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The ability of regional air quality models to skilfully represent pollutant distributions throughout the atmospheric column is important to enabling their skilful prediction at the surface. This provides a requirement for model evaluation at elevated altitudes, though observation datasets available for this purpose are limited. This is particularly true of those offering sampling over extended time periods. To address this requirement and support evaluation of regional air quality models such as the UK Met Offices Air Quality in the Unified Model (AQUM), a long-term, quality-assured dataset of the three-dimensional distribution of key pollutants was collected over the southern United Kingdom from July 2019 to April 2022. Measurements were collected using the Met Office Atmospheric Survey Aircraft (MOASA), a Cessna 421 instrumented for this project to measure gaseous nitrogen dioxide, ozone, sulfur dioxide and fine-mode (PM 2.5) aerosol. This paper introduces the MOASA measurement platform, flight strategies and instrumentation and is not intended to be an in-depth diagnostic analysis but rather a comprehensive technical reference for future users of these data. The MOASA air quality dataset includes 63 flight sorties (totalling over 150 h of sampling), the data from which are openly available for use. To illustrate potential uses of these upper-air observations for regional-scale model evaluation, example case studies are presented, which include analyses of the spatial scales of measured pollutant variability, a comparison of airborne to ground-based observations over Greater London and initial work to evaluate performance of the AQUM regional air quality model. These case studies show that, for observations of relative humidity, nitrogen dioxide and particle counts, natural pollutant variability is well observed by the aircraft, whereas SO 2 variability is limited by instrument precision. Good agreement is seen between observations aloft and those on the ground, particularly for PM 2.5. Analysis of odd oxygen suggests titration of ozone is a dominant chemical process throughout the column for the data analysed, although a slight enhancement of ozone aloft is seen. Finally, a preliminary evaluation of AQUM performance for two case studies suggests a large positive model bias for ozone aloft, coincident with a negative model bias for NO 2 aloft. In one case, there is evidence that an underprediction in the modelled boundary layer height contributes to the observed biases at elevated altitudes. [ABSTRACT FROM AUTHOR]

Ensemble predictions of atmospheric dispersion that account for the meteorological uncertainties in a weather forecast are constructed by propagating the individual members of an ensemble numerical weather prediction forecast through an atmospheric dispersion model. Two event scenarios involving hypothetical atmospheric releases are considered: a near-surface radiological release from a nuclear power plant accident, and a large eruption of an Icelandic volcano releasing volcanic ash into the upper air. Simulations were run twice-daily in real time over a four month period to create a large data set of cases for this study. Performance of the ensemble predictions is measured against retrospective simulations using analysed meteorological fields. The focus of this paper is on comparing the spread of the ensemble members against forecast errors and on the calibration of probabilistic forecasts derived from the ensemble distribution. Results show good overall performance by the dispersion ensembles in both studies, but with simulations for the upper air ash release generally performing better than those for the near-surface release of radiological material. The near-surface results demonstrate a sensitivity to the release location, with good performance in areas dominated by the synoptic-scale meteorology and generally poorer performance at some other sites where, we speculate, the global-scale meteorological ensemble used in this study has difficulty in adequately capturing the uncertainty from local and regional scale influences on the boundary layer. The ensemble tends to be under-spread, or over-confident, for the radiological case in general, especially at earlier forecast steps. The limited ensemble size of 18 members may also affect its ability to fully resolve peak values or adequately sample outlier regions. Probability forecasts of threshold exceedances show a reasonable degree of calibration, though the over-confident nature of the ensemble means that it tends to be too keen on using the extreme forecast probabilities. Ensemble forecasts for the volcanic ash study demonstrate an appropriate degree of spread and are generally well-calibrated, particularly for ash concentration forecasts in the troposphere. The ensemble is slightly over-spread, or under-confident, within the troposphere at the first output time step T+6, thought to be attributable to a known deficiency in the ensemble perturbation scheme in use at the time of this study, but improves with probability forecasts becoming well-calibrated here by the end of the period. Conversely, an increasing tendency towards over-confident forecasts is seen in the stratosphere, which again mirrors an expectation for ensemble spread to fall away at higher altitudes in the met ensemble. Results in the volcanic ash case are also broadly similar between the three different eruption scenarios considered in the study, suggesting that good ensemble performance might apply to a wide range of eruptions with different heights and mass eruption rates. [ABSTRACT FROM AUTHOR]

