indication of special events such as dust or smoke plumes.

**Brewer and UVVIS spectrometers** can extend the **aerosol optical depth** measurements towards the UV spectral range and provide a link to observations of **ozone** and **other trace gases**.

Collocated **surface radiation measurements** following the standards of the Baseline Surface Radiation Network (BSRN) help in aerosol-radiation closure studies and trend analysis and facilitate satellite validation activities.

### 4.1.2.4 Auxiliary measurements

Vertical profiles of **meteorological parameters** (temperature, pressure, relative humidity) are required in the retrievals for calculation of Rayleigh scattering profiles. They can be taken from the output of a high-
resolution weather forecast model, radiosoundings or standard atmospheric profiles based on surface measurements of **meteorological parameters** (temperature, pressure, relative humidity). The stations should thus be equipped at least with automatic sensors for **standard meteorological measurements** (e.g., **automatic weather station**) to provide useful information on surface conditions. **Radiosondes** measuring vertical profiles of **temperature, pressure, humidity** and **winds** allow calculation of actual Rayleigh scattering profiles independent from model information. Radiosondes should be launched at least twice per day to provide useful information for the processing.

### 4.1.3 Instrumentation and calibration

#### 4.1.3.1 High-power aerosol lidar

ACTRIS Observational Platforms apply sophisticated **high-power aerosol lidars** making use of the **Raman or HSR lidar techniques**, which allow the independent determination of **aerosol extinction and backscattering profiles** based on the separation of pure molecular and particulate atmospheric backscattering. Polarization discrimination is required in order to apply **aerosol typing** algorithms. Measurements of the **particle linear depolarization ratio** permit the identification of large, non-spherical dust particles, which strongly depolarize the backscattered light in contrast to less-depolarizing pollution, smoke or marine aerosols. Measurements of aerosol extinction, backscatter and depolarization-ratio profiles are to be performed at least at one wavelength, either 355 or 532 nm. For an optimized setup, a three-wavelength system is recommended, permitting the measurement of backscatter-coefficient profiles at 355, 532 and 1064 nm and thus the **backscatter-related Ångström exponent** (day and night), extinction-coefficient profiles at 355 and 532 nm and thus the **extinction-related Ångström exponent** (at least at night), and **depolarization-ratio profiles** at 355 and/or 532 nm (day and night). In this way, advanced retrievals of aerosol type and microphysical parameters based on spectral information are possible.

Technical system parameters such as laser power, telescope aperture, receiver bandwidth and data acquisition system must be chosen such that profiles can be acquired throughout the troposphere up to the lower stratosphere with the required accuracy and temporal and spatial resolution. A separate near-range receiving system is recommended for observations in the lower planetary boundary layer, starting at least at 200 m height. Configurations may vary to account for climatic circumstances, e.g., the typical height of the boundary layer for the location of the NF.

Quality assurance measures for aerosol lidars include external intercomparisons and internal check-ups. Intercomparison measurements with one of the reference lidar systems operated by the Centre for Aerosol Remote Sensing shall be performed for new systems and after major upgrades following the methodology described in Wandinger et al. (2015). Internal check-up tools have to be applied regularly based on the recommendations and under the supervision of the Centre for Aerosol Remote Sensing. The checks are outlined by Freudenthaler et al. (2018). Polarization measurements require a detailed system characterization and calibration as described in Freudenthaler (2016).
resolution weather forecast model, radiosoundings or standard atmospheric profiles based on surface measurements of **meteorological parameters** (temperature, pressure, relative humidity). The stations should thus be equipped at least with automatic sensors for **standard meteorological measurements** (e.g., **automatic weather station**) to provide useful information on surface conditions. **Radiosondes** measuring vertical profiles of **temperature, pressure, humidity** and **winds** allow calculation of actual Rayleigh scattering profiles independent from model information. Radiosondes should be launched at least twice per day to provide useful information for the processing.

### 4.1.3 Instrumentation and calibration

#### 4.1.3.1 High-power aerosol lidar

ACTRIS Observational Platforms apply sophisticated **high-power aerosol lidars** making use of the **Raman or HSR lidar techniques**, which allow the independent determination of **aerosol extinction and backscattering profiles** based on the separation of pure molecular and particulate atmospheric backscattering. Polarization discrimination is required in order to apply **aerosol typing** algorithms. Measurements of the **particle linear depolarization ratio** permit the identification of large, non-spherical dust particles, which strongly depolarize the backscattered light in contrast to less-depolarizing pollution, smoke or marine aerosols. Measurements of aerosol extinction, backscatter and depolarization-ratio profiles are to be performed at least at one wavelength, either 355 or 532 nm. For an optimized setup, a three-wavelength system is recommended, permitting the measurement of backscatter-coefficient profiles at 355, 532 and 1064 nm and thus the **backscatter-related Ångström exponent** (day and night), extinction-coefficient profiles at 355 and 532 nm and thus the **extinction-related Ångström exponent** (at least at night), and **depolarization-ratio profiles** at 355 and/or 532 nm (day and night). In this way, advanced retrievals of aerosol type and microphysical parameters based on spectral information are possible.

Technical system parameters such as laser power, telescope aperture, receiver bandwidth and data acquisition system must be chosen such that profiles can be acquired throughout the troposphere up to the lower stratosphere with the required accuracy and temporal and spatial resolution. A separate near-range receiving system is recommended for observations in the lower planetary boundary layer, starting at least at 200 m height. Configurations may vary to account for climatic circumstances, e.g., the typical height of the boundary layer for the location of the NF.

Quality assurance measures for aerosol lidars include external intercomparisons and internal check-ups. Intercomparison measurements with one of the reference lidar systems operated by the Centre for Aerosol Remote Sensing shall be performed for new systems and after major upgrades following the methodology described in Wandinger et al. (2015). Internal check-up tools have to be applied regularly based on the recommendations and under the supervision of the Centre for Aerosol Remote Sensing. The checks are outlined by Freudenthaler et al. (2018). Polarization measurements require a detailed system characterization and calibration as described in Freudenthaler (2016).
#### 4.1.3.2 Automatic sun/sky and moon photometer

For columnar aerosol measurements, the required instrument is a **fully automatic sun/sky photometer**. Only this kind of photometer can provide **aerosol radiative properties** (e.g., size distribution, absorption, complex refractive index, spherical/non spherical fraction, phase matrix) required for aerosol forcing determination. Similarly, for synergetic use of observations (joint photometer/lidar retrievals) **direct-sun and diffuse sky radiance** are absolutely mandatory (together with at least two elastic-backscatter lidar signals) to derive, e.g., daytime **aerosol concentration** (fine and coarse modes) and **absorption profiles**. Since moon photometry has recently made significant technical progress, the new sun/sky photometer standard must now include the direct-moon extinction measurement capability, which is again very relevant for aerosol profiling.

Direct-sun and direct-moon-extinction **aerosol optical depth** (AOD) are derived using the Beer-Bouguer-Lambert law. The extra-terrestrial signal has to be determined very accurately to extract AOD with the requested accuracy (0.01 for field instruments and 0.005 for reference instruments). During the operation, optical and electronic components degrade and change the calibration for sun, moon, and sky radiance and polarization observations. Therefore, the regular recalibration of each instrument is mandatory and should be performed every 12 month on average. After the calibration, it is required to set the instrument up again as quickly as possible.

For the standard sun/sky/moon photometer, four major distinct calibrations have to be performed at the Centre for Aerosol Remote Sensing. Day and night AOD calibration must be performed on specific outdoor calibration platforms, whereas radiometric calibrations (sky radiance and polarisation) are performed in the laboratory (for details see the concept paper of the Centre for Aerosol Remote Sensing in D4.1). Since this procedure can last several weeks, spare instruments will be provided by the Topical Centre to cover continuity observation needs at each NF during the calibration phase. The requirement of the annual re-calibration is very strong. A 5% variation in the direct-sun or moon calibration coefficients (caused by filter degradation, deposit of dust on external lenses, etc.) yields to typical 0.05 absolute bias on AOD at noon (Holben et al. 2001, Smirnov et al. 2000). If the period between two calibrations is too long, the assumption of linear variation of the instrument response will be not valid, neither the temporal interpolation performed in the reprocessing phase for getting QA Level 1/Level 2 data products.

### 4.1.4 Operation

#### 4.1.4.1 High-power aerosol lidar

It is recommended to operate ACTRIS high-power aerosol lidars continuously, weather permitting, i.e., in the absence of precipitation or fog. If the instrument is not automated, it must at least provide unbiased long-term regular observations following the pre-defined schedule with 5 observations per week as agreed within the community to avoid a meteorological bias. Furthermore, measurements are to be performed upon alert, e.g., in hazardous situations such as volcanic eruptions, for special events such as dust outbreaks or forest fires, and for dedicated satellite validation purposes. Satellite validation measurements will follow a specific strategy. Alerts and measurement schedules for collocated observations during satellite overpasses will be distributed by the lidar unit of the DC.
#### 4.1.3.2 Automatic sun/sky and moon photometer

For columnar aerosol measurements, the required instrument is a **fully automatic sun/sky photometer**. Only this kind of photometer can provide **aerosol radiative properties** (e.g., size distribution, absorption, complex refractive index, spherical/non spherical fraction, phase matrix) required for aerosol forcing determination. Similarly, for synergetic use of observations (joint photometer/lidar retrievals) **direct-sun and diffuse sky radiance** are absolutely mandatory (together with at least two elastic-backscatter lidar signals) to derive, e.g., daytime **aerosol concentration** (fine and coarse modes) and **absorption profiles**. Since moon photometry has recently made significant technical progress, the new sun/sky photometer standard must now include the direct-moon extinction measurement capability, which is again very relevant for aerosol profiling.

Direct-sun and direct-moon-extinction **aerosol optical depth** (AOD) are derived using the Beer-Bouguer-Lambert law. The extra-terrestrial signal has to be determined very accurately to extract AOD with the requested accuracy (0.01 for field instruments and 0.005 for reference instruments). During the operation, optical and electronic components degrade and change the calibration for sun, moon, and sky radiance and polarization observations. Therefore, the regular recalibration of each instrument is mandatory and should be performed every 12 month on average. After the calibration, it is required to set the instrument up again as quickly as possible.

For the standard sun/sky/moon photometer, four major distinct calibrations have to be performed at the Centre for Aerosol Remote Sensing. Day and night AOD calibration must be performed on specific outdoor calibration platforms, whereas radiometric calibrations (sky radiance and polarisation) are performed in the laboratory (for details see the concept paper of the Centre for Aerosol Remote Sensing in D4.1). Since this procedure can last several weeks, spare instruments will be provided by the Topical Centre to cover continuity observation needs at each NF during the calibration phase. The requirement of the annual re-calibration is very strong. A 5% variation in the direct-sun or moon calibration coefficients (caused by filter degradation, deposit of dust on external lenses, etc.) yields to typical 0.05 absolute bias on AOD at noon (Holben et al. 2001, Smirnov et al. 2000). If the period between two calibrations is too long, the assumption of linear variation of the instrument response will be not valid, neither the temporal interpolation performed in the reprocessing phase for getting QA Level 1/Level 2 data products.

### 4.1.4 Operation

#### 4.1.4.1 High-power aerosol lidar

It is recommended to operate ACTRIS high-power aerosol lidars continuously, weather permitting, i.e., in the absence of precipitation or fog. If the instrument is not automated, it must at least provide unbiased long-term regular observations following the pre-defined schedule with 5 observations per week as agreed within the community to avoid a meteorological bias. Furthermore, measurements are to be performed upon alert, e.g., in hazardous situations such as volcanic eruptions, for special events such as dust outbreaks or forest fires, and for dedicated satellite validation purposes. Satellite validation measurements will follow a specific strategy. Alerts and measurement schedules for collocated observations during satellite overpasses will be distributed by the lidar unit of the DC.
Regular observations shall last for 4 hours, to adequately capture the atmospheric state and to allow for enough observation time to reduce random noise in the measurements and to retrieve a set of aerosol profiles throughout the troposphere. Longer measurement periods are to be considered for special events. In any case, raw data (Level 0) must be recorded with a resolution of at least 60 s in time and 15 m in height in order to permit proper cloud screening and the provision of quicklook images for visual assessment of the atmospheric conditions.

#### 4.1.4.2 Automatic sun/sky and moon photometer

Photometers must be automatic and are operated continuously weather permitting, i.e., in the absence of precipitation. Considering the diversity of photometers operated in regional or global networks, three types of observations can be taken into account: direct sun/moon extinction measurements to retrieve spectral AOD; sky angular radiance measurements; and sky angular polarized radiance measurements. In some cases, cloud radiance observations can also provide Cloud Optical Depth (COD). Spectral AOD and sky angular radiance measurements are mandatory. In addition, sky angular polarization measurements can provide the full picture of the radiation characteristics, but remain optional until a mature data processing chain is implemented.

Direct sun/moon and sky radiance measurements follow a consistent scheduled scenario. Spectral AOD is measured very frequently, whereas simultaneous AOD and sky radiance measurements are performed less frequently, but provide necessary information to retrieve scattering and absorption properties. It must be outlined that new algorithm developments (e.g., Torres et al. 2017) based on the Generalized Retrieval of Aerosol and Surface Properties (GRASP) offer now the possibility to invert spectral AOD to retrieve, both during day and night, the aerosol size distribution. Sky radiance is not measurable at night with the current technology, however, a multi-pixel approach based on GRASP should allow, e.g., the retrieval of absorption at night in the future.

The standard measurement mode of a sun/sky/polarization (and moon) photometer includes two basic measurements, either direct-sun/moon or sky measurements, both within several programmed sequences or protocols at wavelengths of 340, 380, 440, 500, 675, 870, 940, 1020 and 1640 nm. The 940-nm channel is used for column water abundance determination, always relevant when investigating aerosols. AOD is calculated from spectral extinction of direct-beam radiation at each wavelength based on the Beer-Bouguer-Lambert law.

A sequence of three measurements is taken 30 seconds apart creating a triplet observation per wavelength to detect cloud contamination. During the large-air-mass periods, direct-sun measurements are made at 0.25 air mass intervals, while at smaller air masses the interval between measurements is typically 15 minutes. The temporal variation of clouds is usually greater than that of aerosols causing an observable variation in the triplets that can be used to screen clouds in many cases. Additionally, the 15-minute interval allows a longer temporal frequency check for cloud contamination.

