The January 2022 Hunga eruption generated the strongest stratospheric aerosol optical depth for 30 years (e.g. Khaykin et al., 2022; Taha et al., 2022; Bourassa et al., 2023), but the eruption emitted only a modest 0.4-0.5Tg of SO2 to the stratosphere (Carn et al., 2022).
The most explosive eruption in the satellite era (Wright et al., 2022), an upper portion of the Hunga plume was initially at ~35-40km altitude (Taha et al., 2022), but the main detrainment occurred lower at ~27-30km, with a highly unusual initial steep descent of the plume seeing the layer of Hunga-enhanced aerosol form at ~22-26km (e.g. Kloss et al., 2022; Legras et al., 2022; Baron et al., 2023).
The shallow underwater explosion also detrained ~150Tg of water vapour deep into the stratosphere (e.g. Millan et al., 2022), shown by Zhu et al. (2022) and Asher et al. (2023) to have accelerated SO2 oxidation and enhanced the growth of volcanic sulphate aerosol to optically-active sizes. The total water vapour present within the Hunga plume was greater, with also an estimated 23 Tg of SO2 present (Colombier et al., 2023), the vast majority of emitted sulphur removed via ice sedimentation in the initial days.
The potential for such an explosive eruption to influence climate and the ozone layer, and the effects from the strong enhancement to stratospheric water vapour, motivated APARC to begin a special “Hunga impacts” cross-activity project. The activity’s main role is to co-ordinate community activity to write a special “Hunga impacts report”, and author teams were convened in early 2024, each chapter 1st draft peer-reviewed in autumn 2024, ahead of publication in summer 2025.
This presentation will focus on the Hunga aerosol, and the initial months after the eruption, aligned to the 2025 report. We will present findings from the interactive stratospheric aerosol HTHH-MOC experiment 3, a co-ordinate multi-model analysis of the Hunga aerosol progression, the protocols to identify how the model predictions of the co-emitted water vapour effects vary.
The Hunga aerosol progression has commonalities with the 1883 Krakatau eruption, both eruptions injecting very large amounts of vaporised seawater deep into the stratosphere. Krakatau is estimated to have emitted 500Tg water vapour (Joshi and Jones, 2009), i.e. 4 times greater than Hunga. Krakatau’s highest plume-altitude explosions are thought to have occurred after caldera collapse, from pyroclastic density currents entering the sea (see Self and Rampino, 1981; Self 1992),
Purple twilight duration observations in the Royal Society Krakatoa committee report (Russell & Archibald, 1888) show the Krakatau cloud descended between August 1883 and January 1884 (see Nature Feb 1888 summary of the report). The observations presented in Pernter (1889) indicate an initial descent from 32km to 24km in the first few weeks. a similar altitude for the subsequent 2-3 months (September to November 1883), then a descent resuming to 19km in December 1883 and 17km in January 1884.
How to cite: Mann, G., Zhu, Y., Randel, B., Clyne, M., Dhomse, S., Taha, G., Colombier, M., and Newman, P.: The progression and global dispersion of the Hunga aerosol cloud, and influence from co-emitted water vapour, aligned to the APARC Hunga impacts report, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21823, https://doi.org/10.5194/egusphere-egu25-21823, 2025.
Targeted climate change mitigation strategies to reduce greenhouse gas emissions benefit from robust and reliable emission quantification. During the first phase of the Integrated Greenhouse Gas Monitoring System for Germany (ITMS), we aim to obtain top-down estimates of German methane emissions with the help of numerical weather prediction models. Accurately representing the effects of convection and turbulent eddies in a numerical weather prediction model is fundamental for simulating the transport of trace gases with emissions located near the surface, as in our case. In the current configuration of our numerical weather prediction model, ICON-ART, convection and turbulence are parameterised. Using a parameterised transport denial approach and obtaining model equivalents for the ICOS European Obspack dataset, we develop a qualitative picture of the parameterised transport errors at each Obspack station. These insights help to identify the potential sources of error in the simulation and thus improve the accuracy of methane emission estimates, which is crucial for concentration data assimilation and top-down observation-based emission estimation, the so-called "inversions".
How to cite: Jiménez de la Cuesta Otero, D., Ellerhoff, B., Mamtimin, B., Rösch, T., Bruch, V., and Kaiser-Weiss, A.: Influence of the parameterised transport in ICON-ART on the simulated methane concentrations over Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1595, https://doi.org/10.5194/egusphere-egu25-1595, 2025.
