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.

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.

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 (${\mathit{\sigma }}_{\mathrm{w}}^{\mathrm{2}}$ ) above the urban canopy layer. It is shown that ${\mathit{\sigma }}_{\mathrm{w}}^{\mathrm{2}}$ is a powerful predictor for regional contrast in air temperature, as the UHI intensity decreases exponentially with increasing ${\mathit{\sigma }}_{\mathrm{w}}^{\mathrm{2}}$ and strong UHI values only occur when ${\mathit{\sigma }}_{\mathrm{w}}^{\mathrm{2}}$ is very weak. This study demonstrates how DWL observations in cities provide valuable insights into near-surface processes relevant to human and environmental health.

Doppler wind lidars (DWL) offer high-resolution wind profile measurements that are valuable for understanding atmospheric boundary layer (ABL) dynamics. Here six ground-based DWL, deployed in a multi-institutional effort along a 40 km transect through the centre of Paris (France), are used to retrieve horizontal wind speed and direction through the ABL at 18–25 m vertical and 1–60 min temporal resolution. Data are available for June 2022–March 2024 (three DWL) and two Intensive Observation Periods (six DWL) across 9 weeks in September 2023–December 2023. Data from all sensors are harmonised in terms of quality control, file format, as well as temporal and vertical resolutions. The quality of this DWL dataset is evaluated against in-situ measurements at the Eiffel Tower and radiosonde profiles. This unique, spatially dense, open dataset will allow urban boundary layer dynamics to be explored in process-studies, and is further valuable for the evaluation of high-resolution weather, climate, inverse and air pollution models that resolve city-scale processes. The dataset is available at https://doi.org/10.5281/zenodo.14761503 (Morrison et al., 2025).

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.

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.