In addition to the direct solar irradiance measurements, the instruments must measure the sun aureole and sky radiance (optionally the polarized sky radiance) in at least four spectral bands (440, 675, 870 and
Regular observations shall last for 4 hours, to adequately capture the atmospheric state and to allow for enough observation time to reduce random noise in the measurements and to retrieve a set of aerosol profiles throughout the troposphere. Longer measurement periods are to be considered for special events. In any case, raw data (Level 0) must be recorded with a resolution of at least 60 s in time and 15 m in height in order to permit proper cloud screening and the provision of quicklook images for visual assessment of the atmospheric conditions.

#### 4.1.4.2 Automatic sun/sky and moon photometer

Photometers must be automatic and are operated continuously weather permitting, i.e., in the absence of precipitation. Considering the diversity of photometers operated in regional or global networks, three types of observations can be taken into account: direct sun/moon extinction measurements to retrieve spectral AOD; sky angular radiance measurements; and sky angular polarized radiance measurements. In some cases, cloud radiance observations can also provide Cloud Optical Depth (COD). Spectral AOD and sky angular radiance measurements are mandatory. In addition, sky angular polarization measurements can provide the full picture of the radiation characteristics, but remain optional until a mature data processing chain is implemented.

Direct sun/moon and sky radiance measurements follow a consistent scheduled scenario. Spectral AOD is measured very frequently, whereas simultaneous AOD and sky radiance measurements are performed less frequently, but provide necessary information to retrieve scattering and absorption properties. It must be outlined that new algorithm developments (e.g., Torres et al. 2017) based on the Generalized Retrieval of Aerosol and Surface Properties (GRASP) offer now the possibility to invert spectral AOD to retrieve, both during day and night, the aerosol size distribution. Sky radiance is not measurable at night with the current technology, however, a multi-pixel approach based on GRASP should allow, e.g., the retrieval of absorption at night in the future.

The standard measurement mode of a sun/sky/polarization (and moon) photometer includes two basic measurements, either direct-sun/moon or sky measurements, both within several programmed sequences or protocols at wavelengths of 340, 380, 440, 500, 675, 870, 940, 1020 and 1640 nm. The 940-nm channel is used for column water abundance determination, always relevant when investigating aerosols. AOD is calculated from spectral extinction of direct-beam radiation at each wavelength based on the Beer-Bouguer-Lambert law.

A sequence of three measurements is taken 30 seconds apart creating a triplet observation per wavelength to detect cloud contamination. During the large-air-mass periods, direct-sun measurements are made at 0.25 air mass intervals, while at smaller air masses the interval between measurements is typically 15 minutes. The temporal variation of clouds is usually greater than that of aerosols causing an observable variation in the triplets that can be used to screen clouds in many cases. Additionally, the 15-minute interval allows a longer temporal frequency check for cloud contamination.

In addition to the direct solar irradiance measurements, the instruments must measure the sun aureole and sky radiance (optionally the polarized sky radiance) in at least four spectral bands (440, 675, 870 and
1020 nm) with different geometries: almucantar, principal plane, hybrid, etc. Sampling must be done
typically every hour, or less at large solar zenith angles. This kind of measurements provides spectral sky
radiance observations over a large range of scattering angles that are inverted, together with spectral
AOD, to retrieve the aerosol size distribution (from 0.04 to 20 µm), absorption and other optical
properties.

4.1.5 Data production and data products

4.1.5.1 High-power aerosol lidar

Aerosol lidar raw data acquired at NFs are mandatorily processed at the ACTRIS DC using the Single Calculus Chain (SCC). The NFs are responsible for reporting the technical information required for the configuration of the SCC for the specific high-power aerosol lidar system. As support to the NFs, the Centre for Aerosol Remote Sensing provides tools for the NFs for on-site data evaluation procedures and plausibility tests to be performed before starting a measurement or at regular interval for automatized systems. The QA/QC tests performed and/or coordinated by the Centre for Aerosol Remote Sensing are codified and embedded in the SCC for traceability and full documentation of the data quality.

Different levels of products are produced. **Level 0 data** are the **raw lidar data**, which are delivered by the NF to the ACTRIS DC. The processing of Level 0 data allows the provision of higher-level products. **Level 1 data** contain not fully quality controlled data, namely the **pre-processed lidar signals** including all the instrumental correction factors and **profiles of aerosol optical properties** and corresponding **layer products**. In particular, standard products are the **profiles of aerosol extinction coefficient, backscatter coefficient and depolarization ratio**, together with the resulting **profiles of lidar ratio, Ångström exponent and backscatter-related Ångström exponent**. Layer products report statistical values of all these quantities for each identified aerosol layer, together with geometrical features of the layer itself. Additional data products resulting from the **combination** of different observational components, like daytime **aerosol microphysical properties** and **spectral absorption profiles** (Lopatin et al. 2013) retrieved through the combination of aerosol lidar and photometer observations, will also be part of the ACTRIS Level 1 data products.

**Level 2 data** are the same Level 1 products, but fully quality controlled and containing devoted flagging.
The full quality control procedure is based on: update of meteorological fields used in the processing;
reporting/updating of QA tests performed and traced by the Centre for Aerosol Remote Sensing; and
results of ACTRIS DC QC procedures and related feedbacks to the NFs (with potential feedback to the
Centre for Aerosol Remote Sensing). **Level 3 data** are climatological, statistical and other products
obtained using the Level 2 data. All quantities are reported together with the statistical and systematic
errors, according to the state of the art.

4.1.5.2 Automatic sun/sky and moon photometer

Photometer data are automatically transferred to the Centre for Aerosol Remote Sensing where the
standard data processing procedures (following AERONET definitions) are performed to convert Level 0
to Level 1 and Level 1 to Level 2 data products. For new products (non-AERONET), Level 0 to Level 1
processing can be performed at the Centre for Aerosol Remote Sensing as well, while the generation of
1020 nm) with different geometries: almucantar, principal plane, hybrid, etc. Sampling must be done
typically every hour, or less at large solar zenith angles. This kind of measurements provides spectral sky
radiance observations over a large range of scattering angles that are inverted, together with spectral
AOD, to retrieve the aerosol size distribution (from 0.04 to 20 µm), absorption and other optical
properties.

4.1.5 Data production and data products

4.1.5.1 High-power aerosol lidar

Aerosol lidar raw data acquired at NFs are mandatorily processed at the ACTRIS DC using the Single Calculus Chain (SCC). The NFs are responsible for reporting the technical information required for the configuration of the SCC for the specific high-power aerosol lidar system. As support to the NFs, the Centre for Aerosol Remote Sensing provides tools for the NFs for on-site data evaluation procedures and plausibility tests to be performed before starting a measurement or at regular interval for automatized systems. The QA/QC tests performed and/or coordinated by the Centre for Aerosol Remote Sensing are codified and embedded in the SCC for traceability and full documentation of the data quality.

Different levels of products are produced. **Level 0 data** are the **raw lidar data**, which are delivered by the NF to the ACTRIS DC. The processing of Level 0 data allows the provision of higher-level products. **Level 1 data** contain not fully quality controlled data, namely the **pre-processed lidar signals** including all the instrumental correction factors and **profiles of aerosol optical properties** and corresponding **layer products**. In particular, standard products are the **profiles of aerosol extinction coefficient, backscatter coefficient and depolarization ratio**, together with the resulting **profiles of lidar ratio, Ångström exponent and backscatter-related Ångström exponent**. Layer products report statistical values of all these quantities for each identified aerosol layer, together with geometrical features of the layer itself. Additional data products resulting from the **combination** of different observational components, like daytime **aerosol microphysical properties** and **spectral absorption profiles** (Lopatin et al. 2013) retrieved through the combination of aerosol lidar and photometer observations, will also be part of the ACTRIS Level 1 data products.

**Level 2 data** are the same Level 1 products, but fully quality controlled and containing devoted flagging.
The full quality control procedure is based on: update of meteorological fields used in the processing;
reporting/updating of QA tests performed and traced by the Centre for Aerosol Remote Sensing; and
results of ACTRIS DC QC procedures and related feedbacks to the NFs (with potential feedback to the
Centre for Aerosol Remote Sensing). **Level 3 data** are climatological, statistical and other products
obtained using the Level 2 data. All quantities are reported together with the statistical and systematic
errors, according to the state of the art.

4.1.5.2 Automatic sun/sky and moon photometer

Photometer data are automatically transferred to the Centre for Aerosol Remote Sensing where the
standard data processing procedures (following AERONET definitions) are performed to convert Level 0
to Level 1 and Level 1 to Level 2 data products. For new products (non-AERONET), Level 0 to Level 1
processing can be performed at the Centre for Aerosol Remote Sensing as well, while the generation of
specific Level 2 or 3 products, especially those combining column and profile Level 1 data (Dubovik et al. 2011, Lopatin et al. 2013, Dubovik et al. 2014), will be done at the ACTRIS DC (see concept papers of the Centre for Aerosol Remote Sensing and the Data Centre in D4.1). The Level 0 to Level 1 standard automatic processing corrects for various instrumental and atmospheric effects. An automatic cloud screening procedure is also applied to generate either Level 2 aerosol or cloud products.

Standard tools are applied at the TC to evaluate the various data levels and, when necessary, to warn the NFs. Several tools (all centralized and associated to a central relational data base) are recommended to be used by the local site manager of the NF to check the status of the instrument (more details are given in the concept paper of the Centre for Aerosol Remote Sensing in D4.1).

All data levels currently provided by an automatic sun/sky and moon photometer operated in existing operational networks are documented in terms of Standard Operation Procedures (SOPs) and uncertainties. The documentation is publically available. In general, Level 0 data is not made available to the public. QA/QC and calibration procedures are standardized. In principle, average uncertainty on each produced variable is equivalent for each station, since QA/QC and calibration are also standardized. Finally, the uncertainty will be associated to each retrieved aerosol variable (directly or indirectly measured).

## 4.1.6 Data delivery and quality control

### 4.1.6.1 High-power aerosol lidar

The ACTRIS high-power aerosol lidar products are collected and made available through the ACTRIS Data Centre and the ACTRIS data user interface. The data repository is a version-controlled database in order to accomplish the need for reprocessing the data and for producing new advanced products from the raw data delivered by the NFs.

The NFs are responsible for submitting the Level 0 data to the ACTRIS DC in the agreed and documented format as specified in the Data Management Plan (D4.2). These data are then centrally processed at the ACTRIS DC. Level 1 data products are retrieved and made available in NRT internally and externally, according to the agreed Data Policy (D2.3). Level 1 data are made publicly available as visualizations and on request for specific use (e.g., for assimilation purposes), while the higher-level numerical data products are available for file download by ACTRIS users.

Level 2 data are open and publicly available, when the quality control procedures are completed. Quality control procedures (performed every 3 months) include some formal and automatic procedures, which are completed already on Level 1 data, but also some procedures related to the identification of anomalous behaviour in the aerosol optical properties and the identification of potential instrumental problems. The time between the NRT provision (Level 1 data) and the provision of fully quality controlled data allows the use of more accurate meteorological fields in the processing, the update of instrumental QA flags and signal corrections. Level 3 data are publicly provided based on Level 2 data annually.
specific Level 2 or 3 products, especially those combining column and profile Level 1 data (Dubovik et al. 2011, Lopatin et al. 2013, Dubovik et al. 2014), will be done at the ACTRIS DC (see concept papers of the Centre for Aerosol Remote Sensing and the Data Centre in D4.1). The Level 0 to Level 1 standard automatic processing corrects for various instrumental and atmospheric effects. An automatic cloud screening procedure is also applied to generate either Level 2 aerosol or cloud products.

Standard tools are applied at the TC to evaluate the various data levels and, when necessary, to warn the NFs. Several tools (all centralized and associated to a central relational data base) are recommended to be used by the local site manager of the NF to check the status of the instrument (more details are given in the concept paper of the Centre for Aerosol Remote Sensing in D4.1).

All data levels currently provided by an automatic sun/sky and moon photometer operated in existing operational networks are documented in terms of Standard Operation Procedures (SOPs) and uncertainties. The documentation is publically available. In general, Level 0 data is not made available to the public. QA/QC and calibration procedures are standardized. In principle, average uncertainty on each produced variable is equivalent for each station, since QA/QC and calibration are also standardized. Finally, the uncertainty will be associated to each retrieved aerosol variable (directly or indirectly measured).

## 4.1.6 Data delivery and quality control

### 4.1.6.1 High-power aerosol lidar

The ACTRIS high-power aerosol lidar products are collected and made available through the ACTRIS Data Centre and the ACTRIS data user interface. The data repository is a version-controlled database in order to accomplish the need for reprocessing the data and for producing new advanced products from the raw data delivered by the NFs.

The NFs are responsible for submitting the Level 0 data to the ACTRIS DC in the agreed and documented format as specified in the Data Management Plan (D4.2). These data are then centrally processed at the ACTRIS DC. Level 1 data products are retrieved and made available in NRT internally and externally, according to the agreed Data Policy (D2.3). Level 1 data are made publicly available as visualizations and on request for specific use (e.g., for assimilation purposes), while the higher-level numerical data products are available for file download by ACTRIS users.

Level 2 data are open and publicly available, when the quality control procedures are completed. Quality control procedures (performed every 3 months) include some formal and automatic procedures, which are completed already on Level 1 data, but also some procedures related to the identification of anomalous behaviour in the aerosol optical properties and the identification of potential instrumental problems. The time between the NRT provision (Level 1 data) and the provision of fully quality controlled data allows the use of more accurate meteorological fields in the processing, the update of instrumental QA flags and signal corrections. Level 3 data are publicly provided based on Level 2 data annually.
The responsibility for the ACTRIS data quality is on the data originator at the NF. The QC performed at ACTRIS DC provides feedback to the data originator, which can be useful for identifying potential problems of the instrument and fixing them at NF level or with the help of the Centre for Aerosol Remote Sensing.

#### 4.1.6.2 Automatic sun/sky and moon photometer

There are three databases for ACTRIS photometer data available. Beside the official (external) AERONET database and processing system operated by NASA, France and Spain have built dedicated databases and processing chains (i.e., Level 0 to Level 1 Data Management and Production) to respond to some specific needs not covered by NASA and to offer an independent production of Level 1 data for ACTRIS and the provision of data through the ACTRIS DC (i.e., production of value-added parameters, Level 2 and higher). Level 0 to Level 1 data processing chains have been developed, e.g., for specific instruments like mobile photometers (Karol et al. 2013, Torres et al. 2017) and for testing of new calibration methodologies (Li et al. 2010, 2013, 2016, 2017, Barreto et al. 2013, 2017) (see concept paper of the Centre for Aerosol Remote Sensing in D4.1).

For standard ACTRIS/AERONET instruments, the Level 0 data are transferred in NRT to one of the three dedicated databases as soon as they have been acquired at the station. All three databases (all levels) are synchronized and are permanently identical. Similarly, the same data management systems (e.g., monitoring, calibration) are in operation in the three units. The data processing system (Level 0 → Level 1 → Level 2) is centralized and the resulting data products (Level 1, Level 2) are immediately synchronized in the French and Spanish databases.

