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Atmospheric boundary layer (ABL) dynamics severely impact the horizontal transport of pollutants as well as their vertical dilution. Despite this, observations of vertical wind profiles and the ABL height are still rare, particularly in cities. Thanks to recent technological advances compact ground-based remote sensing instruments are now available to monitor the heterogeneous urban atmosphere across dense sensor networks. In urban settings, Doppler wind lidars (DWL) and automatic lidars and ceilometers (ALC) are particularly useful as they operate continuously and automatically with very low maintenance under all weather conditions. Thanks to those novel profiling instruments, high-resolution (time and vertical) wind information as well as aerosol backscatter profiles can be recorded.Based on the RI-URBANS (and ICOS-cities) pilot city of Paris, France, we demonstrate what advanced products can be derived using different detailed algorithms, including vertical profiles of horizontal wind and turbulence, boundary layer heights based on aerosol or turbulence indicators, as well as low-level jet characteristics. In Paris, RI-URBANS is embedded in the PANAME initiative that coordinates the synergy between numerous projects that are investigating the urban atmosphere. Clear measurement standards, careful quality control and advanced processing algorithms are required to ensure harmonised products are obtained from the diverse sensor networks that involve instruments of different models from various manufacturers with respective capabilities and limitations.Using the synergy of the different ABL products obtained in the Paris region, it is investigated how the urban boundary layer interacts with the synoptic scale flow, the underlying topography and the urban surface. A combination of wind direction, atmospheric stability and terrain clearly affect shallow boundary layer heights and the low-level jet characteristics. But also spatial variations across the region are registered during deep convective boundary layer development.

The international ACROSS (Atmospheric ChemistRy Of the Suburban forest) intensive field measurement campaign (supported by the Make Our Planet Great Again initiative) took place from June 13 to July 25 2022 in and around the Ile-de-France during an exceptional hot (more than 3°C above the climatological mean for France) and dry summer season. This work will provide a broad classification of weather conditions that occurred during the campaign, useful for further analysis of observations and simulations. Weather scenarios will be analysed in terms of synoptic weather situations, relating transport patterns (regional advection to the Ile-de-France region, plumes from Paris agglomeration), temperature and relative humidity evolution. During the June-July 2022 period, several distinct weather and pollution patterns occurred, among which: (1) two strong heatwaves, with large photochemical activity promoting secondary pollutants build-up (O3, SOA), (2) advection of relatively clean oceanic air masses, and (3) Saharan dust and intense forest fire events with dust and fire aerosol advection to the Ile-de-France region. The analysis is based on meteorological data from several models (GFS, WRF, ARPEGE, AROME) and observations, pollution forecasts from the French Prev’Air system (http://www2.prevair.org/), and from the AirParif Esmeralda platform (http://www.esmeralda-web.fr/accueil/) that have been used for the airborne campaign support. In addition, dedicated simulations with the CHIMERE model (see companion abstract by Di Antonio et al. for this session), and major pollutant observations by air quality networks, from satellites, or performed within ACROSS will be used to analyse how meteorological conditions and weather patterns impacted the spatial distributions of major primary and secondary chemical species. Especially tracers of anthropogenic and biogenic emissions and photochemical activity (O3, PM2.5, OA, BC, NOx, BVOC) will be analysed. Keywords: Meteorology, pollutant distributions, transport patterns, ACROSS, MOPGA

In the context of climate change with increasingly pronounced impacts, year 2022 in France has been the warmest year ever observed. It has been the case more specifically during July, when a severe heatwave occurred across North-Western Europe. Sea-level temperature anomalies jumped over +12°C, especially in the Western half of France, pushing back all-time records. Associated to extreme temperature occurred a severe drought, with long-lasting precipitation deficit throughout the year, which was amplified during July. With around 80% deficit, July 2022 in France has also been the driest ever recorded. These singular meteorological conditions were therefore a fertile ground for forest fires. Around 70 000 ha of forest have burned in France during 2022. The South-West region has been particularly impacted during July 2022, as well as other areas which are not usually subject to this kind of events (Brittany, Center, Paris region). During July 2022 occurred the ACROSS field campaign within the Paris region, where several sites following a South-West to North-East transect provided comprehensive characterization of atmospheric composition. Namely Rambouillet station (forest, SW of Paris), SIRTA (peri-urban, SW of Paris), PRG (urban, Paris center) and Chatelet Les Halles (urban, Paris center). These sites were equiped with state-of-the-art instrumentations to characterize aerosols and reactive gases. On July 19th, an intense biomass burning plume went through the Paris region, with PM10 concentrations reaching more than 150µg/m3. The experimental effort during that period is therefore a unique asset in order to characterize the composition of this episode. Most of the sites, during that episode, record high concentrations of carbonaceous aerosols (organic matter and black carbon) and Volatile Organic Compounds tracers of biomass burning (eg acetonitrile, benzene, toluene). Temporality of the episode, atmospheric dynamics and air mass pathways, suggest the transport of a biomass burning plume from South-Western France, more than 600km away from Paris. However, aerosol size distribution surpringly reveals the influence of local fires occuring near some of the sites, at almost the same time. This work is therefore focusing on the different properties of different plumes mixing at the same time over a densely urbanized region. These differences can be highlighted by source apportionment, combined with size distribution, as well as oxydation state of organic aerosols.

The urban is at the heart of many disciplinary projects covering very broad scientific areas. Acquired data or simulations are often accessible (when they are) via targeted thematic portals. However, the need for transdisciplinarity has been essential for several years to answer specific scientific questions or societal demands. For this, the crossing of human sciences data, health, air quality, land use, emissions inventories, biodiversity, etc., would allow new innovative studies in connection with the city.PANAME (PAris region urbaN Atmospheric observations and models for Multidisciplinary rEsearch) developed by AERIS was designed as the first brick of a data portal that can promote the discovery, access, cross-referencing and representation of urban data from various sectors with air quality and urban heat islands as a starting point. The portal and future developments will be discussed in this presentation.

Existing CO2 emissions reported by city inventories usually lag real-time by a year or more and are prone to large uncertainties. This study responds to the growing need for timely and precise estimation of urban CO2 emissions to support the present and future mitigation measures and policies. We focus on the Paris metropolitan area, the largest urban region in the European Union and the city with the densest atmospheric CO2 observation network in Europe. We performed long-term atmospheric inversions to quantify the citywide CO2 emissions, both fossil fuel and biogenic sources and sinks, over six years (2016–2021) using a Bayesian inverse modeling system. Our inversion framework benefits from a novel near-real-time hourly fossil fuel CO2 emission inventory (Origins.earth) at 1 km spatial resolution. In addition to the mid-afternoon observations, we attempt to assimilate morning CO2 concentrations based on the ability of the WRF-Chem transport model to simulate atmospheric boundary layer dynamics constrained by observed layer heights. Our results show a long-term decreasing trend by around 2 % per year in annual CO2 emissions over the Paris region. The impact of COVID-19 pandemic led to a 13 %±1 % reduction in annual fossil fuel CO2 emissions in 2020 with respect to 2019. Then, annual emissions increased by 5.2 % from 32.6±2.2 MtCO2 in 2020 to 34.3±2.3 MtCO2 in 2021. Based on a combination of up-to-date inventories, high-resolution atmospheric modeling, and high-precision observations, our current capacity could deliver near real-time CO2 emission estimates at city scale in less than a month, and the results agree within 10 % with independent estimates from multiple city-scale inventories.