Dust weather is a type of hazardous weather phenomenon predominantly occurring in arid and semi-arid regions. It arises from the interaction between specific desert ecological environments and meteorological conditions, significantly affecting climate change, ecological systems, human health, and transportation. Over the past decade, the frequency of blowing dust and dust storms in China has exhibited an upward trend, particularly with multiple occurrences of severe dust storms during the spring in the last four years. This study employs the CMA_Meso/CUACE_SDS model to analyze 15 typical dust events that transpired in China from 2021, 2023, and 2024, systematically assessing the model's forecasting performance concerning key characteristics of dust, such as transmission paths, distribution ranges, and durations. Furthermore, based on the different systems that trigger dust weather (Mongolian cyclones and cold fronts or only cold fronts), we selected four representative dust cases with varying process types (blowing dust or dust storms), focusing on analyzing the model's forecast accuracy in relation to weather systems (Mongolian cyclone intensity and cold front intensity) and local meteorological factors (temperature, wind speed, etc.). The results demonstrate that the CMA_Meso/CUACE_SDS model can accurately simulate the transmission paths, distribution ranges, and durations of dust events; however, it tends to overestimate the intensity of both Mongolian cyclones and cold fronts, as well as wind speeds near dust source areas. This overestimation further exacerbates dust emissions from these areas, ultimately diminishing the model's forecasting accuracy for dust events. This study offers valuable insights for enhancing the model's capability to simulate transboundary dust events in the future.
How to cite: Zhao, Y., Wang, H., Zhang, W., Peng, Y., Ning, H., Han, C., and Wang, J.: The evaluation of model simulations and analysis of error sources for typical spring dust events in China., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2859, https://doi.org/10.5194/egusphere-egu25-2859, 2025.
New generation of the multi-angular polarimeters (3MI, PACE/SPEX and HARP2) will essentially improve the aerosol characterization from remote sensing measurements, providing extended set of the advanced optical and microphysical properties. This will open new possibility for aerosol composition assimilation.
Significant differences exist at present time between aerosol modelling methodologies employed in various remote sensing algorithms and global climate models. This complicates the aerosol data assimilations in reanalysis. This gap also impacts remote sensing approaches, as global climate models provide global information about aerosol masses emissions, accounting for atmospheric states, aerosol sources, and sinks. Consequently, the aerosol information predicted or derived climatologically from global climate models, such as aerosol type and vertical profile, serves as valuable a priori information to constrain remote sensing measurements.
Directly applying the CAMS aerosol modelling approach to remote sensing introduces complexities in the forward model and significantly increases the number of retrieved parameters. However, achieving harmonization between aerosol approaches in global climate modelling and remote sensing holds the potential to enhance the accuracy of aerosol retrieval, as well provide new possibility for aerosol parameters assimilations.
Here we discuss feasibility studies on harmonization of aerosol model approaches in remote sensing and transport models. Different retrieval approaches harmonized with CAMS aerosol model representation are tested on synthetic and real PARASOL measurements as a proxy for future 3MI instrument. The retrieved optical properties are compared with CAMD reanalysis data collocated to PARASOL measurements and will be discussed.
How to cite: Litvinov, P., Dubovik, O., Behera, A., Herrera, M., Jafariserajehlou, S., Fougnie, B., Chimot, J., Remy, S., and Flemming, J.: Harmonization of the aerosol models in remote sensing and global transport models for data assimilation from new generation of polarimetric measurements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19585, https://doi.org/10.5194/egusphere-egu25-19585, 2025.
Mineral dust plays a significant role in climate systems, air quality, and human health, making its accurate prediction essential. This study explores the impact of satellite data assimilation (DA) on mineral dust forecasts, with a focus on the first pre-operational DA system at the Barcelona Supercomputing Center (BSC) using VIIRS aerosol optical depth (AOD) observations.
Results from the DA system which employs the MONARCH (Multiscale Online Non-hydrostatic AtmospheRe Chemistry) model for VIIRS AOD assimilation will be presented. MONARCH contributes to the Barcelona Sand and Dust Storm Warning Advisory System (SDS-WAS) and has previously been used to produce a decadal dust reanalysis based on MODIS, showcasing its reliability in modeling and assimilating mineral dust related observations. The new NRT MONARCH DA system produces daily dust analyses and DA initialized forecasts with a 3 days range since October 2024. The presentation will discuss key methodological choices, including ensemble perturbations, with a particular emphasis on meteorological perturbations and their influence on dust assimilation. Evaluation against the operational control simulation, AERONET ground-based observations and other leading dust forecasting systems will provide a comprehensive assessment of forecast accuracy as a function of forecast range and insights about the impact of different DA setups for mineral dust predictions.
Additionally, the impact of offline satellite-estimated dust emissions on forecast quality will be analyzed with MONARCH. These emissions are derived using an ensemble Kalman Smoother applied to multi-year MONARCH simulations and VIIRS observations, providing a robust estimate of dust sources. This work underscores the importance of integrating diverse data sources to enhance dust modeling and prediction capabilities. The findings contribute to the development of more robust operational dust forecasting systems, with implications for climate research, air quality management, and health risk mitigation.
How to cite: Emili, E., Escribano, J., Karnezi, E., Olid, M., Meikle, C., Jorba, O., and Péréz Garcia-Pando, C.: On the Impact of Satellite Data Assimilation on Mineral Dust Predictions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6940, https://doi.org/10.5194/egusphere-egu25-6940, 2025.