As mentioned above, the major requirement is to transfer the data NRT to the database, which is mandatory to monitor the instrument and the NRT processing and product delivery. Standardization of the instrument and the data transmission protocol are imposed by the TC. Tools are distributed to each operator to allow NRT transmission through different channels (internet, satellite transmission for very special and remote places). The data are made public immediately.

The Centre for Aerosol Remote Sensing is in charge of most of the quality control activities, as explained previously, by following regularly a list of instrumental parameters, detecting anomalies, informing operators and solving problems. The operators must be continuously involved in the quality control. The visual inspection of the instrument (optics, collimator and cables to solar panel and data transmission system) once a week is required. Furthermore, the operators must react efficiently and immediately on alerts sent by the TC. All operators have access to the monitoring webpage and can see the status of the main instrumental parameters (battery level, tracking system, humidity and temperature sensors, etc.). They also have access to Level 1 data in NRT so that they can potentially detect themselves apparent calibration shifts due to collimator obstruction or similar issues.
The responsibility for the ACTRIS data quality is on the data originator at the NF. The QC performed at ACTRIS DC provides feedback to the data originator, which can be useful for identifying potential problems of the instrument and fixing them at NF level or with the help of the Centre for Aerosol Remote Sensing.

#### 4.1.6.2 Automatic sun/sky and moon photometer

There are three databases for ACTRIS photometer data available. Beside the official (external) AERONET database and processing system operated by NASA, France and Spain have built dedicated databases and processing chains (i.e., Level 0 to Level 1 Data Management and Production) to respond to some specific needs not covered by NASA and to offer an independent production of Level 1 data for ACTRIS and the provision of data through the ACTRIS DC (i.e., production of value-added parameters, Level 2 and higher). Level 0 to Level 1 data processing chains have been developed, e.g., for specific instruments like mobile photometers (Karol et al. 2013, Torres et al. 2017) and for testing of new calibration methodologies (Li et al. 2010, 2013, 2016, 2017, Barreto et al. 2013, 2017) (see concept paper of the Centre for Aerosol Remote Sensing in D4.1).

For standard ACTRIS/AERONET instruments, the Level 0 data are transferred in NRT to one of the three dedicated databases as soon as they have been acquired at the station. All three databases (all levels) are synchronized and are permanently identical. Similarly, the same data management systems (e.g., monitoring, calibration) are in operation in the three units. The data processing system (Level 0 → Level 1 → Level 2) is centralized and the resulting data products (Level 1, Level 2) are immediately synchronized in the French and Spanish databases.

As mentioned above, the major requirement is to transfer the data NRT to the database, which is mandatory to monitor the instrument and the NRT processing and product delivery. Standardization of the instrument and the data transmission protocol are imposed by the TC. Tools are distributed to each operator to allow NRT transmission through different channels (internet, satellite transmission for very special and remote places). The data are made public immediately.

The Centre for Aerosol Remote Sensing is in charge of most of the quality control activities, as explained previously, by following regularly a list of instrumental parameters, detecting anomalies, informing operators and solving problems. The operators must be continuously involved in the quality control. The visual inspection of the instrument (optics, collimator and cables to solar panel and data transmission system) once a week is required. Furthermore, the operators must react efficiently and immediately on alerts sent by the TC. All operators have access to the monitoring webpage and can see the status of the main instrumental parameters (battery level, tracking system, humidity and temperature sensors, etc.). They also have access to Level 1 data in NRT so that they can potentially detect themselves apparent calibration shifts due to collimator obstruction or similar issues.
## 4.2 Cloud remote sensing

### 4.2.1 Introduction

Clouds form a major component of the Earth's radiation budget. They vary rapidly in space and time, with associated rapid variation in the radiation and precipitation impinging on the Earth's surface. To obtain quantitative information on how clouds evolve in space and time requires instruments that can capture both the vertical profile and the temporal variation with sufficient resolution.

Within ACTRIS, the primary objective is to obtain the **vertical cloud structure** within an atmospheric column, utilising the temporal dimension to yield the equivalent of a two-dimensional slice through the three-dimensional atmosphere. The resolution required to capture such highly variable entities necessitates the use of active remote sensing in the form of cloud radar and lidar or ceilometer. Both instruments, each sensing a different portion of the electromagnetic spectrum, are required to disentangle ambiguities in the **atmospheric targets** that are detected.

This recommended combination of instruments was instigated in the European FP5 project Cloudnet starting in 2003, where the original objective was the routine automated evaluation of the representation of clouds in numerical models using observations. Evaluating the representation of clouds in climate and numerical weather prediction (NWP) models is not straightforward. For NWP models, this task is compounded by the expectation of a good forecast, as well as the reliable representation of the specific cloud parameters themselves. Cloudnet developed and implemented a comprehensive suite of **objective metrics for the evaluation of model cloud parameters**, in continual joint collaboration with operational modellers. The set of evaluation metrics is designed to investigate both the climatological aspects required of a climate model, and the ability to forecast the correct cloud at the right time, a necessary validation for NWP.

### 4.2.2 Observational capabilities

The objective to provide continuous long-term vertical profiles of **cloud fraction** and **water** and **ice cloud properties** requires the synergistic use of three core instruments—Doppler cloud radar, lidar/ceilometer and microwave radiometer—and an optional Doppler wind lidar. To qualify as an ACTRIS compatible cloud-profiling station, the instrument setup must be capable of continuously providing vertical profiles of clouds at the nominal Cloudnet resolution of 30 seconds and 60 metres. The list of variables is provided in Annex A.2.

#### 4.2.2.1 Optimum setup

With the optimum setup, cloud-profiling stations deliver a comprehensive set of atmospheric quantities such as **cloud and aerosol target classification**, **cloud fraction**, **liquid water content**, **ice water content**, **drizzle properties** (size distribution, water content, water flux), **dissipation rate of turbulent kinetic energy**, **liquid water path**, **integrated water vapour path**, **temperature** and **humidity profiles**. To enable these quantities to be derived, the instruments must have the following capabilities:

**Doppler cloud radar** providing the full Doppler spectrum, together with the first 3 moments of the Doppler spectrum (reflectivity, Doppler velocity, and spectral width) and linear depolarisation ratio is the basic
## 4.2 Cloud remote sensing

### 4.2.1 Introduction

Clouds form a major component of the Earth's radiation budget. They vary rapidly in space and time, with associated rapid variation in the radiation and precipitation impinging on the Earth's surface. To obtain quantitative information on how clouds evolve in space and time requires instruments that can capture both the vertical profile and the temporal variation with sufficient resolution.

Within ACTRIS, the primary objective is to obtain the **vertical cloud structure** within an atmospheric column, utilising the temporal dimension to yield the equivalent of a two-dimensional slice through the three-dimensional atmosphere. The resolution required to capture such highly variable entities necessitates the use of active remote sensing in the form of cloud radar and lidar or ceilometer. Both instruments, each sensing a different portion of the electromagnetic spectrum, are required to disentangle ambiguities in the **atmospheric targets** that are detected.

This recommended combination of instruments was instigated in the European FP5 project Cloudnet starting in 2003, where the original objective was the routine automated evaluation of the representation of clouds in numerical models using observations. Evaluating the representation of clouds in climate and numerical weather prediction (NWP) models is not straightforward. For NWP models, this task is compounded by the expectation of a good forecast, as well as the reliable representation of the specific cloud parameters themselves. Cloudnet developed and implemented a comprehensive suite of **objective metrics for the evaluation of model cloud parameters**, in continual joint collaboration with operational modellers. The set of evaluation metrics is designed to investigate both the climatological aspects required of a climate model, and the ability to forecast the correct cloud at the right time, a necessary validation for NWP.

### 4.2.2 Observational capabilities

The objective to provide continuous long-term vertical profiles of **cloud fraction** and **water** and **ice cloud properties** requires the synergistic use of three core instruments—Doppler cloud radar, lidar/ceilometer and microwave radiometer—and an optional Doppler wind lidar. To qualify as an ACTRIS compatible cloud-profiling station, the instrument setup must be capable of continuously providing vertical profiles of clouds at the nominal Cloudnet resolution of 30 seconds and 60 metres. The list of variables is provided in Annex A.2.

#### 4.2.2.1 Optimum setup

With the optimum setup, cloud-profiling stations deliver a comprehensive set of atmospheric quantities such as **cloud and aerosol target classification**, **cloud fraction**, **liquid water content**, **ice water content**, **drizzle properties** (size distribution, water content, water flux), **dissipation rate of turbulent kinetic energy**, **liquid water path**, **integrated water vapour path**, **temperature** and **humidity profiles**. To enable these quantities to be derived, the instruments must have the following capabilities:

**Doppler cloud radar** providing the full Doppler spectrum, together with the first 3 moments of the Doppler spectrum (reflectivity, Doppler velocity, and spectral width) and linear depolarisation ratio is the basic
instrument. The minimum sensitivity should be at least –60 dBZ at 1 km for 10 second integration time enabling the detection of almost all radiatively significant **ice clouds** in non-precipitating conditions. This sensitivity can be achieved with both pulsed and frequency-modulated continuous-wave (FMCW) Doppler radars operating at 35 or 94 GHz (and potentially at higher frequencies). At these frequencies, correction for attenuation by atmospheric gases and, more importantly, liquid water clouds is necessary; attenuation is less at 35 GHz, so this frequency is preferable to 94 GHz. In addition, radome wetting during periods of rainfall leads to large signal losses and unreliable data.

Lower-frequency radar (e.g., 10 GHz more typical of weather radar) can also achieve the desired sensitivity in vertically-pointing mode and be able to provide reliable data during periods of rainfall. However, lower frequencies pose additional constraints such as enhanced sensitivity to insects and clutter issues at near range (larger sidelobes due to antenna size constraints limiting how focused the transmit beam is), and a minimum range that may preclude measurements of liquid layers in the boundary layer.

Higher temporal resolution (especially 1 second integration time) permits the generation of products based on the velocity variation (e.g., **turbulence**, **drizzle products** based on skewness) in addition to the standard products. The full Doppler spectrum permits the analysis of situations where more than one distinct hydrometeor population is occupying the same volume (e.g., **mixed-phase clouds** with both supercooled liquid and ice particles, stratocumulus containing liquid droplets and drizzle drops). The velocity resolution, usually determined from the Nyquist velocity and the number of points in the FFT (Fast Fourier Transform) used to obtain the Doppler spectrum, should be 10 cm s⁻¹ or better to make maximum use of these capabilities.

Inclusion of additional radar frequencies also expands the range and accuracy of products that can be derived. Dual-frequency (e.g., 35 and 94 GHz) permits the vertical **profile of liquid water content** to be derived without restrictive assumptions, and triple-frequency (e.g., 10, 35, 94 GHz) permits **ice microphysical properties** to be deduced without certain density and habit assumptions through accounting for Mie scattering at higher frequencies. Multiple frequencies also assist in accounting for atmospheric attenuation, especially if one of the selected frequencies is around 10 GHz or less.

**High-power aerosol lidar** capable of detecting **thin ice clouds**, **elevated aerosol layers** and the molecular backscatter signal provides additional retrieval capabilities and improves high-altitude cloud statistics. A depolarization channel is desirable for unambiguous **particle phase discrimination**. The ability to retrieve extinction directly through high-spectral-resolution or Raman methods also greatly improves retrieval uncertainties and provides a consistency check. Aerosol properties at high resolution permit synergy between aerosol (see Sec. 4.1) and cloud remote sensing retrievals allowing investigation of **aerosol-cloud interactions** in the same profile.

**Multi-channel scanning microwave radiometer** permits the retrieval of **temperature** and **humidity profiles**, in addition to the standard column-integrated measurements. The addition of an extra-high frequency channel (such as 89 or 180 GHz) provides extra sensitivity to thin clouds with very low liquid water path (LWP) that are still radiatively important (such as supercooled layers found in the Arctic).

**Doppler wind lidar** provides the **dynamical features** of the atmospheric boundary layer, including winds, turbulent properties, shear, mixing-level-height, low-level jets, and classification of the turbulent sources.
instrument. The minimum sensitivity should be at least -60 dBZ at 1 km for 10 second integration time enabling the detection of almost all radiatively significant **ice clouds** in non-precipitating conditions. This sensitivity can be achieved with both pulsed and frequency-modulated continuous-wave (FMCW) Doppler radars operating at 35 or 94 GHz (and potentially at higher frequencies). At these frequencies, correction for attenuation by atmospheric gases and, more importantly, liquid water clouds is necessary; attenuation is less at 35 GHz, so this frequency is preferable to 94 GHz. In addition, radome wetting during periods of rainfall leads to large signal losses and unreliable data.

Lower-frequency radar (e.g., 10 GHz more typical of weather radar) can also achieve the desired sensitivity in vertically-pointing mode and be able to provide reliable data during periods of rainfall. However, lower frequencies pose additional constraints such as enhanced sensitivity to insects and clutter issues at near range (larger sidelobes due to antenna size constraints limiting how focused the transmit beam is), and a minimum range that may preclude measurements of liquid layers in the boundary layer.

Higher temporal resolution (especially 1 second integration time) permits the generation of products based on the velocity variation (e.g., **turbulence**, **drizzle products** based on skewness) in addition to the standard products. The full Doppler spectrum permits the analysis of situations where more than one distinct hydrometeor population is occupying the same volume (e.g., **mixed-phase clouds** with both supercooled liquid and ice particles, stratocumulus containing liquid droplets and drizzle drops). The velocity resolution, usually determined from the Nyquist velocity and the number of points in the FFT (Fast Fourier Transform) used to obtain the Doppler spectrum, should be 10 cm s⁻¹ or better to make maximum use of these capabilities.

Inclusion of additional radar frequencies also expands the range and accuracy of products that can be derived. Dual-frequency (e.g., 35 and 94 GHz) permits the vertical **profile of liquid water content** to be derived without restrictive assumptions, and triple-frequency (e.g., 10, 35, 94 GHz) permits **ice microphysical properties** to be deduced without certain density and habit assumptions through accounting for Mie scattering at higher frequencies. Multiple frequencies also assist in accounting for atmospheric attenuation, especially if one of the selected frequencies is around 10 GHz or less.

**High-power aerosol lidar** capable of detecting **thin ice clouds**, **elevated aerosol layers** and the molecular backscatter signal provides additional retrieval capabilities and improves high-altitude cloud statistics. A depolarization channel is desirable for unambiguous **particle phase discrimination**. The ability to retrieve extinction directly through high-spectral-resolution or Raman methods also greatly improves retrieval uncertainties and provides a consistency check. Aerosol properties at high resolution permit synergy between aerosol (see Sec. 4.1) and cloud remote sensing retrievals allowing investigation of **aerosol-cloud interactions** in the same profile.

