Search

Land–atmosphere interactions govern the process of dust emission and transport. An accurate depiction of these physical processes within numerical weather prediction models allows for better estimating the spatial and temporal distribution of the dust burden and the characterisation of source and recipient areas. In the presented study, the ECMWF-IFS (European Centre for Medium-Range Weather Forecast – Integrated Forecasting System) outputs, produced with and without the assimilation of Aeolus quality-assured Rayleigh–clear and Mie–cloudy horizontal line-of-sight wind profiles, are used as initial or boundary conditions in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to simulate 2-month periods in the spring and autumn of 2020, focusing on a case study in October. The experiments have been performed over the broader eastern Mediterranean and Middle East (EMME) region, which is frequently subjected to dust transport, as it encompasses some of the most active erodible dust sources. Aerosol- and dust-related model outputs (extinction coefficient, optical depth and concentrations) are qualitatively and quantitatively evaluated against ground- and satellite-based observations. Ground-based columnar and vertically resolved aerosol optical properties are acquired through AERONET sun photometers and PollyXT lidar, while near-surface concentrations are taken from EMEP. Satellite-derived vertical dust and columnar aerosol optical properties are acquired through LIVAS (LIdar climatology of Vertical Aerosol Structure) and MIDAS (ModIs Dust AeroSol), respectively. Overall, in cases of either high or low aerosol loadings, the model predictive skill is improved when WRF-Chem simulations are initialised with the meteorological fields of Aeolus wind profiles assimilated by the IFS. The improvement varies in space and time, with the most significant impact observed during the autumn months in the study region. Comparison with observation datasets saw a remarkable improvement in columnar aerosol optical depths, vertically resolved dust mass concentrations and near-surface particulate concentrations in the assimilated run against the control run. Reductions in model biases, either positive or negative, and an increase in the correlation between simulated and observed values was achieved for October 2020.

Land–atmosphere interactions govern the process of dust emission and transport. An accurate depiction of these physical processes within numerical weather prediction models allows for better estimating the spatial and temporal distribution of the dust burden and the characterisation of source and recipient areas. In the presented study, the ECMWF-IFS (European Centre for Medium-Range Weather Forecast – Integrated Forecasting System) outputs, produced with and without the assimilation of Aeolus quality-assured Rayleigh–clear and Mie–cloudy horizontal line-of-sight wind profiles, are used as initial or boundary conditions in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to simulate 2-month periods in the spring and autumn of 2020, focusing on a case study in October. The experiments have been performed over the broader eastern Mediterranean and Middle East (EMME) region, which is frequently subjected to dust transport, as it encompasses some of the most active erodible dust sources. Aerosol- and dust-related model outputs (extinction coefficient, optical depth and concentrations) are qualitatively and quantitatively evaluated against ground- and satellite-based observations. Ground-based columnar and vertically resolved aerosol optical properties are acquired through AERONET sun photometers and PollyXT lidar, while near-surface concentrations are taken from EMEP. Satellite-derived vertical dust and columnar aerosol optical properties are acquired through LIVAS (LIdar climatology of Vertical Aerosol Structure) and MIDAS (ModIs Dust AeroSol), respectively. Overall, in cases of either high or low aerosol loadings, the model predictive skill is improved when WRF-Chem simulations are initialised with the meteorological fields of Aeolus wind profiles assimilated by the IFS. The improvement varies in space and time, with the most significant impact observed during the autumn months in the study region. Comparison with observation datasets saw a remarkable improvement in columnar aerosol optical depths, vertically resolved dust mass concentrations and near-surface particulate concentrations in the assimilated run against the control run. Reductions in model biases, either positive or negative, and an increase in the correlation between simulated and observed values was achieved for October 2020.

