Quantification of atmospheric dust deposition into the Atlantic Ocean is provided. The estimates rely on the four-dimensional structure of atmospheric dust provided by the ESA-LIVAS climate data record established on the basis of CALIPSO-CALIOP observations. The data record of the atmospheric dust deposition rate is provided for the Atlantic Ocean region, between latitudes 60 degrees S and 40 degrees N, and is characterized by 5 degrees (zonal) x 2 degrees (meridional) spatial resolution and seasonal-mean temporal resolution for the period December 2006-November 2022. The estimates of dust deposition are evaluated on the basis of sediment-trap measurements of deposited lithogenic material. The evaluation intercomparison shows a good agreement between the two datasets, revealing the capacity of the satellite-based product to quantitatively provide the amount of dust deposited into the Atlantic Ocean, characterized by a correlation coefficient of 0.79 and a mean bias of 5.42 mg m(-2) d(-1). Integration of the satellite-based dust deposition rate dataset into AeroVal allowed assessment comparison of the dust deposition product against dust deposition field estimates provided by the MONARCH, EMEP MSC-W, and EC-Earth3-Iron models. The comparison revealed the capacity of the satellite-based product to follow the seasonal activation of dust source regions and the four-dimensional migration of dust transport pathways. Overall, the annual-mean amount of dust deposition into the Atlantic Ocean is estimated at 274.79 +/- 31.64 Tg yr(-1), of which 243.98 +/- 23.89 Tg yr(-1) of dust is deposited into the North Atlantic Ocean and 30.81 +/- 10.49 Tg yr(-1) of dust is deposited into the South Atlantic Ocean. Moreover, a negative statistically significant trend in atmospheric dust deposition over the Atlantic Ocean is revealed. The satellite-based dust deposition product is considered unique with respect to a wide range of potential applications, including compensating for geographical and temporal gaps of sediment-trap measurements, supporting evaluation assessments of model simulations, unraveling physical processes related to the atmospheric cycle of dust, and providing a deeper understanding of dust biogeochemical impacts on oceanic ecosystems, weather, and eventually climate. The atmospheric dust in terms of optical depth and dust deposition rate climate data records relevant to this paper (Proestakis et al., 2025) are available at https://doi.org/10.5281/zenodo.14608539.

To tackle the planetary environmental and climate crisis and meet the United Nations’ Sustainable Development Goals (SDGs), we must fully leverage the potential of Earth observations (EO). This involves integrating globally sourced data on the atmosphere, hydrosphere, cryosphere, lithosphere, along with ecological and socio-economic information. By harmonizing and integrating these diverse data sources, we can more effectively incorporate observational data into multi-scale modeling and artificial intelligence (AI) frameworks. This paper is based on discussions from the “Towards Global Earth Observatory” workshop held from May 8–10, 2023, organized by the World Meteorological Organization (WMO) and the Atmosphere and Climate Competence Center (ACCC), in collaboration with the Institute for Atmospheric and Earth System Research (INAR) at the University of Helsinki. The current state of EO and data repositories is fragmented, highlighting the need for a more integrated approach to establish a new global Ground-Based Earth Observatory (GGBEO). Here, we summarize the current status of selected in-situ and ground-based remote sensing observation systems and outline future actions and recommendations to meet scientific, societal, and economic needs. In addition, we identify key steps to create a coordinated and comprehensive GGBEO system that leverages existing investments, networks, and infrastructures. This system would integrate regional and global ground-based in situ and remote sensing systems, marine, and airborne observational data. An integrated approach should aim for seamless coordination, interoperable and harmonized data repositories, easily searchable and accessible data, and sustainable long-term funding.

We present an example of how a doctoral network can bring together multidisciplinary expertise and novel scientific advances in atmospheric dust. This network (Dust-DN) has started operations and is a strategic alliance of high-profile partners, able to leverage unique facilities for atmospheric research and innovative space missions. The network aims to improve our understandings of dust processes and microphysics, identify the signature of source regions, address the socio-economic impacts of dust transport and improve the quantification of the role of dust in the climate system. The first results have already been achieved and are shown here, and many more are expected to follow.

