The potential of MTG-IRS and S4-TIR to detect high pollution events at urban and regional scales

Transcription

1 Final Report Delivered to Eumetsat/ESA in December 2008 The Slightly potential modified of MTG-IRS on January and 2009 S4-TIR to detect pollution events November 2008 Cathy Clerbaux et Pierre Coheur Contract Eumetsat EUM/CO/07/ /SAT Cathy CLERBAUX Pierre COHEUR Université Libre de Bruxelles - Service de Chimie Quantique et Photophysique CPI 160/09 50 Av. F.D. Roosevelt 1050 Bruxelles The potential of MTG-IRS and S4-TIR to detect high pollution events at urban and regional scales Cathy Clerbaux 1,2, Pierre-François Coheur 1, Oliver Scharf 1, Daniel Hurtmans 1, Anne Boynard 2 1. Université Libre de Bruxelles - Service de Chimie Quantique et Photophysique (ULB) CPI 160/09, 50, Av. F.D. Roosevelt, 1050 Bruxelles, Belgique 2. Service d Aéronomie (SA), Université Pierre et Marie Curie, Boite 102, 4, Place Jussieu Paris Cedex 05, France

2 Executive summary - The levels of all controlled pollutants (NO 2, SO 2, CO, O 3, PM 10 and PM 2.5 ) are continuously decreasing over Europe, except for O 3. The warning and alert levels of the latter are exceeded every year, and it will be worse in the future. Ozone peaks occur between 12 and 15 pm. - Current thermal IR instruments that work operationally are the polar orbiting AIRS/AQUA, IASI/METOP. They provide limited information for boundary layer ozone pollution either because of the coarse instrumental specifications (AIRS) and time of observation (IASI). Thermal contrast ( T=T surface - T atm@pbl ) controls the error budget and the vertical sensitivity. - MTG-IRS: Although the instrumental specifications for MTG-IRS are not optimized for chemistry, the instrument will provide tropospheric columns of O 3 and CO, with significant improvement on our prior knowledge, most remarkably for high pollution events (photochemical pollution in the case of ozone; fires in the case of CO). The diurnal variability might be difficult to capture if thermal contrast remains low. However, as ozone pollution mainly occurs along with high temperatures, pollution peaks will likely be monitored. - The instrumental specifications for S4-TIR are optimized for chemistry. The gain in the spectral resolution and the instrumental noise as compared to MTG-IRS allows to improve the vertical resolution (profile retrievals in the troposphere are then possible) and the error budget. The diurnal variability can be observed to some extent, and a good accuracy is obtained even in the boundary layer if the thermal contrast is favourable. As such, the S4- TIR would provide a significant add for the monitoring of pollution episodes. - More generally, one may expect to take benefit of the high sampling rate of the GEO sounders (0.5 to one hour) in order to set-up a specific retrieval strategy that would use the information at different times of the day (hence different thermal contrast) to extract the peak pollution events at the right time and place. Moreover the smaller MTG-IRS pixel size (3 to 6 km) would allow to average data in order to increase accuracy. This has, however, not been investigated here

3 The potential of MTG-IRS and S4-TIR to detect high pollution events at urban and regional scales A. INTRODUCTION: BACKGROUND AND AIMS OF THE STUDY...4 A.1 PROGRAMMATIC CONTEXT...4 A.2 THE MTG INFRARED SOUNDING MISSION: GOALS...5 A.3 INSTRUMENTAL SPECIFICATIONS FOR MTG-IRS AND S4-TIR...5 A.4 AIMS OF THE STUDY AND DEFINITION OF TASKS...7 A.4.1 Atmospheric scenarios...7 A.4.2 Capabilities of current TIR nadir sounders to detect CO and O 3 for air quality purposes...8 A.4.3 Capabilities of MTG-IRS and S4-TIR to detect CO and O 3 for air quality purposes...8 B. ATMOSPHERIC SCENARIOS...9 B.1 POLLUTION FORMATION...9 B.2 POLLUTION PEAKS...10 B.2.1 Chemical weather...10 B.2.2 Diurnal cycle...10 B.2.3 Long-range transport of pollutants and aerosols...12 B.3 THRESHOLD AND ALERT LEVELS IN EUROPE...13 B.3.1 EU-27 emission trends...13 B.3.2 Air quality exceedances in France...14 B.4 THE ROLE OF THERMAL CONTRAST...16 B.4.1 Definition...16 B.4.2 Thermal contrast over Europe...16 B.5 THE CHIMERE REGIONAL MODEL...17 B.5.1 Description of the model...17 B.5.2 Performances of the PREV AIR system to predict pollution events...20 C. CAPABILITIES OF CURRENT TIR NADIR SOUNDERS TO DETECT CO AND O 3 FOR AIR QUALITY PURPOSES...22 C.1 CURRENT THERMAL INFRARED SOUNDERS...22 C.2 CARBON MONOXIDE OBSERVATIONS...25 C.3 TROPOSPHERIC OZONE OBSERVATION...28 C.4 THERMAL CONTRAST...30 D. CAPABILITIES OF MTG-IRS AND S4-TIR TO DETECT CO AND O 3 FOR AIR QUALITY PURPOSES...32 D.1 THEORY AND METHODOLOGY...32 D.1.1 D.1.2 General formulation of the Inversion Method...32 Methodology for the TIR retrieval assessment...34 D.2 RESULTS...39 D.2.1 Ozone retrievals...39 D.2.2 Carbon monoxide retrievals

4 A. Introduction: Background and aims of the study A.1 Programmatic context EUMETSAT and the European Space Agency (ESA) have initiated joint preparatory activities for the definition of the Meteosat Third Generation (MTG) geostationary system to be available in the timeframe, as a replacement for the second generation satellites (MSG) [Stuhlmann et al., 2005]. Preparatory activities started in 2000 with the EUMETSAT post-msg User Consultation process in order to define the high-level needs and priorities that the EUMETSAT customers and users foresee for the timeframe. The mission definition review process was concluded in March 2006, leading to a phase A reference configuration supporting an improved imaging instrument and an infrared sounding mission (IRS), on two separated platforms MTG-I and MTG-S 1. Both the UV/vis and the lightning missions were not recommended to be included in the payload but are assessed in the Global Monitoring for Environment and Security (GMES) context. ESA is in charge of the instrumental concepts and developments, and EUMETSAT is responsible for the definition of the scientific requirements and for the overall programme. The MTG Mission Team (MMT) defined needs and priorities for observations relevant to the operational applications and services in the This study is one of the R&T studies performed in the framework of the MMT and focuses on the potential of IRS onboard MTG to detect high pollution events at urban and regional scales. At the same time, ESA has progressed in the Sentinel family definition. It includes the detailed definition of the satellite, its requirements on the ground segment and the operations concept based on the requirements for an operational GMES system. Sentinel 4 (S4) 2 is directly relevant to MTG, and as it is focused on atmospheric chemistry from a geostationary orbit, and it includes a thermal infrared instrument, hereafter referred as S4-TIR, as well as a UV-vis sounder. The current instrumental specifications of the thermal infrared sounder onboard Sentinel 4 present similarities with those envisaged for MTG-IRS, although are more demanding in terms of spectral resolution and radiometric performance. This is because the scientific requirements were driven by chemistry needs only, whereas for MTG it results from a compromise between meteorology and chemistry needs, with a priority on Numerical Weather Prediction (NWP). An extension to the ongoing study started in 2007 at ESA s request, in order to access the differences to be expected, in terms of accuracy and vertical profile retrieval capability, between the two concepts of infrared sounders (MTG-IRS and S4- TIR). In July 2008, the implementation group for the Global Monitoring for Environment and Security Atmosphere Core Service (GACS) has released his final report 3, providing the following recommendations for the space architecture from geo: It is accordingly recommended to develop and deploy the following new capacities: - A UVN spectrometer (Sentinel-4) to be embarked on MTG-S, to serve needs of regional operational Air Quality applications requiring dense sampling, in Europe, - To allow optimal use of the synergies of an UVN on MTG with the FDHSI (clouds, 1 MTG Mission Requirements Document, Eumetsat EUM/MTG/SPE/06/0011, Issue : v2b, 6 October GMES Sentinels 4 and 5 Mission Requirements Document, issue 1, revision 3, 9 Avril 2008, EOP-SMA/1507/JL-rd. 3 Report of the GAS implementation group : Global Monitoring for Environment and Security Atmosphere Core Service (GACS), Final report, July 2008, GMES Bureau

5 aerosol) and IRS (tropospheric O 3 and CO) instruments. In order to synchronise the MTG and Sentinel-4 it is then recommended to ESA and EUMETSAT to further harmonise their respective requirements on these projects. About thermal infrared spectrometers for atmospheric chemistry, the GACS requirements also include needs for thermal infrared spectrometers to sound the troposphere for atmospheric chemistry purposes and provide profile measurements of CO, ozone, HNO 3, CH 4 and volcanoes SO 2 to complement Sentinels 4 and 5 observations. As thermal infrared instruments are already part of the core payload of MTG and Post-EPS, as they are the baseline meteorological instruments to sound temperature, humidity and winds, specific recommendations are not provided here. But these instruments will also provide important information for air quality only if the instrumental specifications are optimized accordingly. It is thus recommended that the instrumental specifications (noise, spectral resolution, and pixel size) are also optimized to answer the GACS air quality and climate requirements. A.2 The MTG infrared sounding mission: goals The Infrared Sounding mission will support NWP by providing atmospheric motion vectors through the tracking of three-dimensional water vapour patterns. It will also deliver more frequent information on vertical temperature and water-vapour profiles in the atmosphere. The emphasis is on high horizontal resolution (better than 10 km), high vertical/spectral resolution (better than 1 km) and frequent observations (better than 1 hour) of the full Earth disk. The primary objective of the IRS mission is to support NWP at regional and global scales, through the provision of: Atmospheric Motion Vectors (AMV) with higher vertical resolution in clear air, to be extracted from the tracking of three dimensional water vapour patterns; More frequent information on temperature and water vapour profiles. The full disk AMV capability has the highest priority for global NWP, as this geostationary observing technique is unique for the extraction of three-dimensional wind fields in clear air. The secondary objective of the IRS mission is to support together with the UV-VIS sounding mission, chemical weather and air quality applications. It is recognised that these operational objectives could be in conflict, and that priorities may need to be captured in an optimum concept, that interleaves various observing modes. A.3 Instrumental specifications for MTG-IRS and S4-TIR MTG-IRS The MTG-IRS instrumental specifications are delivered by Eumetsat and are evolving continuously after iteration with industry (through ESA) and the MMT scientific experts that perform specific studies. Table A.1 provides the IRS target species, spatial resolution, noise - 5 -