The atmospheric boundary layer (ABL) defines the volume of air adjacent to the Earth’s surface for the dilution of heat, moisture, and trace substances. Quantitative knowledge on the temporal and spatial variations in the heights of the ABL and its sub-layers is still scarce, despite their importance for a series of applications (including, for example, air quality, numerical weather prediction, greenhouse gas assessment, and renewable energy production). Thanks to recent advances in ground-based remote-sensing measurement technology and algorithm development, continuous profiling of the entire ABL vertical extent at high temporal and vertical resolution is increasingly possible. Dense measurement networks of autonomous ground-based remote-sensing instruments, such as microwave radiometers, radar wind profilers, Doppler wind lidars or automatic lidars and ceilometers are hence emerging across Europe and other parts of the world. This review summarises the capabilities and limitations of various instrument types for ABL monitoring and provides an overview on the vast number of retrieval methods developed for the detection of ABL sublayer heights from different atmospheric quantities (temperature, humidity, wind, turbulence, aerosol). It is outlined how the diurnal evolution of the ABL can be monitored effectively with a combination of methods, pointing out where instrumental or methodological synergy are considered particularly promising. The review highlights the fact that harmonised data acquisition across carefully designed sensor networks as well as tailored data processing are key to obtaining high-quality products that are again essential to capture the spatial and temporal complexity of the lowest part of the atmosphere in which we live and breathe., European Cooperation in Science and Technology (COST) PTDC/CTAMET/29678/2017 European Commission, French National Research Agency (ANR) Region Ile-de-France, Spanish Government, Junta de Andalucia, University of Granada, Programa Operativo FEDER Andalucia 2014-2020, Earth System Excellence Units Program, Marie Sklodowska-Curie Action Cofund 2016 EU project - Athenea3i national Portuguese funds through FCT - Fundacao para a Ciencia e Tecnologia, I.P. CA18235 871115 101036245 ANR-20-CE22-0013 PID2020-120015RB-I00 PID2020.117825GB.C21 P18-RT-3820 P20-00136 A-RNM-430-UGR20 754446 UIDB/04683/2020

Photovoltaic production is highly variable with a limited predictability due to stochastic variation of cloud attenuation of surface solar irradiance. Forecasting this irradiance helps to manage the safe and stable operation of the grid and thus enables to increase solar electricity penetration for a lower carbonated energy mix. In particular, intraday horizon forecasts (typically up to 6 h) are more and more required for the grid power reserve management, the increase of intra-day auctions in the electricity market and the current worldwide development of micro-grids.Forecast irradiance using images from geostationary meteorological satellite is particularly appropriate for intraday horizon. It gives better performance than NWP models, does not require instrumentation, or costly computing resource. However, the accuracy of such methods is very sensitive to the cloud cover state and its short-term evolution. For instance, methods based on the temporal extrapolation of cloud motion present better results for passing cloud events than for sudden cloud appearance or disappearance. In several studies, reliability predictors have been identified for satellite-based irradiance forecast. They showed clear signals when uncertainty is computed as a function of season, solar zenith angle, cloud albedo and more recently synoptic weather regimes. This anticipation of error range would help grid managers to prepare the sizing of storage capacity or ancillary electricity resource.A pertinent predictor must have a significant influence on forecast error. It also must be easy to obtain in operational forecast conditions. In this work, we propose to exploit the only observation source required in satellite-based forecast: the satellite image itself. Using 5 years (2017-2021) of image-derived forecasts at 15 min time step over Palaiseau (France), we computed the forecast uncertainties as a function of multiple parameters derived from the HRV channel of Meteosat Second Generation satellite. We highlighted the influence of cloud albedo spatial variance and cloud motion vector field spatial-temporal features in the forecast uncertainties. In addition to the error range provided to users, this work can help forecasters to better characterize their sources of error and to select new predictors for machine-learning approaches.

The atmospheric boundary layer (ABL) is the layer closest to the Earth’s surface within which most human activities take place. The vertical profile of atmospheric thermodynamic parameters in the ABL impact weather, air quality and climate. However, surface sensor networks and satellite observations do not provide sufficient information on the high temporal variability and strong vertical gradients that occur in the ABL. Thus, the ABL represents an important but rather under-sampled part of the atmosphere. This observational gap currently hampers progress in numerical weather prediction, air quality forecasting and climate assessment. Due to recent technological and methodological advances, ground-based remote sensing instruments are now able to provide high-quality profiles of ABL parameters such as temperature, humidity, wind, aerosol and cloud properties. However, even though state-of-the-art ABL profilers are deployed at numerous sites in Europe, efficient science and technology networking and coordination is still required to exploit this rich dataset effectively. The current lack of data and procedure harmonization often diminishes the potential societal benefits of the existing ABL profiling data. This paper introduces PROBE, a new initiative funded by the European Cooperation in Science and Technology (COST), that aims to broaden the bridge between a wide range of user needs and the science and technology expertise residing in industry and academia, while strengthening and harmonizing methods and procedures to yield higher quality ABL observational data. Here, the challenges, objectives and implementation plan for PROBE are described, highlighting some preliminary results that will be further developed into operational applications during the 4-year duration (2019–2023) of this collaborative project.

Air quality and meteorology in urban environments are strongly affected by dynamical and turbulent processes occurring in the atmospheric boundary layer. These are largely driven by the interaction between the surface and the atmosphere, including the exchange of momentum, heat, moisture, and various gases and aerosols. Vertical ventilation, horizontal advection, and atmospheric stratification are key processes.To improve the understanding of the exchange processes in the urban atmosphere and to assess the implications of spatial variations in surface roughness, spatially resolved vertical profiles of the horizontal wind are required. In this work, we are implementing a novel “volume wind processing” approach to retrieve horizontal wind information on a 3D spatial grid from observations of a scanning Doppler wind lidar (Vaisala Windcube 400s). Deployed on the rooftop of a tall building in downtown Paris, France, the Doppler lidar is operated with a series of scan strategies to monitor the vertical and horizontal variations of the mean wind field across the city center.In order to quantify the performance of the volume wind processing, an evaluation measurement campaign was performed combining measurements at the Vaisala measurement site and the SIRTA atmospheric observatory (Paris-Saclay) located 3.5 km from each other. The Windcube 400s, located on the Vaisala site, gathered measurements based on different scan patterns (full or sector (>30°) Plan-position Indicator (PPI)), from which wind profiles were retrieved using the volume wind processing. These retrievals were then compared to vertical wind profiles obtained from a previously validated and calibrated Doppler lidar (WLS70) running in a vertical profiling mode located at SIRTA. The comparison is performed over a 30-days period. We found a mean difference (Volume Wind – Vertical Stare) of -0.69 m/s and a standard deviation of 1.32 m/s for 10-min averaged profiles.The ongoing work consists of identifying the sources of uncertainty in the volume wind processing and improving the quality of the retrievals by improving quality control procedures. High-quality wind profile products will then be available for research on the spatial variability of the wind speed profiles, in order to determine the influence of the surface roughness on exchange processes in the Paris urban atmosphere.

An improved version of the near-real-time decision tool PARAFOG (PFG2) is presented to retrieve pre-fog alert levels and to discriminate between radiation (RAD) and stratus lowering (STL) fog situations. PFG2 has two distinct modules to monitor the physical processes involved in RAD and STL fog formation and is evaluated at European sites. The modules are based on innovative fuzzy logic algorithms to retrieve fog alert levels (low, moderate, high) specific to RAD/STL conditions, minutes to hours prior to fog onset. The PFG2-RAD module assesses also the thickness of the fog. Both the PFG2-RAD and PFG2-STL modules rely on the combination of visibility observations and automatic lidar and ceilometer (ALC) measurements. The overall performance of the PFG2-RAD and PFG2-STL modules is evaluated based on 9 years of measurements at the SIRTA (Instrumented Site for Atmospheric Remote Sensing Research) observatory near Paris and up to two fog seasons at the Paris-Roissy, Vienna, Munich, and Zurich airports. At all sites, pre-fog alert levels retrieved by PFG2 are found to be consistent with the local weather analysis. The advanced PFG2 algorithm performs with a hit rate of about 100 % for both considered fog types and presents a false alarm ratio on the order of 10 % (30 %) for RAD (STL) fog situations. Finally, the first high alerts that result in a subsequent fog event are found to occur for periods of time ranging from −120 min to fog onset, with the first high alerts occurring earlier for RAD than STL cases.