EUMETSAT’s (European Organisation for the Exploitation of Meteorological Satellites) contribution to global air quality monitoring is multifaceted, encompassing technological advancements, long-term commitments, and a collaborative approach to address environmental challenges. The organization has been actively involved in satellite observations since 1990 through programs like Meteosat and Metop, and since 2015, it has been contributing to the Copernicus EU program. This effort is particularly significant for supporting air quality monitoring in developing countries, where reliable in-situ observatories are limited, and there is high vulnerability to pollutants and climate change impacts. The data provision is set to continue for the next two decades thanks to next-generation missions such as Meteosat Third Generation (MTG). These missions, along with instruments like Sentinel-4 and Sentinel-5 under the Copernicus EU program, are dedicated to air quality monitoring in specific regions, including North Africa and globally. Here, we will showcase how the atmospheric composition data obtained from EUMETSAT's satellites can be utilized for air quality analysis at the continental and local scale. Recent scientific applications based on datasets from Infrared Atmospheric Sounding Interferometer (IASI) and Sentinel instruments will be reviewed. In addition, examples on how EUMETSAT's satellite data is used to monitor phenomena that have direct implications for health and security, such as desert dust storms and wildfire emissions. A critical part of the discussion will focus on the advantages and drawbacks of satellite data due to observational configurations. This may involve addressing challenges and limitations while highlighting the strengths of satellite observations for air quality monitoring. Finally, it will be shown the importance of data access and training for effective utilization of satellite data. Additional value can be derived from satellite information through techniques like data assimilation and the application of artificial intelligence and machine learning (AI-ML methods).

The global ocean is a key component to the Earth’s climate system, absorbing atmospheric energy in excess and exchanging as a sink climate-relevant gases with the atmosphere. More specifically, through the uptake of atmospheric CO2 and acting as carbon storage, through the processes of biological pump and solubility pump, helps to mitigate anthropogenic CO2 increase. Moreover, the ocean enables phytoplankton photosynthesis, impacts ocean color, light penetration into deeper layers, and sea surface temperature, eventually modulating weather and resulting to feedback effects on climate. However, primary production highly depends on the spatial distribution of input nutrients from the atmosphere, with iron (Fe) availability the most important limiting factor for phytoplankton growth. Across the open ocean, the principal source of Fe is considered atmospheric mineral dust, transported over distances of thousands of kilometers prior removal through wet deposition or gravitational settling.The present study provides quantification of the amount of atmospheric dust deposited into the broader Atlantic Ocean. Based on Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) routine observations on atmospheric dust, the primary instrument onboard Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and meridional and zonal wind components provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5), the atmospheric dust fluxes and the dust deposited component across the tans-Atlantic transits are estimated. On the basis of more than sixteen years (12/2006-11/2022) of Earth Observations, and for the Atlantic Ocean region extending between latitudes 60°S and 40°N, the annual-mean amount of deposited dust is estimated at 274.79 ± 31.64 Tg, of which 243.98 ± 23.89 Tg is deposited into the North Atlantic Ocean and 30.81 ± 10.49 Tg into the South Atlantic Ocean. Moreover, a negative statistically significant decreasing trend in dust deposition into the Atlantic Ocean for this period is revealed, characterized by slope -13.35 Tg yr-1 and offset 306.97 Tg.The climate data record is evaluated against high quality sediment-trap measurements of deposited lithogenic material implemented as reference dataset, demonstrating the protentional of the established dataset to be used in a wide range of applications, including filling geographical and temporal gaps in sediment-trap measurements, aiding model simulation evaluations, uncovering physical processes in the dust cycle from emission to deposition, and enhancing our understanding of dust's biogeochemical impacts on ocean ecosystems, as well as its effects on weather and climate. Acknowledgements This research was supported by the Dust Observation and Modelling Study (DOMOS) under ESA contract number 4000135024/21/I-NB. Emmanouil Proestakis acknowledges support by the AXA Research Fund for postdoctoral researchers under the project entitled “Earth Observation for Air-Quality – Dust Fine-Mode (EO4AQ-DustFM)”.