**Multi-channel scanning microwave radiometer** permits the retrieval of **temperature** and **humidity profiles**, in addition to the standard column-integrated measurements. The addition of an extra-high frequency channel (such as 89 or 180 GHz) provides extra sensitivity to thin clouds with very low liquid water path (LWP) that are still radiatively important (such as supercooled layers found in the Arctic).

**Doppler wind lidar** provides the **dynamical features** of the atmospheric boundary layer, including winds, turbulent properties, shear, mixing-level-height, low-level jets, and classification of the turbulent sources.
In combination with aerosol and cloud retrievals, such properties permit the identification of aerosol layers and clouds that are directly coupled to the surface, extend the *in situ* measurements at the surface vertically through the atmospheric column and provide the **timescales for turbulent transport**.

### 4.2.2.2 Minimum requirements

The minimum setup of a cloud-profiling station consists of less powerful core instruments that may operate on just one channel. The measurements are used to derive the minimum set of atmospheric quantities: **cloud fraction**, **liquid water content**, **ice water content** and **drizzle properties**. To enable these quantities to be derived, the three core instruments must have the following capabilities:

**Cloud radar** with Doppler capability providing profiles of radar reflectivity factor and Doppler velocity: Minimum sensitivity should be about -50 dBZ at 1 km in the absence of attenuation.

**Ceilometer** (low-power lidar) capable of detecting liquid water layers, including supercooled liquid layers, up to 7.5 km in altitude: In this mode, the primary function of the ceilometer is purely the detection of liquid water in the lower troposphere.

**Dual-frequency microwave radiometers**, where the liquid water path and water vapour path are derived from two brightness temperatures ideally measured at frequencies close to 23.8 and 36.5 GHz. The approach by Gaussiat et al. (2007) is then used to derive an accurate liquid water path by correcting for instrumental drifts in calibration and unknown absorption coefficients by adding a calibration offset to the derived optical depths. The offset is determined in clear-sky periods as indicated by the ceilometer, when it is expected that the liquid water path be zero.

### 4.2.2.3 Complementary added-value observations

**Water vapour** and **temperature** profiles measured with **Raman lidar** can provide useful information on the atmospheric conditions under which clouds develop. The profiling capabilities for water vapour content and relative humidity can be further improved when Raman lidar and **microwave radiometer** measurements are combined in synergistic retrievals. For this purpose, the high-power lidar used in the optimum setup can be equipped with additional measurement channels.

**Radar wind profilers** provide the vertical profile of **horizontal and vertical winds**. The horizontal wind profile can be used to determine appropriate advection-based averaging for cloud variables, provide suitable length scales for deriving turbulent parameters and statistical-based algorithms and determine the beam-broadening component for radar vertical velocity uncertainties. The profile of the vertical wind (determined from clear-air returns) can be used to provide the 'true' vertical wind. The cloud radar measures the hydrometeor velocity, which is the sum of the hydrometeor fall velocity and vertical air motion. The hydrometeor fall velocity provides excellent information on the hydrometeor size, density and shape; after correction for the vertical air motion, algorithms can use the cloud radar Doppler velocity as an additional component to constrain microphysical properties. Winds throughout the full tropospheric profile are of interest.

Combination of a **sonic anemometer** and a **Licor gas analyser** permits the calculation of the **sensible and latent heat flux** (and CO₂ flux) at the surface. Together with radiation measurements, the full surface
In combination with aerosol and cloud retrievals, such properties permit the identification of aerosol layers and clouds that are directly coupled to the surface, extend the *in situ* measurements at the surface vertically through the atmospheric column and provide the **timescales for turbulent transport**.

### 4.2.2.2 Minimum requirements

The minimum setup of a cloud-profiling station consists of less powerful core instruments that may operate on just one channel. The measurements are used to derive the minimum set of atmospheric quantities: **cloud fraction**, **liquid water content**, **ice water content** and **drizzle properties**. To enable these quantities to be derived, the three core instruments must have the following capabilities:

**Cloud radar** with Doppler capability providing profiles of radar reflectivity factor and Doppler velocity: Minimum sensitivity should be about -50 dBZ at 1 km in the absence of attenuation.

**Ceilometer** (low-power lidar) capable of detecting liquid water layers, including supercooled liquid layers, up to 7.5 km in altitude: In this mode, the primary function of the ceilometer is purely the detection of liquid water in the lower troposphere.

**Dual-frequency microwave radiometers**, where the liquid water path and water vapour path are derived from two brightness temperatures ideally measured at frequencies close to 23.8 and 36.5 GHz. The approach by Gaussiat et al. (2007) is then used to derive an accurate liquid water path by correcting for instrumental drifts in calibration and unknown absorption coefficients by adding a calibration offset to the derived optical depths. The offset is determined in clear-sky periods as indicated by the ceilometer, when it is expected that the liquid water path be zero.

### 4.2.2.3 Complementary added-value observations

**Water vapour** and **temperature** profiles measured with **Raman lidar** can provide useful information on the atmospheric conditions under which clouds develop. The profiling capabilities for water vapour content and relative humidity can be further improved when **Raman lidar** and **microwave radiometer** measurements are combined in synergistic retrievals. For this purpose, the high-power lidar used in the optimum setup can be equipped with additional measurement channels.

**Radar wind profilers** provide the vertical profile of **horizontal and vertical winds**. The horizontal wind profile can be used to determine appropriate advection-based averaging for cloud variables, provide suitable length scales for deriving turbulent parameters and statistical-based algorithms and determine the beam-broadening component for radar vertical velocity uncertainties. The profile of the vertical wind (determined from clear-air returns) can be used to provide the 'true' vertical wind. The cloud radar measures the hydrometeor velocity, which is the sum of the hydrometeor fall velocity and vertical air motion. The hydrometeor fall velocity provides excellent information on the hydrometeor size, density and shape; after correction for the vertical air motion, algorithms can use the cloud radar Doppler velocity as an additional component to constrain microphysical properties. Winds throughout the full tropospheric profile are of interest.

Combination of a **sonic anemometer** and a **Licor gas analyser** permits the calculation of the **sensible and latent heat flux** (and CO₂ flux) at the surface. Together with radiation measurements, the full surface
energy balance can be obtained using micrometeorological methods by including a **soil heat plate** to determine the **heat flux into the ground**.

The **atmospheric emitted radiance interferometer** (AERI) is a ground-based instrument that measures the **downwelling infrared radiance** with sufficient spectral resolution to discriminate among gaseous emitters (e.g., carbon dioxide and water vapour) and suspended matter (e.g., aerosols, water droplets and ice crystals). These upward-looking surface observations can be used to obtain vertical profiles of tropospheric **temperature and water vapour**, as well as measurements of **trace gases** (e.g., ozone, carbon monoxide and methane) and **downwelling infrared spectral signatures** of clouds and aerosols.

The **micro rain radar** (MRR) is a very low-power vertically-pointing FMCW Doppler radar operating at 24 GHz providing the full Doppler spectrum, from which vertical profiles of the **drop size distribution of precipitation** are derived. This instrument complements the cloud radar in the nearest 2 km by providing the precipitation profile to the surface, enabling correction for any saturation in the cloud radar near field, and aiding calibration activities.

**Drop-counting raingauges** and **snowgauges** provide closure methods for microphysical profiles of precipitation derived from the active remote sensing vertical profiles. Various types of **disdrometers** and **cameras** can provide information on the **hydrometeor size distribution, phase, shape and density**.

**Broad-band shortwave** and **longwave radiation** measurements at the surface (e.g., BSRN station) are an obvious complement to cloud profiling, and also provide an opportunity to implement closure studies on the vertical profile of microphysical properties derived from the cloud-profiling measurements. For full closure, it is recommended that all radiation components are measured (shortwave down, net, direct and diffuse, and longwave down and net).

#### 4.2.2.4 Auxiliary measurements

Vertical profiles of **meteorological parameters** (temperature, pressure, specific humidity and winds) are required for quantifying instrument, atmospheric attenuation and retrieval uncertainties. They may be taken from the output of a high-resolution weather forecast model or radiosoundings. **Radiosondes** measure high-resolution vertical profiles of **temperature, humidity and winds**, thus providing the parameters necessary for specific instrument corrections within the cloud profile processing scheme, independent from any model that might be evaluated. However, only radiosonde launches at sites where the frequency is twice per day or higher are suitable for inclusion within the processing.

**Standard meteorological measurements** (e.g., **automatic weather station**) provide useful information on surface conditions, especially those that may impact the core measurements, such as precipitation, strong winds, extreme heat or cold, and can be used within the QA system. The measurement system should include a precipitation detection system with fast response times (i.e., optical rain gauge or present weather detector).
energy balance can be obtained using micrometeorological methods by including a **soil heat plate** to determine the **heat flux into the ground**.

The **atmospheric emitted radiance interferometer** (AERI) is a ground-based instrument that measures the **downwelling infrared radiance** with sufficient spectral resolution to discriminate among gaseous emitters (e.g., carbon dioxide and water vapour) and suspended matter (e.g., aerosols, water droplets and ice crystals). These upward-looking surface observations can be used to obtain vertical profiles of tropospheric **temperature and water vapour**, as well as measurements of **trace gases** (e.g., ozone, carbon monoxide and methane) and **downwelling infrared spectral signatures** of clouds and aerosols.

The **micro rain radar** (MRR) is a very low-power vertically-pointing FMCW Doppler radar operating at 24 GHz providing the full Doppler spectrum, from which vertical profiles of the **drop size distribution of precipitation** are derived. This instrument complements the cloud radar in the nearest 2 km by providing the precipitation profile to the surface, enabling correction for any saturation in the cloud radar near field, and aiding calibration activities.

**Drop-counting raingauges** and **snowgauges** provide closure methods for microphysical profiles of precipitation derived from the active remote sensing vertical profiles. Various types of **disdrometers** and **cameras** can provide information on the **hydrometeor size distribution, phase, shape and density**.

**Broad-band shortwave** and **longwave radiation** measurements at the surface (e.g., BSRN station) are an obvious complement to cloud profiling, and also provide an opportunity to implement closure studies on the vertical profile of microphysical properties derived from the cloud-profiling measurements. For full closure, it is recommended that all radiation components are measured (shortwave down, net, direct and diffuse, and longwave down and net).

#### 4.2.2.4 Auxiliary measurements

Vertical profiles of **meteorological parameters** (temperature, pressure, specific humidity and winds) are required for quantifying instrument, atmospheric attenuation and retrieval uncertainties. They may be taken from the output of a high-resolution weather forecast model or radiosoundings. **Radiosondes** measure high-resolution vertical profiles of **temperature, humidity and winds**, thus providing the parameters necessary for specific instrument corrections within the cloud profile processing scheme, independent from any model that might be evaluated. However, only radiosonde launches at sites where the frequency is twice per day or higher are suitable for inclusion within the processing.

**Standard meteorological measurements** (e.g., **automatic weather station**) provide useful information on surface conditions, especially those that may impact the core measurements, such as precipitation, strong winds, extreme heat or cold, and can be used within the QA system. The measurement system should include a precipitation detection system with fast response times (i.e., optical rain gauge or present weather detector).
## 4.2.3 Instrumentation and calibration

### 4.2.3.1 Cloud radar

Cloud radars are active systems similar to weather radar, but operating at higher frequencies, typically 35 and 94 GHz. These higher frequencies permit much smaller antennae and power consumption than weather radar frequencies but suffer more from gaseous and liquid attenuation, which must be corrected for. Higher frequencies also have an advantage with clutter suppression, due to the more focused radar beam, and a reduction in the amount of scattering by insects ubiquitous in the lower atmosphere; insect sizes relative to the radar wavelength mean they are scattering in the Mie regime. The radar must be Dopplerized, as Doppler velocity measurements are required for hydrometeor diagnosis (e.g., liquid cloud droplets versus drizzle drops, identification of the melting layer as slowly falling snowflakes melt to become rain drops with significant terminal fall speeds), and for discriminating between insects and other hydrometeors. The second moment of the Doppler spectrum, Doppler spectral width, is a useful but not strictly necessary additional parameter. Both pulsed and frequency-modulated continuous-wave (FMCW) Doppler radars are suitable, provided there is no saturation. Cloud radars capable of measuring Linear Depolarisation Ratio (LDR) provide significant advantages in insect discrimination, melting layer determination and ice crystal classification.

The preferred measurement mode is vertical pointing, since this permits the direct use of Doppler velocities (after correction for the motion of the air) in microphysical retrieval and reduces the severity of attenuation due to gases and liquid. Scanning is permitted but is not directly applicable for ACTRIS measurements. Note that without measurements of liquid water path, it is not possible to determine how strongly the profile of radar reflectivity is attenuated by an intervening liquid layer. Sensitivity to ice clouds above a liquid layer is then reduced by an unknown amount, potentially causing issues in determining cloud fraction, and there is an unknown bias in the reflectivity profile harming measurements in ice above the liquid layer.

The amount of radar reflectivity attenuation due to liquid is frequency dependent. The two-way attenuation due to a cloud with a liquid water path of 500 g m⁻² is around 4.5 dB at 94 GHz, but only 1.2 dB at 35 GHz, and less at lower frequencies. Hence, it is possible to sidestep the issue of liquid attenuation in particular locations under certain conditions through the use of a lower-frequency radar – such as the Arctic in winter when there are no warm liquid layers present, only thin supercooled liquid layers with very low liquid water path.