Comprehensive field campaigns dedicated to fog life cycle observation were conducted during the winters of 2010–2013 at the Instrumented Site for Atmospheric Remote Sensing Research (SIRTA) observatory in a suburb of Paris. In order to document their properties, in situ microphysical measurements collected during 23 fog events induced by both radiative cooling and stratus lowering are examined here. They reveal large variability in number, concentration and size of both aerosol background before the fog onset and fog droplets according to the different cases. The objective of this paper is to evaluate the impact of aerosol particles on the fog microphysics. To derive an accurate estimation of the actual activated fog droplet number concentration Nact, we determine the hygroscopicity parameter κ, the dry and the wet critical diameter and the critical supersaturation for each case by using an iterative procedure based on the κ-Köhler theory that combines cloud condensation nuclei (CCN), dry particle and droplet size distribution measurements. Our study reveals low values of the derived critical supersaturation occurring in fog with a median of 0.043 %. Consequently, the median dry and wet activation diameters are 0.39 and 3.79 µm, respectively, leading to a minor fraction of the aerosol population activated into droplets. The corresponding Nact values are low, with median concentrations of 53.5 and 111 cm−3 within the 75th percentile. The activated fraction of aerosols exhibits remarkably low correlation with κ values, which reflects the chemical composition of the aerosols. On the contrary, the activated fraction exhibits a strong correlation with the inferred critical diameter throughout the field campaigns. This suggests that the variability in the activated fraction is mostly driven by particle size, while variations in aerosol composition are of secondary importance. Moreover, our analysis suggests that the supersaturation reached in fog could be lowered by the aerosol number concentration, which could contribute to the sink term of water vapor during the radiative cooling. Although radiative fogs are usually associated with higher aerosol loading than stratus-lowering events, our analysis also reveals that the activated fraction at the beginning of the event is similar for both types of fog. However, the evolution of the droplet concentration during the fog life cycle shows significant differences between both types of fog. This work demonstrates that an accurate calculation of supersaturation is required to provide a realistic representation of fog microphysical properties in numerical models.

Observations of profiles of winds, aerosol, clouds, winds, temperature and humidity in the lowest few km of the atmosphere from networks of ceilometers, Doppler wind lidars and microwave radiometers are starting to flow in real time to forecasting centers in Europe.\ud \ud To realise the promise of improved predictions of hazardous weather such as flash floods, wind storms, fog and poor air quality from high-resolution mesoscale models, the forecast models must be initialized with an accurate representation of the current state of the atmosphere, but the lowest few km are hardly accessible by satellite, especially in dynamically-active conditions. We report on recent European developments in the exploitation of existing ground-based profiling instruments so that they are networked and able to send data in real-time to forecast centers. The three classes of instruments are: (i) Automatic lidars and ceilometers providing backscatter profiles of clouds, aerosols, dust, fog and volcanic ash, the last two being especially important for air traffic control; (ii) Doppler wind lidars deriving profiles of wind, turbulence, wind shear, wind-gusts and low-level jets; and (iii) Microwave radiometers estimating profiles of temperature and humidity in nearly all weather conditions. Twenty-two European countries and fifteen European National Weather Services are collaborating in the project, that involves the implementation of common operating procedures, instrument calibrations, data formats and retrieval algorithms. Currently, data from 220 ceilometers in 17 countries are being distributed in near real-time to national weather forecast centers; this should soon rise to many hundreds. The wind lidars should start delivering real time data in late 2018, and the plan is to incorporate the microwave radiometers in 2019. Initial data assimilation tests indicate a positive impact of the new data.

By reducing the atmospheric visibility, fog events have strong impacts on several humans activities. Transport security, military operations, air quality forecast and solar energy production are critical activities considering fog dissipation time as a high valuable information.Fog dissipation occurs through these two following processes. (1) An adiabatic cloud elevation converts the fog into a low stratus, increasing the visibility at ground level while keeping an overcast sky. (2) A radiative warming can break through a large continuous fog deck. Then, the cleared area increases progressively by heating the ground of the neighboured fog covered area.These two events are particularly difficult to forecast using NWP models as many non-linear local processes at short-time scale are involved. Moreover, current network of fog presence sensors is too scarce to analyse and/or anticipate the phenomena. Subsequent images of geostationary meteorological satellite offer a high temporal resolution that enables to monitor large fog decks and detect punctual clear areas that induce dissipation (case 2). However, fog detection using satellite images suffers from a lack of distinction between fog and very low stratus.In this work, we explored the potential of MSG SEVIRI radiometer through radiance observations and more advanced cloud products to analyse fog events effectively observed at the SIRTA atmospheric observatory (Palaiseau, France). We assumed that, during these events, pixels classified as “very low cloud” according to SAF-NWC algorithm were covered by fog. We monitored the evolution of these pixels using a cloud index derived from HRV channels, providing a more detailed spatial distribution of cloud cover during day time. We analysed the evolution of brightness temperature spatial gradient from the SEVIRI infrared window channel (IR 10.8µm). We isolated cases where ground warming situation could anticipate an irreversible fog dissipation. Then we deduced some fog dissipation forecasting principles.This approach has the potential to provide to users information on morning fog sustainability with a higher accuracy and finer temporal resolution than NWP. Ongoing work focuses on characterizing favourable situations for accurate forecasts, while further predictors are investigated using recent products providing a smart distinction between fog and low stratus using SEVIRI images.

A detailed understanding of atmospheric boundary layer (ABL) processes is key to improve forecasting of pollution dispersion and cloud dynamics in the context of future climate scenarios. International networks of automatic lidars and ceilometers (ALC) are gathering valuable data that allow for ABL layers to be derived in near real time. A new generation of advanced methods to automatically detect the ABL heights now exist. However, diversity in ALC models means these algorithms need to be tailored to instrument-specific capabilities. Initial evaluation of the advanced algorithms STRATfinder (for application to high signal-to-noise ratio (SNR) ALC observations) and CABAM (low-SNR measurements) to automatically derive ABL heights indicates promising performances (Kotthaus et al. 2020).In the framework of the ABL testbed project (funded by ICOS, ACTRIS and E-PROFILE) the two algorithms are now being assessed for application in an operational network setting, such as EUMETNET E-PROFILE. A subset of 11 E-PROFILE sites in a range of geographical and land cover settings across Europe are selected where data from low-SNR and/or high-SNR ALC are available for multiple years. Automatic layer detection is implemented, including instrument-specific corrections and calibrations. Algorithm performance for layer height detection is being evaluated via comparison of results from different ALC and by including reference data from thermodynamic- and turbulence derived layer heights from radiosondes and other ground-based profiling sensors where available. Recommendations are formulated for implementation of automatic ABL height retrievals across a diverse sensor network. A prime example of collaborations within the EU COST action PROBE on profiling the atmospheric boundary layer, the ABL testbed is a crucial step towards harmonised ABL height products at the European scale.ReferencesKotthaus, S, M Haeffelin, MA Drouin, JC Dupont, S Grimmond, A Haefele, M Hervo, Y Poltera, M Wiegner, 2020: Tailored Algorithms for the Detection of the Atmospheric Boundary Layer Height from Common Automatic Lidars and Ceilometers (ALC). Remote Sens, 12, 3259, DOI: 10.3390/rs12193259.