Airborne mineral dust poses a safety challenge for aviation. Several fatal accidents have happened in dust-laden air due to reduced visibility, strong gusty winds, and wind shear. Dust-induced icing also contributed at least to two fatal accidents. Furthermore, atmospheric dust has long- and short-term effects on aircraft operating conditions due to corrosion and abrasion on the aircraft surfaces and molten ingress deterioration of engine hot section components. The combined impact can increase operating and maintenance costs and increase the overall cost of ownership. While the scientific community has started preparing and providing products based on atmospheric dust modeling and observation, there are still important data and information gaps in the fundamental science. These include (i) insufficient data which could be used to better understand the effects of dust on aircraft as well as on ground systems and operations (e.g., four-dimensional information of dust mineralogy, cost-benefit analysis of the impact of dust on aviation along flight routes), (ii) the identification of airborne dust monitoring and modeling products and services that could enable the flow of relevant information in commercial aviation and in decision-making workflows, and (iii) the underdeveloped, unclear, or absent role of dust hazards in regulations and operational procedures as well as in the training, skill set, and knowledge base of pilots. This review is aimed at both academic and aviation stakeholders and presents the current state-of-the-art knowledge at the intersection of dust hazards, aviation safety, and impacts on flight operations and aircraft maintenance.

Sand and dust storms increasingly threaten global environmental and public health. To date, 150 countries are directly affected, with more than 100 classified as non-dust source regions. With climate change, these storms are expected to become more frequent and severe. Despite international awareness and initiatives, such as those led by the UN, crucial knowledge gaps continue to hinder effective, evidence-based public responses to sand and dust storms. In this Viewpoint, we review existing gaps in health research and highlight four key research priorities: the comprehensive health effects of sand and dust storms, including short-term and long-term exposures, diseases, regions, and health outcomes; the key particle sizes and toxic components of particles during sand and dust storms; the design of multicentre studies accounting for region-specific exposure patterns; and research on health outcomes attributable to particulate matter mixtures dominated by windblown dust versus other sources. We urgently call for international, collaborative, and multidisciplinary health studies considering sand and dust storm exposure characteristics and for the adoption of scientifically robust epidemiological methods in these studies.

During the months of February–March (FM) of the 2020–2022 period, several intense dust intrusions from northern Africa affected Europe. The frequency of dust events was exceptional, considering that wintertime is the season with minimum dust activity in the Mediterranean, and some episodes displayed a duration and/or intensity never recorded before, affecting large areas of the western Euro-Mediterranean (WEM) region. The main objective of this work is to construct a catalogue of FM dust events over the WEM for the 2003–2022 period based on satellite aerosol retrievals and to analyse their atmospheric drivers at the synoptic and large scales, paying special attention to the recent 2020–2022 period of high dust activity. Overall, our results indicate large intraseasonal and interannual variability in the occurrence of wintertime dust events over the WEM. Dust events of FM 2020–2022 were characterized by enhanced dust concentration and high maximum altitudes in comparison with those of previous years (2003–2019). WEM dust events are associated with enhanced activity of high-pressure systems over the Euro-Atlantic sector, which favour the obstruction of the westerlies and the occurrence of cut-off lows at subtropical latitudes. However, these high-pressure systems can exhibit a large variety of configurations, including meridional dipole blocking patterns with poleward shifted jets or Mediterranean subtropical ridges with an intensified mid-latitude jet. The former is the dominant favourable pattern for WEM dust occurrence, but the latter was relatively common during the 2020–2022 period.