6 The potential of MTG-IRS and S4-TIR to detect pollution events December 2008 specifications and spectral resolutions as quoted in the 'Post-MSG Mission Requirement Document', EUM/MTG/SPE/02/0015 (hereafter referred as MRD-MTG). These specifications were defined in priority to meet the needs for NWP global and regional applications. S4-TIR The study relies on the S4-TIR instrumental specifications from the GEMS Sentinels-4 and -5 Requirements Document EOP-SMA/1507/JL-rd (hereafter referred as MRD GMES). These specifications were defined in priority to meet the needs for atmospheric chemistry applications. Table A.1 provides the TIR target species, spatial resolution, noise specifications and spectral resolution. From these two missions, ozone (O3) and carbon monoxide (CO), which are both key atmospheric species for atmospheric composition measurement, can be retrieved. This has been demonstrated from previous and current missions based on thermal infrared sounders onboard satellite (see Section C), such as the IASI/METOP instrument [Clerbaux et al., 2007]. Figure A.1 illustrates an atmospheric spectrum measured by the IASI with a zoom on the ozone and CO absorbing spectral ranges, as well as radiative transfer simulations of the absorbing species in the same spectral range. Figure A.1 Atmospheric spectrum as measured by the IASI instrument onboard the METOP satellite (top panel) along with radiative transfer simulated spectra for the main absorbers in the ozone and carbon monoxide spectral ranges (middle panel). The ozone and carbon monoxide soundings uses the bands located around cm-1 and cm, respectively. These spectral intervals also contain weak signatures of other species, some of which, chemically reactive, would provide additional strength to the mission [Image courtesy P.F. Coheur, ULB]. -6-

7 Table A.1 Specifications of the proposed MTG IR Sounding (MTG-IRS) and Sentinel 4 Thermal Infrared Sounder (S4-TIR) as defined in the respective mission requirement documents. For S4-TIR Goal (G) and Threshold (T) values are provided. Geo instr. MTG-IRS S4-TIR Species O 3, CO O 3, CO, (HNO 3) Spatial res. 4 x 4 km 2 5 x 5-15 x 15 km 2 Nedt@280K 0.2 (O 3), 0.85 (CO) (G -T) OPD Spectral res. (unapodized) 0.8 cm 0.75 cm cm (G -T) cm -1 A.4 Aims of the study and definition of tasks This study investigates the capability of a thermal infrared sounder onboard a geo-orbiting satellite to detect enhanced levels of carbon monoxide (CO) and ozone (O 3 ) at local and regional scales, in particular over Europe. This is achieved using a combination of model simulations of the boundary layer and tropospheric pollution, existing measurements from on-board thermal infrared satellite instruments (MOPITT, IASI) and further sensitivity studies using the current MTG-IRS and S4-TIR instrumental specifications. To answer the questions, the following tasks are tackled: A.4.1 Atmospheric scenarios Air quality relates to the concentration of primary or secondary pollutants in the generally wellmixed boundary layer. These concentrations depend upon emission sources but also meteorological conditions, which regulate to a large extent the outflow of pollutants from the boundary layer to the free troposphere. Monitoring air-quality from satellites is a major challenge as it requires probing the atmospheric composition in the first one or two kilometers of the atmosphere. For a thermal infrared instrument the possibility to detect enhanced levels of concentration for any pollutant in the boundary layer is limited by the low thermal contrast between the surface and the first layer of the atmosphere. The capability of an infrared nadir sounder to probe the lower atmospheric layers, where local pollution occurs, therefore strongly depends on location, temperature, type of surface and time of the day. The first part of the report investigates how the boundary layer is affected by these parameters, which provides a good indication of thermal contrast. This is achieved using atmospheric models simulations (CTM). We also compare the model climatology to the occurrence of the major pollution events in Europe (e.g. peaks of ozone in summer)

8 A.4.2 Capabilities of current TIR nadir sounders to detect CO and O 3 for air quality purposes There are currently several thermal infrared nadir sounders in operation, which provide measurements of carbon monoxide and ozone from local to global scale. These instruments differ in the measurement technique (gas correlation radiometry vs. FTIR spectroscopy), ground pixel size and temporal sampling. We investigate the capabilities of these TIR sounders to measure carbon monoxide and ozone with sufficient accuracy and vertical sensitivity to deliver useful information for air quality applications. IASI and MOPITT will be taken as a basis for this study. Sub-tasks include: Assessment of vertical profile retrievals from MOPITT and IASI, in terms of vertical resolution, error sources in the troposphere. Analyses of the sensitivity to the surface under different types of conditions, including weak to strong thermal contrasts, low to high pollution events. A.4.3 Capabilities of MTG-IRS and S4-TIR to detect CO and O 3 for air quality purposes The current specifications of MTG-IRS and S4-TIR resemble those of IASI to some extent (all based on a Fourier transform spectrometer, with different instrumental specifications). Based on the differences in terms of spectral resolution and radiometric performances between these two sounders, we assess the capabilities of the MTG-IRS to contribute to air quality issues by a frequent monitoring of carbon monoxide and ozone and compare these to those of S4-TIR (for a range of specifications)

9 B. Atmospheric scenarios B.1 Pollution Formation Half of the global population presently lives in urban centers. In these large urban areas air quality is becoming a major concern, and an important area of research has emerged during the last decade in order to quantify the emission sources of pollutants, to model the chemical and physical transformation that leads to the production of secondary pollutants, and to study the transport pathways for the dispersal of pollution. Figure B.1 provides a schematic representation of the main sources, sinks, and photochemical reaction driving the concentration of the main atmospheric pollutants. For this report we will only report on ozone and CO, as these are the two molecules that can be measured from the radiance spectra measured using thermal infrared techniques. Carbon monoxide is a primary pollutant produced from methane and non-methane hydrocarbon oxidation, from fossil fuel combustion (associated with car traffic, industry and domestic heating) and from vegetation burning (for agricultural purposes or from wildfires). Its primary sink is oxidation by the hydroxyl radical (OH), which in turn controls the removal of most of the atmospheric pollutants as it is usually the predominant atmospheric oxidant. The CO atmospheric lifetime ranges from a few weeks to a few months depending on location and season, making it particularly suitable as a tracer of pollutant emissions. Ozone is a molecule of three oxygen atoms bound together. It is unstable and highly reactive. Ozone is found naturally in small concentrations in the stratosphere. In this upper atmosphere, ozone is made when ultraviolet light from the sun splits an oxygen molecule (O 2 ), forming two single oxygen atoms. If a freed atom collides with an oxygen molecule, it becomes ozone. Tropospheric ozone is generated from chemical reactions linked to internal combustion engines and power plants. Automobile exhaust and industrial emissions release a family of nitrogen oxide gases (NO x ) and volatile organic compounds (VOC), by-products of burning gasoline and coal. NO x and VOC combine chemically with oxygen to form ozone during sunny, high-temperature conditions of late spring, summer and early fall. High levels of ozone are usually formed in the heat of the middle of the day, and dissipates during the cooler nights. V Figure B.1 Schematic representation of the tropospheric keys species and reactions. CO and ozone are two of the most important drivers of atmospheric chemistry

10 B.2 Pollution peaks B.2.1 Chemical weather Air pollution is a problem in many parts of the world. High concentrations of fine particles and trace gases like ozone increase health problems, in particular pulmonary and cardiovascular diseases. Air pollution results in reductions of the average life expectancy, and excess mortality during summer heat waves. Health regulations in Europe, USA and other parts of the world set alert and thresholds for pollutants like ozone, nitrogen dioxide (NO 2 ) and fine particles (particulate matter, PM). Air pollution has contributions at many different scales. Above threshold conditions occur close to busy streets with large traffic pollution, but the contribution of far-away sources to local air pollution is considerable. A good quantification and understanding of the pollution levels requires dedicated observations and modelling efforts from the global scale down to street level. Apart from the direct impacts on human health, air pollution also has an adverse effect on the biosphere in general. The daily variability and real time composition of the atmosphere on the global and regional scale in relation to the meteorological situation has become an active area of research in recent years. The term chemical weather was introduced for this new field, and can be defined as the local, regional, and global distributions of important trace gases and aerosols and their variability on time scales of minutes to hours to days, particularly in light of their various impacts, such as on human health, ecosystems, the meteorological weather, and climate. The hot summer of 2003 (Figure B.2) has stimulated the development of models for the provision of operational air quality forecasts in West-European countries to inform the public on predicted high pollution levels. The quality of the simulations and forecasts of these air quality and chemical weather models is presently limited by the uncertainties in the modelled emissions and knowledge of the physical and chemical processes. For a detailed description of chemical weather a dedicated (real time) observing system is required, consisting of both ground- and space-based observations. B.2.2 Diurnal cycle Air quality in the planetary boundary layer, from a regional-scale down to street-level, is changing with a typical time scale of one to a few hours (see Figure B.3). The temporal changes in atmospheric composition are related to variable surface emissions (e.g. rush hour), chemical processes such as the speed of ozone formation and residence time of NO X, and physical processes like the diurnal cycle of the depth of the planetary boundary layer, frontal and convective systems and variations in actinic flux related to clouds and time of the day. The combination of these aspects determines the air quality at a specific place and time. The complexity and process interactions also reduce the predictability of the evolution of boundary layer air quality over the day and multiple observations per day are needed to well characterize the pollution levels

11 Figure B.2 Summer 2003: number of days for which the threshold value for information to the public (ozone concentration > 180 ug/m 3 ) was observed at rural and background stations (April-August) [Image courtesy European Topic Center on Air and Climate change]