Since early 2020, the COVID-19 pandemic has led to lockdowns at national scales. These lockdowns resulted in large cuts of atmospheric pollutant emissions, notably related to the vehicular traffic source where daily commuting of light-duty vehicles was almost completely stopped in numerous urban areas worldwide, especially during Spring 2020. As a result, air quality changed in manners that are still currently under investigation. Long-term in-situ monitoring of atmospheric composition provides, to this perspective, essential information. However, a robust quantitative assessment of the impact of lockdown measures on ambient concentrations is hindered by weather variability. Basic comparisons with previous years may thus be flawed, especially regarding secondary pollutants, whose concentrations strongly depends on meteorological conditions. In order to circumvent this difficulty, an innovative methodology has been developed. The Analog Application for Air Quality (A3Q) method is based on the comparison of each day of lockdown to a group of analog days having similar meteorological conditions. The A3Q method has been successfully evaluated and applied to a comprehensive in-situ dataset of primary and secondary pollutants obtained at the SIRTA observatory, a suburban background site of the Paris megacity (France). The overall slight decrease of PM1 concentrations (−14 %) compared to business-as-usual conditions conceals contrasting behaviours. Primary traffic tracers (NOx and traffic-related carbonaceous aerosols) dropped by 42–66 % during the lockdown period. Further, the A3Q method enabled us to characterize of changes triggered by NOx decreases. Particulate nitrate and secondary organic aerosols (SOA), two of the main springtime aerosol components in North-Western Europe, decreased by −45 % and −25 %, respectively. A NOx-relationship emphasizes the interest of NOx mitigation policies at the regional (i.e. city) scale, although long-range pollution advection sporadically overcompensated regional decreases. Variations of the oxidation state of SOA suggests discrepancies in SOA formation processes. At the same time, the expected ozone increase (+20 %) underlines the negative feedback of NO titration. These results provide a quasi-comprehensive observation-based insight on mitigation policies regarding air quality in future low-carbon urban areas.

An improved version of the near-real time decision tool PARAFOG (PFG2) is presented to retrieve pre-fog alert levels and to discriminate between radiation (RAD) and stratus lowering (STL) fog situations. PFG2 has two distinct modules to monitor the physical processes involved in RAD and STL fog formation and is evaluated at European sites. The modules are based on innovative fuzzy logic algorithms to retrieve fog alert levels (low, moderate, high) specific to RAD/STL conditions, minutes to hours prior to fog onset. The PFG2-RAD module assesses also the thickness of the fog. Both the PFG2-RAD and PFG2-STL modules rely on the combination of visibility observations and automatic lidar and ceilometer (ALC) measurements. The overall performance of the PFG2-RAD and -STL modules is evaluated based on 9 years of measurements at the SIRTA observatory near Paris and up to two fog seasons at the Paris-Roissy, Vienna, Munich and Zurich airports. At all sites, pre-fog alert levels retrieved by PFG2 are found to be consistent with the local weather analysis. The advanced PFG2 algorithm performs with a hit rate of about 100 % for both considered fog types, and presents a false alarm ratio on the order of 10 % (30 %) for RAD (STL) fog situations. Finally, the first high alerts that result in a subsequent fog event are found to occur for periods of time ranging from −120 minutes to fog onset, with first high alerts occurring earlier for RAD than STL cases.

Volatile Organic Compounds (VOCs) have direct influences on air quality and climate. They also play a key role in atmospheric chemistry, as they are precursors of secondary pollutants, such as ozone (O3) and secondary organic aerosols (SOA).Long-term datasets of in-situ atmospheric measurements are crucial to characterize the variability of atmospheric chemical composition. Online and continuous measurements of O3, NOx and aerosols have been achieved at the SIRTA-ACTRIS facility (Paris region, France), since 2012. Regarding VOCs, they have been measured there for several years thanks to bi-weekly samplings followed by offline Gas Chromatography analysis. However, this method doesn’t provide a good representation of the temporal variability of VOC concentrations. To tackle this issue, online VOC measurements using a Proton-Transfer-Reaction Quadrupole Mass-Spectrometer (PTR-Q-MS) have been started in January 2020.The dataset acquired during the first year of online VOC measurements is analyzed, which gives insights on VOC seasonal variability. The additional long-term datasets obtained from co-located measurements (O3, NOx, aerosol physical and chemical properties, meteorological parameters) are also used for the sake of this study.Due to Covid-19 pandemic, the year 2020 notably comprised a total lockdown in France in Spring, and a lighter one in Autumn. Therefore, a focus can be made on the impact of these lockdowns on the VOC variability and sources. To this end, the diurnal cycles of VOCs considered markers for anthropogenic sources are carefully investigated. Results notably indicate that markers for traffic and wood burning sources behave quite differently during the Spring lockdown in comparison to the other periods. A source apportionment analysis using positive matrix factorization allows to further document the seasonal variability of VOC sources and the impacts on air quality associated with the lockdown measures.

The WMO World Weather Research Programme (WWRP) “promotes international and interdisciplinary research for more accurate and reliable forecasts from minutes to seasons, expanding the frontiers of weather science to enhance society’s resilience to high-impact weather and the value of weather information for users. In the 2016-2023 WWRP implementation plan, activities focus on 4 challenges: High-Impact Weather, Water, Urbanization, Evolving technologies. Furthermore, the WMO Global Atmosphere Watch Urban Research Meteorology and Environment (GURME) focus on the development of models and associated research activities to enhance the capabilities in providing urban-environmental forecasting and air quality services, illustrating the linkages between meteorology and air quality (https://public.wmo.int/en/programmes).This talk presents an international Research Demonstration Project (RDP), that will focus on international research on scientific urban issues addressed by both WWRP and GURME. The strategic objective of this RDP is to focus on the Olympic Games of Paris in 2024 in order to advance research on the theme of the “future Meteorological Forecasting systems at 100m (or finer) resolution for urban areas”. Such systems would prefigure the numerical weather prediction at the horizon 2030. The focus will be on themes related to extreme weather events in summer which both are influenced by and impacts urbanization: thunderstorms and strong Urban Heat Islands, and their consequences.There are 5 scientific questions that will be addressed during this Paris RDP:Nowcasting & Numerical Weather Prediction in cities at order 100m resolution High resolution thunderstorm nowcasting (probabilistic and deterministic) in the urban environment, Urban heat islands, cool areas and air quality Nowcasting and forecast in coastal cities (for the Marseilles site) How to improve and better use observational networks in urban areas, including (big) non-conventional data Conception and Communication of tailored weather, climate, environmental information at infra-urban resolution. Several High-Impact weather case studies were selected. Storm cases (starting with one the 10th July 2017) will allow to evaluate the role of the urban area on their enhancement. Extreme Heat wave aggravated by a strong Urban Heat Island are also studied (July 2019). Open urban data describing the agglomerations at very high resolution are provided. New innovative methods to produce maps of urban form characteristics (e.g. from street images) and meteorological data (from personal meteorological stations) will be explored.This talk will describe these scientific questions, as well as the common methodology approach that is being discussed within the partners. A focus will be the international experimental campaign that will take place in 2022 over the Paris agglomeration, with an Intensive Observation Period in the summer 2022. Interactions between urban surface and the atmospheric boundary layer, the interactions between air quality and aerosols between city and biogenic plumes, and the local effect of urban trees on micro-climate and chemistry are some of the axes of the campaign. It will provide additional meteorological and air quality observations, to both help to improve the nowcasting and NWP systems at urban scale, and aim to define the required additional instrumentation that should be deployed during the Olympics games themselves.