GHOST (Globally Harmonised Observations in Space and Time) represents one of the biggest collections of harmonised measurements of atmospheric composition at the surface. In total, 7 275 148 646 measurements from 1970 to 2023, of 227 different components from 38 reporting networks, are compiled, parsed, and standardised. The components processed include gaseous species, total and speciated particulate matter, and aerosol optical properties. The main goal of GHOST is to provide a dataset that can serve as a basis for the reproducibility of model evaluation efforts across the community. Exhaustive efforts have been made towards standardising almost every facet of the information provided by major public reporting networks, which is saved in 21 data variables and 163 metadata variables. Extensive effort in particular is made towards the standardisation of measurement process information and station classifications. Extra complementary information is also associated with measurements, such as metadata from various popular gridded datasets (e.g. land use) and temporal classifications per measurement (e.g. day or night). A range of standardised network quality assurance flags is associated with each individual measurement. GHOST's own quality assurance is also performed and associated with measurements. Measurements pre-filtered by the default GHOST quality assurance are also provided. In this paper, we outline all steps undertaken to create the GHOST dataset and give insights and recommendations for data providers based on the experiences gleaned through our efforts.

Abstract. During the winters of the 2020–2022 period, several intense North African dust intrusions affected Europe. Some of them displayed a duration never recorded before. They were referred to as exceptional by several international operational and research institutions considering that wintertime is the season with minimum dust activity in the Mediterranean and Europe. These anomalous winter events with origin in North Africa largely affected western Mediterranean. The main objective of the present work is to analyse the atmospheric drivers (synoptic and large-scale environments) of wintertime (from January to March) dust events over the region covering North Africa, the Western Mediterranean and the Euro-Atlantic during the period 2003–2022. Overall, our results indicate large interannual variability over the study period. A dust catalogue of dust events identified by aerosols retrievals from satellite and aerosol reanalysis products shows a very irregular record and large differences between winter months. The analyses demonstrate a positive anomaly in dust concentration and maximum altitude during the dust events of 2020–2022 in comparison with those of previous years (2003–2019). Winter dust events over western Mediterranean are associated with enhanced blocking activity over the Euro-Atlantic sector, which favours the obstruction of the westerlies and the occurrence of cut-off lows at subtropical latitudes. However, these high-pressure systems can exhibit a large variety of configurations, including meridional dipole blocking patterns with poleward shifted jets or Mediterranean subtropical ridges with an intensified mid-latitude jet. The former was more frequent during the reference 2003–2019 period, whereas the latter was relatively common during the anomalous 2020–2022 period.