12 Figure B.3 Diurnal and day-to-day variations of key trace gases and fine particulate matter (PM 2.5 and PM 10 ) in London at street-level. The build-up of air pollution during the day demonstrates the advantage of having more than one observation per day. The day-to-day variability is also very large [Image courtesy I. Kilbane-Dawe, AEATI]. B.2.3 Long-range transport of pollutants and aerosols Since the photochemical lifetime of O 3 and CO can be more than one month in the free troposphere, transport from source to remote regions affects the background levels on intercontinental scales. Local air pollution is determined not only by local sources and sinks but increasingly also by long-range transport of pollutants. Global observations are more and more needed to supplement the local and regional surface networks in order to get a full picture and better understand the observed air quality levels on the local scale. Figure B.4 shows the ozone change calculated for Europe in summer 2030, illustrating that, under current legislation conditions, the long-range transport of ozone and its precursors will cause ozone to increase over Europe. This effect will counteract the reduction in ozone due to decreasing precursor emissions in Europe (see next Section). Transport of ozone and its precursors from North America and chemical processing over the Atlantic Ocean already influence background concentrations over Europe. Similarly, export of pollutants from Asia influences the distribution of species over the Pacific Ocean and North America. The rapid economical growth in Eastern Asia will certainly result in increased northern hemisphere background levels of both ozone and fine particles (PM 2.5 )

13 Figure B.4 Impact of emissions and long-range transport on ozone in July 2030 as simulated by a model (LMDz-INCA and Chimere). The right panel shows the surface ozone change (in parts per billion (ppb)) calculated for 2030 under the IIASA current legislation scenario. The upper left panel shows that the corresponding decrease of NO x, CO and VOC emissions reduces ozone over large parts of Europe. The lower left figure shows that long-range transport from North-America and also Asia causes an ozone increase over Europe [Image courtesy: D. Hauglustaine/S. Szopa, LSCE]. B.3 Threshold and alert levels in Europe B.3.1 EU-27 emission trends 4 The European Environment Agency LRTAP report acknowledges that most EU-27 countries have reduced their emissions of air pollutants over the past decades. However, other studies show that pollution continues to undermine local air quality, particularly in urban areas. The report identifies road transport, manufacturing industries and construction, the residential sector and agriculture as the main sources of air pollution in Europe today. Aggregated EU-27 emission trends for NOX, CO, NMVOCs, SO X, NH 3, PM 10 and PM 2.5 are presented in Figure B.5. Total emissions of these air pollutants in the EU-27 still cannot be estimated for all years because of gaps in the underlying data reported by some countries. Across the EU-27 the largest reduction in emissions in percentage terms has been achieved for the acidifying pollutant SO X : emissions in 2006 were almost 70 % less than in Emissions of other key air pollutants also fell during this period, including emissions of the three air pollutants primarily responsible for the formation-destruction of ground-level ozone in the atmosphere: CO (53 % reduction), NMVOCs (44 % reduction) and NO X (35 % reduction). Trends of particulate matter (PM 10 and PM 2.5 ) levels have been compiled for the years 2000 to 2006 only. According to the data reported by the states, emissions of both these pollutants decreased by approximately 10 % in the EU-27 during this period. 4 Adapted from the Annual European Community LRTAP Convention emission inventory report , Submission to EMEP through the Executive Secretary of the UNECE, No 7/2008, EEA Technical report

14 Figure B.5 EU-27 emission trends in absolute (Gg) and relative terms for NO X, CO, NMVOCs, SO X, NH 3, between 1990 and 2006 (index year 1990 = 100, and for PM 10 and PM 2.5 between 2000 and 2006 (index year 2000 = 100) [Image courtesy LRTAP 2007 report]. B.3.2 Air quality exceedances in France 5 In agreement with the LRTAP EU-27 findings, regular measurements of air quality in Ile-de- France show a general decrease of all pollution sources emitted at the ground level, but a continuous increase of ozone. Figure B.6 reports the number of days for which the alerts or threshold levels of pollution were exceeded for the last 10 years, for three pollutants: O 3, SO 2, and NO 2. It is worth noting than for SO 2 the harmful levels are not met anymore, due to the decreasing use of heating based on coal. Due to the heat wave, the year 2007 is a record year for pollution peaks. Ozone Between 1992 and 2006, the average annual level of ozone has doubled over Paris, and in particular in the suburbs where averaged levels are higher. The air quality standards are exceeded during a few days every year. Figure B.7 shows the averaged increase of O 3 concentration levels as measured for three stations located around Paris. Carbon monoxide Between 1994 and 2003, a decrease of more than 60% of CO emissions has been observed close to traffic roads. This is a direct consequence of the use of catalytic filters for car exhaust and the increasing use of diesel engines. Figure B.8 illustrates the continuous decrease of O 3 concentration levels as measured for three stations located around Paris. Air quality standards are not exceeded any more over Paris. Elsewhere in Europe, only big fire events such as those that occurred in the Greek Peninsula in the summer 2007 can lead to exceedances of alert/threshold levels. 5 Report Air-Parif, La qualité de l air en Ile de France 2007, 2008 (available from

15 Figure B.6 Number of days for which the Air Quality alert levels have been exceeded during the last ten years, over the Ile-de-France area, for three major pollutants: SO 2, NO 2 and O 3 [Image courtesy Air Parif, Figure B.7 Averaged concentrations measurements for ozone over three stations near Paris, from 1992 to A general increase can be observed, and a peak concentration record was measured during the 2003 summer heat wave [Image courtesy Air Parif, Figure B.8 Averaged concentrations measurements for carbon monoxide over three stations near Paris, from 1992 to The alert levels of µg/m 3 has not been exceeded since 2000 owing to the improvement of catalytic filters on car exhausts [Image courtesy Air Parif,

16 B.4 The role of thermal contrast B.4.1 Definition For space instruments using the thermal infrared spectral range to sound the atmosphere, thermal contrast is a critical parameter for observing the planetary boundary layer (PBL), where all the pollution events occur. The thermal contrast between the surface and the PBL determines to what extend we can see sources of short lived species emitted near the surface. It is directly linked to the difference of temperature between the ground and the first atmospheric layer (see Figure B.9.), that reverberates into the radiance spectrum recorded by the instrument. Figure B.9 The radiance spectrum measured by an instrument onboard a satellite is the resultant of the atmospheric absorption and emission at different layers of the atmosphere. Of crucial importance for the study of the boundary layer is thermal contrast which is the difference (in radiance) induced by the temperature difference between the surface temperature (T skin ) and a reference temperature in the boundary layer (T 1 ) [Image courtesy L. Clarisse, ULB]. In the morning, the Earth starts absorbing UV, for which the atmosphere is roughly transparent. The Earth then transmits this energy on to the atmosphere via IR radiation. When the sun sets, the Earth is still giving of energy, while the atmosphere continues to absorb. The earth heats up/cools down faster than the atmosphere and therefore, the diurnal variation is larger, and hence thermal contrast more pronounced during day than night. B.4.2 Thermal contrast over Europe In this section we access the climatological variability of thermal contrast over Europe, in order to evaluate where and when thermal infrared sounders are sensitive to the boundary

17 layer. In Section C, we will link this model fields with measurements provided by the IASI/METOP instrument. The following results rely on model distributions of temperatures and boundary layer heights as provided by ECMWF fields, from which we extracted surface temperature (T skin ) and temperature at 10 m (T 10m ). Figure B.10. represents averaged thermal contrasts (difference between the two temperatures) for July 2007 (left) and January 2008 (right), above Europe, at 9am and 9pm. These time periods were chosen as they correspond to the IASI morning and IASI evening local time measurements. We also plotted the distribution as modelled at 3pm, as this corresponds to the time where maximum of ozone occur (see Section A of this report). These plots demonstrate the high spatial and temporal variability of the thermal contrast. Maxima are observed during daytime (9am and 3pm) for July as well as above continents. On the other hand, thermal contrast is very low at 9pm. This leads to the conclusion that relevant observation near the surface can only be observed by thermal infrared using daytime measurements. Figure B.11. represents the variations of the boundary layer height above Europe for the same time period (July 2007 and January 2008). These distributions show the good correlation between the thermal contrast and the boundary layer height. The more the thermal contrast is important the more elevated is the PBL, with some delay in time. Boundary layer height variations also become more significant in July above continents during daytime. This period of high PBL height matches with the timing and place of O 3 pollution episodes (as both need maximal solar radiation). B.5 The Chimere regional model B.5.1 Description of the model The CHIMERE Eulerian chemistry-transport model [Schmidt et al., 2001; Bessagnet et al., 2004] is designed to produce daily forecasts of ozone, aerosols and other pollutants and allows performing long-term simulations over several seasons for emission control scenarios. The model area covers scales from the regional to the urban one. In the vertical, the model contains eight layers up to 500 hpa pressure level, defined using hybrid coordinates. Meteorological input data used in CHIMERE result from external meteorological models such as ECMWF. Emissions derived from the EMEP yearly data are used (for details see the Web site CHIMERE offers the possibility to include different gas phase chemical mechanisms. The original, complete scheme [Lattuati, 1997] describes more than 300 reactions of 80 gaseous species. In order to reduce the computing time, a reduced version containing more than 110 reactions of 44 gaseous species is derived from MELCHIOR [Derognat, 2003]. CHIMERE is forced at its boundaries by climatological monthly means, calculated from the LMDzINCA model [Hauglustaine et al, 2004] or the MOZART model [Brasseur et al, 1998; Hauglustaine et al, 1998] for gas phase chemical species. Monthly climatologies computed from the global aerosol model GOCART [Chin et al., 2004] is used for mineral aerosols concentrations. CHIMERE can be downloaded from the Web site The model has been used in many studies, including the analysis of extreme events such as the 2003 heat wave in Europe [Vautard et al, 2005]. Model uncertainty has been analysed using a Bayesian Monte Carlo approach [Beekmann et al, 2003]. Several evaluations of the model have been performed [Hodzic et al, 2005]. The model has been used for data assimilation of ozone surface [Blond et al, 2004]. Also, the model participated in the assessment of the operational air quality forecasting and monitoring system PREV AIR [Honore et al, 2008]

18 Figure B.10 Averaged thermal contrast derived from ECMWF temperature fields, above Europe at 9am UTC, 3pm UTC and 9pm UTC, for July 2007 (left) and January 2008 (right) [Image courtesy A. Boynard]