Transportation especially aviation sector all around the world is severely hindered due to Fog and hence observations and specific research for fog is necessary. The SOFOG3D (SOuth west FOGs 3D) experiment took place in South-West of France which is particularly prone to fog occurrence, during the period between November 2019 to March 2020 with primary objective to advance our understanding of fog processes and to improve fog forecast. Simultaneous measurements from various remote sensing instruments like BASTA: a 95 GHz cloud radar with scanning capability, HATPRO Microwave radiometer (MWR), doppler lidar, and balloon-borne in-situ measurements were collected to characterize the spatio-temporal evolution of Fog. On the supersite, detailed measurements of meteorological conditions, aerosol properties, fog microphysics, water deposition, radiation budget, heat, and momentum fluxes are collected to provide 3D structure of the boundary layer during fog events. The improvement in the retrieval of fog parameters and understanding of fog dynamics based on cloud radar and microwave (MWR) synergy will be addressed. We will present our work on the retrieval of key fog parameters like dynamics and microphysics using a combination of cloud radar and MWR observations. The retrievals will be validated with the tethered-balloon and radio-sounding observations. In-situ measurements and remote-sensing retrievals of fog microphysical properties will be compared. We will show a detailed analysis of retrieved LWP derived from BASTA radar only with LWP derived from HATPRO microwave radiometer, considering instrumental uncertainty and sensitivity. A closer analysis of the in-situ data (measured by granulometer) will be presented in order to assess and improve the retrieval derived with cloud radar in vertically pointing mode. Radar attenuation will be quantified by measuring the backscattered radar signal on well-known calibrated reflectivity metallic targets installed at the top of 20 m mast. The integrated attenuation along the radar beam path will be measured by the cloud radar and used as a new constraint to improve the microphysical properties.

Air quality in cities is influenced not only by emissions and chemical transformations but also by the physical state of the atmosphere which varies both temporally and spatially. Increasingly, tall buildings (TB) are common features of the urban landscape, yet their impact on urban air flow and dispersion is not well understood, and their effects are not appropriately captured in parameterisation schemes. Here, hardware models of areas within two global mega-cities (London and Beijing) are used to analyse the impact of TB on flow and transport in isolated and cluster settings. Results show that TB generate strong updrafts and downdrafts that affect street-level flow fields. Velocity differences do not decay monotonically with distance from the TB, especially in the near-wake region where the flow is characterised by recirculating winds and jets. Lateral distance from an isolated TB centreline is crucial, and flow is still strongly impacted at longitudinal distances of several TB heights. Evaluation of a wake-flow scheme (ADMS-Build) in the isolated TB case indicates important characteristics are not captured. There is better agreement for a slender, shorter TB than a taller non-cuboidal TB. Better prediction of flow occurs horizontally further away and vertically further from the surface. TB clusters modify the shape of pollutant plumes. Strong updrafts generated by the overlapping wakes of TB clusters lift pollutants out of the canopy, causing a much deeper tracer plume in the lee of the cluster, and an elevated plume centreline with maximum concentrations around the TB mean height. Enhanced vertical spread of the pollutants in the near-wake of the cluster results in overall lower maximum concentrations, but higher concentrations above the mean TB height. These results have important implications for interpreting observations in areas with TB. Using real world ceilometer observations in two mega-cities (Beijing and Paris), we assess the diurnal seasonal variability of the urban boundary layer and evaluate a mixed layer height (MLH) empirical model with parameters derived from a third mega-city (London). The MLH model works well in central Beijing but less well in suburban Paris. The variability of the physical meteorology across different vertical scales discussed in this paper provides additional context for interpreting air quality observations.

The worldwide COVID-19 pandemic has led to lockdowns at national scales in the Spring of 2020. Large cuts in emissions occurred, but the quantitative assessment of their role from observations is hindered by weather variability. In order to circumvent this difficulty, we developed here an innovative analog methodology and applied it to a comprehensive in-situ dataset of primary and secondary pollutants obtained at the SIRTA observatory, a suburban background site of the Paris megacity (France). We find that concentrations of primary traffic dropped by 69-76% during the lockdown period. Further, the decrease of NOx triggered a decrease of particulate nitrate (-41%), one of the main springtime aerosol components in North-Western Europe. We reveal a threshold effect highlighting the need of substantial NOx decrease to affect particulate nitrate. At the same time, the expected ozone increase (+21%) underlines the negative feedback of NO titration. Finally, an increase of residential wood burning sporadically compensated primary traffic, and influenced the oxidation state of secondary organic aerosols. Our results provide a quasi-comprehensive observation-based insight on mitigation policies regarding air quality in future low-carbon urban areas.

International audience

The worldwide growing development of PV capacity requires an accurate forecast for a safer and cheaper PV grid penetration. Solar energy variability mainly depends on cloud cover evolution. Thus, relationships between weather variables and forecast uncertainties may be quantified to optimize forecast use. An intraday solar energy forecast algorithm using satellite images is fully described and validated over three years in the Paris (France) area. For all tested horizons (up to 6 h), the method shows a positive forecast skill score compared to persistence (up to 15%) and numerical weather predictions (between 20% and 40%). Different variables, such as the clear-sky index (Kc), solar zenith angle (SZA), surrounding cloud pattern observed by satellites and northern Atlantic weather regimes have been tested as predictors for this forecast method. Results highlighted an increasing absolute error with a decreasing SZA and Kc. Root mean square error (RMSE) is significantly affected by the mean and the standard deviation of the observed Kc in a 10 km surrounding area. The highest (respectively, lowest) errors occur at the Atlantic Ridge (respectively, Scandinavian Blocking) regime. The differences of relative RMSE between these two regimes are from 8% to 10% in summer and from 18% to 30% depending on the time horizon. These results can help solar energy users to anticipate—at the forecast start time and up to several days in advance—the uncertainties of the intraday forecast. The results can be used as inputs for other solar energy forecast methods.

Air pollution, in particular high concentrations of particulate matter smaller than 1 µm in diameter (PM1), continues to be a major health problem, and meteorology is known to substantially influence atmospheric PM concentrations. However, the scientific understanding of the ways in which complex interactions of meteorological factors lead to high-pollution episodes is inconclusive. In this study, a novel, data-driven approach based on empirical relationships is used to characterize and better understand the meteorology-driven component of PM1 variability. A tree-based machine learning model is set up to reproduce concentrations of speciated PM1 at a suburban site southwest of Paris, France, using meteorological variables as input features. The model is able to capture the majority of occurring variance of mean afternoon total PM1 concentrations (coefficient of determination (R2) of 0.58), with model performance depending on the individual PM1 species predicted. Based on the models, an isolation and quantification of individual, season-specific meteorological influences for process understanding at the measurement site is achieved using SHapley Additive exPlanation (SHAP) regression values. Model results suggest that winter pollution episodes are often driven by a combination of shallow mixed layer heights (MLHs), low temperatures, low wind speeds, or inflow from northeastern wind directions. Contributions of MLHs to the winter pollution episodes are quantified to be on average ∼5 µg/m3 for MLHs below m a.g.l. Temperatures below freezing initiate formation processes and increase local emissions related to residential heating, amounting to a contribution to predicted PM1 concentrations of as much as ∼9 µg/m3. Northeasterly winds are found to contribute ∼5 µg/m3 to predicted PM1 concentrations (combined effects of u- and v-wind components), by advecting particles from source regions, e.g. central Europe or the Paris region. Meteorological drivers of unusually high PM1 concentrations in summer are temperatures above ∼25 ∘C (contributions of up to ∼2.5 µg/m3), dry spells of several days (maximum contributions of ∼1.5 µg/m3), and wind speeds below ∼2 m/s (maximum contributions of ∼3 µg/m3), which cause a lack of dispersion. High-resolution case studies are conducted showing a large variability of processes that can lead to high-pollution episodes. The identification of these meteorological conditions that increase air pollution could help policy makers to adapt policy measures, issue warnings to the public, or assess the effectiveness of air pollution measures.