The cloud radar must be capable of detecting the vast majority of optically relevant clouds, which can be directly expressed in terms of radar reflectivity. Comparing radar returns with the optical depth derived from lidar, for ice clouds up to a height of 9 km, Protat et al. (2006) show that a radar with a sensitivity of -55 dBZ at 1 km should detect 80% of the ice clouds with an optical depth above 0.05 and 97% of clouds with an optical depth greater than 0.1. For a radar with -60 dBZ sensitivity at 1 km the percentages are 98% and 100%, respectively. Hence, a minimum requirement is -50 dBZ at 1 km with a temporal resolution of 30 seconds and 60 m resolution in the vertical. Calibration of the reflectivity is vital for removing bias in both calculating cloud fraction amounts and in deriving ice water contents; a systematic 1 dB radar calibration error results in a 15% error in mean ice water content when using the radar reflectivity-
## 4.2.3 Instrumentation and calibration

### 4.2.3.1 Cloud radar

Cloud radars are active systems similar to weather radar, but operating at higher frequencies, typically 35 and 94 GHz. These higher frequencies permit much smaller antennae and power consumption than weather radar frequencies but suffer more from gaseous and liquid attenuation, which must be corrected for. Higher frequencies also have an advantage with clutter suppression, due to the more focused radar beam, and a reduction in the amount of scattering by insects ubiquitous in the lower atmosphere; insect sizes relative to the radar wavelength mean they are scattering in the Mie regime. The radar must be Dopplerized, as Doppler velocity measurements are required for hydrometeor diagnosis (e.g., liquid cloud droplets versus drizzle drops, identification of the melting layer as slowly falling snowflakes melt to become rain drops with significant terminal fall speeds), and for discriminating between insects and other hydrometeors. The second moment of the Doppler spectrum, Doppler spectral width, is a useful but not strictly necessary additional parameter. Both pulsed and frequency-modulated continuous-wave (FMCW) Doppler radars are suitable, provided there is no saturation. Cloud radars capable of measuring Linear Depolarisation Ratio (LDR) provide significant advantages in insect discrimination, melting layer determination and ice crystal classification.

The preferred measurement mode is vertical pointing, since this permits the direct use of Doppler velocities (after correction for the motion of the air) in microphysical retrieval and reduces the severity of attenuation due to gases and liquid. Scanning is permitted but is not directly applicable for ACTRIS measurements. Note that without measurements of liquid water path, it is not possible to determine how strongly the profile of radar reflectivity is attenuated by an intervening liquid layer. Sensitivity to ice clouds above a liquid layer is then reduced by an unknown amount, potentially causing issues in determining cloud fraction, and there is an unknown bias in the reflectivity profile harming measurements in ice above the liquid layer.

The amount of radar reflectivity attenuation due to liquid is frequency dependent. The two-way attenuation due to a cloud with a liquid water path of 500 g m⁻² is around 4.5 dB at 94 GHz, but only 1.2 dB at 35 GHz, and less at lower frequencies. Hence, it is possible to sidestep the issue of liquid attenuation in particular locations under certain conditions through the use of a lower-frequency radar – such as the Arctic in winter when there are no warm liquid layers present, only thin supercooled liquid layers with very low liquid water path.

The cloud radar must be capable of detecting the vast majority of optically relevant clouds, which can be directly expressed in terms of radar reflectivity. Comparing radar returns with the optical depth derived from lidar, for ice clouds up to a height of 9 km, Protat et al. (2006) show that a radar with a sensitivity of -55 dBZ at 1 km should detect 80% of the ice clouds with an optical depth above 0.05 and 97% of clouds with an optical depth greater than 0.1. For a radar with -60 dBZ sensitivity at 1 km the percentages are 98% and 100%, respectively. Hence, a minimum requirement is -50 dBZ at 1 km with a temporal resolution of 30 seconds and 60 m resolution in the vertical. Calibration of the reflectivity is vital for removing bias in both calculating cloud fraction amounts and in deriving ice water contents; a systematic 1 dB radar calibration error results in a 15% error in mean ice water content when using the radar reflectivity-
temperature method of Hogan et al. (2006). Hence, radar reflectivity calibration should take place at least twice a year (see calibration methods described in the concept paper of the Centre for Cloud Remote Sensing in D4.1). Doppler velocity calibration is usually not necessary but can be checked by pointing at a hard target. Ensuring that the instrument is indeed pointing at vertical is very important; off-vertical Doppler velocity measurements will contain a component from the horizontal wind which may be significant at altitudes close to the tropopause where horizontal wind speeds can easily exceed 50 m s⁻¹.

Precipitation can cause wetting of the radome or antenna, causing significant attenuation which is difficult to ascertain. Reflectivity data is not reliable in these conditions and should be flagged. Atmospheric attenuation, including gaseous, liquid rain, and the melting layer, is diagnosed and corrected for, if appropriate, in the data processing stage.

### 4.2.3.2 Ceilometer/Lidar

Ceilometers are single-wavelength, low-power, automatic lidars typically operating at wavelengths ranging from 850 to 1064 nm, originally designed to provide the cloud base altitude. Since clouds are very strong scatterers at near-infrared wavelengths, these systems do not require the high power expected for aerosol lidar systems. However, the sensitivity of current ceilometers is sufficient to detect aerosol within the boundary layer, and thicker ice clouds. High-power aerosol lidar systems can also be used (see Sec. 4.1).

The key parameter for the ceilometer or lidar is detection of **liquid layers**, including supercooled liquid layers. To improve sensitivity, averaging of data to 15–30 m in the vertical and 15–30 seconds in time is therefore appropriate. The minimum range should be 200 m or less to enable detection of fog layers, which might be missed by the cloud radar causing unaccounted-for attenuation. Maximum range should exceed 7.5 km to enable detection of supercooled liquid layers down to almost –40 °C. Note that ceilometers with extended maximum range are required in regions where supercooled layers may be expected above 7.5 km, such as the tropics. Instrument design may mean that full overlap between receiver and transmitter is not reached until 1 km or so, with correction of the signal possible in part of the overlap region; this is permissible as long as detection of liquid layers at 200 m is still possible. Possible saturation of the lidar signal in clouds at low altitudes must be flagged and mitigation procedures adopted to prevent this occurring.

Instrument sensitivity is dependent on the signal-to-noise ratio (SNR), which is a function of the emitted power, telescope design, averaging time, background light and the strength of the backscattered return from atmospheric targets. Thus, stray background light (solar radiation) entering the detector chain leads to a drop in sensitivity during the day.

The required sensitivity for liquid-layer detection can be expressed in terms of extinction, with typical values exceeding 10 km⁻¹. Since the lidar ratio for cloud droplets is about 20 sr at almost all lidar wavelengths, this translates to a backscatter coefficient of about 0.5 km⁻¹ sr⁻¹ which is achieved by all current ceilometer systems up to 7 km even during daytime as long as the instrument is operating at designed performance levels. Note that this sensitivity is not sufficient to detect high ice clouds, which can have lower extinction than 0.05 km⁻¹.
temperature method of Hogan et al. (2006). Hence, radar reflectivity calibration should take place at least twice a year (see calibration methods described in the concept paper of the Centre for Cloud Remote Sensing in D4.1). Doppler velocity calibration is usually not necessary but can be checked by pointing at a hard target. Ensuring that the instrument is indeed pointing at vertical is very important; off-vertical Doppler velocity measurements will contain a component from the horizontal wind which may be significant at altitudes close to the tropopause where horizontal wind speeds can easily exceed 50 m s⁻¹.

Precipitation can cause wetting of the radome or antenna, causing significant attenuation which is difficult to ascertain. Reflectivity data is not reliable in these conditions and should be flagged. Atmospheric attenuation, including gaseous, liquid rain, and the melting layer, is diagnosed and corrected for, if appropriate, in the data processing stage.

### 4.2.3.2 Ceilometer/Lidar

Ceilometers are single-wavelength, low-power, automatic lidars typically operating at wavelengths ranging from 850 to 1064 nm, originally designed to provide the cloud base altitude. Since clouds are very strong scatterers at near-infrared wavelengths, these systems do not require the high power expected for aerosol lidar systems. However, the sensitivity of current ceilometers is sufficient to detect aerosol within the boundary layer, and thicker ice clouds. High-power aerosol lidar systems can also be used (see Sec. 4.1).

The key parameter for the ceilometer or lidar is detection of **liquid layers**, including supercooled liquid layers. To improve sensitivity, averaging of data to 15–30 m in the vertical and 15–30 seconds in time is therefore appropriate. The minimum range should be 200 m or less to enable detection of fog layers, which might be missed by the cloud radar causing unaccounted-for attenuation. Maximum range should exceed 7.5 km to enable detection of supercooled liquid layers down to almost –40 °C. Note that ceilometers with extended maximum range are required in regions where supercooled layers may be expected above 7.5 km, such as the tropics. Instrument design may mean that full overlap between receiver and transmitter is not reached until 1 km or so, with correction of the signal possible in part of the overlap region; this is permissible as long as detection of liquid layers at 200 m is still possible. Possible saturation of the lidar signal in clouds at low altitudes must be flagged and mitigation procedures adopted to prevent this occurring.

Instrument sensitivity is dependent on the signal-to-noise ratio (SNR), which is a function of the emitted power, telescope design, averaging time, background light and the strength of the backscattered return from atmospheric targets. Thus, stray background light (solar radiation) entering the detector chain leads to a drop in sensitivity during the day.

The required sensitivity for liquid-layer detection can be expressed in terms of extinction, with typical values exceeding 10 km⁻¹. Since the lidar ratio for cloud droplets is about 20 sr at almost all lidar wavelengths, this translates to a backscatter coefficient of about 0.5 km⁻¹ sr⁻¹ which is achieved by all current ceilometer systems up to 7 km even during daytime as long as the instrument is operating at designed performance levels. Note that this sensitivity is not sufficient to detect high ice clouds, which can have lower extinction than 0.05 km⁻¹.
Ceilometer instruments are calibrated by the manufacturer, and the calibration is regarded as stable over time. However, the calibration should be checked on installation, and then periodically over time using a standard naturally occurring atmospheric target with a known backscatter, such as molecular backscatter or liquid clouds that fully extinguish the signal (see calibration methods described in the concept paper of the Centre for Cloud Remote Sensing in D4.1). These calibration methods yield a calibration accurate to about 10% (O'Connor et al. 2004, Wiegner and Geiß 2012), suitable for cloud detection and target categorization.

High-power aerosol lidar systems can also be used instead of ceilometers. These should utilise a near-range channel to avoid overlap issues, and include neutral-density filters to mitigate saturation in liquid layers. Full calibration should follow the specifications given in the concept paper of the Centre for Aerosol Remote Sensing in D4.1.

Further use of the lidar signal in specific algorithms may require much more stringent tests on calibration, stability, and the calculation of the influence of multiple scattering.

#### 4.2.3.3 Microwave radiometer

Microwave radiometers are passive receivers calibrated to measure the down-welling thermal emission from the atmosphere; from which some atmospheric thermodynamic properties can be deduced. The quantity measured is atmospheric radiance (W m⁻² sr⁻¹ Hz⁻¹), typically converted into brightness temperature (Tb, in Kelvin). Thermal emission is usually measured in the 20–60 GHz range. The 22–35 GHz band provides information on water vapour and cloud liquid water, with at least two channels (usually 23.8 and 30–31 GHz) required to retrieve the column-integrated water vapour path and liquid water path. Additional channels in this region can provide information on the vertical distribution of water vapour content. Some instruments make observations at 50–60 GHz to derive temperature information, with a vertical profile usually obtained through scanning at multiple elevation angles. Current systems have a temporal resolution better than 1 minute.

Microwave radiometers are continuously calibrated internally using a black-body target at ambient temperature and noise power injected by diode sources. In addition, external calibration should be performed using a tipping curve method (for scanning systems) and by an external target (liquid nitrogen). Calibrated microwave radiometers are then expected to be capable of providing brightness temperatures with an absolute accuracy of 0.3–0.5 K. This results in measurements of LWP with an accuracy of about 20 g m⁻². External calibration with a liquid nitrogen target is recommended at least twice a year (see calibration methods described in the concept paper of the Centre for Cloud Remote Sensing in D4.1).

Precipitation causes wetting of the radome, with similar attenuation issues as experience by the cloud radars. The data is not reliable in these conditions and must be flagged.

#### 4.2.3.4 Doppler wind lidar

Doppler wind lidars are single-wavelength lidar systems that provide profiles of SNR and radial Doppler velocity. These instruments are typically low-power, eye-safe, automatic lidar systems operating at wavelengths close to 1500 nm, and Doppler velocity is usually obtained using the heterodyne technique.
Ceilometer instruments are calibrated by the manufacturer, and the calibration is regarded as stable over time. However, the calibration should be checked on installation, and then periodically over time using a standard naturally occurring atmospheric target with a known backscatter, such as molecular backscatter or liquid clouds that fully extinguish the signal (see calibration methods described in the concept paper of the Centre for Cloud Remote Sensing in D4.1). These calibration methods yield a calibration accurate to about 10% (O'Connor et al. 2004, Wiegner and Geiß 2012), suitable for cloud detection and target categorization.

High-power aerosol lidar systems can also be used instead of ceilometers. These should utilise a near-range channel to avoid overlap issues, and include neutral-density filters to mitigate saturation in liquid layers. Full calibration should follow the specifications given in the concept paper of the Centre for Aerosol Remote Sensing in D4.1.

Further use of the lidar signal in specific algorithms may require much more stringent tests on calibration, stability, and the calculation of the influence of multiple scattering.

#### 4.2.3.3 Microwave radiometer

Microwave radiometers are passive receivers calibrated to measure the down-welling thermal emission from the atmosphere; from which some atmospheric thermodynamic properties can be deduced. The quantity measured is atmospheric radiance ($W m^{-2} sr^{-1} Hz^{-1}$), typically converted into brightness temperature (Tb, in Kelvin). Thermal emission is usually measured in the 20-60 GHz range. The 22-35 GHz band provides information on water vapour and cloud liquid water, with at least two channels (usually 23.8 and 30-31 GHz) required to retrieve the column-integrated water vapour path and liquid water path. Additional channels in this region can provide information on the vertical distribution of water vapour content. Some instruments make observations at 50-60 GHz to derive temperature information, with a vertical profile usually obtained through scanning at multiple elevation angles. Current systems have a temporal resolution better than 1 minute.

Microwave radiometers are continuously calibrated internally using a black-body target at ambient temperature and noise power injected by diode sources. In addition, external calibration should be performed using a tipping curve method (for scanning systems) and by an external target (liquid nitrogen). Calibrated microwave radiometers are then expected to be capable of providing brightness temperatures with an absolute accuracy of 0.3-0.5 K. This results in measurements of LWP with an accuracy of about 20 g m⁻². External calibration with a liquid nitrogen target is recommended at least twice a year (see calibration methods described in the concept paper of the Centre for Cloud Remote Sensing in D4.1).

Precipitation causes wetting of the radome, with similar attenuation issues as experience by the cloud radars. The data is not reliable in these conditions and must be flagged.

#### 4.2.3.4 Doppler wind lidar

Doppler wind lidars are single-wavelength lidar systems that provide profiles of SNR and radial Doppler velocity. These instruments are typically low-power, eye-safe, automatic lidar systems operating at wavelengths close to 1500 nm, and Doppler velocity is usually obtained using the heterodyne technique.
The scattering targets are aerosol particles and cloud droplets, which act as tracers from which winds and turbulent properties can be derived, and larger hydrometeors such as ice and rain drops. Vertical profiles of the horizontal wind are obtained by pointing off-zenith, whether by combining three or four orthogonal beams (Doppler Beam Swinging - DBS) or by conical scanning (Velocity Azimuth Display - VAD), hence one key requirement is that the Doppler lidar instrument is configured with multi-beam or scanning capability.