This presentation will provide an overview of the Community Fire Behavior model (CFBM, Jimenez y Munoz et al, 2024). Fire behavior models allow for an explicit representation of the fire progression accounting for feedback between the atmosphere and fires. The CFBM has been designed to facilitate coupling with different atmospheric models. To this end, we rely on modern software engineering standards and the Earth System Modeling Framework (ESMF) libraries (https://github.com/NCAR/fire_behavior). In its current version (v0.2.0), CFBM closely follows the methods of the Weather Research and Forecasting (WRF) model with fire extensions (WRF-Fire). This allowed us to ensure the adequacy of our implementations. The CFBM has been implemented in the National Oceanic and Atmospheric Administration (NOAA) Unified Forecast System (UFS) and we are in the process of coupling the model to WRF. The results from idealized fire simulations, and the consistency shown between UFS-CFBM and WRF-Fire, as well as WRF-CFBM and WRF-Fire, were the starting point for our ongoing extensions. This includes accounting for the impact of the fuel moisture content on smoke emissions from wildland fires. Our strategy to enhance the evolution of fire emissions for air quality systems will be also outlined. The results obtained so far and the interoperability of the model, that allows for coupling to other atmospheric models, should facilitate its adoption; which would foster collaborative developments to improve fundamental understanding of fire-atmosphere processes including wildland fire impacts on atmospheric composition.
Jimenez y Munoz, P.A., M. Frediani, M. Eghdami, D. Rosen, M. Kavulich, and T.W. Juliano, 2024: The Community Fire Behavior model for coupled fire-atmosphere modeling: Implementation in the Unified Forecast System. Geosci. Model Dev. Discuss. Preprint.
How to cite: Jimenez, P., Frediani, M., Eghdami, M., Rosen, D., Kavulich, M., and Katherine, K.: An interoperable fire behavior model for coupling with atmospheric models: The Community Fire Behavior model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21242, https://doi.org/10.5194/egusphere-egu25-21242, 2025.
Fungal spores have been recognized as a significant source of particulate matter as PM10. They also represent a public health and air quality topic, as high concentrations of fungal spores can cause respiratory issues. Within the Copernicus Atmosphere Monitoring Service (CAMS), ECMWF operates the Integrated Forecasting System with atmospheric composition extension (IFS-COMPO) to provide global forecasts and reanalyses of aerosols and trace gases. In the context of the Horizon Europe CAMAERA (CAMS Aerosol Advancement) project, a first attempt has been made to include a simple representation of fungal spores in IFS-COMPO. Several emission schemes from the literature have been tested, using a variety of meteorological and land use variables as precursors. Evaluation is carried out against: 1) a growing database of fungal spores related observations composed of surface concentration of polyols (arabitol and mannitol) over Europe, which are a good proxy for fungal spores, fungal spores counts over the U.S. from the American Academy of Allergy, Asthma and Immunology (AAAI) and fungal spores DNA abundance as collected worldwide by the Global Spore Sampling Project (GSSP); and 2) against PM10 observations worldwide. In this contribution, we compare the skill of different fungal spores emission schemes and discuss the opportunity of adding fungal spores to the portfolio of CAMS products.
How to cite: Remy, S., Lange, G. F., Fagerli, H., Huijnen, V., Jaffrezzo, J.-L., Uzu, G., Elias, T., and Flemming, J.: Towards a representation of fungal spores in IFS-COMPO, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12848, https://doi.org/10.5194/egusphere-egu25-12848, 2025.
Accurate and efficient retrieval of atmospheric chemical concentrations across space and time is crucial for weather prediction and health assessments. However, existing model-measurement fusion methods suffer from limitations due to imbalanced samples from ground measurements or less effective assimilation of satellite data along with numerical modeling. To address these limitations, this study introduces a novel Deep-learning Measurement-Model Fusion method (DeepMMF) constrained by physical and chemical laws inferred from numerical chemical transport models (CTM). This method is applied to NO₂ species over the Continental United States (CONUS) domain for the years 2019 and 2020. By pre-training with abundant CTM simulations, fine-tuning with satellite and ground measurements, and employing a novel optimization strategy for selecting weighting loss and prior emissions, the retrieved spatiotemporally continuous surface NO₂ concentrations present consistent values and daily variations with observations (NMB reduced from -0.3 to -0.1 compared to original CTM simulation). Importantly, the corresponding emissions have been simultaneously adjusted, showing good agreement with changes reported in the national emission inventory (NEI) between 2019 and 2020. Interpretation analysis suggests that the DeepMMF model effectively identifies the importance of satellite data at the regional level and ground measurements at the city level, which is scientifically sound. It exhibits consistent prediction of ground measurements while successfully avoiding the sample imbalance problem that leads to overestimation (up to +100%) of downwind/rural concentrations compared to other existing methods. These results demonstrate the great potential of DeepMMF in data assimilation and retrieval studies for other pollutants and regions, to better support weather forecasting and heatlh studies.