19 Figure B.11 Averaged boundary layer height as derived from ECMWF fields, above Europe at 9am UTC, 3pm UTC and 9pm UTC, for July 2007 (left) and January 2008 (right) [Image courtesy A. Boynard]

20 In this study, the continental-scale version covering Western Europe with a 0.5 horizontal resolution is used. The vertical grid resolution extends up to 200hPa. The CHIMERE model is forced by ECMWF meteorological fields, by the EMEP emission database and the LMDzINCA monthly fields. B.5.2 Performances of the PREV AIR system to predict pollution events PREV AIR is the French operational forecasting and mapping system for air quality in Europe. It relies on the CHIMERE model, and other inputs as described in the previous section. For the European and French domains, PREV AIR system delivers every day, forecasts of ozone up to two days ahead, together with nitrogen dioxide and particulate matter (PM 10 and PM 2.5 ) concentrations forecasts. These outputs are made available on the PREV AIR website ( early in the morning. Global scale concentrations of ozone, nitrogen dioxide and dusts are also predicted up to two days ahead. The forecasts are evaluated every day, and then every year using an exhaustive comparison between observations and forecasts. Statistical indicators such as bias, root mean square errors, and correlation coefficients are computed for each species. An extensive analysis of these results is reported in [Honoré et al., 2007]. Generally speaking, it is demonstrated that the PREV AIR system performances comply with the state of the art, which was also demonstrated in European model intercomparisons [Vautard et al., 2007]. The scores are particularly satisfactory for ozone with a correlation of The scores are less reliable for pollution peaks, as shown in Table B.1 which reports the percentage of correct prediction, false alarms and missed events related to the ozone information threshold (180 µg/m 3 ), in 2003, 2004 and Table B.1 Skill scores for the ozone daily maxima, over spring/summers 2004 to 2006, for the European forecast. Years 2003, 2004 et 2005 (49 days over limit) All pollutants + all regions Ozone Paris Ozone Suburb Nitrogen oxide Paris Event forecasted Un-detected event 63% 45% 68% 33% 29% 26% 31% 28% False alarm 37% 55% 29% 67%

21 Conclusions Atmospheric scenarios: O 3 and CO pollution episodes Pollution (Europe) - Pollution episodes are generally limited to the boundary layer (BL). - The levels of all controlled pollutants (NO 2, SO 2, CO, O 3, PM 10 and PM 2.5 ) are continuously decreasing over Europe, except for O 3. - Ozone levels are increasing in Europe, as a result of decreasing NOx and increasing O 3 concentration coming from other continents (mainly Asia). O 3 - Pollution peaks are observed mostly in summer as a result of enhanced photochemistry. - The warning and alert levels for O 3 pollution have been reached several times in the recent year in Europe (eg 58 days of exceedance over Paris, including 19 in summer 2003). CO - The warning and alert levels for CO pollution have not been reached since a few year for CO and should not be reach anytime soon in the future (in Europe). - The only exceedances occur locally, when strong fire episodes (e.g. Greece in August 2007) occur. Thermal contrast - Thermal contrast, which is defined as the temperature difference between the ground and the atmosphere close to the surface is a critical parameter impacting on the sensitivity of TIR sounders to the BL. - Thermal contrast varies as a function of location and time of the day. The highest values (and hence the best sensitivity for TIR instrument in the BL) is achieved during the summer, between 12 and 15h, over land. - This period of highest sensitivity matches with the timing and place of O 3 pollution episodes (as both need maximal solar radiation)

22 C. Capabilities of current TIR nadir sounders to detect CO and O 3 for air quality purposes C.1 Current thermal infrared sounders There are currently four spaceborne thermal IR instruments providing CO and/or O3 measurements from polar-orbiting satellites : MOPITT/TERRA, AIRS/AQUA, TES/AURA and IASI/METOP. Hereafter is a description of the four missions, and Table C.1 is providing a summary of the instrumental specifications of each platform/instrument, as well as a list of the retrieved products. These four missions use different instrumental designs (Fourier transform spectrometer (FTS), gas correlation and radiometer), with different instrumental specifications (spectral ranges, spectral resolution, radiometric noise, and pixel size). MOPITT/TERRA The Measurements Of Pollution In The Troposphere (MOPITT) remote sensing instrument, which was developed by Canada and US, was launched aboard the EOS Terra satellite in December 1999 and became operational in March MOPITT includes nadir-viewing channels for monitoring both carbon monoxide [Deeter et al., 2003, 2004] and methane, to quantify and track the movement of pollution in the troposphere. MOPITT operates by sensing infrared radiation from either the thermal emission/absorption at 4.7 µm for CO profiles, or reflected sunlight at about µm for CO and CH 4 column measurements in daylight. The measurement technique exploits gas correlation radiometry to determine tropospheric concentrations. The MOPITT instrument is equipped with four CO modulation cells operating in two spectral bands (a solar band around 2.3 µm and a thermal band around 4.6 µm) to produce a total of six CO-sensitive channels. Due to the loss of one of the instrument's two coolers in May, 2001, two of the four thermal-band CO channels (Channels 1 and 3) no longer provide useful information. The TERRA spacecraft operates in a near-circular, sun-synchronous orbit with an inclination of approximately 98.2 degrees. The descending node crossing time is 10:30am. MOPITT views the Earth over all latitudes with a pixel size of 22 km by 22 km and a cross-track swath that measures a near-global distribution of CO every 3 days. AIRS/AQUA The Atmospheric Infrared Sounder (AIRS) is a high-resolution infrared sounder selected to fly on the EOS Aqua platform with two operational microwave sounders, AMSU and HSB. It is an high-spectral resolution, grating multispectral infrared sounder (3.74 to 15.4 µm), with a spectral resolving power of 1200, operating in a cross-track-scanning mode. Measurements from the three instruments are analyzed jointly to filter out the effects of clouds from the IR data in order to derive clear-column air-temperature profiles and surface temperatures with high vertical resolution and accuracy. Together, these three instruments constitute an advanced operational sounding system. The AIRS retrieved temperature profiles have an accuracy of 1K per 1 km thick layer in the troposphere and moisture profiles have an accuracy of 20% per 2 km thick layer in the lower

23 troposphere (20%-60% in the upper troposphere). A description of CO retrievals is provided in [Mc Millan et al., 2005]. web: TES/AURA The Troposperic Emission Spectrometer (TES) is one of four instruments onboard NASA s AURA satellite launched in July TES is a Fourier Transform Spectrometer designed to infer the vertical distribution of tropospheric ozone and carbon monoxide using both nadir and limb viewing geometries from outgoing spectrally resolved IR radiation. During routine operation, the radiometrically calibrated nadir spectra [Worden et al., 2006] cover the thermal IR region in four bands from (band 2B1), (1B2), (2A1) and (1A1) cm -1 at a spectral resolution of 0.1 cm -1 (apodized). The AURA spacecraft is in a sun-synchronous orbit at an altitude of about 705 km, with a 13:38 local mean solar time ascending node. For the nadir observation, TES employs 1x16 linear arrays of rectangular pixels. The projections of these arrays used in nadir are 8 km along-track and 5 km crosstrack at the Earth's surface. The vertical distribution of the following atmospheric products are retrieved operationally: atmospheric temperature and H 2 O, CO [Luo et al., 2007], O 3 [Worden et al., 2007], among others. IASI/METOP IASI, the Infrared Atmospheric Sounding Interferometer, was launched in 2006 onboard the METOP-A satellite. This is the first European meteorological polar-orbiting satellite, the second and third instruments will be mounted on the METOP-B and C satellites with launches scheduled in 2010 and It is a joint undertaking of the French spatial agency CNES (Centre National d'etudes Spatiales) and EUMETSAT, with CNES managing the instrumental development part and EUMETSAT operating the instrument in orbit. This meteorological platform is the space segment of the EUMETSAT Polar System (EPS). IASI provides accurate measurements of the temperature profiles in the troposphere and lower stratosphere, as well as moisture profiles in the troposphere in order to improve the quality of numerical weather forecasts [Schlüssel et al, 2005]. IASI is also monitoring some of the chemical components playing a key role in the climate monitoring, global change and atmospheric chemistry: CO 2, CH 4, N 2 O, CO, O 3, HNO 3. [Turquety et al, 2004]. The instrument consists of a Fourier Transform spectrometer associated with an imaging system, designed to measure the infrared spectrum emitted by the Earth in the thermal infrared using a nadir geometry. The instrument provides spectra of high radiometric quality at 0.5 cm -1 resolution (apodized), from 645 to 2760 cm -1. The MetOp polar orbit is at an altitude around 817 km. The satellite is slightly slanted at a 98.7 inclination to the equator, and the Earth pas s beneath the satellite ground track at 09:30 in the morning. The time to complete an orbit is about 101 minutes, which implies that MetOp makes a little more than 14 revolutions a day. IASI has a squared field of view sampled by a matrix of 2x2 circular pixels of 12 km each. The measurements are taken every 50 km at nadir with an excellent horizontal coverage due to its ability to scan across track over a swath width of 2200 km

24 Table C.1 Current thermal infrared atmospheric sounders. Mission/Plate-form Agency MOPITT/TERRA NASA AIRS/AQUA NASA TES/AURA NASA IASI/MetOp EUMETSAT/CNES Instrument Correlation radiometer Spectrometer I/ I=1200 FTS OPD : 8.45 cm (nadir) FTS OPD : 2 cm Sounding geometry Pixel size Altitude coverage Nadir + scan 22 x 22 km 1 pixel 0-25 km Nadir + scan 13.5 x 13.5 km 9 pixels 0-30 km Nadir + pointing 5.3 x 8.3 km 1 pixel 0-32 km Nadir + scan 12 x 12 km 4 pixels 0-30 km Measured species Launch date CO December 1999 H2O, CO2, CH4, O3, CO, (SO2) H2O, CH4, N2O, O3, CO, NO, NO2, HNO3 H2O, HDO, CO2, CH4, N2O, O3, CO, CFC-11, CFC-12, HCFC22, HNO3, (SO2), NH3, C2H4 May 2002 July 2004 October 2006 All 4 missions measure CO and TES, AIRS and IASI measure ozone. It is worth noting that only AIRS and IASI provide operational measurements as they are onboard meteorological plateforms, and that AIRS with its crossing time of 1:30 pm has a better timing to sound tropospheric ozone. But due to the quite coarse spectral resolution of the instrument, the contamination from the stratosphere is expected to be large. Thermal IR instrument measure total columns or low vertical resolution profiles, the accuracy and the vertical sensitivity of which are significantly depending on the instrument specifications (spectral resolution and radiometric noise), as well as on geophysical situations (thermal contrast). Generally, TIR instruments have higher sensitivity in the free troposphere, between 5-10 km for CO and O 3. Figure C.1 provides radiance measurements as observed from AIRS, TES and IASI, and shows that the spectral range, the spectral resolution and the S/N ratio varies. The exact location of the measurement is also provided, these observations were selected as they are co-located, although the time of the observation differs