The other key requirement for the Doppler lidar is that it must have sufficient sensitivity to obtain velocity information throughout the extent of the boundary layer. The majority of the scattering targets in the boundary layer are aerosol particles and the required sensitivity is therefore similar to ceilometer systems. The minimum measurement height should be 100 m or less, but this can be mitigated through the use of scanning at low elevation angles (minimum range may exceed 100 m), and the maximum range should extend beyond the boundary layer (i.e., maximum range should be at least 2 km in most cases).

Quantitative measurements of winds and turbulent mixing in the atmosphere require accurate characterization of the uncertainties in the radial SNR and Doppler velocities. Since radial Doppler velocity uncertainty estimates are derived from SNR, full characterization of the background noise behaviour of the instrument, and possible subsequent post-processing, is necessary (Manninen et al. 2016). This improvement in the radial Doppler velocity uncertainty estimate propagates directly through to wind retrievals and is **vital for deriving reliable higher order velocity statistics** such as variance, skewness and dissipation rates; for example, the observed variance contains contributions from both the true variance and the error variance (e.g., O'Connor 2010).

Doppler velocity calibration is performed by the manufacturer and the calibration is regarded as stable over time. However, the velocity calibration should be checked periodically using stationary targets in the far field of the instrument, such as towers, masts, buildings, or hills. Such targets, especially towers, masts and buildings, also enable the azimuthal and elevation pointing accuracy to be assessed.

If the telescope function is known accurately, the attenuated backscatter profile can be obtained from the SNR profile (Hirsikko et al. 2014), and the Doppler lidar can act as a surrogate ceilometer. Calibration should be checked periodically over time using a standard naturally occurring atmospheric target with a known backscatter, such as liquid clouds that fully extinguish the signal (see calibration methods described in the concept paper of the Centre for Cloud Remote Sensing in D4.1). This method yields a calibration accurate to about 15% (O'Connor et al. 2004, Westbrook et al. 2012), suitable for cloud detection and target categorization. Note that the molecular backscatter return is too low to detect at typical Doppler lidar operating wavelengths.

Further use of the Doppler lidar signal in specific algorithms may require much more stringent tests on calibration, stability and the post-processing corrections applied.

#### 4.2.3.5 Auxiliary data sources: temperature and humidity profiles

The target categorization includes profiles of temperature, humidity and horizontal winds for assisting in quantifying uncertainties in particular retrievals, initialising a background state, estimating temporal or spatial scales, and for determining constants or variables that are temperature or humidity dependent where appropriate. Certain instrument uncertainties also require these parameters as an input. The
The scattering targets are aerosol particles and cloud droplets, which act as tracers from which winds and turbulent properties can be derived, and larger hydrometeors such as ice and rain drops. Vertical profiles of the horizontal wind are obtained by pointing off-zenith, whether by combining three or four orthogonal beams (Doppler Beam Swinging - DBS) or by conical scanning (Velocity Azimuth Display - VAD), hence one key requirement is that the Doppler lidar instrument is configured with multi-beam or scanning capability.

The other key requirement for the Doppler lidar is that it must have sufficient sensitivity to obtain velocity information throughout the extent of the boundary layer. The majority of the scattering targets in the boundary layer are aerosol particles and the required sensitivity is therefore similar to ceilometer systems. The minimum measurement height should be 100 m or less, but this can be mitigated through the use of scanning at low elevation angles (minimum range may exceed 100 m), and the maximum range should extend beyond the boundary layer (i.e., maximum range should be at least 2 km in most cases).

Quantitative measurements of winds and turbulent mixing in the atmosphere require accurate characterization of the uncertainties in the radial SNR and Doppler velocities. Since radial Doppler velocity uncertainty estimates are derived from SNR, full characterization of the background noise behaviour of the instrument, and possible subsequent post-processing, is necessary (Manninen et al. 2016). This improvement in the radial Doppler velocity uncertainty estimate propagates directly through to wind retrievals and is **vital for deriving reliable higher order velocity statistics** such as variance, skewness and dissipation rates; for example, the observed variance contains contributions from both the true variance and the error variance (e.g., O'Connor 2010).

Doppler velocity calibration is performed by the manufacturer and the calibration is regarded as stable over time. However, the velocity calibration should be checked periodically using stationary targets in the far field of the instrument, such as towers, masts, buildings, or hills. Such targets, especially towers, masts and buildings, also enable the azimuthal and elevation pointing accuracy to be assessed.

If the telescope function is known accurately, the attenuated backscatter profile can be obtained from the SNR profile (Hirsikko et al. 2014), and the Doppler lidar can act as a surrogate ceilometer. Calibration should be checked periodically over time using a standard naturally occurring atmospheric target with a known backscatter, such as liquid clouds that fully extinguish the signal (see calibration methods described in the concept paper of the Centre for Cloud Remote Sensing in D4.1). This method yields a calibration accurate to about 15% (O'Connor et al. 2004, Westbrook et al. 2012), suitable for cloud detection and target categorization. Note that the molecular backscatter return is too low to detect at typical Doppler lidar operating wavelengths.

Further use of the Doppler lidar signal in specific algorithms may require much more stringent tests on calibration, stability and the post-processing corrections applied.

#### 4.2.3.5 Auxiliary data sources: temperature and humidity profiles

The target categorization includes profiles of temperature, humidity and horizontal winds for assisting in quantifying uncertainties in particular retrievals, initialising a background state, estimating temporal or spatial scales, and for determining constants or variables that are temperature or humidity dependent where appropriate. Certain instrument uncertainties also require these parameters as an input. The
profiles of temperature, humidity and horizontal winds are normally taken from the output of a high-resolution weather forecast model. Radiosoundings can also be used if available, and the ingest code will provide them in a format that emulates the weather forecast model input file. In addition, horizontal winds from a wind profiler can also be incorporated if preferred.

#### 4.2.3.6 Auxiliary sources: raingauge

Precipitation has several potential sources of impact, arising from radome wetting (both cloud radar and radiometer), scattering assumptions no longer applicable (microwave radiometer), and attenuation in the atmospheric column (cloud radar). Periods when the measurements are potentially affected by precipitation can usually be determined from the measurements themselves. However, the addition of a raingauge provides the surface rainrate which will aid identifying those periods where only radome wetting is an issue (weak precipitation), and periods where the precipitation is strong enough to cause serious attenuation of the radar reflectivity profile. For identifying radome wetting conditions, a fast response instrument sensitive to weak precipitation must be employed, such as optical sensors and disdrometers; a standard tipping bucket raingauge has very slow response times to weak precipitation (has to wait for the internal bucket to fill before tipping) and is not suited for this application. A tipping bucket raingauge may be more accurate in strong precipitation conditions and when calculating accumulations.

### 4.2.4 Operation

Cloud profiling operates on a continuous basis, rather than targeted or routine dedicated measurement periods. The goal is to operate at or above the nominal temporal resolution of 30 s continuously, i.e., 24 hours a day/7 days a week. The preferred measurement mode is continuous vertically-pointing, although occasional scanning is also permitted as long as it does not dominate the measurement schedule. Usually, only extended continuous measurement periods (greater than 1 month) are considered suitable.

The required temporal resolution for mandatory instrumentation is nominally 30 seconds, although this can be relaxed to 1 minute for the multi-channel microwave radiometer. The minimum required vertical resolution is 60 m for the active instruments: cloud radar and ceilometer/lidar. The required temporal and spatial resolution is necessarily high, since cloud parameters also vary rapidly at these measurement scales. The temporal and spatial requirements are not necessary for any complementary instrumentation, since most other atmospheric parameters vary much less rapidly than do clouds.

Standard operation procedures relating to the mandatory instrumentation are described in the concept paper of the Centre for Cloud Remote Sensing in D4.1.

### 4.2.5 Data production and data products

Raw data acquired from the cloud profiling instruments at NFs are processed at the ACTRIS DC using the Cloudnet processing suite, which provides the necessary tools for data processing, automated quality control and product generation, together with a standard template for generating new products and tools within the community. In addition, the processing suite provides an extensive set of procedures for both model evaluation and for data evaluation.
profiles of temperature, humidity and horizontal winds are normally taken from the output of a high-resolution weather forecast model. Radiosoundings can also be used if available, and the ingest code will provide them in a format that emulates the weather forecast model input file. In addition, horizontal winds from a wind profiler can also be incorporated if preferred.

#### 4.2.3.6 Auxiliary sources: raingauge

Precipitation has several potential sources of impact, arising from radome wetting (both cloud radar and radiometer), scattering assumptions no longer applicable (microwave radiometer), and attenuation in the atmospheric column (cloud radar). Periods when the measurements are potentially affected by precipitation can usually be determined from the measurements themselves. However, the addition of a raingauge provides the surface rainrate which will aid identifying those periods where only radome wetting is an issue (weak precipitation), and periods where the precipitation is strong enough to cause serious attenuation of the radar reflectivity profile. For identifying radome wetting conditions, a fast response instrument sensitive to weak precipitation must be employed, such as optical sensors and disdrometers; a standard tipping bucket raingauge has very slow response times to weak precipitation (has to wait for the internal bucket to fill before tipping) and is not suited for this application. A tipping bucket raingauge may be more accurate in strong precipitation conditions and when calculating accumulations.

### 4.2.4 Operation

Cloud profiling operates on a continuous basis, rather than targeted or routine dedicated measurement periods. The goal is to operate at or above the nominal temporal resolution of 30 s continuously, i.e., 24 hours a day/7 days a week. The preferred measurement mode is continuous vertically-pointing, although occasional scanning is also permitted as long as it does not dominate the measurement schedule. Usually, only extended continuous measurement periods (greater than 1 month) are considered suitable.

The required temporal resolution for mandatory instrumentation is nominally 30 seconds, although this can be relaxed to 1 minute for the multi-channel microwave radiometer. The minimum required vertical resolution is 60 m for the active instruments: cloud radar and ceilometer/lidar. The required temporal and spatial resolution is necessarily high, since cloud parameters also vary rapidly at these measurement scales. The temporal and spatial requirements are not necessary for any complementary instrumentation, since most other atmospheric parameters vary much less rapidly than do clouds.

Standard operation procedures relating to the mandatory instrumentation are described in the concept paper of the Centre for Cloud Remote Sensing in D4.1.

### 4.2.5 Data production and data products

Raw data acquired from the cloud profiling instruments at NFs are processed at the ACTRIS DC using the Cloudnet processing suite, which provides the necessary tools for data processing, automated quality control and product generation, together with a standard template for generating new products and tools within the community. In addition, the processing suite provides an extensive set of procedures for both model evaluation and for data evaluation.
NFs are responsible for providing the instrument technical information necessary for performing QA/QC tests coordinated by the Centre for Cloud Remote Sensing, which then embeds this within the metadata to ensure full traceability of the data within the processing chain. This includes updates to instrument calibration, performance and parameter information.

Instrument synergy is necessary to create cloud products. Level 1 data deals with the processing of Level 0 instrument data provided by the NF to the DC, and their subsequent combination to provide a single synergistic product on a well-defined time-height grid. All individual observations are pre-processed and quality-checked. The target categorization dataset (Hogan and O'Connor 2004) is the basis from which all ACTRIS cloud products are created, and provides the harmonized dataset that contains all instrument uncertainties and quality control flags for propagation through the various retrieval algorithms, as well as providing the underlying target identification for these retrievals to operate on (Illingworth et al. 2007).

High-resolution products, at the native instrument resolution if possible, are created in Level 2a, and are used for all scientific studies. Specific products for model evaluation are created in Level 2b, where the high-resolution products are averaged onto the grid of each individual model, and in Level 3 and beyond, amassed into monthly and yearly files containing a wide range of statistical measures. These include: means, distributions, and joint-pdfs for creating the contingency tables used for deriving the skill score of choice. From these files, a wide range of metrics are then routinely plotted and analysed. The full list of products and accompanying metadata is detailed in the Data Management Plan (D4.2).

### 4.2.6 Data delivery and quality control

The ACTRIS cloud profiling products are collected and made available through the ACTRIS DC and the ACTRIS data user interface. The data repository is a version-controlled database enabling an iterative process concerning calibration updates and manual quality control.

NFs are responsible for providing Level 0 or Level 1 data in RRT to the ACTRIS DC in the agreed and documented format specified in the Data Management Plan (D4.2). Processing through Levels 1–3 is then performed centrally at the ACTRIS DC and made available according to the agreed Data Policy (see D2.3).
NFs are responsible for providing the instrument technical information necessary for performing QA/QC tests coordinated by the Centre for Cloud Remote Sensing, which then embeds this within the metadata to ensure full traceability of the data within the processing chain. This includes updates to instrument calibration, performance and parameter information.

Instrument synergy is necessary to create cloud products. Level 1 data deals with the processing of Level 0 instrument data provided by the NF to the DC, and their subsequent combination to provide a single synergistic product on a well-defined time-height grid. All individual observations are pre-processed and quality-checked. The target categorization dataset (Hogan and O'Connor 2004) is the basis from which all ACTRIS cloud products are created, and provides the harmonized dataset that contains all instrument uncertainties and quality control flags for propagation through the various retrieval algorithms, as well as providing the underlying target identification for these retrievals to operate on (Illingworth et al. 2007).

High-resolution products, at the native instrument resolution if possible, are created in Level 2a, and are used for all scientific studies. Specific products for model evaluation are created in Level 2b, where the high-resolution products are averaged onto the grid of each individual model, and in Level 3 and beyond, amassed into monthly and yearly files containing a wide range of statistical measures. These include: means, distributions, and joint-pdfs for creating the contingency tables used for deriving the skill score of choice. From these files, a wide range of metrics are then routinely plotted and analysed. The full list of products and accompanying metadata is detailed in the Data Management Plan (D4.2).

### 4.2.6 Data delivery and quality control

The ACTRIS cloud profiling products are collected and made available through the ACTRIS DC and the ACTRIS data user interface. The data repository is a version-controlled database enabling an iterative process concerning calibration updates and manual quality control.

NFs are responsible for providing Level 0 or Level 1 data in RRT to the ACTRIS DC in the agreed and documented format specified in the Data Management Plan (D4.2). Processing through Levels 1-3 is then performed centrally at the ACTRIS DC and made available according to the agreed Data Policy (see D2.3).
## 4.3 Reactive trace gases remote sensing

### 4.3.1 Introduction

Trace gases play an important role in the atmosphere, e.g., in environmental issues like air quality and climate change, and it is not only the concentration of the trace gas at the surface that is relevant but also the abundance of the trace gas in the troposphere and above, as well as the vertical distribution of the trace gas throughout the atmosphere. For example, ozone in the troposphere plays a very different role in air quality and climate change than ozone in the stratosphere.