How to cite: Xing, J., Baek, B. H., Li, S., Wang, C.-T., Song, G., Ma, S., Tong, D., and Fu, J.: A Deep Learning Method for Model-Measurement Fusion of Atmospheric Concentrations with Physical Constraints, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12599, https://doi.org/10.5194/egusphere-egu25-12599, 2025.
The daily analyses and forecasts of atmospheric composition delivered by the Copernicus Atmosphere Monitoring Service (CAMS) are produced by the ECMWF Integrated Forecasting System configured for COMPOsition (IFS-COMPO). On 27 June 2023, this system was upgraded to Cy48R1 which solves explicitly for stratospheric chemistry through a module extracted from the Belgian Assimilation System for Chemical ObsErvations (BASCOE). On 12 November 2024, the system was further upgraded to Cy49R1 which improves the representation of stratospheric composition with an adjusted parameterization of Polar Stratospheric Clouds (PSC), updated chemical rates for heterogeneous chemistry, and the implementation of missing processes to model the distribution of sulfate aerosols in the stratosphere.
We report on these improvements and evaluate the resulting stratospheric composition in forecast mode, i.e. with no assimilation of composition observations. These evaluations focus on aerosol extinction in the global stratosphere and on ozone depletion processes in the polar lower stratosphere. For ozone depletion events we compare forecasts of ozone, water vapor, N2O, HNO3, HCl and ClO with a reanalysis of observations by the Aura Microwave Limb Sounder (MLS) for three Antarctic events (2008, 2009, 2020) and four Arctic events (ending in 2009, 2011, 2012 and 2020). We show that the model configuration currently used by CAMS (IFS-COMPO Cy49R1) simulates successfully these processes and events. This enables the assimilation of multiple satellite observations of stratospheric composition in an operational Data Assimilation System developed primarily for Numerical Weather Forecasting and provides a useful tool for further studies of the couplings between stratospheric aerosols and gas-phase chemistry.
How to cite: Chabrillat, S., Rémy, S., Huijnen, V., Bingen, C., Debosscher, J., Errera, Q., Metzger, S., Minganti, D., Opdebeek, M., Williams, J., Eskes, H., and Flemming, J.: Modelling stratospheric composition for the Copernicus Atmosphere Monitoring Service Cy49R1: polar ozone depletion and sulfate aerosols, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18231, https://doi.org/10.5194/egusphere-egu25-18231, 2025.
The Mount Etna eruptions are not included in the anthropogenic emission inventories used to simulate air quality in Europe. This study examines the potential of using satellite data assimilation techniques for “adding” the volcanic contribution to atmospheric concentrations. Sulfur dioxide (SO₂) column data from the Sentinel-5p L2 COBRA retrievals (5.5 km x 3.5 km resolution and 2660 km swath) is incorporating through Data Assimilation in 3D SO2 concentrations (0.15° x 0.1°, 14 vertical levels) simulated with MINNI, an atmospheric modelling system member of the CAMS regional air quality ensemble (https://atmosphere.copernicus.eu/charts/packages/cams_air_quality/products/europe-air-quality-forecast-regulated?base_time=202501140000&layer_name=composition_europe_o3_forecast_surface&level=key_0&originating_centre=85_205&projection=opencharts_europe&valid_time=202501140000) which is based on the Chemical Transport Model FARM. The work is part of the CAMs EvOlution (CAMEO, https://ww.cameo.project.eu/) project.
The Data Assimilation framework based on the Ensemble Adjustment Kalman Filter (EnAKF) is for the first time designed and implemented to couple FARM with the Data Assimilation Research Testbed (DART) tool (http://doi.org/10.5065/D6WQ0202). The system runs a 20-member ensemble over an hourly assimilation window. The model perturbations are achieved by varying anthropogenic emissions and boundary conditions to estimate model uncertainties. A specific forward operator based on the Copernicus Satellite Operator (CSO, CAMS / CSO · GitLab) is implemented in DART to obtain the simulation of retrieval products from the model state using averaging kernels. Vertical localization was performed using the 5th-order Gaspari-Cohn (GC) rational function, while prior inflation was applied to minimize filter divergence due to insufficient variance. The Quantile Conserving Ensemble Filter Framework Method was applied to preserve the positivity of the trace gas concentrations. Furthermore, it is assumed that the impact of increments due to SO2 observations only influences the model SO2states.
The DA experiment has been conducted for August 2023. The comparison of the time series, illustrated in Figure 1, shows the vertical column TROPOMI SO2, the prior ensemble mean and the posterior ensemble mean averaged on a subdomain including Sicily, Malta and part of the Mediterranean Sea. Both the prior and the posterior exhibit a negative bias, highlighting the necessity of incorporating volcanic emissions to address this discrepancy. During the period influenced by volcanic activity (08/13 – 08/17), the modelled concentrations after assimilation are enhanced in correspondence of the volcanic plume across the Mediterranean Sea, as depicted in Figure 2.