Fog strongly perturbs the aviation, marine and land transportation, leading to human losses and high financial costs. The primary objective of SOFOG3D is to advance our understanding of fog processes at the smallest scale to improve forecasts of fog events by numerical weather prediction (NWP) models.Specifically, SOFOG3D conducts process studies on very well documented situations, using synergy between 3D high-resolution Large Eddy Simulation (LES) and unprecedented 3D detailed observations. SOFOG3D will particularly focus on the impact of surface heterogeneities (types of vegetation, rivers, orography) on the fog life cycle, on fog microphysics properties, on entrainment at fog top, on the surface energy budget, and on the impact of aerosols. SOFOG3D will also investigate how improving the initial conditions of NWP models can improve fog forecasts. To that end, data from a ground-based MWR network will be assimilated using an innovative ensemble-based variational data assimilation scheme.A 6 months field experiment took place during wintertime 2019/2020 in the South-West of France to provide 3D mapping of the boundary layer during fog events. The observation strategy is to combine vertical profiles derived from new remote sensing instruments (microwave radiometer (MWR), Doppler cloud radar and Doppler lidars) and balloon-borne in-situ measurements, with local observations provided by a network of surface stations, and a fleet of Unmanned Aerial Vehicles (UAV) to explore fog spatial heterogeneities. Three nested domains has been instrumented with increasing density to provide observations from regional scale (300x200 km) down to local scale on the super-site (10x10 km), thanks to Meteo France and U.K. Meteorological Office sensors. On the super site, meteorological conditions, visibility, aerosol optical, microphysical and hygroscopic properties, fog microphysics and liquid water content, water deposition, radiation budget, heat and momentum fluxes on flux-masts has been performed on different areas to investigate the impacts of surface heterogeneities on fog processes, as well as turbulence anisotropy. Combination of cloud radar and MWR measurements will allow optimal retrieval of temperature, humidity and liquid water content profiles.We will present the instrumental set-up that has been deployed during this campaign and discuss the main objectives of the project. An overview of fog events that occurred during the 6 months experiment will be given, and preliminary analysis of data collected during IOPs with a tethered balloon and UAVs will be presented.

Radiative cooling and heating impact the liquid water balance of fog and therefore play an important role in determining their persistence or dissipation. We demonstrate that a quantitative analysis of the radiation-driven condensation and evaporation is possible in real time using ground-based remote sensing observations (cloud radar, ceilometer, microwave radiometer). Seven continental fog events in midlatitude winter are studied, and the radiative processes are further explored through sensitivity studies. The longwave (LW) radiative cooling of the fog is able to produce 40–70 g m−2 h−1 of liquid water by condensation when the fog liquid water path exceeds 30 g m−2 and there are no clouds above the fog, which corresponds to renewing the fog water in 0.5–2 h. The variability is related to fog temperature and atmospheric humidity, with warmer fog below a drier atmosphere producing more liquid water. The appearance of a cloud layer above the fog strongly reduces the LW cooling relative to a situation with no cloud above; the effect is strongest for a low cloud, when the reduction can reach 100 %. Consequently, the appearance of clouds above will perturb the liquid water balance in the fog and may therefore induce fog dissipation. Shortwave (SW) radiative heating by absorption by fog droplets is smaller than the LW cooling, but it can contribute significantly, inducing 10–15 g m−2 h−1 of evaporation in thick fog at (winter) midday. The absorption of SW radiation by unactivated aerosols inside the fog is likely less than 30 % of the SW absorption by the water droplets, in most cases. However, the aerosols may contribute more significantly if the air mass contains a high concentration of absorbing aerosols. The absorbed radiation at the surface can reach 40–120 W m−2 during the daytime depending on the fog thickness. As in situ measurements indicate that 20–40 % of this energy is transferred to the fog as sensible heat, this surface absorption can contribute significantly to heating and evaporation of the fog, up to 30 g m−2 h−1 for thin fog, even without correcting for the typical underestimation of turbulent heat fluxes by the eddy covariance method. Since the radiative processes depend mainly on the profiles of temperature, humidity and clouds, the results of this paper are not site specific and can be generalised to fog under different dynamic conditions and formation mechanisms, and the methodology should be applicable to warmer and moister climates as well. The retrieval of approximate emissivity of clouds above fog from cloud radar should be further developed.

This article presents a new Cloud Radar calibration methodology using solid reference reflectors mounted on masts, developed during two field experiments held in 2018 and 2019 at the SIRTA atmospheric observatory, located in Palaiseau, France, in the framework of the ACTRIS-2 research and innovation program. The experimental setup includes 10 cm and 20 cm triangular trihedral targets installed at the top of 10 m and 20 m masts, respectively. The 10 cm target is mounted on a pan-tilt motor at the top of the 10 m mast to precisely align its boresight with the radar beam. Sources of calibration bias and uncertainty are identified and quantified. Specifically, this work assesses the impact of receiver compression, incomplete antenna overlap, temperature variations inside the radar, clutter and experimental setup misalignment. Setup misalignment is a source of bias previously undocumented in the literature, that can have an impact on the order of tenths of dB in calibration retrievals of W band Radars. A detailed analysis enabled the design of a calibration methodology which can reach a cloud radar calibration uncertainty of 0.3 dB based on the equipment used in the experiment. Among different sources of uncertainty, the two largest terms are due to signal-to-clutter ratio and radar-to-target alignment. The analysis revealed that our 20 m mast setup with an approximate alignment approach is preferred to the 10 m mast setup with the motor-driven alignment system. The calibration uncertainty associated with signal-to-clutter ratio of the former is ten times smaller than for the latter. Cloud radar calibration results are found to be repeatable when comparing results from a total of 18 independent tests. Once calibrated the cloud radar provides valid reflectivity values when sampling mid-tropospheric clouds. Thus we conclude that the method is repeatable and robust, and that the uncertainties are precisely characterized. The method can be implemented under different configurations as long as the proposed principles are respected. It could be extended to reference reflectors held by other lifting devices such as tethered balloons or unmanned aerial vehicles.

International audience; A detailed understanding of atmospheric boundary layer (ABL) processes is key to improve forecasting of pollution dispersion and cloud dynamics in the context of future climate scenarios. International networks of automatic lidars and ceilometers (ALC) are gathering valuable data that allow for the height of the ABL and its sublayers to be derived in near real time. A new generation of advanced methods to automatically detect the ABL heights now exist. However, diversity in ALC models means these algorithms need to be tailored to instrument-specific capabilities. Here, the advanced algorithm STRATfinder is presented for application to high signal-to-noise ratio (SNR) ALC observations, and results are compared to an automatic algorithm designed for low-SNR measurements (CABAM). The two algorithms are evaluated for application in an operational network setting. Results indicate that the ABL heights derived from low-SNR ALC have increased uncertainty during daytime deep convection, while high-SNR observations can have slightly reduced capabilities in detecting shallow nocturnal layers. Agreement between the ALC-based methods is similar when either is compared to the ABL heights derived from temperature profile data. The two independent methods describe very similar average diurnal and seasonal variations. Hence, high-quality products of ABL heights may soon become possible at national and continental scales.