25 Figure C.1 Radiance measurements as observed from AIRS, TES and IASI over Brazil on August 1, 2008 [Courtesy M. George, SA]. C.2 Carbon monoxide observations Carbon monoxide was one of the first tropospheric trace gases observed from space. As a result of its relatively long lifetime, it undergoes long range transport from its sources and mixes both horizontally and vertically. Space borne observations from MOPITT reveal a hemispheric gradient and high values in the outflow of regions with intense biomass burning [Edwards et al., 2004]. Main source regions are fires in Africa and South America but also fires in boreal forests in Siberia and Alaska emit large quantities of CO [Petron et al., 2004; Pfister et al., 2005]. As it is dominated by biomass burning emissions, the global CO burden shows large interannual variations following changes in the amount of biomass burning. This indicates that future CO distributions will largely depend on the evolution of anthropogenic and natural fires in response to climate change and farming practice. Although MOPITT measurements have only limited sensitivity in the lower troposphere, the signature of anthropogenic pollution can be observed over industrialised regions in long-term averages [Clerbaux et al., 2008; Kar et al., 2008]. The anthropogenic signal can also be retrieved using measurements in the solar IR by SCIAMACHY [Buchwitz et al., 2007] as it has full sensitivity down to the ground. However, the signal-to-noise ratio of these measurements is limited. As mentioned here above, AIRS and IASI provide operational measurements that could be used for air quality observation and forecast. Figure C. 3 provides an illustration of a one

26 month averaged distribution of CO as observed by IASI. The performance of each mission in terms of accuracy and vertical profile capability varies as a function of latitude, season and land/see surface. Values of accuracy and DOFS are reported in Part D of this report, along with the results simulated for MTG-IRS and S4-TIR, as the IASI specifications match with those of S4-TIR threshold. Figure C.3 IASI CO total column one month averaged data, for day (left) and night (right). In the Northern hemisphere, most of the pollution is associated with urban activity, with persistent high values above China and elevate levels over US, Europe and Asia in spring and winter. In the tropics and Southern hemisphere, most of the CO is emitted where biomass burning occurs, such as in Africa, Central and South America. The CO pollution plumes emitted locally spread from regional to global scales, depending on meteorological conditions and photochemistry. In the framework of this study, detailed investigation were undertaken in order to access the capability of MOPITT (as it is the instrument that has the longest recording series of CO observation) to detect pollution from cities and urban areas. This study uses the full 7.5 years of available MOPITT global data. MOPITT was not designed to detect local pollution plumes emitted from cities. The large pixel size (22 km x 22 km) and the long revisit time interval are drawbacks to use these datasets for operational air quality purposes. Moreover, over the oceans, as emphasized by Deeter et al. [2004], the measurement has its maximum sensitivity to CO in the lower free troposphere and lacks sensitivity to the planetary boundary layer due to reduced thermal contrast. In order to separate the continuous localized signal generated by urban activity from the changing background, we have averaged the MOPITT L2 data over long time periods. This increases the precision due to the redundancy of the accumulated information. Figures C.4 and C.5 illustrate the average of the CO mixing ratios at the surface level, as derived from the measurements from March 2000 to June Figure C.4 compares the global L2 MOPITT surface CO distribution to population density [CIESIN, 2000, both averaged over a 1 x 1 grid, over China and parts of India and Japan, which are among the most populated areas of the globe. A very good correlation, especially for China, is observed between the MOPITT surface CO mixing ratios and the population density, with the highest CO levels observed where the higher population density occurs. In China and India, the combination of old car fleet and large use of coal as anthropogenic fuels contributes to elevated levels of CO. In contrast, over Europe and the US, the level of CO is lower and the signal is a continuous

27 mixing of the background CO loading (long-range transported from China and the USA, and from the boreal fires in Siberia and Canada) and the local production of urban areas. Figure C.4 Top: Population density (source CIESIN, in million inhabitants) over China and surroundings. All cities with more than 2 millions of inhabitants are indicated. Middle: MOPITT CO mixing ratios at the surface level (obtained by averaging the MOPITT L2 measurements from March 2000 to June Adapted from [Clerbaux, et al., 2008]. Figure C.5 presents time averages of MOPITT CO over specific regions (US west and east coasts, the Milan area (Italy), Mexico City, Teheran (Iran), Tokyo (Japan), Moscow (Russia), Jakarta (Indonesia) and Johannesburg (South Africa)), where intense emissions originating from large cities can be observed. The enhancement of CO can clearly be detected, either above the city, or in the general vicinity, such as the Pô valley in Italy where pollution accumulates south of the Alps

28 The most favorable situations to observe these plumes are 1) places surrounded by mountains such as Milan, Jakarta, Teheran and Mexico City, where the pollution is trapped and the source is isolated to some extent from the surroundings, and 2) locations where high thermal contrast conditions are found, such as Tokyo, Moscow, LA-San Diego, San Francisco, NY-Philadelphia and Johannesburg. It is worth mentioning that some notably polluted cities in Africa (e.g. Lagos, Nigeria) and South America (e.g. Sao Paulo, Brazil) cannot be detected, firstly as they are often under the outflow coming from regional biomass burning activity, and secondly because thermal contrast conditions are relatively weak, presumably due to evaporation and evapotranspiration processes associated with surrounding forests [Deeter et al., 2007]. Figure C.5 MOPITT CO mixing ratios at the surface level (obtained by averaging the MOPITT L2 measurements from March 2000 to June 2007, on a 0.5 x 0.5 grid) over selected places. The linear color bars were scaled to each maximum for sake of clarity, with the lower limit fixed at 80 ppbv. The averaged number of data at the city location is indicated at the bottom of each subplot. Adapted from [Clerbaux, et al., 2008]. C.3 Tropospheric ozone observation Satellite remote sounders already contribute to the monitoring of tropospheric ozone chemistry and to forecasting air quality by providing quantitative information on these ozone precursors. A significant breakthrough in observing the atmospheric composition from satellites comes from the direct observation of tropospheric ozone itself. Although conceptually straightforward, the practical implementation of this task is challenging as it comes in retrieving a small fraction of ozone from the measured integrated column, largely dominated by the stratospheric ozone layer. The UV-visible instrument provide only weak

29 sensitivity to tropospheric ozone while thermal sounders offer maximum sensitivity in this layer, with some vertical profiling (~6 km resolution) capabilities (e.g. [Coheur, et al., 2005, Worden, et al., 2007a]). The potential of probing the boundary layer remains, however, limited in most situations. Extended analyses have been performed, using TES and AIRS in particular. They have highlighted seasonal trends [Divakarla, et al., 2008], enhanced pollution patterns and longrange transport [Jourdain, et al., 2007; Parrington, et al., 2008; Zhang, et al., 2006]. More recently, the enhanced capabilities of TIR sounders to probe tropospheric ozone have been used to perform an analysis of the photochemical pollution events that occurred during the summer 2007 heat wave in Southern Europe, with the recently launched IASI sounder [Eremenko, et al., 2008]. Overall this study is a first step in using infrared satellite observations to monitor tropospheric ozone and to improve the forecasts of air quality and climate models. On the theoretical side, evidences that improvements in measuring tropospheric ozone could be gained by combining information from complementary observations in the UV-visible and the TIR have been obtained [Landgraf and Hasekamp, 2007, Coheur et al, 2005]. Yet the demonstration on the field still needs to be made. Figure C.6 (Top) Ozone concentrations from IASI during the summer 2007 heatwave in Europe, expressed as the 0-6 km partial column, and (bottom) regional predictions of the CHIMERE chemistry-transport model. Adapted from [Eremenko, et al., 2008]

30 C.4 Thermal contrast Following the work on thermal contrast performed using climatological values from ECMWF temperatures and boundary layers distributions (see Section B), a similar work was undertaken using the IASI/METOP data. Figure C.7 provides the T distribution derived from IASI observations, where T skin is the surface temperature as derived from the IASI L2 data, and T 1 is the first level of altitude retrieved for temperature. As expected, the thermal contrast is higher during daytime observations than during nighttime observations, and is usually higher over land than over sea. The direct consequence of this is that the IASI instrument will provide more accurate and vertically resolved measurements for the morning orbit and over land. Figure C.7. Thermal contrast distribution as observed by IASI in May 2007, for the morning orbit (top panel) and the evening orbit (bottom panel) [Courtesy L. Clarisse, ULB]

31 Conclusions Capabilities of current TIR nadir sounders to detect CO and O3 for air quality purposes Current TIR - Current thermal IR instruments providing CO and/or O3 measurements are the polarorbiting MOPITT/TERRA, AIRS/AQUA, TES/AURA and IASI/METOP. - The only near real time measurements (needed for pollution forecast) are provided by AIRS (1:30 AM and PM local time) and IASI (9:30 AM and PM local time). - Thermal IR instrument measure total columns or low vertical resolution profiles, the accuracy and the vertical sensitivity of which are significantly depending on the instrument spectral resolution and radiometric specifications. - Generally, TIR instruments have higher sensitivity in the free troposphere, between 5-10 km for CO and O3. Thermal contrast - Thermal contrast as observed from the IASI/METOP mission shows that the daytime observation will be more sensitive to the PBL than the night-time measurements