Figure1. Time series of sulphur dioxide (SO₂) retrieval over a selected region (inset map) during August 2023. black: S5P_PAL_L2_SO2CBR observations, orange: prior ensemble mean, blue: posterior ensemble mean.
Figure 2. Comparison of S5P (left), prior ensemble mean (centre) and posterior ensemble mean (right) on 2023-08-14 13:00.
How to cite: D'Ausilio, A., De Moliner, G., Silibello, C., Bolignano, A., Briganti, G., Russo, F., and Mircea, M.: Assessing the impacts of assimilating SO2 TROPOMI retrievals with MINNI and DART at the European scale: a case study of the Mount Etna eruption, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16042, https://doi.org/10.5194/egusphere-egu25-16042, 2025.
Within the EUMETSAT program EPS-SG, CNES is responsible of purchasing the IASI-NG system, consisting in three instruments on-board of METOP-SG satellites, the L1C products processors, the Mission Performances Expertise Centre and all the simulators needed for these developments.
IASI-NG mission will produce data for the meteorological, atmospheric chemistry and climatology user's community. L1C products are the first level of products that will be distributed to end-users.
IASI-NG space segment transmits the measurements to ground where they are organized in raw L0 products. L0 products are the main input of the L1C processor that calibrates the spectra and images radiometrically and spectrally, and enriches these data with geolocation and additional information for further spectra exploitation.
CNES has developed several L1C processors in order to generate this L1C product:
- L1CPOP (L1C Product Operation Processor) for the global and regional mission, that will be integrated in EUMETSAT PDAP (Payload Data Acquisition and Processing) Ground Segment to support the routine phase
- L1CLOP (L1C Local Operation Processor) for the local mission, that will be integrated in SAF (Satellite Application Facilities) ground stations
- L1CTOP (L1C Temporary Operational Processor) extending the L1CLOP to the global mission, that will be integrated in the EUMETSAT T-GPS (Temporary Ground Processing System) Ground segment to support the commissioning of METOP-SGA-1 satellite.
This presentation:
- Présentation de la mission IASI-NG et présentation des produits L1C qui seront distribués par EUMETSAT aux utilisateurs finaux
- présente les traitements et algorithmes de base de L1C et leur processus de vérification basé sur l’utilisation des outils de simulation du CNES et des données IASI-NG réelles issues d’essais d’instruments
- L’intégration du traitement L1C dans L1CPOP dans l’infrastructure PDAP complexe basée sur une solution Big Data in-memory
- L’intégration des traitements L1C dans L1CLOP et L1CTOP dans l’infrastructure simplifiée basée sur une solution d’échange de fichiers.
How to cite: Petrucci, B., Nosovan, J., Bijac, S., Cebe, Q., Le Fevre, C., and Bermudo, F.: Traitement IASI-NG L1C : développement, validation et produits, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1166, https://doi.org/10.5194/egusphere-egu25-1166, 2025.
Compared with climate models, the role of aerosol-cloud interaction (ACI) in mesoscale numerical weather prediction (NWP) models still needs to be better understood, especially in haze regions with relatively high aerosol concentration. Here, we perform two sensitivity experiments with and without ACI (ACI and NO-ACI) in the atmospheric chemistry model CMA_Meso/CUACE to investigate the impact of ACI on mesoscale NWP during the low-cloud period in winter 2016 over varying haze regions (severe polluted Jing-Jin-Ji (JJJ), polluted Yangtze River Delta (YRD), and weak polluted Pearl River Delta (PRD)) in China. The study results show that the real-time ACI improves underestimated cloud optical thickness (COT) and cloud water liquid path (CLWP) in haze regions, with the mean bias of simulated COT (CLWP) decreased by 27% (3%), 60% (14%), and 55% (3%) in JJJ, YRD, and PRD, respectively. The increased COT and CLWP lead to a decrease of 6.8, 21, and 13 W m-2 in daytime surface downward shortwave radiation (SDSR) in JJJ, YRD, and PRD, helping to reduce the mean bias of daytime SDSR by 6%, 13%, and 9%. In addition, ACI mitigates the warm bias of temperature at 2 m and dry bias of relative humidity (RH) at 2 m to a certain extent in haze regions, particularly in YRD with the mean absolute bias improved by 13% and 6%. The simulated vertical structure of temperature and RH in the ACI experiment is more consistent with observations than in the NO-ACI experiment. Further investigations find that the ACI effects on mesoscale NWP strongly depend on COT and CLWP magnitude over varying haze regions. Higher COT and CLWP, hence more significant meteorology changes due to ACI, occur in YRD, followed by PRD and JJJ. This study demonstrates the importance and complexity of ACI in modifying mesoscale NWP over varying haze regions of China, which promotes the further understanding of ACI in operational NWP models and bridges the gap with climate models.