Measurement of water vapor or humidity in the atmosphere is fundamental for many applications. Relative humidity measurements with a capacitive sensor in radiosondes are affected by several factors that need to be assessed and corrected. This work aims to address corrections for the main effects for the Meteomodem M10 radiosonde as a step to meet the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) requirements. The considered corrections are 1) the calibration correction; 2) a slow regime due to the slow diffusion of molecules through the sensor, especially at very high and very low relative humidity conditions; 3) the relative humidity sensor dependence on the gradient of temperature; and 4) the time lag at cold temperatures, which affects measurements in regions of strong relative humidity gradients. These corrections were tested for 26 nighttime and 25 daytime radiosondes in two midlatitude locations for which both Meteomodem M10 and Vaisala RS92 measurements were available. The results show that, after correcting for the four effects, M10 relative humidity measurements are, on average, consistent with the Vaisala RS92 relative humidity values within 2% RH at all altitudes for the nighttime launches (against 6% RH before the correction) and within 5% RH at all altitudes for the daytime launches (against 9% RH before the correction).

Six months of stratospheric aerosol observations with the European Aerosol Research Lidar Network (EARLINET) from August 2017 to January 2018 are presented. The decay phase of an unprecedented, record-breaking stratospheric perturbation caused by wildfire smoke is reported and discussed in terms of geometrical, optical, and microphysical aerosol properties. Enormous amounts of smoke were injected into the upper troposphere and lower stratosphere over fire areas in western Canada on 12 August 2017 during strong thunderstorm–pyrocumulonimbus activity. The stratospheric fire plumes spread over the entire Northern Hemisphere in the following weeks and months. Twenty-eight European lidar stations from northern Norway to southern Portugal and the eastern Mediterranean monitored the strong stratospheric perturbation on a continental scale. The main smoke layer (over central, western, southern, and eastern Europe) was found at heights between 15 and 20 km since September 2017 (about 2 weeks after entering the stratosphere). Thin layers of smoke were detected at heights of up to 22–23 km. The stratospheric aerosol optical thickness at 532 nm decreased from values > 0.25 on 21–23 August 2017 to 0.005–0.03 until 5–10 September and was mainly 0.003–0.004 from October to December 2017 and thus was still significantly above the stratospheric background (0.001–0.002). Stratospheric particle extinction coefficients (532 nm) were as high as 50–200 Mm−1 until the beginning of September and on the order of 1 Mm−1 (0.5– 5 Mm−1 ) from October 2017 until the end of January 2018. The corresponding layer mean particle mass concentration was on the order of 0.05–0.5 µg m−3 over these months. Soot particles (light-absorbing carbonaceous particles) are efficient ice-nucleating particles (INPs) at upper tropospheric (cirrus) temperatures and available to influence cirrus formation when entering the tropopause from above. We estimated INP concentrations of 50–500 L−1 until the first days in September and afterwards 5–50 L−1 until the end of the year 2017 in the lower stratosphere for typical cirrus formation temperatures of −55 ◦C and an ice supersaturation level of 1.15. The measured profiles of the particle linear depolarization ratio indicated a predominance of nonspherical smoke particles. The 532 nm depolarization ratio decreased slowly with time in the main smoke layer from values of 0.15–0.25 (August–September) to values of 0.05–0.10 (October–November) and < 0.05 (December–January). The decrease of the depolarization ratio is consistent with aging of the smoke particles, growing of a coating around the solid black carbon core (aggregates), and thus change of the shape towards a spherical form. We found ascending aerosol layer features over the most southern European stations, especially over the eastern Mediterranean at 32–35◦ N, that ascended from heights of about 18–19 to 22–23 km from the beginning of October to the beginning of December 2017 (about 2 km per month). We discuss several transport and lifting mechanisms that may have had an impact on the found aerosol layering structures.

Since 1992, the Olympic and Paralympic Games have committed, as part of Agenda 21, to a sport that supports sustainable development. This includes, of course, the issue of air quality, which is a major concern for most organizing cities, which are often megacities with several million inhabitants. These environments with very high population density are associated with intense pollutant emissions due to various human activities related to transport, energy production, industry and even agriculture. This issue of air quality has in the past been a more or less important preoccupation of candidate cities with the highlight of the candidacy of Beijing in 2008. The measures taken, particularly focused on transport, on this occasion but also in the case of other Olympiads had different consequences with an impact often dominated by weather conditions. The persistence over time of the measures taken during the games is generally low but the impact on awareness, less tangible, is perhaps non negligible, especially in the case of games in Beijing. In the case of Paris 2024, the question of pollution with ozone and particles arises. Major efforts have been made for several decades to reduce pollution in Europe. These policies have been quite effective in reducing the levels of primary pollutants (e.g. NOx), but the levels of these pollutants are still high enough to have an impact on the health of exposed populations. Policies to reduce primary pollutants will continue but with possible negative implications for ozone levels and in the context of potential changes in weather such as summer heatwaves, the effects of which can be very detrimental to the quality of the ozone. In this chapter, we describe the areas of research in the field of air quality that are directly or indirectly related to a mega-event such as the Olympic Games. In that context, the following axes will be addressed: estimate of Paris area air quality in the future in the context of mitigation policies and climatic change; air quality in Olympic facilities; planning mobility and traffic with air quality perspectives; air quality and athletes.

Wood burning is widely used for domestic heating and has been identified as a ubiquitous pollution source in urban areas, especially during cold months. The present study is based on a three and a half winter months field campaign in the Paris region measuring Volatile Organic Compounds (VOCs) by Proton Transfer Reaction Mass Spectrometry (PTR-MS) in addition to Black Carbon (BC). Several VOCs were identified as strongly wood burning-influenced (e.g., acetic acid, furfural), or traffic-influenced (e.g., toluene, C8-aromatics). Methylbutenone, benzenediol and butandione were identified for the first time as wood burning-related in ambient air. A Positive Matrix Factorization (PMF) analysis highlighted that wood burning is the most important source of VOCs during the winter season. (47%). Traffic was found to account for about 22% of the measured VOCs during the same period, whereas solvent use plus background accounted altogether for the remaining fraction. The comparison with the regional emission inventory showed good consistency for benzene and xylenes but revisions of the inventory should be considered for several VOCs such as acetic acid, C9-aromatics and methanol. Finally, complementary measurements acquired simultaneously at other sites in Île-de-France (the Paris region) enabled evaluation of spatial variabilities. The influence of traffic emissions on investigated pollutants displayed a clear negative gradient from roadside to suburban stations, whereas wood burning pollution was found to be fairly homogeneous over the region.

International audience; Six months of stratospheric aerosol observations with the European Aerosol Research Lidar Network (EARLINET) from August 2017 to January 2018 are presented. The decay phase of an unprecedented, record-breaking stratospheric perturbation caused by wildfire smoke is reported and discussed in terms of geometrical, optical, and microphysical aerosol properties. Enormous amounts of smoke were injected into the upper troposphere and lower stratosphere over fire areas in western Canada on 12 August 2017 during strong thunderstorm-pyrocumulonimbus activity. The stratospheric fire plumes spread over the entire Northern Hemisphere in the following weeks and months. Twenty-eight European lidar stations from northern Norway to southern Portugal and the eastern Mediterranean monitored the strong stratospheric perturbation on a continental scale. The main smoke layer (over central, western, southern, and eastern Europe) was found at heights between 15 and 20 km since September 2017 (about 2 weeks after entering the stratosphere). Thin layers of smoke were detected at heights of up to 22-23 km. The stratospheric aerosol optical thickness at 532 nm decreased from values > 0.25 on 21-23 August 2017 to 0.005-0.03 until 5-10 September and was mainly 0.003-0.004 from October to December 2017 and thus was still significantly above the stratospheric background (0.001-0.002). Stratospheric particle extinction coefficients (532 nm) were as high as 50-200 Mm-1 until the beginning of September and on the order of 1 Mm-1 (0.5-5 Mm-1) from October 2017 until the end of January 2018. The corresponding layer mean particle mass concentration was on the order of 0.05-0.5 µg m-3 over these months. Soot particles (light-absorbing carbonaceous particles) are efficient ice-nucleating particles (INPs) at upper tropospheric (cirrus) temperatures and available to influence cirrus formation when entering the tropopause from above. We estimated INP concentrations of 50-500 L-1 until the first days in September and afterwards 5-50 L-1 until the end of the year 2017 in the lower stratosphere for typical cirrus formation temperatures of -55 ∘C and an ice supersaturation level of 1.15. The measured profiles of the particle linear depolarization ratio indicated a predominance of nonspherical smoke particles. The 532 nm depolarization ratio decreased slowly with time in the main smoke layer from values of 0.15-0.25 (August-September) to values of 0.05-0.10 (October-November) and < 0.05 (December-January). The decrease of the depolarization ratio is consistent with aging of the smoke particles, growing of a coating around the solid black carbon core (aggregates), and thus change of the shape towards a spherical form. We found ascending aerosol layer features over the most southern European stations, especially over the eastern Mediterranean at 32-35∘ N, that ascended from heights of about 18-19 to 22-23 km from the beginning of October to the beginning of December 2017 (about 2 km per month). We discuss several transport and lifting mechanisms that may have had an impact on the found aerosol layering structures.