32 D. Capabilities of MTG-IRS and S4-TIR to detect CO and O 3 for air quality purposes In this task we primarily assess the capabilities of MTG-IRS to retrieve tropospheric ozone and carbon monoxide using the instrument specifications provided in Table A1. In a first step, we use standard diagnostics to characterize the instrument performance at geophysical level: we focus on the vertical sensitivity to the trace gas profile and on the retrieval error, essentially on the tropospheric columns. We also investigate the performance losses as compared to the S4-TIR sounder, optimized for atmospheric chemistry purpose. In a second step, we investigate the capabilities of these sounders to capture elevated photochemical pollution events (for ozone) and to track their diurnal build up. The theoretical method and the approach followed in this study, including assumptions on the state of the atmosphere etc., are briefly recalled in section D.1. The results are presented and discussed in section D.2. D.1 Theory and Methodology D.1.1 General formulation of the Inversion Method For the TIR sensitivity simulations and studies, we use the Atmosphit software [Coheur, et al., 2005], which contains ray tracing for various geometries, a line-by-line radiative transfer model as complete and as precise as possible, and an inversion scheme that relies on the Optimal Estimation theory [Rodgers, 2000]. Given the general radiative transfer equation, the measurement y can be expressed as: y = F( x,b) + ε (01) where F is the forward radiative transfer function, b represents model parameters affecting the measurement and ε is the measurement noise. A synthetic spectrum is computed in Atmosphit using the line parameters (positions, intensities, broadening and shifting parameters, including their dependence on temperature), and absorption cross sections for the heavier molecules, as collected in various spectroscopic databases. Here the HITRAN 2004 database [Rothman, et al., 2005] is used, with, for the molecular lines, a Voigt line shape calculated in each atmospheric layer. The water vapor (MT-CKD model [Clough, et al., 2005]), carbon dioxide, oxygen and nitrogen continua are also included when relevant. The resulting spectrum is processed to account for the Instrumental Line Shape (ILS). The Atmosphit software is able to provide the derivatives of the radiance with respect to the parameters to retrieve, K = y x (x includes the vertical abundances of the target species) as well as to the model parameters K = b y b. Starting from relevant a priori information, composed of a mean state x a, and an a priori covariance matrix, S a, which has to represent the best statistical knowledge of the state prior to the measurements, the retrieved state can then be found using the Optimal Estimation Method. Assuming a linear problem, the optimal vertical profile can be written as [Rodgers, 2000]: T T 1 1 ˆ = ( ε + a ) ( ε + a a ) x K S K S K S y S x (02)

33 where S ε is the measurement covariance matrix. Introducing respectively the gain and averaging kernels matrices G and A, xˆ T T 1 G = = ( K Sε K + Sa ) K Sε y ˆ x A = = GK x equation (02) can also be rewritten as: xˆ = x + A( x x ) + Gε ( + K ( b b ˆ)) (05) a a b Information content The element A(i; j) is the relative contribution of the element x(j) of the true state to the element x ˆ( i) of the retrieved state. The vertical resolution of the retrieved profile can be defined as the Full Width at Half Maximum (FWHM) of the rows of the averaging kernel matrix. The number of independent elements of information contained in the measurement can also be estimated as the Degrees Of Freedom for Signal (DOFS) defined as the trace of the averaging kernel matrix [Rodgers, 2000]. (03) (04) Error budget In the linear approximation, the total error is computed from the linear retrieval equation (Equation ULB-05) as the difference between the true state and the retrieved state: xˆ x = ( A I)( x x ) + Gε + GK ( b - bˆ ) + A τ ( τ τ ) (06) a b x a The estimated total error can be decomposed in four terms: 1. The smoothing error, ( A I)( x x a ), accounts for the smoothing of the true state by the averaging kernels. The covariance matrix of the smoothing error is given by: T S s = (A - I)S a (A - I) (07) 2. The measurement error, Gε, is due to the instrumental noise. Its covariance matrix is given by: T Sm = GSnG (08) S n is a real noise matrix, which can be taken similar as S, which is used as a constrain on the retrieval. 3. The model parameters error, GK ( ˆ ) b b - b, is accounting for the imperfect knowledge of the model parameters. The covariance of this error term is given by: S = GK S ( GK ) T (09) p b b b Where S b is the covariance matrix representing the uncertainty on the forward model parameters, which are held fixed. In this study, only the temperature profile falls in this category; it is considered with an uncertainty of 0.5 K at each level. 4. The cross-state error, Ax τ ( τ τ a ) quantifies how the uncertainty on fitted model parameters reverberates on the error in the retrieved target quantity. The covariance of this error is S A S A (10) T cs = x τ τ x τ where S ε is the uncertainty on the fitted parameters, which include surface temperature (5 K), emissivity (2 %), water vapour profile (10 % at each level) and CO 2 columns (5 %)

34 The total error covariance matrix is then given by: S = ( S + S ) + ( S + S ) (11) T s m p cs The sum of the first two terms (smoothing and measurement errors) will be referred to as the internal error, which is inherent to the observing geometry and instrumental capabilities, whereas the sum of the last two terms, which are related to the knowledge of the atmosphere, constitute the external error. D.1.2 Methodology for the TIR retrieval assessment The approach is twofold as follows: 1. Sensitivity studies are performed using the Optimal Estimation method in the linear approximation. The results are expressed in terms of vertical sensitivity (DOFS and averaging kernels) and accuracy on the target species vertical profile and partial columns. The European background scenario is chosen as the single baseline scenario for this task. 2. Retrieval experiments are performed using a priori information on the target species (a priori profile and associated covariance matrix) to retrieve vertical profile typical for a scenario produced in Section B (Task 1). Details of these two approaches are given below. For both, the instrument specifications, in terms of spectral resolution and radiometric noises considered for the simulations, are as provided in Table D1 6. More complete information for the MTG-IRS noise specifications and pre-phase A industry performance is provided on the next page (extra plots added at a later stage). Table D.1 Set of instrument specifications considered for this study. Instrument Band MOPD (cm) Sampling (cm -1 ) FWHM (cm -1 )* NeDT (K) NESR (W / cm 2 sr cm -1 ) MTG-IRS IRS-MWIR IRS-LWIR S4-TIR (goal) GEO band GEO band S4-TIR (threshold) GEO band GEO band * The FWHM is here for the non-apodized ILS. D Linear approximation Model atmospheres In the linear approximation, the sensitivity to the a priori profile on the results is not significant. Here the ozone or carbon monoxide a priori profiles considered are the MOZART global annual mean ones, already used in previous studies [Clerbaux, 2006]. For 6 Note that the MTG-IRS specifications adopted for the present study rely on pre-phase A industrial assessment of instrumental requirements, whereas the adopted S4-TIR specifications have not been assessed by industry at time of writing

35 temperature and the vmr of all other modeled trace gases, a standard atmosphere is considered. For the CO retrievals the profiles are all interpolated on 1 km thick layers and the first 15 levels are considered for the retrievals. For ozone the profiles count 36 levels, from the ground to 50 km. It was constructed as in Turquety et al. [2004]. 1.0 IRS-3 ozone IRS-5 Methane IRS-7 Carbon monoxide NeDT (K of bb@280k) K MRD1.2 specifications K 0.2 K wavenumber (cm -1 ) IRS-5 Methane NeDT (K of bb@280k) IRS-3 ozone MRD1.2 Reference scenes: ABB FTS ASTRIUM FTS ASTRIUM DS IRS-7 Carbon monoxide wavenumber (cm -1 ) Top: Noise MRD specifications for IRS-3 (ozone), IRS-5 (methane) and IRS-7 (CO). Bottom: Noise performance specifications provided by ABB and Astrium during the pre-phase A study (extracted from Clerbaux et al, 2006). These cases, referred to as ABB FTS, Astrium FTS and Astrium DS, are plotted here in NEDT units, considering a reference blackbody at 280 K, along with the initial noise specification provided in the MRD1.2 document

36 Prior information For both the sensitivity studies in the linear approximation and also the retrieval experiments (see below), we use the a priori covariance matrices associated to the above reference profiles. They are built from the MOZART ensemble of profiles (considering two profiles per month on the degrees MOZART grid) and which thus accounts for realistic correlations amongst the level. The a priori profiles and associated covariance matrices for O 3 and CO are shown in Figure D.1. Altitude (km) Altitude (km) vmr (ppm) Altitude (km) Altitude (km) ,00 0,05 0,10 0,15 vmr (ppm) Figure D.1 MOZART a priori information considered throughout the study Left: annual mean profile and variance (diagonal elements of Sa) for ozone (top) and carbon monoxide (bottom). Right: Full covariance matrix 7. Tested parameters The relevant parameters that are addressed in the linear approximation include: Level1B requirements. These are analyzed in terms of spectral band and trade-off between spectral resolution and radiometric noise, following mainly the differences in specifications between MTG-IRS and Sentinel-4. Only the nadir with a viewing zenith angle at 0 is considered, 7 All the plots in this Section are Courtesy O. Scharf/P. Coheur, ULB

37 Thermal contrast: Important parameters for the TIR retrievals are emissivity and surface temperature, which characterize the source radiation from the surface and hence the radiance difference between the surface and the first layer in the atmosphere (see Section B). Except when explicitly written, the sensitivity studies use a constant value ε = 0.96 for the spectral emissivity, whereas different values of T are considered, between + 10 and -10 K. A vanishing thermal contrast, T=0K is adopted as the reference case. D Retrieval experiments (for ozone only) The retrieval experiments aim at adjusting a spectrum corresponding to a specific scenario considering a priori information different from the truth. For the purpose of this study the truth is taken as a highly polluted ozone profile modeled by CHIMERE, representative of the 2007 heatwave in North-Eastern Europe (Figure D.2, upper left). The situation at 15h is taken as the reference. This atmosphere is characterized by relative high temperature (above 305 K at ground level). A reference simulation is made with vanishing thermal contrast (surface temperature equivalent to that of the atmosphere) but the dependence towards this parameter is also studied within an acceptable range of values (between -10 and +10 K). The selected case shows obviously elevated ozone mixing ratios throughout the troposphere but in particular in the planetary boundary layer, where it reaches 0.15 ppm (Figure D.2 bottom). Figure D.2 Top left: Surface ozone (alt=1.3 km) in Europe on August 8, 2003 at 15 h. Top right: Temperature and ozone profile for a situation on the North Eastern part of Europe, where surface ozone reaches its maximum (location marked by the black rectangle on the left panel). The blue line is the MOZART a priori with corresponding variance (1σ). Bottom: Zoom