How to cite: Zhang, W., Wang, H., Zhang, X., and Peng, Y.: The impact of aerosol-cloud interaction on mesoscale numerical weather prediction in winter over major polluted regions of China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5017, https://doi.org/10.5194/egusphere-egu25-5017, 2025.
In the Korean Peninsula, intricate meteorological conditions influence fine particulate matter (PM) concentrations, making the improvement of both air-quality and meteorological forecasts crucial for better PM predictions. Data assimilation (DA) can help enhancing air-quality forecasts by reducing initial condition uncertainties of air-quality and meteorology. This study investigates the impacts of chemical and meteorological DA on air-quality and meteorological forecasts during a high PM event in the Korean Peninsula. Observational verification showed that the combined application of chemical and meteorological DA yielded the greatest improvements in air-quality and meteorological forecasts. While chemical DA primarily enhanced air-quality predictions, meteorological DA was essential for improving meteorological forecasts.
The study also assessed the effects of chemical and meteorological DA on air-quality and meteorological forecasts in both DA cycling and non-cycling processes, with respect to the forecasts without DA. Based on the root-mean-square differences between forecasts with and without DA, the impacts of chemical and meteorological DA on air-quality forecasts were found to be similar in cycling and non-cycling processes. In the simultaneous chemical–meteorological DA experiment, the effects of the chemical DA and meteorological DA complemented each other. In the cycling DA process, chemical DA influenced meteorological forecasts, and meteorological DA affected air-quality forecasts due to cumulative DA effects. Chemical DA improved the absolute levels of PM in forecasts, while meteorological DA enhanced the spatiotemporal accuracy of PM distribution by refining transport processes.
Consequently, simultaneous chemical–meteorological DA proved to be the most effective approach for changing air-quality and meteorological forecasts, and could offer substantial improvements in air-quality and meteorological forecasts in the Korean Peninsula.
Acknowledgements
This study was supported by the National Research Foundation of Korea (2021R1A2C1012572) funded by the South Korean government (Ministry of Science and ICT) and the Yonsei Signature Research Cluster Program of 2024 (2024-22-0162).
How to cite: Cho, Y., Kim, H. M., Seo, M.-G., and Kim, D.-H.: Impacts of chemical and meteorological data assimilation on air-quality and meteorological predictions in the Korean Peninsula, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5670, https://doi.org/10.5194/egusphere-egu25-5670, 2025.
Wind speed is one of the most sensitive meteorological factors influencing dust storm simulations. The dust emission threshold wind speed (ut) represents the critical point, beyond which dust emission increases notably as wind speed intensifies. However, ut exhibits spatial and temporal variability, lacks direct measurement compared to conventional meteorological parameters, and is challenging to estimate. This study integrates in-situ observations, satellite data, and reanalysis datasets to develop a global dust emission threshold wind speed dataset. Site-specific ut values are determined using in-situ observations by defining and optimizing a dust emission threshold score (DuTS). Based on these site-level ut values, along with satellite dust optical depth (DOD) and wind speed reanalysis data, a global ut distribution dataset is created. This dataset is implemented and validated in a NWP-grade global dust-weather integrated model, iDust. Evaluating iDust against particulate matter (PM) concentration and DOD observations demonstrates that incorporating this dataset significantly improves the seasonal variation of dust simulations and enhances PM concentration simulations across multiple regions.
How to cite: Chong, M., Chen, X., Wang, S., Liang, Y., Lin, S.-J., and Liang, Z.: Heterogeneous Observation-Based Threshold Velocity Dataset for Wind Erosion and Its Implementation in the iDust Prediction System, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2805, https://doi.org/10.5194/egusphere-egu25-2805, 2025.
Volcanic eruptions are one of the major natural hazards, exerting profound effects on the environment, economy, and infrastructure. The emissions associated with such eruptions pose substantial risks to terrestrial systems and public health, particularly through the induction of acid rain and air pollution. Additionally, these emissions impact the climate by releasing sulfur dioxide (SO2), which subsequently undergoes conversion into sulfate aerosols due to oxidation by hydroxyl radicals (OH) and hydrogen peroxide (H2O2). Sulfate aerosols, SO2, and volcanic ash influence extensive populations at distances reaching several thousand kilometers from the erupted volcano. In addition, information on ash concentration and the location of the volcanic cloud is crucial for air traffic control. Considering these aspects, accurate modeling of the transport and deposition of volcanic debris is essential. Among the available forecasting tools, the online WRF-Chem Eulerian model is distinguished for its capability to simulate the transport and deposition of volcanic debris.
Here, we enhance the existing and add new functionalities to the WRF-Chem code. In particular, we account for major sinks (wet and dry deposition of ash, sulfate, and chemical transformation of SO2). We identified and rectified a bug in the subroutine for gravitational deposition of the ash. Due to this bug, the ash mass balance was violated. Furthermore, we established a mass balance for sulfate, SO2, and ash by incorporating diagnostic variables into the model's output. Additionally, we corrected the deposition velocity of the coarse (>60 microns in diameter) ash particles and integrated gravitational settling for sulfate aerosols.