These datasets correspond to soil and airbone mass mineral fractions as described and generated for "Modeling dust mineralogical composition: sensitivity to soil mineralogy" by Gonçalves Ageitos, M., Obiso, V., Miller, R.L., Jorba, O., Klose, M., Dawson, M., Balkanski, Y., Perlwitz, J., Basart, S., Di Tomaso, E., Escribano, J., Macchia, F., Montané, G., Mahowald, M.M., Green, R.O., Thompson, D.R. and Pérez García-Pando, C., ACP, 2023. There are 4 netCDF files that include the soil mass mineralogical fractions (0-1) in the clay (0-2 \(\mu\)m in diameter) and silt (2-63 \(\mu\)m in diameter) size classes as derived from the works of Claquin et al., (1999), and updated by Nickovic et al. (2012): C1999-SMA, and Journet et al. (2014): J2014-SMA. The data is mapped in a regular global grid with a horizontal resolution of 0.083º. Additional information on the FAO soil units, and soil texture data from HWSDv1.2 is provided in the J2014-SMA files. File details: C1999-SMA_CLAY_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction. C1999-SMA_SILT_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction. J2014-C2-SMA_CLAY_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction. J2014-C2-SMA_SILT_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction. There are 2 additional files that report the multiannual (2006-2010 period) monthly mean of the aerosol mass mineral fractions as obtained from the MONARCH model simulations described in Gonçalves Ageitos et al. (2023). The mass fractions are provided in each of the 8 size bins used in the model (ranging from 0.2 to 20 \(\mu\)m in diameter), and normalized so as to sum 1 (i.e., the sum of all minerals in all bins equals 1). Note that in order to reduce the size of these files, the variables have been compressed to short format and include an offset and scale factor as attributes. File details: 20062010_monarch_minfrac_C1999.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH C1999 experiment. 20062010_monarch_minfrac_J2014.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH J2014 experiment. Legend for the minerals: quar: quartz, feld: feldspars, calc: calcite, gyps: gypsum, illi: illite, mont: montmorillonite/smectite, kaol: kaolinite, verm:vermiculite, chlo: chlorite, mica: mica, hema: hematite, goet: goethite, irox:iron oxides (hematite and goethite). References: Claquin, T., Schulz, M., and Balkanski, Y. J.: Modeling the mineralogy of atmospheric dust sources, Journal of Geophysical Research Atmospheres, https://doi.org/10.1029/1999JD900416, 1999. FAO-UNESCO: Soil Map of the World- Volume I Legend, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Paris, http://www.fao.org/3/as360e/as360e.pdf, 1974. FAO-UNESCO: Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization. Digital Soil Map of the World and Derived Soil Properties, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Rome, 1995. FAO/IIASA/ISRIC/ISSCAS/JRC: Harmonized World Soil Database (version 1.2), Food and Agriculture Organization, FAO, Rome, Italy and IIASA, Laxenburg, Austria, 2012. Journet, E., Balkanski, Y., and Harrison, S. P.: A new data set of soil mineralogy for dust-cycle modeling, Atmospheric Chemistry and Physics, 14, 3801–3816, https://doi.org/10.5194/acp-14-3801-2014, 2014. Nickovic, S., Vukovic, A., Vujadinovic, M., Djurdjevic, V., and Pejanovic, G.: Technical Note: High-resolution mineralogical database of dust-productive soils for atmospheric dust modeling, Atmospheric Chemistry and Physics, 12, 845–855, https://doi.org/10.5194/acp-12-845-2012, 2012.

Aerosol reanalyses are a well-established tool for monitoring aerosol trends, for validation and calibration of weather chemical models, as well as for the enhancement of strategies for environmental monitoring and hazard mitigation. By providing a consistent and complete data set over a sufficiently long period, they address the shortcomings of aerosol observational records in terms of temporal and spatial coverage and aerosol speciation. These shortcomings are particularly severe for dust aerosols. A 10-year dust aerosol regional reanalysis has been recently produced on the Barcelona Supercomputing Center HPC facilities at the high spatial resolution of 0.1 $$^{\circ }$$ . Here we present a brief description and an initial assessment of this data set. An innovative dust optical depth data set, derived from the MODIS Deep Blue products, has been ingested in the dust module of the MONARCH model by means of a LETKF with a four-dimensional extension. MONARCH ensemble has been generated by applying combined meteorology and emission perturbations. This has been achieved using for each ensemble member different meteorological fields as initial and boundary conditions, and different emission schemes, in addition to stochastic perturbations of emission parameters, which we show is beneficial for dust data assimilation. We prove the consistency of the assimilation procedure by analyzing the departures of the assimilated observations from the model simulations for a two-month period. Furthermore, we show a comparison with AERONET coarse optical depth retrievals during a period of 2012, which indicates that the reanalysis data set is highly accurate. While further analysis and validation of the whole data set are ongoing, here we provide a first evidence for the reanalysis to be a useful record of dust concentration and deposition extending the existing observational-based information intended for mineral dust monitoring.

Mineral dust produced by wind erosion of arid and semiarid surfaces is a major component of atmospheric aerosol that affects climate, weather, ecosystems, and socioeconomic sectors such as human health, transportation, solar energy, and air quality. Understanding these effects and ultimately improving the resilience of affected countries requires a reliable, dense, and diverse set of dust observations, fundamental for the development and the provision of skillful dust-forecast-tailored products. The last decade has seen a notable improvement of dust observational capabilities in terms of considered parameters, geographical coverage, and delivery times, as well as of tailored products of interest to both the scientific community and the various end-users. Given this progress, here we review the current state of observational capabilities, including in situ, ground-based, and satellite remote sensing observations in northern Africa, the Middle East, and Europe for the provision of dust information considering the needs of various users. We also critically discuss observational gaps and related unresolved questions while providing suggestions for overcoming the current limitations. Our review aims to be a milestone for discussing dust observational gaps at a global level to address the needs of users, from research communities to nonscientific stakeholders.

