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Characterizing inter‐instrument variability of sensors is crucial to assessing uncertainties in observational campaigns, networks, and for data assimilation. Here, we co‐locate six high signal‐to‐noise ratio Vaisala CL61 lidar‐ceilometers for a period of 10 days to quantify instrument‐related differences in several observed variables: profiles of attenuated backscatter, its components (parallel‐ and cross‐polarized backscatter) and the volume linear depolarisation ratio (δ $$ \\delta $$ ), as well as derived cloud variables and mixed‐layer height. Analysing intervals between 5 and 60 min, median absolute differences between sensors (AD50 $$ {}_{50} $$ ) and percentiles (e.g., AD75 $$ {}_{75} $$ ) are used to quantify instrument related uncertainties. For backscatter and δ $$ \\delta $$ , we differentiate between conditions with rain, clear sky, and clouds. Here we address instrument precision rather than accuracy, with instrument accuracy assumed. The detected agreement between instruments suggests a distributed measurement network should be capable of providing context for interpretation of spatial differences. If instruments measure accurately, it is possible to resolve spatial differences (e.g., urban–rural) for attenuated backscatter, derived cloud variables and layer heights. However, differences exist and vary with signal‐to‐noise ratio and atmospheric conditions. The AD50 $$ {}_{50} $$inter‐sensor results for 15 min intervals for total cloud‐cover fraction (excluding clear sky and fully overcast conditions) is 1.9%, and for cloud base height 7.3 m. Agreement of all cloud variables is better for boundary layer clouds (when first cloud layer < $$ < $$4 km agl) than for all five cloud layers recorded by the sensor firmware. The 15 min mixed‐layer height AD50 $$ {}_{50} $$is 0 m and the AD75 $$ {}_{75} $$21.5 m. We show that instrument precipitation flags are in good agreement, but do not link closely with ground‐level rainfall observations, hence an alternative algorithm is proposed. We provide quality control recommendations for data processing to improve inter‐instrument agreement of cloud variables and mixed‐layer height. Instrument‐related differences between six co‐located Vaisala CL61 automatic lidar‐ceilometers are quantified for several observed variables: profiles of attenuated backscatter (β) and the linear depolarisation ratio, as well as derived cloud variables and mixed‐layer height. Figure shows variation of relative differences (%) in β (m−1 sr−1) with atmospheric conditions as absolute differences divided by observed values. When instruments are measuring accurately, it is possible to resolve spatial differences (e.g., urban‐rural).

In this study, we use a synergy of in situ and remote sensing measurements collected during the SOuthwest FOGs 3D experiment for processes study (SOFOG3D) field campaign in autumn and winter 2019–2020 to analyse the thermodynamic and turbulent processes related to fog formation, evolution, and dissipation across southwestern France. Based on a unique measurement dataset (synergy of cloud radar, microwave radiometer, wind lidar, and weather station data) combined with a fog conceptual model, an analysis of the four deepest fog episodes (two radiation fogs and two advection–radiation fogs) is conducted. The results show that radiation and advection–radiation fogs form under deep and thin temperature inversions, respectively. For both fog categories, the transition period from stable to adiabatic fog and the fog adiabatic phase are driven by vertical mixing associated with an increase in turbulence in the fog layer due to mechanical production (turbulence kinetic energy (TKE) up to 0.4 m2 s−2 and vertical velocity variance (σw2) up to 0.04 m2 s−2) generated by increasing wind and wind shear. Our study reveals that fog liquid water path, fog top height, temperature, radar reflectivity profiles, and fog adiabaticity derived from the conceptual model evolve in a consistent manner to clearly characterise this transition. The dissipation time is observed at night for the advection–radiation fog case studies and after sunrise for the radiation fog case studies. Night-time dissipation is driven by horizontal advection generating mechanical turbulence (TKE at least 0.3 m2 s−2 and σw2 larger than 0.04 m2 s−2). Daytime dissipation is linked to the combination of thermal and mechanical turbulence related to solar heating (near-surface sensible heat flux larger than 10 W m−2) and wind shear, respectively. This study demonstrates the added value of monitoring fog liquid water content and depth (combined with wind, turbulence, and temperature profiles) and diagnostics such as fog liquid water reservoir and adiabaticity to better explain the drivers of the fog life cycle.