Emissions of sulfate along with water vapor, which are other (along with ash and SO2) constituents of a typical volcanic eruption, were also added to the model code. Water vapor is an important greenhouse gas. The recent underwater eruption of the Hunga-Tonga volcano released approximately 150 Mt of water vapor, which affected the dynamics of the debris cloud as a result of the radiative cooling of the water vapor cloud.
The WRF-Chem code has been further enhanced to incorporate the direct radiative effects of ash and sulfate aerosols, acknowledging the substantial radiative forcing exerted by volcanic eruptions on the climate system. In particular, the ash released into the upper atmosphere can inhibit sunlight from reaching the Earth's surface for an extended period, cooling the surface and causing disruptions in ecosystems and agriculture. The stratospheric sulfate aerosol clouds can persist from a few months to a couple of years, reflecting solar radiation into space and causing global cooling.
In addition, we developed an open-source emission preprocessor written in Python. In comparison with the existing PREP-CHEM-SRC utility, our tool facilitates the workflow and adds flexibility in prescribing the volcanic eruption process given the eruption source parameters.
We demonstrate the effect of changes and additions implemented into the WRF-Chem code. The capabilities added to the code allow for significant advancement in volcanic debris forecasting and studies of the effects of volcanic eruptions on climate.
How to cite: Ukhov, A. and Hoteit, I.: Enhancing Volcanic Eruption Simulations with the WRF-Chem Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12830, https://doi.org/10.5194/egusphere-egu25-12830, 2025.
Global reanalyses of atmospheric composition (AC) have become a valuable data source to study trends of aerosols, reactive gases, and greenhouse gases. These reanalyses are produced by data assimilation of satellite retrievals of atmospheric composition with atmospheric composition models. The AC reanalysis data are consistent gridded data sets at high temporal resolution (“maps without gaps”) covering decades. Reanalyses are well suited for the study of trends as the users do not have to deal with spatial and temporal gaps in the assimilated observations and their inter-instrument biases. However, the trends and variability of the atmospheric composition fields in AC reanalysis are also influenced by the trends of the model input data such as emissions from anthropogenic sources and wildfires as well as from the meteorological conditions and changes in the availability of the assimilated satellite data.
The Copernicus Atmosphere Monitoring Service (CAMS, atmosphere.copernicus.eu) has produced several AC reanalyses for the period starting in 2003 by assimilating satellite retrievals of atmospheric composition with the ECMWF model. The latest version is the CAMS Reanalysis (EAC4, Inness et al 2019) which is continued in near-real-time with a delay of a few weeks. It has been used for a wide range of applications such as the monitoring of the ozone hole, trends of surface PM2.5 and AOD, tropospheric ozone, and carbon monoxide.
CAMS currently prepares the production of a new AC reanalysis (EAC5). EAC5 has a more advanced modelling approach that also includes stratospheric chemistry and a wider range of secondary aerosols than EAC4. Further, more satellite retrievals in particular from the TropOMI instrument will be assimilated. In this presentation we will give a status update of the preparations for EAC5 and present the efforts on modelling and data assimilation to ensure the production of a consistent data set. Results of scouting analysis and model simulations will be shown to indicate the expected improvements of EAC5 with respect to EAC4.
How to cite: Flemming, J., Inness, A., Ades, M., Di Tomaso, E., Kluge, F., Paschalidi, Z., Robas, R., Kelly, C., Remy, S., and Huijnen, V.: Towards the next CAMS reanalysis of atmospheric composition, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12348, https://doi.org/10.5194/egusphere-egu25-12348, 2025.
We report on the analysis of formaldehyde (HCHO) simulations performed by the CAMS (Copernicus Atmosphere Monitoring Service) atmospheric composition forecasting system (IFS-COMPO) in different tropospheric regions, seasons, altitudes and air masses using a comprehensive data set of airborne measured HCHO vertical column densities and mixing ratios. The observations are derived from measurements of the HALO mini-DOAS instrument operated from aboard the German research aircraft DLR HALO during six international research missions in the years 2017 to 2019 and TROPOMI S5P satellite observations. In addition, measurements over the South American tropical rain forest in 2014 are included, as this region is of particular interest in the analysis of global biogenic emissions. In particular, the presented analysis evaluates HCHO in biogenic air masses and the impact of recent advances of biogenic emission estimation in IFS-COMPO on simulated biogenic VOCs. For this purpose, we evaluate operational IFS-COMPO HCHO simulations, which apply a climatology of monthly averaged biogenic emissions (Cams-Glob-BioV3.1), and HCHO simulations based on a recently developed online biogenic emission estimation module.