While the *in situ* observation techniques focus on local concentrations at the surface and at specific heights in the troposphere, the remote-sensing techniques provide access to column abundances and vertical profiles of the trace gases. Both techniques are therefore complementary to each other. Moreover, the ground-based remote-sensing techniques provide column and vertical profile data that are better comparable to satellite measurements than the local *in situ* observations. The remote-sensing observations together with the local *in situ* observations can be used to get information about the transport in the atmosphere.

The trace-gas products that can be obtained by ground-based remote-sensing techniques and that ACTRIS will be focusing on at start include the **ozone ($O_3$) column, partial column and vertical profile, nitrogen dioxide ($NO_2$) column, partial columns and lower tropospheric profile,** as well as the **formaldehyde (HCHO) column and lower tropospheric profile** and **ethane ($C_2H_6$) column**. The latter two are important components in the family of volatile organic compounds (VOC) that play an important role in the abundance of ozone and the self-cleansing capacity of the atmosphere. The **ammonia column ($NH_3$)** will be provided in special conditions. The characteristics of the different products (columns, partial columns, profiles) are further described in Sec. 4.3.1.

In ACTRIS the following trace-gas remote-sensing techniques are considered: **Fourier Transform Infrared spectrometry (FTIR), Ultra-Violet and Visible spectrometry (UVVIS) and Differential Absorption Lidar (DIAL)**. These techniques have been developed and demonstrated in the Network for the Detection of Atmospheric Composition Change (NDACC). Also protocols for observations and data QA/QC have been developed within that network (http://www.ndsc.ncep.noaa.gov/organize/protocols/). They will be adopted and further developed in ACTRIS. For the history, structure and observational capacities of NDACC, we refer to the AMT/ACP/ESSD Inter-journal Special issue 'Twenty-five years of operations of the Network for the Detection of Atmospheric Composition Change (NDACC)' – in particular the overview paper by De Mazière et al. (2018).
## 4.3 Reactive trace gases remote sensing

### 4.3.1 Introduction

Trace gases play an important role in the atmosphere, e.g., in environmental issues like air quality and climate change, and it is not only the concentration of the trace gas at the surface that is relevant but also the abundance of the trace gas in the troposphere and above, as well as the vertical distribution of the trace gas throughout the atmosphere. For example, ozone in the troposphere plays a very different role in air quality and climate change than ozone in the stratosphere.

While the *in situ* observation techniques focus on local concentrations at the surface and at specific heights in the troposphere, the remote-sensing techniques provide access to column abundances and vertical profiles of the trace gases. Both techniques are therefore complementary to each other. Moreover, the ground-based remote-sensing techniques provide column and vertical profile data that are better comparable to satellite measurements than the local *in situ* observations. The remote-sensing observations together with the local *in situ* observations can be used to get information about the transport in the atmosphere.

The trace-gas products that can be obtained by ground-based remote-sensing techniques and that ACTRIS will be focusing on at start include the **ozone ($O_3$) column, partial column and vertical profile, nitrogen dioxide ($NO_2$) column, partial columns and lower tropospheric profile,** as well as the **formaldehyde (HCHO) column and lower tropospheric profile** and **ethane ($C_2H_6$) column**. The latter two are important components in the family of volatile organic compounds (VOC) that play an important role in the abundance of ozone and the self-cleansing capacity of the atmosphere. The **ammonia column ($NH_3$)** will be provided in special conditions. The characteristics of the different products (columns, partial columns, profiles) are further described in Sec. 4.3.1.

In ACTRIS the following trace-gas remote-sensing techniques are considered: **Fourier Transform Infrared spectrometry (FTIR), Ultra-Violet and Visible spectrometry (UVVIS) and Differential Absorption Lidar (DIAL)**. These techniques have been developed and demonstrated in the Network for the Detection of Atmospheric Composition Change (NDACC). Also protocols for observations and data QA/QC have been developed within that network (http://www.ndsc.ncep.noaa.gov/organize/protocols/). They will be adopted and further developed in ACTRIS. For the history, structure and observational capacities of NDACC, we refer to the AMT/ACP/ESSD Inter-journal Special issue 'Twenty-five years of operations of the Network for the Detection of Atmospheric Composition Change (NDACC)' – in particular the overview paper by De Mazière et al. (2018).
## 4.3.2 Observational capabilities

### 4.3.2.1 Optimum setup

The optimum setup of an ACTRIS Observational Platform dedicated to remote sensing of reactive trace gases consists of a configuration of instruments that enables the measurement of **total columns of HCHO, C₂H₆ and O₃**, as well as **partial columns or profiles of HCHO, O₃ and NO₂**. This can be achieved in the following configurations:

(1) The Observational Platform hosts together (co-located) an FTIR instrument and a UVVIS instrument, both satisfying the optimum technical specifications, as described in Sec. 4.3.3; these are the so-called **double FTIR and UVVIS MAXDOAS instruments**, or

(2) The Observational Platform hosts together (co-located) an FTIR instrument satisfying the minimum technical specifications (so-called **single FTIR**, see Sec. 4.3.3) **plus an O₃ DIAL and a UVVIS MAXDOAS instrument** (satisfying the optimum requirements for the UVVIS instrument).

Each FTIR and UVVIS instrument must satisfy the layout, observation procedures, data production and data delivery requirements established by the Centre for Reactive Trace Gases Remote Sensing, as described in the respective concept document (see D4.1) and in the following sections. The O₃ DIAL must provide O₃ vertical profiles according to the standard operation procedures established in NDACC. The list of produced variables is provided in Annex A.3, and the products are further described below.

### 4.3.2.2 Minimum requirements

To satisfy the minimum requirements for an ACTRIS Observational Platform dedicated to remote sensing of reactive trace gases, it is required to have a configuration of instruments that enables the measurement of **total columns of HCHO and C₂H₆ and total/partial columns of O₃ and NO₂**. This can be achieved in two configurations:

(1) The Observational Platform hosts an FTIR instrument satisfying the optimum technical specifications (so-called **double FTIR**, see Sec. 4.3.3), or

(2) The Observational Platform hosts together (co-located) an FTIR and a UVVIS instrument, both satisfying the minimum technical specifications (i.e., the so-called **single FTIR and UVVIS-zenith-sky** spectrometers, see Sec. 4.3.3).

In all configurations, the FTIR and UVVIS instruments must comply with the technical specifications as described below (see Sec. 4.3.3) and with all the observation procedures, data processing and data delivery requirements established by the Centre for Reactive Trace Gases Remote Sensing, as described in the respective concept document (see D4.1) and in the following sections.

### 4.3.2.3 Complementary added-value observations

For any reactive-trace-gases remote-sensing facility, co-location with instrument(s) for **in situ trace-gas monitoring** provides an added value.

For the sites equipped with a UVVIS-type instrument, co-location with an **aerosol lidar** instrument and/or a **sun photometer** provides an additional added value. MAXDOAS UVVIS systems can provide **aerosol**
## 4.3.2 Observational capabilities

### 4.3.2.1 Optimum setup

The optimum setup of an ACTRIS Observational Platform dedicated to remote sensing of reactive trace gases consists of a configuration of instruments that enables the measurement of **total columns of HCHO, C₂H₆ and O₃**, as well as **partial columns or profiles of HCHO, O₃ and NO₂**. This can be achieved in the following configurations:

(1) The Observational Platform hosts together (co-located) an FTIR instrument and a UVVIS instrument, both satisfying the optimum technical specifications, as described in Sec. 4.3.3; these are the so-called **double FTIR and UVVIS MAXDOAS instruments**, or

(2) The Observational Platform hosts together (co-located) an FTIR instrument satisfying the minimum technical specifications (so-called **single FTIR**, see Sec. 4.3.3) **plus an O₃ DIAL and a UVVIS MAXDOAS instrument** (satisfying the optimum requirements for the UVVIS instrument).

Each FTIR and UVVIS instrument must satisfy the layout, observation procedures, data production and data delivery requirements established by the Centre for Reactive Trace Gases Remote Sensing, as described in the respective concept document (see D4.1) and in the following sections. The O₃ DIAL must provide O₃ vertical profiles according to the standard operation procedures established in NDACC. The list of produced variables is provided in Annex A.3, and the products are further described below.

### 4.3.2.2 Minimum requirements

To satisfy the minimum requirements for an ACTRIS Observational Platform dedicated to remote sensing of reactive trace gases, it is required to have a configuration of instruments that enables the measurement of **total columns of HCHO and C₂H₆ and total/partial columns of O₃ and NO₂**. This can be achieved in two configurations:

(1) The Observational Platform hosts an FTIR instrument satisfying the optimum technical specifications (so-called **double FTIR**, see Sec. 4.3.3), or

(2) The Observational Platform hosts together (co-located) an FTIR and a UVVIS instrument, both satisfying the minimum technical specifications (i.e., the so-called **single FTIR and UVVIS-zenith-sky** spectrometers, see Sec. 4.3.3).

In all configurations, the FTIR and UVVIS instruments must comply with the technical specifications as described below (see Sec. 4.3.3) and with all the observation procedures, data processing and data delivery requirements established by the Centre for Reactive Trace Gases Remote Sensing, as described in the respective concept document (see D4.1) and in the following sections.

### 4.3.2.3 Complementary added-value observations

For any reactive-trace-gases remote-sensing facility, co-location with instrument(s) for **in situ trace-gas monitoring** provides an added value.

For the sites equipped with a UVVIS-type instrument, co-location with an **aerosol lidar** instrument and/or a **sun photometer** provides an additional added value. MAXDOAS UVVIS systems can provide **aerosol**
**extinction profiles in the lower troposphere** as well as the total **AOD**. If the platform hosts an O₃ DIAL, this also provides **aerosol backscatter coefficient** and information of **cloud height** using the lidar signal less absorbed by ozone.

Complementary co-located **water-vapour lidar profiles** or **water-vapour sonde measurements** are useful but not mandatory for the retrieval of the FTIR data (water vapour is often an interfering species).

Since cloud occurrences and the types of clouds can be useful for the quality assessment of both UVVIS and FTIR observations, the availability of **cloud information** at the site is of interest.

The availability of regular nearby O₃ **sonde profiles**, or complementary **Dobson/Brewer total ozone column** measurements, is helpful for the quality control of the ACTRIS O₃ products.

#### 4.3.2.4 Auxiliary measurements

For any of the three techniques for remote sensing of reactive trace gases, it is useful for the geophysical exploitation of the data to also have **wind** (direction and strength) information at the observation site. Also local soundings of **pressure**, **temperature** and **relative humidity profiles** are useful but not mandatory. For the automated FTIR observations that require cloud-free conditions, meteorological information is needed, but in general, a small **meteorological station** is part of the FTIR experimental setup. The mandatory parameters are **wind speed and direction**, **precipitation**, **surface pressure and temperature** and **direct solar irradiance**. If the FTIR system is not automated and such small meteorological station is not included, then the availability of complementary meteorological information should be assured to interpret the quality of the recorded interferogrammes.

### 4.3.3 Instrumentation and calibration

#### 4.3.3.1 FTIR

The potential of modern ground-based, fast, wide-bandpass FTIR spectrometers using the sun as source of radiation is their ability to regularly record solar spectra over the entire middle and thermal-infrared spectral domain (from 2 to 15 µm) under daytime clear-sky conditions, with very high spectral resolution and large signal-to-noise ratios. Today, they are the standard instruments in the NDACC for making solar-absorption remote-sensing measurements of total and partial columns of a large variety of trace gases in the atmosphere.

The FTIR instruments at ACTRIS Observational Platforms must satisfy the minimum criteria outlined in the NDACC Measurements and Validation Protocols (http://www.ndsc.ncep.noaa.gov/organize/protocols/) and in particular the criteria spelled out in the Appendix II ‘Infrared Instruments’ of the Validation Protocol.

The main technical specifications are:

1. Optical path difference (OPD): a minimum of 120 cm is acceptable; however, 250 cm is recommended for high-resolution instruments for optimal profile retrieval;

2. Spectral range: **minimum** 1900–4100 cm⁻¹ (CaF₂ beamsplitter), **optimum** 700–5000 cm⁻¹ (KBr beamsplitter);
**extinction profiles in the lower troposphere** as well as the total **AOD**. If the platform hosts an O₃ DIAL, this also provides **aerosol backscatter coefficient** and information of **cloud height** using the lidar signal less absorbed by ozone.

Complementary co-located **water-vapour lidar profiles** or **water-vapour sonde measurements** are useful but not mandatory for the retrieval of the FTIR data (water vapour is often an interfering species).

Since cloud occurrences and the types of clouds can be useful for the quality assessment of both UVVIS and FTIR observations, the availability of **cloud information** at the site is of interest.

The availability of regular nearby O₃ **sonde profiles**, or complementary **Dobson/Brewer total ozone column** measurements, is helpful for the quality control of the ACTRIS O₃ products.

#### 4.3.2.4 Auxiliary measurements

For any of the three techniques for remote sensing of reactive trace gases, it is useful for the geophysical exploitation of the data to also have **wind** (direction and strength) information at the observation site. Also local soundings of **pressure**, **temperature** and **relative humidity profiles** are useful but not mandatory. For the automated FTIR observations that require cloud-free conditions, meteorological information is needed, but in general, a small **meteorological station** is part of the FTIR experimental setup. The mandatory parameters are **wind speed and direction**, **precipitation**, **surface pressure and temperature** and **direct solar irradiance**. If the FTIR system is not automated and such small meteorological station is not included, then the availability of complementary meteorological information should be assured to interpret the quality of the recorded interferogrammes.

### 4.3.3 Instrumentation and calibration

#### 4.3.3.1 FTIR

The potential of modern ground-based, fast, wide-bandpass FTIR spectrometers using the sun as source of radiation is their ability to regularly record solar spectra over the entire middle and thermal-infrared spectral domain (from 2 to 15 µm) under daytime clear-sky conditions, with very high spectral resolution and large signal-to-noise ratios. Today, they are the standard instruments in the NDACC for making solar-absorption remote-sensing measurements of total and partial columns of a large variety of trace gases in the atmosphere.

The FTIR instruments at ACTRIS Observational Platforms must satisfy the minimum criteria outlined in the NDACC Measurements and Validation Protocols (http://www.ndsc.ncep.noaa.gov/organize/protocols/) and in particular the criteria spelled out in the Appendix II ‘Infrared Instruments’ of the Validation Protocol.

