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Nine methods to determine local-scale aerodynamic roughness length ( z 0 ) and zero-plane displacement ( z d ) are compared at three sites (within 60 m of each other) in London, UK. Methods include three anemometric (single-level high frequency observations), six morphometric (surface geometry) and one reference-based approach (look-up tables). A footprint model is used with the morphometric methods in an iterative procedure. The results are insensitive to the initial z d and z 0 estimates. Across the three sites, z d varies between 5 and 45 m depending upon the method used. Morphometric methods that incorporate roughness-element height variability agree better with anemometric methods, indicating z d is consistently greater than the local mean building height. Depending upon method and wind direction, z 0 varies between 0.1 and 5 m with morphometric z 0 consistently being 2–3 m larger than the anemometric z 0 . No morphometric method consistently resembles the anemometric methods. Wind-speed profiles observed with Doppler lidar provide additional data with which to assess the methods. Locally determined roughness parameters are used to extrapolate wind-speed profiles to a height roughly 200 m above the canopy. Wind-speed profiles extrapolated based on morphometric methods that account for roughness-element height variability are most similar to observations. The extent of the modelled source area for measurements varies by up to a factor of three, depending upon the morphometric method used to determine z d and z 0 .

Beijing has been suffering from frequent severe air pollution events, with concentrations affected significantly by the mixed-layer height. Major efforts have been made to study the physico-chemical properties, compositions, and sources of aerosol particles at ground level. However, little is known about the morphology, elemental composition, and mixing state of aerosol particles above the mixed layer. In this work, we collected individual aerosol particles simultaneously at ground level (2 m above ground) and above the mixed layer in urban Beijing (within the Atmospheric Pollution and Human Health in a Chinese Megacity, APHH-Beijing, 2016 winter campaign). The particles were analyzed offline by transmission electron microscopy coupled with energy dispersive X-ray spectroscopy. Our results showed that the relative number contribution of mineral particles to all measured particles was much higher during non-haze periods (42.5 %) than haze periods (18.1 %); in contrast, internally mixed particles contributed more during haze periods (21.9 %) than non-haze periods (7.2 %) at ground level. In addition, more mineral particles were found at ground level than above the mixed-layer height. Around 20 % of individual particles showed core–shell structures during haze periods, whereas only a few core–shell particles were observed during non-haze periods (2 %). The results showed that the particles above the mixed layer were more aged, with a larger proportion of organic particles originating from coal combustion. Our results indicate that a large fraction of the airborne particles above the mixed layer come from surrounding areas influenced by coal combustion activities. This source contributes to the surface particle concentrations in Beijing when polluted air is mixed down to the ground level.

ABSTRACT 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).

Urban areas are a hotspot for the interactions between the built environment, its inhabitants, and weather. Unlike the impact of temperatures through the well-known urban heat island effect, urban effects on cloud formation remain unknown. In this study we show observational evidence of a systematic enhancement of cloud cover in the afternoon and evening over two large metropolitan areas in Europe (Paris and London). Long-term measurements in and around London show that during late-spring and summer, even though less moisture is available at the surface and the atmosphere is drier, low clouds can persist longer over the urban area as vertical mixing of the available moisture is maintained for a longer period of time, into the evening transition. Our findings show that urban impacts on weather extend beyond temperature effects. These prolonged clouds over the city might enhance the urban heat island via night-time radiative forcing. Urban climato l ogy: A cloudy picture for hot cities Compared to nearby rural areas, mega-cities can produce an atmosphere that is, paradoxically, drier yet persistently more cloudy. Cities are known to create an “urban heat island”, but a multi-institution team led by Natalie Theeuwes from the University of Reading now shows that the urban fingerprint extends to an increase in cumulus cloud cover. In an analysis of the Paris and London mega-cities, the team finds that afternoon cloud cover during spring and summer is, on average, several percent higher over the urban core than over surrounding rural areas. The results are initially counter-intuitive, because reduced vegetation in cities also tends to dry the atmosphere, which should reduce cloud cover. By probing detailed ground-based observations in and near London, the researchers explain that buildings release their heat throughout the afternoon, enhancing turbulent mixing and thus the delivery of moisture to clouds. The findings illuminate the widening impact of cities on their environment.