38 into the tropospheric ozone profile. The red line is the target case and the blue line the MOZART a priori. The error bar show the variance (1σ in blue and 3σ in cyan). The diurnal variations at this location are shown in Figure D.3. It can be seen that the highest ozone values at the surface are observed throughout the afternoon, as anticipated from the increased photochemical activity. The capabilities of MTG-IRS and S4-TIR sounder to capture these diurnal variations in the planetary layer are also studied. For the retrievals, the ozone a priori profile used is that from MOZART, which is associated in the retrievals to the corresponding covariance matrix (Figures D.1 and D.2). At this stage it is already worth pointing out that the high values characterizing the reference polluted profile at 15h lies outside the 3σ variability allowed in the covariance matrix (Figure D.2, bottom), thereby compromising the retrievals capabilities for purely methodological limitations. This will be discussed in the next section. The temperature and pressure profiles for the retrievals are those of the truth, meaning that the impact on the retrievals of possible uncertainties on these parameters has not been investigated here. The spectral range for the retrieval experiments spans 1030 to 1080 cm 1. It fulfills, as will be shown in section D.2., most of the requirements in terms of vertical sensitivity and retrievals errors. In processing the retrieval experiment, the following quantities are adjusted: Ground temperature T g O 3 profiles on 36 levels from the ground to 50 km with the same grid as that of the input atmosphere H 2 O profiles on 16 levels from the ground to 15 km CO 2 total column. Figure D.3 Diurnal variations of the tropospheric ozone profile for the case shown in Figure D

39 D.2 Results D.2.1 Ozone retrievals D Assessment of MTG-IRS retrievals for a reference standard atmosphere Spectral range In order to assess the adequacy of the prescribed cm -1 spectral range for the ozone retrievals, plots have been generated to represent, in false color, a map of the evolution of the error with respect to the centre of the wavenumber window (abscissa) and to the size of the window (ordinate). The optimal wavenumber window (centre and width) can then be selected based upon the lowest achieved uncertainty and the highest DOFS. The results are shown for MTG-IRS specification in Figure D.4. From this Figure we conclude that MTG-IRS enables retrieving ozone vertical profiles with close to 4 independent pieces of information, and an error on the tropospheric column of the order of 10 %. These values, obtained using the entire MTG-IRS band 3, meet the chemistry requirements [Lelieveld, 2003]. Reducing the spectral interval to a subset of MTG-IRS-3 (e.g or cm-1) does not result in a significant loss of vertical sensitivity (DOFS close to 3.5) nor to an increase in the retrieval errors, with an error on the tropospheric column that remains around 15%. Following this and in agreement with previous studies and the MRD, the cm -1 spectral range for the following simulation has been adopted. It is covered by MTG-IRS band 3 and for a small part by MTG- IRS band 4 to add baseline information. The weak impact of the spectral range on the retrieval performance is such that we recommend it be optimized, if needed, considering the benefit for the mission that is anticipated from the measurements of other reactive chemicals absorbing. This is the case of NH 3 and CH 3 OH, now monitored by TES [Beer et al., 2008] and IASI (Figure D.5). The same holds true for S4-TIR spectral range specification. Figure D.4 Ozone retrieval achievements in terms of vertical sensitivity expressed as the DOFS (left panel), and retrieval error on the tropospheric (0-12 km) column (right panel). The results are given in 2D as function of band centre and band width. The dots indicate specific intervals: cm -1, which is the entire MTG-IRS3 (green dot), cm -1 (cyan dot) and cm -1 (white dot). The results are obtained with MOZART as a priori profile, and with vanishing thermal contrast

40 The potential of MTG-IRS and S4-TIR to detect pollution events December 2008 Figure D.5 IASI normalized spectrum in the region of the ozone 9.7 µm band (top panel), and associated molecular contributions. The middle and bottom panel show contributions from strong and weak absorbers, respectively. The latter include chemically active species, such as NH3, CH3OH, C2H4, HCOOH, previously measured by IASI [Coheur et al., 2008]. C2H6 and PAN have not been measured to know in the nadir view. Vertical sensitivity The vertical sensitivity is analysed by means of the averaging kernel functions, which are plotted in Figure D.6 in terms of 6 km thick atmospheric columns. The corresponding value for the DOFS is 3.5, in agreement with Figure D.4. The sensitivity of the instrument covers the troposphere and lower stratosphere, with essentially three independent kernels, corresponding to the 0-12, and 18 km partial columns. This means that with the current MTG-IRS specifications a tropospheric column will be retrieved independently but that it will not be possible to separate the latter in different contribution. We note vanishing sensitivity to the surface but recall that the simulation set-up assumes a vanishing thermal contrast. Error budget The error budget for the reference simulation is given in Figure D.7, where it is compared to the MOZART a priori variability. It can be seen that the error is strongly reduced as compared to the a priori over the entire altitude range from the troposphere to the lower stratosphere, except in the planetary boundary layer. In this layer the error remains large, whereas above 2 km it is between 20 and 40 % on each retrieved level of the profile. The main improvement is in the free and upper troposphere, where the sensitivity is largest and the a priori variability high. The very dominant source of error is the smoothing error, with

41 additional contribution from the measurement noise. The low sensitivity near the surface is partly due to the vanishing thermal contrast and reflects a relatively unfavourable case. Figure D.6 Merged averaging kernels, in partial column space for ozone retrievals by MTG-IRS. The spectral range is cm-1. Spectral resolution and noise as as given in Table D1. The simulation assumes vanishing thermal contrast. Figure D.7 Error budget for ozone retrievals by MTG-IRS. The spectral range is cm -1. Spectral resolution and noise are as given in Table D.1. The simulation assumes vanishing thermal contrast. D Comparison between MTG-IRS and S4-TIR retrieval performances The merged averaging kernels representative of MTG-IRS ozone retrievals shown above are compared to those of a TIR with improved performance in Figure D.8. Both an improvement on the noise level (0.05 K instead of 0.2K) and the spectral resolution (4 cm instead of 0.8 cm), matching the differences between MTG-IRS and the S4-TIR goal values, are considered. It can be seen that the improvements on these two specifications have almost the same impact on the

42 retrievals: they increase vertical sensitivity in the troposphere, with now two distinctive but still highly correlated kernels. The errors on the retrieved partial columns 8 are lowered as a result. The dependence of the retrieval performances to the spectral resolution and the radiometric noise, within the range of values listed in Table D.1 (thus covering from MTG-IRS to S4-TIR goal specifications) is further summarized in Figure D.9. The figure reveals that in the best situation, corresponding to the low noise (0.05 K) and high spectral resolution of S4-TIR in its goal configuration, the retrievals of ozone profile is performed with a DOFS as high as 7 and an error on the tropospheric ozone column of about 10 %. S4-TIR threshold and MTG-IRS in comparison reach a DOFS of about 5 and 3.5 respectively. Despite this obvious loss in vertical sensitivity, the error on the tropospheric column is not significantly affected, remaining well below 20 % in all cases. Similarly the total column error is not much dependent on the level1 specifications, being always below 2%. In fact, the differences are mainly on the tropospheric sub-columns considered here, with the best instrument configuration allowing for the smallest errors. As an example, S4- TIR goal provide retrievals with 5 % in the upper troposphere, which is a factor 4 improvement as compared to MTG-S. The same is true in the PBL and on the 0-6 km column, although less marked. Finally from Figure D. 9, it can be seen that the S4-TIR threshold configuration provides results similar to IASI, as could be anticipated from the similar instrument characteristics. Figure D.8 Error budget for ozone retrievals. The spectral range is cm -1. Spectral resolution and noise as given in Table D1. The simulation assumes vanishing thermal contrast. 8 In Atmosphit we fit multiplicative factors eg M = x/xa. The latter are transformed to partial columns C by using a matrix B that accounts for the change of units. The error on the column S c is then calculated from the error on the multiplicative factor S M with S c =BS M B T. The relationship between the errors on the profile, which in Figure D.7. are only the diagonal elements of S M and the given errors on the partial columns, which include also the correlations, is therefore not straightforward. For the ozone retrievals, the correlations have significant importance in the troposphere and that is reflected onto the partial column errors

43 DOFS UT (6-12 km) column a priori variability MTG-IRS Figure D.9 Summary of retrieval performances for TIR instruments with 0.8 (cyan), 2 (green) and 4 cm (blue) OPD, as function of radiometric noise. The a priori variability from MOZART is shown by a red line. A red dot corresponding to IASI specifications (estimate on flight) is shown for reference

44 D Impact of thermal contrast on the retrieval performance All results shown above are for a reference standard and unpolluted atmosphere. Furthermore they have been performed assuming a vanishing thermal contrast between the surface and the first atmospheric layer. Figure D.10 illustrates for MTG-IRS the impact of thermal contrast variations on the measurement of the ozone tropospheric partial columns. The effect is studied in a reasonable range of T values, between +10 and -10 K; the emissivity is constant at The effect is clear in the troposphere with the error on the 0-12 km column decreasing from about 17 % for a vanishing thermal contrast (see also Figure D.9) to 12 % at + 10 K thermal contrast. The effect is especially strong in the lowest part of the troposphere, (PBL and 0-6 km column), being essentially negligible on the 6-12 km column. The main conclusion is that the retrieval results are better in the case of a high positive thermal contrast. Figure D.10 Error on the retrieval of ozone partial columns in the troposphere, as function of thermal contrast. The results are for MTG-IRS specifications. Surface emissivity is