Aerosol reanalysis datasets are model-based, observationally constrained, continuous 3D aerosol fields with a relatively high temporal frequency that can be used to assess aerosol variations and trends, climate effects, and impacts on socioeconomic sectors, such as health. Here we compare and assess the recently published MONARCH (Multiscale Online Non-hydrostatic AtmospheRe CHemistry) high-resolution regional desert dust reanalysis over northern Africa, the Middle East, and Europe (NAMEE) with a combination of ground-based observations and space-based dust retrievals and products. In particular, we compare the total and coarse dust optical depth (DOD) from the new reanalysis with DOD products derived from MODIS (MODerate resolution Imaging Spectroradiometer), MISR (Multi-angle Imaging SpectroRadiometer), and IASI (Infrared Atmospheric Sounding Interferometer) spaceborne instruments. Despite the larger uncertainties, satellite-based datasets provide a better geographical coverage than ground-based observations, and the use of different retrievals and products allows at least partially overcoming some single-product weaknesses in the comparison. Nevertheless, limitations and uncertainties due to the type of sensor, its operating principle, its sensitivity, its temporal and spatial resolution, and the methodology for retrieving or further deriving dust products are factors that bias the reanalysis assessment. We, therefore, also use ground-based DOD observations provided by 238 stations of the AERONET (AErosol RObotic NETwork) located within the NAMEE region as a reference evaluation dataset. In particular, prior to the reanalysis assessment, the satellite datasets were evaluated against AERONET, showing moderate underestimations in the vicinities of dust sources and downwind regions, whereas small or significant overestimations, depending on the dataset, can be found in the remote regions. Taking these results into consideration, the MONARCH reanalysis assessment shows that total and coarse-DOD simulations are consistent with satellite- and ground-based data, qualitatively capturing the major dust sources in the area in addition to the dust transport patterns. Moreover, the MONARCH reanalysis reproduces the seasonal dust cycle, identifying the increased dust activity that occurred in the NAMEE region during spring and summer. The quantitative comparison between the MONARCH reanalysis DOD and satellite multi-sensor products shows that the reanalysis tends to slightly overestimate the desert dust that is emitted from the source regions and underestimate the transported dust over the outflow regions, implying that the model's removal of dust particles from the atmosphere, through deposition processes, is too effective. More specifically, small positive biases are found over the Sahara desert (0.04) and negative biases over the Atlantic Ocean and the Arabian Sea (−0.04), which constitute the main pathways of the long-range dust transport. Considering the DOD values recorded on average there, such discrepancies can be considered low, as the low relative bias in the Sahara desert (< 50 %) and over the adjacent maritime regions (< 100 %) certifies. Similarly, over areas with intense dust activity, the linear correlation coefficient between the MONARCH reanalysis simulations and the ensemble of the satellite products is significantly high for both total and coarse DOD, reaching 0.8 over the Middle East, the Atlantic Ocean, and the Arabian Sea and exceeding it over the African continent. Moreover, the low relative biases and high correlations are associated with regions for which large numbers of observations are available, thus allowing for robust reanalysis assessment.