The ability of regional air quality models to skilfully represent pollutant distributions throughout the atmospheric column is important to enabling their skilful prediction at the surface. This provides a requirement for model evaluation at elevated altitudes, though observation datasets available for this purpose are limited. This is particularly true of those offering sampling over extended time periods. To address this requirement and support evaluation of regional air quality models such as the UK Met Offices Air Quality in the Unified Model AQUM), a long-term, quality assured, dataset of the three-dimensional distribution of key pollutants has been collected over the southern United Kingdom from June 2019 to April 2022. This sampling period encompasses operations during the global COVID-19 pandemic, and as such the dataset serves an additional application in providing a unique resource with which to explore changes in atmospheric composition associated with reduced emissions during this period. Measurements were collected using the Met Office Atmospheric Survey Aircraft MOASA), a Cessna-421 instrumented for this project to measure gaseous nitrogen dioxide, ozone, sulphur dioxide and fine mode (PM2.5) aerosol. This paper provides a technical introduction to the MOASA measurement platform, flight strategies and instrumentation. The MOASA air quality dataset includes 63 flight sorties (totalling over 150 hours of sampling), the data from which are openly available for use. Example case studies using data from these sorties are presented, which include an analysis of the spatial scales of measured pollutant variability, initial work to evaluate performance of the AQUM regional air quality model, and an introduction to the vertical structure of pollutants observed during repeated flight patterns over Greater London, including during the COVID-19 impacted period. [ABSTRACT FROM AUTHOR]

Atmospheric dispersion model output is frequently used to provide advice to decision makers, for example, about the likely location of volcanic ash erupted from a volcano or the location of deposits of radioactive material released during a nuclear accident. Increasingly scientists and decision makers are requesting information on the uncertainty of these dispersion model predictions. One source of uncertainty is in the meteorology used to drive the dispersion model and in this study ensemble meteorology from the Met Office ensemble prediction system is used to provide meteorological uncertainty to dispersion model predictions. Two hypothetical scenarios, one volcanological and one radiological, are repeated every 12 hours over a period of 4 months. The scenarios are simulated using ensemble meteorology and deterministic forecast meteorology and compared to output from simulations using analysis meteorology using the Brier skill score. Adopting the practice commonly used in evaluating numerical weather prediction models (NWP) where observations are sparse or non-existent we consider output from simulations using analysis NWP data to be truth. The results show that on average the ensemble simulations per- form better than the deterministic simulations although not all individual ensemble simulations outperform their deterministic counterpart. The results also show that greater skill scores are achieved by the ensemble simulation for later time steps rather than earlier time steps and at those later time steps for deposition than for air concentration. For the volcanic ash scenarios it is shown that the performance of the ensemble at one flight level can be different to that at a different flight level, e.g. a negative skill score might be obtained for FL350-550 and a positive skill score for FL200-350. This study does not take into account any source term uncertainty but it does take the first steps towards demonstrating the value of ensemble dispersion model predictions. [ABSTRACT FROM AUTHOR]

This paper presents a study of haze in Singapore caused by biomass burning in Southeast Asia over the six year period from 2010 to 2015, using the Lagrangian dispersion model, NAME. The major contributing source regions are shown to be Riau, Peninsular Malaysia, South Sumatra, and Central and West Kalimantan. However, we see differences in haze concentrations and variation in the relative contributions from the various source regions between monitoring stations across Singapore, as well as on an inter-annual timescale. These results challenge the current popular assumption that haze in Singapore is dominated by emissions/burning from only Indonesia. It is shown that Peninsular Malaysia is a large source for the Maritime Continent off-season biomass burning impact on Singapore. As should be expected, the relatively stronger southeast monsoonal winds that coincide with increased biomass burning activities in the Maritime Continent create the main haze season from August to October (ASO), which brings particulate matter from several and varying source regions to Singapore. In contrast, atypical haze episodes in Singapore are characterised by atypical weather conditions, ideal for biomass burning, and emissions dominated by a single source region (for each event). The two most recent atypical haze events in mid 2013 and early 2014 have different source regions, whereas a different set of five regions dominate as major contributing source regions for most of the recent ASO haze seasons. Haze in Singapore varies across year, season, and location it is influenced by local and regional weather, climate, and regional burning. The study shows that even across small scales, such as in Singapore, variation in local meteorology can impact concentrations of particulate matter significantly, and emphasises the importance of the scale of modelling both spatially and temporally. [ABSTRACT FROM AUTHOR]