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.

When the urban heat island (UHI) effect coincides with a heat wave (HW), thermal stress in cities is exacerbated. Understanding the surface energy balance (SEB) responses to HWs is critical for improving predictions of the synergies between UHIs and HWs. This study evaluates observed SEB characteristics in four cities (Beijing, Łódź, London and Swindon), along with their ambient meteorological conditions, for both HW and background summer climate scenarios. Using the Analytical Objective Hysteresis Model (AnOHM), particular emphasis is on the heat storage. The results demonstrate that in London and Swindon the amount of daytime heat storage and its fraction relative to the net all-wave radiation increase under HWs. Results further demonstrate that such increases are strongly tied to lower wind speeds. The effects of different UHI mitigation measures on heat storage are assessed using AnOHM. Results reveal that use of reflective materials and maintaining higher soil moisture availability can offset the adverse effects of increased heat storage.

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 <500 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.

Black carbon (BC) is known to have major impacts on both human health and climate. The populated megacity represents the most complex anthropogenic BC emissions where the sources and related impacts are very uncertain. This study provides source attribution and characterization of BC in the Beijing urban environment during the joint UK–China APHH (Air Pollution and Human Health) project, in both winter (November–December 2016) and summer (May–June 2017). The size-resolved mixing state of BC-containing particles was characterized by a single-particle soot photometer (SP2) and their mass spectra was measured by a soot particle aerosol mass spectrometer (SP-AMS). The refractory BC (rBC) mass loading was around a factor of 2 higher in winter relative to summer, and more variable coatings were present, likely as a result of additional surface emissions from the residential sector and favourable condensation in the cold season. The characteristics of the BC were relatively independent of air mass direction in summer, whereas in winter air masses from the Northern Plateau were considerably cleaner and contained less-coated and smaller BC, but the BC from the Southern Plateau had the largest core size and coatings. We compare two online source apportionment methods using simultaneous measurements made by the SP2, which measures physical properties of BC, and the chemical approach using the positive matrix factorization (PMF) of mass spectra from the SP-AMS for the first time. A method is proposed to isolate the BC from the transportation sector using a mode of small BC particles (core diameter Dc<0.18 µm and coating thickness ct < 50 nm). This mode of BC highly correlated with NOx concentration in both seasons (∼14 ng m−3 BC ppb−1 NOx) and corresponded with the morning traffic rush hour, contributing about 30 % and 40 % of the total rBC mass (35 % and 55 % in number) in winter and summer respectively. The BC from coal burning or biomass burning was characterized by moderate coatings (ct = 50–200 nm) contributing ∼20 %–25 % of rBC mass. Large uncoated BC particles (Dc>0.18 µm and ct < 50 nm) were more likely to be contributed by coal combustion, as these particles were not present in urban London. This mode was present in Beijing in both winter (∼30 %–40 % rBC mass) and summer (∼40 % rBC mass) but may be dominated by the residential and industrial sector respectively. The contribution of BC thickly coated with secondary species (ct > 200 nm) to the total rBC mass increased with pollution level in winter but was minor in summer. These large BC particles importantly enhanced the absorption efficiency at high pollution levels – in winter when PM1 > 100 µg m−3 or BC > 2 µg m−3, the absorption efficiency of BC increased by 25 %–70 %. The reduction of emissions of these large BC particles and the precursors of the associated secondary coating will be an effective way of mitigating the heating effect of BC in urban environments.

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 sub-layer 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.

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, it is assessed how the evening and night-time cooling effect of urban parks (relative to near-by built-up settings) varies with the park size and the meso-scale 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 one-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 park cooling effect. We find that very low turbulent vertical mixing in the urban boundary layer (< 0.05 m2s-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 that 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 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.