A critical measure for our understanding of the complex non-linear processes which determine atmospheric composition is through the use of Chemical Transport Models and Earth System Models. In order to evaluate the veracity of these models, observations are required, however the availability and quality of these observations serves as a major impediment to this process. The most temporally consistent measurements have been made at the surface by established measurement networks, typically for the purpose of monitoring local exceedances of air quality limits. There are multiple networks which report this data, in disparate formats, requiring harmonisation to allow for synthesis.On the occasion that evaluation efforts use data from multiple networks, there is typically little to no detail given about the methodology used for the data synthesis across the different networks, or regarding the quality assurance (QA) or station classifications employed to subset the data. Therefore, evaluation efforts across different research groups are often incomparable.As a response to this common challenge, we established GHOST (Globally Harmonised Observational Surface Treatment). GHOST can be succinctly stated as an effort to standardise the data / metadata from the major public reporting networks which provide in situ atmospheric measurements at the surface. In total the dataset comprises of ~20 billion processed measurements, for ~200 components, across 32 networks, from 1970 to 2022. This represents the biggest collection of harmonised atmospheric composition surface measurements ever composed. Substantial efforts have been made towards standardising almost every facet of provided data / metadata from across the networks. On top of this, additional metadata was added by processing various commonly utilised globally gridded datasets (e.g. land use), as well as adding temporal classifications per measurement (e.g. weekday / weekend). As the dataset spans many decades, metadata is handled dynamically and allowed to vary through the record, important for instances when there are changes in measurement instrumentation or the measurement position.Major efforts were made for the standardisation of the numerous metadata fields detailing measurement procedures, with all measurements linked to a dictionary of standard measurement methods and standard instruments. Great effort was also spent in the standardisation of station classifications, providing large flexibility for the subsetting of stations. Rather than dropping any measurements which are labelled as potentially erroneous by the measurement provider, standardised data flags are associated with each individual measurement. On top of this, GHOST own QA flags are also associated per measurement.All data is now freely available to the community.

Sand and Dust Storms (SDS) play a significant role in different aspects of the Earth system and can represent a severe hazard for life, health, property, environment and economy. SDS can severely disrupt communications, energy production and transportation. The Barcelona Dust Regional Center manages and coordinates the activities related to sand and dust storms of the World Meteorological Organization (WMO) for research and operations. The Center provides access to available dust products and coordinates a network of collaborators (researchers, data providers and users’ communities) in Northern Africa, the Middle East and Europe. The activities of the Center focus on facilitating access to the available dust information. The network around the Center promotes scientific collaborations that aim to deepen our understanding of the dust cycle and its variability, along with its impacts on key socio-economic sectors. Additionally, one of the core activities of the Center is to build the capacity of end-users with the aim of promoting the use of dust products to address the risks associated with airborne dust. Here, we introduce two examples of the use of SDS information accessible through the Barcelona Dust Regional Center website. First, we revise the benefits of using multi-model products for the early prediction of extreme events as the ones occurred in February 2020 in the Canary Islands and February 2021 in Europe. On the other hand, we introduce the results for 2019–2020 of the SDS-WAS Warning Advisory System for Burkina Faso which is a tailored daily product based on colour-coded maps that started in October 2018.

AQ-WATCH (Air Quality: Worldwide Analysis and Forecasting of Atmospheric Composition for Health) is an international consortium, which co-develops and co-produces tailored products and services derived from space and in situ observational data for improving air quality forecasts and attribution. For this purpose, AQ-WATCH develops a supply chain leading to innovative downstream products and services for providing air quality information tailored to the identified needs of international users. This presentation will focus on one of the AQ-WATCH products, the AQ-WATCH air quality forecast system. Air quality forecast models provided by the AQ-WATCH consortium are set up for the focus regions in Asia and the Americas, based on the templates of Copernicus European and MarcoPolo-Panda Asian ensembles, but with much higher resolution and reliance on regional emission and observational information. The models are established over the focus regions using the meteorological and emission data taken from Copernicus repositories and other national archives and refined with local information wherever available. Each forecast model is then evaluated using local observational datasets and with the needs of the stakeholders. Machine learning workflows are being incorporated into the forecast system to improve both results from individual models and the model ensembles based on bias correction from observation data. Lessons learnt from model comparison in the focus regions will be presented. At last, the potential application of the system prototype, as well as the other AQ-WATCH products, namely the global and regional air quality atlas, the air quality attribution & mitigation, the dust and fire forecasts, and the fracking analysis tool, to other regions of the world will be discussed.