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.

Accurate measurements of upper troposphere humidity are essential to enhance understanding of contrail formation and guiding mitigation efforts. This study evaluates the ability of the IPRAL Raman Lidar, located south of Paris, to provide high-resolution water vapour mixing ratio (WVMR) profiles at contrail-relevant altitudes. Raman signals are screened on hourly bases, and a universal calibration method, independent of acquisition mode, is proposed towards operational Lidar water vapour profiles, using co-located ERA5 data. Calibration factors are derived from comparisons between 4 and 6 km, and nightly coefficients determined from hourly factors. Instrumental stability is monitored through the temporal evolution of calibration factors, and stable-period medians are adopted as final values. The uncertainty of calibrated WVMR profiles is assessed by comparison with GRUAN processed Meteomodem M10 radiosondes and ERA5 data. Results show a high agreement (>90%), with IPRAL exhibiting a small negative bias (~10%) below 8 km, reducing to ~5% up to 10.5 km to radiosondes. ERA5 systematically underestimates water vapour at cruise altitudes, with a dry bias increasing from 10% at 9 km to >20% at 11 km. Recent IAGOS corrections to ERA5, improving supersaturation representation, are validated over Paris. This calibrated Lidar data set supports improved atmospheric modelling and contributes to future air traffic management strategies.

Radiation fog occurs over many locations around the world in stable atmospheric conditions. Air traffic at busy airports can be significantly disrupted because low visibility at the ground makes it unsafe to take off, land and taxi on the ground. Current numerical weather prediction forecasts are able to predict general conditions favorable for fog formation, but not the exact time or location of fog occurrence. A selected set of observations available in near-real time at strategic locations could also be useful to track the evolution of key processes and key parameters that drive fog formation. Such observations could complement the information predicted by numerical weather prediction (NWP) models that is made available to airport forecasters in support of their fog forecast. This paper presents an experimental setup based on collocated automatic lidar and ceilometer measurements, relative humidity measurements and horizontal visibility measurements to study hygroscopic growth of fog condensation nuclei. This process can take several minutes to hours, and can be tracked using lidar- or ceilometer-attenuated backscatter profiles. Based on hygroscopic growth laws we derive a set of parameters that can be used to provide alerts minutes to hours prior to formation of radiation fog. We present an algorithm that uses the temporal evolution of attenuated backscatter measurements to derive pre-fog formation alerts. The performance of the algorithm is tested on 45 independent pre-fog situations at two locations (near Paris, France, and Brussels, Belgium). We find that an alert for pre-fog conditions predominantly occurs 10–50 min prior to fog formation at an altitude ranging 0 to 100 m above ground. In a few cases, alerts can occur up to 100 min prior to fog formation. Alert durations are found to be sensitive to the relative humidity conditions found a few hours prior to the fog.

A scientific approach is presented to aggregate and harmonize a set of 60 geophysical variables at hourly timescale over a decade, and to allow multiannual and multi-variable studies combining atmospheric dynamics and thermodynamics, radiation, clouds and aerosols from ground-based observations. Many datasets from ground-based observations are currently in use worldwide. They are very valuable because they contain complete and precise information due to their spatio-temporal co-localization over more than a decade. These datasets, in particular the synergy between different type of observations, are under-used because of their complexity and diversity due to calibration, quality control, treatment, format, temporal averaging, metadata, etc. Two main results are presented in this article: (1) a set of methods available for the community to robustly and reliably process ground-based data at an hourly timescale over a decade is described and (2) a single netCDF file is provided based on the SIRTA supersite observations. This file contains approximately 60 geophysical variables (atmospheric and in ground) hourly averaged over a decade for the longest variables. The netCDF file is available and easy to use for the community. In this article, observations are “re-analyzed”. The prefix “re” refers to six main steps: calibration, quality control, treatment, hourly averaging, homogenization of the formats and associated metadata, as well as expertise on more than a decade of observations. In contrast, previous studies (i) took only some of these six steps into account for each variable, (ii) did not aggregate all variables together in a single file and (iii) did not offer an hourly resolution for about 60 variables over a decade (for the longest variables). The approach described in this article can be applied to different supersites and to additional variables. The main implication of this work is that complex atmospheric observations are made readily available for scientists who are non-experts in measurements. The dataset from SIRTA observations can be downloaded at http://sirta.ipsl.fr/reobs.html (last access: April 2017) (Downloads tab, no password required) under https://doi.org/10.14768/4F63BAD4-E6AF-4101-AD5A-61D4A34620DE.