The main technical specifications are:

1. Optical path difference (OPD): a minimum of 120 cm is acceptable; however, 250 cm is recommended for high-resolution instruments for optimal profile retrieval;

2. Spectral range: **minimum** 1900–4100 cm⁻¹ (CaF₂ beamsplitter), **optimum** 700–5000 cm⁻¹ (KBr beamsplitter);
3. Continuous spectral coverage (except for the 6–7 µm H₂O region) in a small number (less than 8) of spectral (filter) bands;

4. Ability to record full-resolution spectrum (in one filter band) in approximately one minute;

5. Ability to make regular timely (sub-diurnal) measurements on an ongoing basis (decadal).

The FTIR spectrometers that satisfy the minimum requirements for specification (2) are indicated as **'single FTIR'** instruments in this document: their measurement spectral range is smaller because they include only one detector of the InSb (Indium-Antimonide) type. In terms of ACTRIS products, it implies that they cannot measure the ozone products (neither total nor partial columns) and the NH₃ product.

The FTIR spectrometers that satisfy the optimum requirements for specification (2) are indicated as **'double FTIR'** instruments hereinafter: their measurement spectral range is larger because they include two detectors, one detector of the InSb type and the other one of the MCT (Mercury-Cadmium-Telluride) type. In terms of ACTRIS products, it implies that they do measure the ozone products (total and partial columns) and they can measure the NH₃ product, if the concentration surpasses the detection threshold (Dammers et al. 2016).

In addition, the ACTRIS FTIR instruments will have to comply with specific quality criteria established by the Centre for Reactive Trace Gases Remote Sensing. Moreover, the operations will have to comply with the Standard Operating Procedures that will be developed and maintained by the Topical Centre.

The FTIR instruments are operated in the solar absorption mode. The Level 1 data are solar absorption spectra in spectral bands in the mid-infrared spectral range, in which the target gases have well-identified absorption lines. Since the zero absorption background is also observed, the spectrum is self-calibrating. Nevertheless, to derive from the spectrum accurate quantitative and traceable information about the target gas' column abundance and/or vertical distribution, a retrieval or spectral inversion procedure must be applied for which it is important to characterize the Instrument Line Shape (ILS) function. The Centre for Reactive Traces Gases Remote Sensing will be the housekeeper of the standard retrieval and ILS characterization procedures and software and associated retrieval strategies to be used by the ACTRIS FTIR NFs.

With the optimum specifications (double FTIR), the target species that can be observed – with the required accuracy and precision – are O₃, NO₂, HCHO, C₂H₆ and NH₃ (if the concentration is above the detection limit). For most species, the retrieved product is the total column. The characteristics of the product and the vertical sensitivities are discussed by Hendrick et al. (2012) for NO₂, by Vigouroux et al. (2018) for HCHO, by Dammers et al. (2016) for NH₃ and by Franco et al. (2015a) for C₂H₆. For O₃, approximately four partial columns (troposphere, lower, middle and upper stratosphere) can be retrieved (Vigouroux et al. 2015). It must be highlighted that the FTIR O₃ partial column product provides information about the O₃ vertical distribution with a vertical resolution not better than 5 to 8 km. This is much lower than the vertical resolution of the order of 0.1 to a few km that the O₃ DIAL can provide.

Mandatory target species are C₂H₆ and HCHO, while O₃ will be provided in the optimized double FTIR setup. NH₃ is considered to be a specialised variable because it can be provided only by the double FTIR and only at some sites where its concentration is high enough to be above the detection limit. Since the vertical sensitivity for FTIR NO₂ measurements lies essentially in the stratosphere, the NO₂ total column is
3. Continuous spectral coverage (except for the 6–7 µm H₂O region) in a small number (less than 8) of spectral (filter) bands;

4. Ability to record full-resolution spectrum (in one filter band) in approximately one minute;

5. Ability to make regular timely (sub-diurnal) measurements on an ongoing basis (decadal).

The FTIR spectrometers that satisfy the minimum requirements for specification (2) are indicated as **'single FTIR'** instruments in this document: their measurement spectral range is smaller because they include only one detector of the InSb (Indium-Antimonide) type. In terms of ACTRIS products, it implies that they cannot measure the ozone products (neither total nor partial columns) and the NH₃ product.

The FTIR spectrometers that satisfy the optimum requirements for specification (2) are indicated as **'double FTIR'** instruments hereinafter: their measurement spectral range is larger because they include two detectors, one detector of the InSb type and the other one of the MCT (Mercury-Cadmium-Telluride) type. In terms of ACTRIS products, it implies that they do measure the ozone products (total and partial columns) and they can measure the NH₃ product, if the concentration surpasses the detection threshold (Dammers et al. 2016).

In addition, the ACTRIS FTIR instruments will have to comply with specific quality criteria established by the Centre for Reactive Trace Gases Remote Sensing. Moreover, the operations will have to comply with the Standard Operating Procedures that will be developed and maintained by the Topical Centre.

The FTIR instruments are operated in the solar absorption mode. The Level 1 data are solar absorption spectra in spectral bands in the mid-infrared spectral range, in which the target gases have well-identified absorption lines. Since the zero absorption background is also observed, the spectrum is self-calibrating. Nevertheless, to derive from the spectrum accurate quantitative and traceable information about the target gas' column abundance and/or vertical distribution, a retrieval or spectral inversion procedure must be applied for which it is important to characterize the Instrument Line Shape (ILS) function. The Centre for Reactive Traces Gases Remote Sensing will be the housekeeper of the standard retrieval and ILS characterization procedures and software and associated retrieval strategies to be used by the ACTRIS FTIR NFs.

With the optimum specifications (double FTIR), the target species that can be observed – with the required accuracy and precision – are O₃, NO₂, HCHO, C₂H₆ and NH₃ (if the concentration is above the detection limit). For most species, the retrieved product is the total column. The characteristics of the product and the vertical sensitivities are discussed by Hendrick et al. (2012) for NO₂, by Vigouroux et al. (2018) for HCHO, by Dammers et al. (2016) for NH₃ and by Franco et al. (2015a) for C₂H₆. For O₃, approximately four partial columns (troposphere, lower, middle and upper stratosphere) can be retrieved (Vigouroux et al. 2015). It must be highlighted that the FTIR O₃ partial column product provides information about the O₃ vertical distribution with a vertical resolution not better than 5 to 8 km. This is much lower than the vertical resolution of the order of 0.1 to a few km that the O₃ DIAL can provide.

Mandatory target species are C₂H₆ and HCHO, while O₃ will be provided in the optimized double FTIR setup. NH₃ is considered to be a specialised variable because it can be provided only by the double FTIR and only at some sites where its concentration is high enough to be above the detection limit. Since the vertical sensitivity for FTIR NO₂ measurements lies essentially in the stratosphere, the NO₂ total column is
essentially a partial stratospheric column. More technical details are provided in the ACTRIS concept document of the Centre for Reactive Trace Gases Remote Sensing (see D4.1).

### 4.3.3.2 UVVIS

Passive UVVIS spectrometry using direct or scattered sunlight as a light source provides one of the most effective methods for routine remote sensing of atmospheric trace gases from the ground. While zenith-sky observations have been used for several decades to monitor stratospheric gases (e.g., Pommereau and Goutail 1988), measurements scanning the sky vertically at elevation angles close to the horizon have been established only more recently. Owing to its multi-angular observation geometry, the MAXDOAS (Multi-Axis Differential Optical Absorption Spectroscopy) technique allows the derivation of vertically resolved information on tropospheric species and aerosols (e.g., Hönninger et al. 2004, Wagner et al. 2004, Frieß et al. 2006) as well as the total tropospheric column of the observed trace gases. When combined with the direct-sun geometry, these systems also provide accurate trace-gas column measurements integrated over the full atmosphere (e.g., Herman et al. 2009).

The trace gases that the UVVIS instruments will provide in ACTRIS are O<sub>3</sub>, NO<sub>2</sub> and HCHO. The UVVIS O<sub>3</sub> product is described in Hendrick et al. (2011) and Herman et al. (2015), the NO<sub>2</sub> products are discussed in Hendrick et al. (2012, 2014) and Vlemmix et al. (2015) and the HCHO products in Franco et al. (2015b) and Vlemmix et al. (2015). As an added value, MAXDOAS UVVIS systems can provide aerosol extinction profiles in the lower troposphere as well as the total AOD (Clémer et al. 2010, Vlemmix et al. 2015).

The UVVIS instruments at ACTRIS National Facilities must at minimum comply with the following requirements:

- Zenith-sky viewing mode with one channel covering the visible range (400-550 nm);

- Fully automated acquisition, including data transfer to central processing system;

- Spectral resolution better than 2.0 nm (full width at half maximum);

- Minimum signal-to-noise ratio of 1500 at the wavelengths where the target gases feature absorption signatures that are used for the retrievals;

- No significant spectral features due to polarization sensitivity (it is recommended to use optical fibre bundle to depolarize the incoming scattered sunlight);

- Instrument Spectral Response Function (ISRF) characterised using emission lines (HgCd) at minimum one wavelength per channel;

- Spectral stray-light < 2% at wavelengths relevant for trace gas retrievals.

The UVVIS instruments satisfying the above minimum requirements are referred to as UVVIS-zenith-sky or UVVIS-ZS instruments.

The **optimum** setup should feature the additional technical specifications:

- MAXDOAS elevation scanner covering the range from 0 to 90° elevation in at least one azimuthal direction, with accuracy and repeatability better than 0.2° and field of view better than 1.5°;

- Sun-tracking system allowing for measurements in direct-sun mode;

- One or more spectral channels covering the UV (300-400 nm) at a resolution of 0.8 nm or better and/or the visible (400-550 nm) at a resolution of 1.5 nm or better.
essentially a partial stratospheric column. More technical details are provided in the ACTRIS concept document of the Centre for Reactive Trace Gases Remote Sensing (see D4.1).

#### 4.3.3.2 UVVIS

Passive UVVIS spectrometry using direct or scattered sunlight as a light source provides one of the most effective methods for routine remote sensing of atmospheric trace gases from the ground. While zenith-sky observations have been used for several decades to monitor stratospheric gases (e.g., Pommereau and Goutail 1988), measurements scanning the sky vertically at elevation angles close to the horizon have been established only more recently. Owing to its multi-angular observation geometry, the MAXDOAS (Multi-Axis Differential Optical Absorption Spectroscopy) technique allows the derivation of vertically resolved information on tropospheric species and aerosols (e.g., Hönninger et al. 2004, Wagner et al. 2004, Frieß et al. 2006) as well as the total tropospheric column of the observed trace gases. When combined with the direct-sun geometry, these systems also provide accurate trace-gas column measurements integrated over the full atmosphere (e.g., Herman et al. 2009).

The trace gases that the UVVIS instruments will provide in ACTRIS are O<sub>3</sub>, NO<sub>2</sub> and HCHO. The UVVIS O<sub>3</sub> product is described in Hendrick et al. (2011) and Herman et al. (2015), the NO<sub>2</sub> products are discussed in Hendrick et al. (2012, 2014) and Vlemmix et al. (2015) and the HCHO products in Franco et al. (2015b) and Vlemmix et al. (2015). As an added value, MAXDOAS UVVIS systems can provide aerosol extinction profiles in the lower troposphere as well as the total AOD (Clémer et al. 2010, Vlemmix et al. 2015).

The UVVIS instruments at ACTRIS National Facilities must at minimum comply with the following requirements:

- Zenith-sky viewing mode with one channel covering the visible range (400-550 nm);

- Fully automated acquisition, including data transfer to central processing system;

- Spectral resolution better than 2.0 nm (full width at half maximum);

- Minimum signal-to-noise ratio of 1500 at the wavelengths where the target gases feature absorption signatures that are used for the retrievals;

- No significant spectral features due to polarization sensitivity (it is recommended to use optical fibre bundle to depolarize the incoming scattered sunlight);

- Instrument Spectral Response Function (ISRF) characterised using emission lines (HgCd) at minimum one wavelength per channel;

- Spectral stray-light < 2% at wavelengths relevant for trace gas retrievals.

The UVVIS instruments satisfying the above minimum requirements are referred to as UVVIS-zenith-sky or UVVIS-ZS instruments.

The **optimum** setup should feature the additional technical specifications:

- MAXDOAS elevation scanner covering the range from 0 to 90° elevation in at least one azimuthal direction, with accuracy and repeatability better than 0.2° and field of view better than 1.5°;

- Sun-tracking system allowing for measurements in direct-sun mode;

- One or more spectral channels covering the UV (300-400 nm) at a resolution of 0.8 nm or better and/or the visible (400-550 nm) at a resolution of 1.5 nm or better.
The UVVIS instruments satisfying these optimum requirements are referred to as **UVVIS-MAXDOAS** instruments.

Moreover, the ACTRIS UVVIS instruments have to comply with specific quality criteria defined by the Centre for Reactive Trace Gases Remote Sensing as well as with certification criteria established as part of regular intercalibration campaigns, organised by the Centre for Reactive Trace Gases Remote Sensing. ACTRIS quality and certification criteria will be compliant with NDACC criteria, but may be more strict. The operations also have to comply with the Standard Operating Procedures to be developed and maintained by the Topical Centre.

Instrument intercalibration campaigns are considered necessary every 5 to 10 years. Such exercises need to be organised in a location that allows optimal observing conditions with respect to the required viewing geometry. Auxiliary observations of the atmospheric column, aerosol profile and cloud properties, as well as *in situ* trace gas observations should be available during such campaigns. The Centre for Reactive Trace Gases Remote Sensing should identify an ACTRIS site for this purpose. More technical details are provided in the concept document of the Centre for Reactive Trace Gases Remote Sensing (see D4.1).

#### 4.3.3.3 $O_3$ DIAL

The **DIAL technique** is used for the measurement of the **ozone vertical distribution**. This technique requires the simultaneous emission of two laser beams with different wavelengths characterised by different ozone absorption cross-sections. The laser wavelengths are chosen in the ultraviolet where ozone absorption is most efficient. Tropospheric and stratospheric measurements require different wavelength pairs and laser sources. Since the ozone profile is derived from the derivatives of the lidar signals, the technique does not require any calibration, but correction terms in the DIAL equation require the use of auxiliary total number density and temperature profile data.

ACTRIS $O_3$ DIAL instruments that are included in the optimum setup of an ACTRIS Observational Platform for remote sensing of reactive trace gases have to comply with the specific procedures defined by the Centre for Reactive Trace Gases Remote Sensing. Such procedures include periodic intercomparison campaigns with a reference mobile lidar instrument, regular comparisons with nearby satellite measurements or ozonesonde profiles and evaluation of the retrieval algorithm using synthetic data.