45 D Assessment of MTG-IRS and S4-TIR retrievals for a polluted atmosphere In order to test the performance of MTG-IRS and by extension S4-TIR sounders to monitor tropospheric ozone for air quality purpose, we have investigated the retrieval performance for the highly polluted case shown in Figure D.2. Retrieval experiments are performed starting from he MOZART a priori, considering the pressure and temperature profiles from the truth. The results are expressed here in terms of relative differences to the true profile, (x-x true )/x true, where x can be either the value of the profile at a given level or that of a partial column. It is calculated for the a priori and the retrieved profile. The results are summarized in Figure D.11. It can be seen that the retrieval improves strongly on the a priori knowledge of the profile, despite the fact is very far from the truth (see for instance Figure D.2 bottom). The improvement is mainly in the altitude range from 2 to 20 km, which is in agreement with the results of the sensitivity analyses presented above. The relative differences on the tropospheric columns with respect to the truth, which amount 13 % for the full troposphere and 58 % for the 0-2 km column (inset of Figure D.11), also fully confirm, both qualitatively and quantitatively the results from the linear analyses. When compared to the relative differences between the a priori and the truth, this is significant improvement: a factor of 1.5 for the PBL 0-2km column (82 down to 58 %), 2 for the lower tropospheric 0-6 km column (70 down to 39 %) and up to a factor of 5 improvement for the total tropospheric column (63 down to 13 %). The results can be extended to S4-TIR goal and threshold specifications, as summarized in Figure D.12. We see that the relative differences with respect to the truth are logically smaller for the goal configuration. They reach for instance 30 % for the PBL (as compared to the 82 % a priori variability), which is a factor of 2 better than MTG-IRS (58 %). Here again we note that the impact on the tropospheric column is limited. The dependence of the retrievals upon thermal contrast is shown in Figure D.13. It shows as above that the most favourable situation, corresponding to high positive values of the thermal contrast allow retrieving tropospheric ozone with less than 10 % error and PBL ozone with 50 % error. In the less favourable cases (negative values of T) the error increase significantly, for instance up to 20 % for the tropospheric column. Figure D.11 Relative difference between the a priori (black curve) and the retrieved (red curve) profiles with respect to the truth. The latter is the ozone profile from the polluted atmosphere shown in Figure D.2. The inset gives similarly the relative differences in terms of partial columns

46 Figure D.12 Relative difference between the truth, the a priori (red curve) and the retrieved partial columns, for varying spectral resolution (given here in terms of spectral sampling). The three color curves are for different noise values (0.2 K in cyan, 0.1 K in green and 0.05 K in blue). Figure D.13 Relative difference between the truth, the a priori (red curve) and the retrieved partial columns (cyan), for varying thermal contrast. MTG-IRS specifications are considered. The results are shown on the left for the tropospheric 0-12 km and on the right for the planetary boundary layer 0-2 km columns. The results are shown here for MTG-IRS. The results from Figure D.11 D.13 clearly demonstrate the usefulness of the MTG-IRS and by extension S4-TIR to detect and monitor high pollution events, despite medium sensitivity to the lowermost atmosphere. As step further in the analysis is made by investigating the capabilities of the TIR sounders to monitor the diurnal variations, including the built up of the ozone pollution in the afternoon. The model diurnal variations of the ozone partial columns are shown in Figure D.14, along with the variations of the thermal contrast. It is seen that the thermal contrast does only vary within a limited range of values (from -2 to +2K) and that it is almost at zero when ozone

47 reaches its maximum in the mid-afternoon. Furthermore, the tropospheric ozone columns only vary from to molecules cm -2, which is a 17 % increase, and from to molecules cm -2 in the PBL, which represents a 70 % increase. These values are close to the expected retrieval for vanishing thermal contrast, as discussed above. The results of the retrieval experiments for the diurnal variations are presented in Figure D.15 for the three reference instrument configurations listed in Table D1. The first conclusion is that none of the instrument correctly captures the diurnal variations and in particular that they all miss the ozone maximum of the afternoon. This is to be explained on one hand by the modest increase in ozone and also the limited thermal contrast, which reduces sensitivity in the PBL. Overall, S4-TIR goal is closest to the model values in the PBL. It is unclear here why that configuration does not provide the best results for the tropospheric column. Figure D.14 Diurnal evolution of the ozone partial column (red curve) and the thermal contrast (green curve) from CHIMERE model between 3 AM and 23 PM. The day and location are those of the polluted profile described in Figure D.2. The left and right panels are for the tropospheric 0-12 km and the PBL 0-2 km columns, respectively. Figure D.15 Comparison between the model (red curve) and the retrieved ozone columns as function of time of the day. The green curve is for MTG-IRS, the blue curve for S4-TIR threshold and the cyan curve for S4-TIR goal. The top panel shows the results for the tropospheric 0-12 km column and the bottom panel for the 0-2 km PBL column

48 D.2.2 Carbon monoxide retrievals D Assessment of retrieval capabilities for a reference standard atmosphere For carbon monoxide, the sensitivity analyses are all performed in the linear approximation, taking the MOZART global annual mean profile and the associated covariance matrix described in Figure D.1 as the reference. As for ozone, the reference case assumes a vanishing thermal contrast. We investigate below, the dependence of the retrieval performance of MTG-IRS in terms of the selected spectral window, highlighting more particularly the vertical sensitivity achieved and the error budget. Spectral range The performance of MTG-IRS CO retrievals as function of the selected window in MTG-IRS 7, expressed in terms of vertical sensitivity (DOFS, left panel) and tropospheric errors (right panel), is depicted Figure D.16. One conclude from these calculations that even in the case where the entire IRS7 band is taken the DOFS is close to 1. This means that the measurements do not allow any CO profile information to be retrieved. The error on the tropospheric CO column is around %, which is close to the chemistry requirements for the mission [Lelieveld, 2003] 9. The selection of a narrow window in IRS7, such as the cm -1 frequently used and also prescribed for S4-TIR, does not affect the retrieval performance significantly. Figure D.16 MTG-IRS carbon monoxide retrieval achievements in terms of vertical sensitivity expressed as the DOFS (left panel), and retrieval error on the tropospheric 0-12 km column (right panel). The results are given in 2D as function of band centre and band width. The white dot indicates the spectral interval cm -1 within MTG-IRS7, which is also the prescribed spectral range for S4-TIR. The results are obtained with the MOZART a priori profile and covariance matrix, and with vanishing thermal contrast. Vertical sensitivity The averaging kernels characterizing the vertical sensitivity of the MTG-IRS measurements to the carbon monoxide vertical structure are shown in Figure D.17. As a result of the medium spectral resolution of the sounder but more especially to its relatively elevated noise in IRS7, the kernels, given for 3 km thick columns in figure D.17, all have the same shape: they span the entire 9 Note that the scientific user requirement of 10% for the CO total column is given on a 10 x 10 km 2 area. As the current instrumental specifications for MTG-IRS sampling distance is 4 km, one could average the IRS samples to meet the 10 x 10 km 2 user requirement, and artificially reduce the radiometric noise in order to lower the retrieval errors

49 troposphere and peak at relatively high altitude (around 10 km), not providing for the vanishing thermal contrast reference- any information at the surface. The result obviously point to the impossibility of MTG-IRS to resolve any vertical structure of the CO profile in the troposphere. Hence, only a tropospheric column is relevant. Error budget The error budget for the reference case shows that despite the absence of vertical sensitivity, MTG-IRS measurements of carbon monoxide significantly improves upon the prior knowledge (Figure D.18). The smoothing error logically dominates the budget. The errors on the profile are of the order of 10-20% in the free troposphere, reaching higher values near the surface. For the more relevant tropospheric or total column, an error of % prevails (e.g. Figure D.16). Figure D.17 Merged averaging kernels, in partial column space (PC) for CO retrievals by MTG-IRS. The spectral range is cm -1. Spectral resolution and noise as as given in Table D.1. The simulation assumes vanishing thermal contrast. Each kernel is for a 3 km thick layer (0-3 km in red, 3-6 km in green; 6-9 km in blue, 9-12 km in magenta and km in cyan)

50 Figure D.18 Error budget for carbon monoxide retrievals by MTG-IRS. The spectral range is cm -1. Spectral resolution and noise are as given in Table D.1. The simulation assumes vanishing thermal contrast. The black curve is the variance (square root of the diagonal elements of the MOZART S a matrix) and the red curve the total retrieval error. D Comparison between MTG-IRS and S4-TIR retrieval performances The comparison between the MTG-IRS and S4-TIR retrieval performances is provided in Figures D.19 and D.20 for the vertical sensitivity and the error budget, respectively. The most remarkable difference is between MTG-IRS and the S4-TIR sounder in its goal configuration, which allows separation of two tropospheric columns (Figure D.19), one covering the lowest part of the troposphere (0-6 km) and the other the upper part. The DOFS for that instrument set-up is 3. Also the threshold configuration of S4-TIR sounder provides some improvement with respect to MTG- IRS: the DOFS is 2, allowing for a reasonable separation of two tropospheric columns. The higher spectral resolution and lower noise of the S4-TIR configurations, as compared to those of MTG-IRS, provides a significant lowering of the errors (Figure D.20). This especially the case for the goal configuration which, due to the higher sensitivity to the CO vertical structure, provides errors of less than 10 % on each level of the profile above 2 km. Also the sensitivity near the surface is significantly improved, recalling here that the reference case is unfavourable for surface monitoring due to the vanishing contrast. A summary of the performances of MTG-IRS versus S4-TIR, including the bdifferent possible sets of instrument configurations from Table D.1 is shown in Figure D.21. In the best case (S4-TIR goal), the DOFS is as high as 3 and the error on the tropospheric column close to 5 %. For that case, as stated above, separation of two independent tropospheric columns is possible, with respective errors of about 7 % for the 0-6 km columns and 3 % for the 6-12 km column; PBL error is lower than 20 %. MTG-IRS performances do not allow retrieving the CO column to better than 10 % if the thermal contrast is weak, mainly due to the high radiometric noise in IRS-7. S4-TIR threshold configuration provides good results for the measurement of the tropospheric column (8 % error) but has less potential for measuring down to the lowest part of the troposphere as compared to the goal configuration

51 Figure D.19 Merged averaging kernels (in partial column space), compared for MTG-IRS and S4-TIR-goal configurations. Figure D.20 Comparative error budget for MTG-IRS (green), and the S4-TIR goal (magenta) and threshold (blue) configurations. The back curve is the variance from MOZART. Only the total retrieval errors are shown

52 Figure D.21 Summary of retrieval performances for TIR instruments with radiometric noise values of 0.85 K (cyan), 0.15 K (green) and 0.05 K (blue), as function of the ILS FWHM. The a priori variability from MOZART is shown by a red line. The MTG-IRS and S4-TIR configurations (goal and threshold) are highlighted