Jean-Charles Dupont

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.In the framework of the ABL testbed project (funded by ICOS, ACTRIS and EUMETNET E-PROFILE), two advanced algorithms for the detection of ABL heights are being assessed for application in an operational network setting. 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. A subset of 11 E-PROFILE sites in diverse geographical and land cover settings across Europe are selected where data from different ALC are available covering 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. Recommendations are formulated for implementation of automatic ABL height retrievals across a diverse sensor network. First results are very promising, revealing consistent temporal and spatial variations in ABL layer heights across the network.

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 (NO_x 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 NO_x 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 NO_x-relationship emphasizes the interest of NO_x 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.

We propose a new paradigm to describe the temporal evolution of continental fog layers. This paradigm defines fog as a layer saturated from the surface to a known upper boundary, and whose liquid water path (LWP) exceeds a critical value, the critical liquid water path (CLWP). When the LWP is less than the CLWP the fog water cannot extend all the way to the surface, leading to a surface horizontal visibility greater than 1 km. On the opposite, when the LWP is larger than the CLWP, the fog water extends all the way to the surface, inducing a horizontal visibility less than 1 km. The excess water with respect to the critical value is then defined as the reservoir liquid water path (RLWP). The new fog paradigm is formulated as a conceptual model that relates the liquid water path of adiabatic fog with its thickness and surface liquid water content, and allows the critical and reservoir liquid water paths to be computed. Both variables can be tracked in real time using vertical profiling measurements, enabling a real time diagnostic of fog status. The conceptual model is tested using data from seven years of measurements performed at the SIRTA observatory, combining cloud radar, microwave radiometer, ceilometer, scatterometer and weather station measurements. In this time period we found 80 fog events with reliable measurements, with 56 of these lasting more than three hours. The paper presents the conceptual model and its capability to derive the LWP from the fog CTH and surface horizontal visibility with an RMS uncertainty of 10.5 g m⁻². The impact of fog liquid water path and fog top height variations on fog life cycle (formation to dissipation) is presented based on four case studies, and statistics derived from 56 fog events. Our results show in particular that the reservoir liquid water path is consistently positive during the mature phase of the fog and that it starts to decrease quasi monotonously about one hour before dissipation, reaching a near-zero value at the time of dissipation. The reservoir liquid water path and its time derivative could hence be used as an indicator for life cycle stage and support short range forecasting of fog dissipation.

The development of ground based cloud radars offers a new capability to continuously monitor the fog structure. Retrievals of fog microphysics is key for future process studies, data assimilation or model evaluation, and can be performed using a variational method. Both the one-dimensional variational retrieval method (1D-Var) or direct 3D/4D-Var data assimilation techniques rely on the combination of cloud radar measurements and a background profile weighted by their corresponding uncertainties to obtain the optimal solution for the atmospheric state. In order to prepare for the use of ground-based cloud radar measurements for future applications based on variational approaches, the different sources of uncertainty due to instrumental, background, and the forward operator errors need to be properly treated and accounted for. This paper aims at preparing 1D-Var retrievals by analysing the errors associated with a background profile and a forward operator during fog conditions. For this, the background was provided by a high-resolution numerical weather prediction model and the forward operator by a radar simulator. Firstly, an instrumental dataset was taken from the SIRTA observatory near Paris, France for winter 2018–19 during which 31 fog events were observed. Statistics were calculated comparing cloud radar observations to those simulated. It was found that the accuracy of simulations could be drastically improved by correcting for significant spatio-temporal background errors. This was achieved by implementing a most resembling profile method in which an optimal model background profile is selected from a domain and time window around the observation location and time. After selecting the best background profile a good agreement was found between observations and simulations. Moreover, observation minus simulation errors were found to satisfy the conditions needed for future 1D-var retrievals (un-biased and normally distributed).

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).

The microphysical properties of low stratus and fog are analyzed here based on simultaneous measurement of an in situ sensor installed on board a tethered balloon and active remote-sensing instruments deployed at the Instrumented Site for Atmospheric Remote Sensing Research (SIRTA) observatory (south of Paris, France). The study focuses on the analysis of 3 case studies where the tethered balloon is deployed for several hours in order to derive the relationship between liquid water content (LWC), effective radius (Re) and cloud droplet number concentration (CDNC) measured by a light optical aerosol counter (LOAC) in situ granulometer and Bistatic Radar System for Atmospheric Studies (BASTA) cloud radar reflectivity. The well-known relationship Z = α × (LWC)^β has been optimized with α ϵ [0.02, 0.097] and β ϵ [1.91, 2.51]. Similar analysis is done to optimize the relationship Re = f(Z) and CDNC = f(Z). Two methodologies have been applied to normalize the particle-size distribution measured by the LOAC granulometer with a visible extinction closure (R² ϵ [0.73, 0.93]) and to validate the LWC profile with a liquid water closure using the Humidity and Temperature Profiler (HATPRO) microwave radiometer (R² ϵ [0.83, 0.91]). In a second step, these relationships are used to derive spatial and temporal variability of the vertical profile of LWC, Re and CDNC starting from BASTA measurement. Finally, the synergistic remote sensing of clouds (SYRSOC) algorithm has been tested on three tethered balloon flights. Generally, SYRSOC CDNC and Re profiles agreed well with LOAC in situ and BASTA profiles for the studied fog layers. A systematic overestimation of LWC by SYRSOC in the top half of the fog layer was found due to fog processes that are not accounted for in the cloud algorithm SYRSOC.