International audience

Doppler cloud radars are amazing tools to characterize cloud and fog properties and to improve their representation in models. However, commercially available cloud radars (35 and 95 GHz) are still very expensive, which hinders their widespread deployment. This study presents the development of a lower-cost semioperational 95-GHz Doppler cloud radar called the Bistatic Radar System for Atmospheric Studies (BASTA). To drastically reduce the cost of the instrument, a different approach is used compared to traditional pulsed radars: instead of transmitting a large amount of energy for a very short time period (as a pulse), a lower amount of energy is transmitted continuously. By using a specific signal processing technique, the radar can challenge expensive radars and provide high-quality measurements of cloud and fog. The latest version of the instrument has a sensitivity of about −50 dBZ at 1 km for 3-s integration and a vertical resolution of 25 m. The BASTA radar currently uses four successive modes for specific applications: the 12.5-m vertical resolution mode is dedicated to fog and low clouds, the 25-m mode is for liquid and ice midtropospheric clouds, and the 100- and 200-m modes are ideal for optically thin high-level ice clouds. The advantages of such a radar for calibration procedures and field operations are also highlighted. The radar comes with a set of products dedicated to cloud and fog studies. For instance, cloud mask, corrected Doppler velocity, and multimode products combining the high-sensitivity mode and high-resolution modes are provided.

Comprehensive field campaigns dedicated to fog life cycle observation were conducted during the winters of 2010–2013 at the SIRTA observatory in the suburb of Paris. In order to document their properties, in situ microphysical measurements collected during 23 fog events are examined here. They reveal large variability in number, concentration and size of both aerosol background before the fog onset and fog droplets according to the different cases. The objective of this paper is to evaluate the impact of aerosol particles on the fog microphysics. To derive an accurate estimation of the actual activated fog droplet number concentration Nact we determine the hygro-scopicity parameter κ, the dry and the wet critical diameter and the critical supersaturation for each case by using an iterative procedure based on the κ-Köhler theory that combines cloud condensation nuclei (CCN), dry particle and droplet size distribution measurements. Resulting values of κ = 0.17 ± 0.05 were found typical of continental aerosols. Our study reveals low values of the derived critical supersaturation with a median of 0.043 % and large values for both wet and dry activation diameters. Consequently, the corresponding Nact values are low with median concentrations of 53.5 cm−3 and 111 cm−3 within the percentile 75th. No detectable trend between the concentration of aerosol particles with diameter > 200 nm and Nact was observed. In contrast the CCN data at 0.1 % supersaturation exhibits a strong correlation with these aerosol concentrations. We therefore conclude that the actual supersaturations reached during these fog episodes are too low and no simultaneous increase of aerosols > 200 nm and droplet concentrations can be observed. Moreover our analysis suggests that a high aerosol loading limits the supersaturation values. It is also found that the activated fraction mainly depends on the aerosol size while the hygroscopicity appears to be of a secondary importance. Although radiative fogs are usually associated with higher aerosol loading rather than to stratus lowering events, our analysis reveals that the activated particle concentrations at the beginning of the event are similar for both types of fog. However the evolution of the droplet concentration during the fog life cycle shows significant differences between both types of fog.

The ultraviolet index (UVI) is an important tool to raise public awareness of the risks of solar radiation overexposure. However, solar ultraviolet radiation instruments are relatively expensive, and the deployment and maintenance of a large-scale network proved impractical. In this paper, we describe a simple method for UVI and daily erythemal dose retrieval from photosynthetically active radiation (PAR – 0.40–0.70 μm) measurements. These latter are generally performed by cheaper instruments and commonly found in any ordinary meteorological station. Despite the different interactions involving UV and PAR radiation with the atmospheric components, our method is based on the solar position and cloud modification factor (CMF) that exert a quite similar influence on these electromagnetic radiation bands. We also show that this method is not suitable for shortwave radiation sensors (0.25–4.00 μm). A large dataset was used to test this method and the comparisons between UVI measurements and inferences are comparable to the instrumental errors. Thus, in this paper we show a 2nd degree regression equation for obtaining erythemal UV from PAR measurements.

Long-range transported smoke aerosols in the UTLS (Upper Troposphere/Lower Stratosphere) over Europe were detected in Summer 2017. The measurements of ground-based instruments and satellite sensors indicate that the UTLS aerosol layers were originated from Canadian wildfires and were transported to Europe by UTLS advection. In this study, the observations of two multi-wavelength Raman Lidar systems in northern France (Lille and Palaiseau) are used to derive aerosol properties, such as optical depth of the UTLS layer, Lidar ratios at 355 and 532 nm and particle linear depolarization ratios at 355, 532 and 1064 nm. The optical depth of the UTLS layers at 532 nm varies from 0.05 to above 0.20, with very weak spectral dependence between 355 and 532 nm. Lidar ratios at 355 nm are in 31 ± 15 sr to 45 ± 9 sr range and at 532 nm, the Lidar ratios are in the range of 54 ± 12 sr to 58 ± 9 sr. Such spectral dependence of Lidar ratio is known to be a characteristic feature of aged smoke. The typical particle depolarization ratios in the UTLS smoke layer are 25 ± 4 % at 355 nm, 19 ± 3 % at 532 nm and 4.5 ± 0.8 % at 1064 nm. The relatively high depolarization ratios and such spectral dependence are an indication of a complicated morphology of aged smoke particles. We found an increase of depolarization ratio versus transport time. The depolarization ratio at 532 nm increases from below 2–5 % for fresh smoke to over 20 % for smoke aged more than 20 days. The 3 β + 2 α observations of two cases at Palaiseau and Lille sites were inverted to the aerosol microphysical properties using regularization algorithm. The particles distribute in the 0.1–1.0 μm range with effective radius of 0.33 ± 0.10 μm for both cases. The derived complex refractive indices are 1.52 (± 0.05) + i 0.021 (± 0.010) and 1.55 (± 0.05) + i 0.028 (± 0.014) for Palaiseau and Lille data. The retrieved aerosol properties were used to calculate the direct radiative forcing (DRF) effect specific to the UTLS aerosol layers. The simulations derive daily net DRF efficiency of −79.6 W m −2 τ −1 at the bottom of the atmosphere for Lille observations. At the top of the atmosphere, the net DRF efficiency is −7.9 W m −2 τ −1 . The results indicate that the UTLS aerosols strongly reduce the radiation reaching the terrestrial surface by absorption. The heating rate of the UTLS layers is estimated to be 3.7 K day −1 . The inversion of Palaiseau data leads to similar results. The heating rate predicts a temperature increase within the UTLS aerosol layer, which has been observed by the radiosonde temperature measurements.