The added heat in cities amplifies the health risks of heat waves. At night under calm winds and cloud-free skies, the air in the urban canopy layer can be several degrees warmer than in rural areas. This lower nocturnal cooling in the built-up settings poses severe health risks to the urban inhabitants, as indoor spaces cannot be ventilated effectively. With heat waves becoming more frequent and more intense in future climates, many cities are expanding their green spaces with the aim to introduce cooling through shading, evaporation and lower heat storage capacities. In this study, we assessed how the evening and nighttime cooling effect of urban parks (relative to nearby built-up settings) varies with the park size and the mesoscale atmospheric conditions during warm summer periods. Using a combination of meteorological surface station data and compact radiosondes, the cooling effect is quantified for several urban parks (about 15 ha) and urban woods (about 900 ha). A profiling Doppler wind lidar deployed in the city centre is used to measure turbulent vertical mixing conditions in the urban boundary layer. We find that the maximum nocturnal cooling effects in urban parks range around 1–5 °C during a 1-week heat wave event in mid-July 2022 but also in general during summer 2022 (June–August). Three atmospheric stability and mixing regimes are identified that explain the night-to-night variability in the park cooling effect. We find that very low turbulent vertical mixing in the urban boundary layer (<0.05 m2 s−2) results in the strongest evening cooling in both rural settings and urban parks and the weakest cooling in the built-up environment. This regime specifically occurs during heat waves in connection with large-scale advection of hot air over the region and corresponding subsidence. When nocturnal turbulent vertical mixing above the city is stronger, the evening cooling in urban green spaces is less efficient, so the atmospheric stratification above both urban parks and woods is less stable, and temperature contrasts compared to the built-up environment are less pronounced. These results highlight the fact that urban green spaces have a significant cooling potential during heat waves, with maximum effects at night as advection and mixing transport processes are minimal. This suggests adapting the opening hours of public parks to enable residents to benefit from these cooling islands.

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.

The low-level jet (LLJ) and the urban heat island (UHI) are common nocturnal phenomena. While the UHI has been studied extensively, interactions of the LLJ and the urban atmosphere in general (and the UHI in particular) have received less attention. In the framework of the PANAME (PAris region urbaN Atmospheric observations and models for Multidisciplinary rEsearch) initiative in the Paris region, continuous profiles of horizontal wind speed and vertical velocity were recorded with two Doppler wind lidars (DWLs) – for the first time allowing for a detailed investigation of the summertime LLJ characteristics in the region. Jets are detected for 70 % of the examined nights, often simultaneously at an urban and a suburban site, highlighting the LLJ regional spatial extent. Emerging at around sunset, the mean LLJ duration is ∼ 10 h, the mean wind speed is 9 m s−1, and the average core height is 400 m above the city. The temporal evolution of many events shows signatures that indicate that the inertial oscillation mechanism plays a role in the jet development: a clockwise veering of the wind direction and a rapid acceleration followed by a slower deceleration. The horizontal wind shear below the LLJ core induces variance in the vertical velocity (σw2) above the urban canopy layer. It is shown that σw2 is a powerful predictor for regional contrast in air temperature, as the UHI intensity decreases exponentially with increasing σw2 and strong UHI values only occur when σw2 is very weak. This study demonstrates how DWL observations in cities provide valuable insights into near-surface processes relevant to human and environmental health.

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, especially during spring 2020. As a result, air quality changed in manners that are still currently under investigation. The robust quantitative assessment of the impact of lockdown measures on ambient concentrations is however hindered by weather variability. 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 megacity of Paris (France). The overall slight decrease of submicron particulate matter (PM1) concentrations (−14 %) compared to business-as-usual conditions conceals contrasting behaviors. 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 changes triggered by NOx decreases. Particulate nitrate and secondary organic aerosols (SOAs), two of the main springtime aerosol components in northwestern 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 for regional decreases. Variations of the oxidation state of SOA suggest 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 for mitigation policies regarding air quality in future low-carbon urban areas.