One of the most important factors towards improved mineral dust mobilization and transport modelling is the representation of wind fields, which determine dust emission and atmospheric lifetime. The potential improvements on regional dust simulations attributed to the assimilation of Aeolus wind profiles is the core objective of the NEWTON (ImproviNg dust monitoring and forEcasting through Aeolus Wind daTa assimilatiON) ESA project. Towards this goal, the Weather Research and Forecasting regional atmospheric model coupled with chemistry (WRF/Chem) is used to simulate the airborne dust concentrations for two-month long periods in the spring and fall season of 2020, with special focus on a dust case in October 2020. The model is driven by ECMWF IFS outputs produced with (hel4) and without (hel1) assimilation of Aeolus quality-assured Rayleigh-clear and Mie-cloudy wind profiles. Our experiments are performed over the broader Eastern Mediterranean region that is subjected frequently to dust transport, encompassing the major natural erodible dust sources of the planet. Dust-related model outputs (extinction coefficient, optical depth and concentrations) are qualitatively and quantitatively evaluated against ground-based columnar and vertically resolved aerosol optical properties acquired by AERONET sun photometers and PollyXT lidar, as well as near-surface concentrations available through EMEP. Our assessment further includes comparison versus LIVAS and MIDAS satellite-derived datasets providing vertical and columnar dust optical properties, respectively. Overall, in cases of either high or low aerosol loadings, the model predictive skill is improved when the regional simulations are initialized with Aeolus wind assimilation (hel4). The improvement varies in space and time, with the inclusion of the assimilated wind profiles into IFS meteorological fields having a larger impact on the spatiotemporal distribution of dust particles during the fall compared to the spring months. During the case study of interest in October 2020, there is strong evidence of a better representation of the Mediterranean desert dust outbreak spatiotemporal patterns based on the hel4 experiment. Such improvements are driven by wind fields throughout the atmosphere affecting mobilization mechanisms through surface winds, and transport and removal processes. Comparison with MIDAS saw a remarkable improvement for the hel4 against the hel1 simulated AODs, over the central and eastern sectors of the Mediterranean and Middle East regions. Confirmed by the drastically reductions of the model biases (either positive or negative) and the increased correlation (up to 0.28), meanwhile for several AERONET stations there was an average improvement in the correlation of assimilated outputs compared to control ones.

The study aims to re-create an NOx emission inventory for the greater London area on an hourly temporal resolution using mostly open-source data. London was chosen as the target location due to the vast amount of data accessible to the public. The main objective of the study is to create pattern formations of intraday variations for NOx emissions and enable predictions of increased temporal accuracy. The inventory focuses on the three most significant emitting sectors; non-road mobile machinery (NRMM), domestic gas and road transport, accounting for 10%, 13% and 45% of the 2016 total budget respectively. The study makes use of a bottom-up approach and compares the results with the annual NOx values stated in the London Atmospheric Emissions Inventory. The estimation of road transport emissions makes use of recorded annual vehicle kilometres broken down per borough and per type of vehicle, the registered London vehicle stock and the estimated probability of a vehicle to be on-road per hour of the day depending on the day and month. The aforementioned data are provided by the Department for Transport and the Digest UK Energy Statistics databank. Furthermore, a traffic index specified by the TomTom traffic dataset is used to identify an hourly average vehicle speed per road type and then is used alongside speed-based emission factors published by Transport for London to determine the geospatial specific emissions per vehicle category and per kilometre travelled. Formulation of domestic gas emissions employs real-time data of gas demand in kWh on 15-minute intervals sourced from the National Grid. The data was used alongside characteristics of the local boiler stock provided by Aether and was spatially distributed using energy efficiency ratings of households in each borough, provided by Digest UK Energy Statistics. Estimating emissions from NRMMs made use of the total number of machineries registered for operation in London since 2015, obtained through the King’s College NRMM registry. The machineries were categorised depending on horsepower and Euro standard as published by the 2009 EMEP/EEA Guidebooks, from which the associated NOx emissions factors were taken from. In addition, emissions were geographically distributed by prorating NRMMs according to the registered construction projects per borough. The discrepancies observed between the modelled and the London Atmospheric Emissions Inventory values was 5.9% for domestic gas emissions, 11.2% for NRMMs and for the vehicle categories; diesel cars, petrol cars, HGVs and LGVs, it differed by -2.0%, 7.0%, -3.0% and 14.2% respectively.