Between 27 June and 14 July 2019 aerosol layers were observed by the United Kingdom (UK) Raman lidar network in the upper troposphere and lower stratosphere. The arrival of these aerosol layers in late June caused some concern within the London Volcanic Ash Advisory Centre (VAAC) as according to dispersion simulations the volcanic plume from the 21 June 2019 eruption of Raikoke was not expected over the UK until early July. Using dispersion simulations from the Met Office Numerical Atmospheric-dispersion Modelling Environment (NAME), and supporting evidence from satellite and in situ aircraft observations, we show that the early arrival of the stratospheric layers was not due to aerosols from the explosive eruption of the Raikoke volcano but due to biomass burning smoke aerosols associated with intense forest fires in Alberta, Canada, that occurred 4 d prior to the Raikoke eruption. We use the observations and model simulations to describe the dispersion of both the volcanic and forest fire aerosol clouds and estimate that the initial Raikoke ash aerosol cloud contained around 15 Tg of volcanic ash and that the forest fires produced around 0.2 Tg of biomass burning aerosol. The operational monitoring of volcanic aerosol clouds is a vital capability in terms of aviation safety and the synergy of NAME dispersion simulations, and lidar data with depolarising capabilities allowed scientists at the Met Office to interpret the various aerosol layers over the UK and attribute the material to their sources. The use of NAME allowed the identification of the observed stratospheric layers that reached the UK on 27 June as biomass burning aerosol, characterised by a particle linear depolarisation ratio of 9 %, whereas with the lidar alone the latter could have been identified as the early arrival of a volcanic ash–sulfate mixed aerosol cloud. In the case under study, given the low concentration estimates, the exact identification of the aerosol layers would have made little substantive difference to the decision-making process within the London VAAC. However, our work shows how the use of dispersion modelling together with multiple observation sources enabled us to create a more complete description of atmospheric aerosol loading.

On 15–16 October 2017, ex-hurricane Ophelia passed to the west of the British Isles, bringing dust from the Sahara and smoke from Portuguese forest fires that was observable to the naked eye and reported in the UK's national press. We report here detailed observations of this event using the UK operational lidar and sun-photometer network, established for the early detection of aviation hazards, including volcanic ash. We also use ECMWF ERA5 wind field data and MODIS imagery to examine the aerosol transport. The observations, taken continuously over a period of 30 h, show a complex picture, dominated by several different aerosol layers at different times and clearly correlated with the passage of different air masses associated with the intense cyclonic system. A similar evolution was observed at several sites, with a time delay between them explained by their different location with respect to the storm and associated meteorological features. The event commenced with a shallow dust layer at 1–2 km in altitude and culminated in a deep and complex structure that lasted ∼12 h at each site over the UK, correlated with the storm's warm sector. For most of the time, the aerosol detected was dominated by mineral dust mixtures, as highlighted by depolarisation measurements, but an intense biomass burning aerosol (BBA) layer was observed towards the end of the event, lasting around 3 h at each site. The aerosol optical depth at 355 nm (AOD₃₅₅) during the whole event ranged from 0.2 to 2.9, with the larger AOD correlated to the intense BBA layer. Such a large AOD is unprecedented in the UK according to AERONET records for the last 20 years. The Raman lidars permitted the measurement of the aerosol extinction coefficient at 355 nm, the particle linear depolarisation ratio (PLDR), and the lidar ratio (LR) and made the separation of the dust (depolarising) aerosol from other aerosol types possible. A specific extinction has also been computed to provide an estimate of the atmospheric concentration of both aerosol types separately, which peaked at 420±200 µg m⁻³ for the dust and 558±232 µg m⁻³ for the biomass burning aerosols. Back trajectories computed using the Numerical Atmospheric-dispersion Modelling Environment (NAME) were used to identify the sources and strengthen the conclusions drawn from the observations. The UK network represents a significant expansion of the observing capability in northern Europe, with instruments evenly distributed across Great Britain, from Camborne in Cornwall to Lerwick in the Shetland Islands, and this study represents the first attempt to demonstrate its capability and validate the methods in use. Its ultimate purpose will be the detection and quantification of volcanic plumes, but the present study clearly demonstrates the advanced capabilities of the network.

Between 27 June and 14 July 2019 aerosol layers were observed by the United Kingdom (UK) Raman lidar network in the upper troposphere and lower stratosphere. The arrival of these aerosol layers in late June caused some concern within the London Volcanic Ash Advisory Centre (VAAC) as according to dispersion simulations the volcanic plume from the 21 June 2019 eruption of Raikoke was not expected over the UK until early July. Using dispersion simulations from the Met Office Numerical Atmospheric-dispersion Modelling Environment (NAME), and supporting evidence from satellite and in-situ aircraft observations, we show that the early arrival of the stratospheric layers was not due to aerosols from the explosive eruption of the Raikoke volcano, but due to biomass burning smoke aerosols associated with intense forest fires in Alberta, Canada that occurred four days prior to the Raikoke eruption. We use the observations and model simulations to describe the dispersion of both the volcanic and forest fire aerosol clouds, and estimate that the initial Raikoke ash aerosol cloud contained around 15 Tg of volcanic ash, and that the forest fires produced around 0.2 Tg of biomass burning aerosol. The operational monitoring of volcanic aerosol clouds is a vital capability in terms of aviation safety and the synergy of NAME dispersion simulations and lidar data with depolarising capabilities allowed scientists at the Met Office to interpret the various aerosol layers over the UK, and attribute the material to their sources. The use of NAME allowed the identification of the observed stratospheric layers that reached the UK on 27 June as biomass burning aerosol, characterised by a particle linear depolarisation ratio of 9 %, whereas with the lidar alone the latter could have been identified as the early arrival of a volcanic ash/sulphate mixed aerosol cloud. In the case under study, given the low concentration estimates, the exact identification of the aerosol layers would have made little substantive difference to the decision making process within the London VAAC. However, our work shows how the use of dispersion modelling together with multiple observation sources enabled us to create a more complete description of atmospheric aerosol loading.

The Met Office has recently established a series of 10 lidar / sun-photometer installations across the UK, consolidating their ash / aerosol remote sensing capabilities [1]. In addition to this network, the Met Office have acquired the Civil Contingency Aircraft (MOCCA) which allows airborne in-situ measurements of ash / aerosol scattering and size-distributions. Two case studies are presented in which mass concentrations of Saharan dust are obtained remotely using lidar returns, and are then compared with those obtained in-situ. A thorough analysis of the mass concentration uncertainty will be provided at the conference.

Measurements are presented from a purpose-built outdoor test facility for the assurance of situational awareness sensors for Autonomous Vehicles (AVs). The testbed included meteorological instrumentation for an accurate high-resolution picture of weather traversing the site. Static targets at ranges up to 250 m were observed simultaneously with multiple radar, LiDAR and camera systems exposed to the natural weather events over a two-year period. The aim of the testbed was to collect data over a prolonged period of time to ensure the maximum amount of variation in weather was encountered. Uniquely, the testbed treats the measurement of weather as an equal metrology challenge to that of measuring the CAV sensor response with the aim of understanding the extent to which it is possible to quantitatively correlate sensor performance degradation with weather parameters. In this paper case study examples of the testbed data for rainfall, fog and ‘ideal’ neutral conditions have been analysed. Correlations between sensor performance degradation and weather parameters were observed in several cases, including comparisons with rain rate and MOR (visibility). However, large uncertainties and complex interactions between weather-related performance reduction and secondary weather effects, like sensor window and target surface wetting, make an explicit determination of the severity at which a weather condition causes a sensor to become untrustworthy, problematic.