The above findings are part of the ongoing research carried out in the Horizon Europe CAMEO (CAMS EvOlution) project, which aims to develop an inversion of biogenic emissions within ECMWF’s Integrated Forecasting System. As a first step towards a successful implementation of HCHO assimilation and inversion capability within the IFS, a tangent linear and adjoint have been derived based on a simplified, linearized HCHO chemistry scheme. The impact of the assimilation of HCHO in IFS-COMPO is currently analyzed using TROPOMI S5P formaldehyde observations, with a particular focus on its impact on other atmospheric reactive trace gases, such as isoprene and ozone, and on aerosols.
How to cite: Kluge, F., Flemming, J., Huijnen, V., Inness, A., Kelly, C., Müller, J.-F., Oomen, G.-M., Pfeilsticker, K., Ribas, R., Stavrakou, T., Weyland, B., and van der Worp, M.: Initial steps towards an inversion system for biogenic isoprene emissions in CAMS: Evaluation of IFS-COMPO formaldehyde simulations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20379, https://doi.org/10.5194/egusphere-egu25-20379, 2025.
Online-coupled meteorology atmospheric composition models (CCMM) have greatly evolved in recent at least three decades. Although mainly developed by the air quality modeling community, these integrated models are also of interest for numerical weather prediction and climate modeling as they can consider both the effects of meteorology on air quality, and the potentially important effects of atmospheric composition on weather. Migration from offline to online integrated modeling and seamless environmental prediction systems are recommended for consistent treatment of processes and allowance of two-way interactions of physical and chemical components, particularly for AQ and numerical weather prediction (NWP) communities.
Regarding AQF and atmospheric composition modelling, the CCMM approach will certainly improve forecast capabilities as it allows a correct way of jointly and consistently describing meteorological and chemical processes within the same model time steps and grid cells. Applications that may benefit from CCMM are numerous and include: chemical weather forecasting (CWF), numerical weather prediction for precipitation, visibility, thunderstorms, etc., integrated urban meteorology, environment and climate services, sand and dust storm modeling and warning systems, wildfire atmospheric pollution and effects, volcano ash forecasting, warning and effects, high impact weather and disaster risk, effects of short-lived climate forcers, earth system modelling and projections, data assimilation for CWF and NWP, and weather modification and geo-engineering. Online integrated models, however, need harmonized formulations of all processes influencing meteorology and chemistry.
This presentation provides an overview and analysis of integrated meteorology & chemistry model developments during the last 30 year focusing on the main achievements, main trends in developments and applications, as well as the challenges and future research priorities. A special focus will be done on new requirements for further development and applications of CCMM for:
- Multi-hazard early warning systems,
- Integrated urban weather, climate and environmental systems,
- Adaptation and mitigation strategy for climate-smart cities,
- Earth System Prediction on different scales.
How to cite: Baklanov, A.: Three Decades of Integrated Atmospheric Composition & Meteorology Model Developments: Current Status and New Requirements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13348, https://doi.org/10.5194/egusphere-egu25-13348, 2025.
Air quality forecasting is essential for protecting public health and the environment. The World Meteorological Organization (WMO) has developed initiatives like the Global Air Quality Forecasting and Information System (GAFIS), the Sand and Dust Storms Warning Advisory and Assessment System (SDS-WAS) and the Vegetation Fire and Smoke Pollution Warning Advisory and Assessment System (VFSP-WAS) to enhance forecasting capabilities.
In this context, low-cost air quality sensor systems (LCS) are transformative tools in modern air quality management, offering new opportunities to complement traditional monitoring methods. By integrating LCS data with established systems, such as satellite observations and reference-grade instrumentation, the reliability and applicability of air quality data for forecasting can be significantly enhanced.
A key strength of LCS is their ability to expand the spatial and temporal reach of monitoring networks. However, their use must address inherent limitations in accuracy and precision. Co-locating LCS with reference-grade monitors is essential to quantify uncertainties and ensure data quality. This calibration step enables the deployment of LCS in advanced applications like air quality forecasting. Successfully implementing LCS networks for global, regional, or urban forecasting requires careful planning to ensure adequate spatial coverage, data quality, and timely updates, as well as seamless integration with other systems for actionable insights.
The World Meteorological Organization (WMO) has been instrumental in coordinating global efforts to optimize LCS deployment. Through guidelines, best practices, and integration frameworks, the WMO supports national and regional initiatives to enhance air quality management. A recent WMO report (WMO, 2024) underscores the importance of incorporating LCS into comprehensive monitoring frameworks for supporting air quality forecasting and reanalysis applications.
Ongoing advancements in LCS technology and standardization—led by organizations like WMO —are vital to unlocking their full potential. These efforts promise a more equitable and effective approach to air quality management, ensuring that LCS contribute meaningfully to global strategies for monitoring and forecasting. The present contribution will overview the main outcomes of the WMO’s 2024 report on the use of LCS for air quality forecasting and reanalysis applications.
References:
WMO, UNEP and IGAC; Integrating Low-cost Sensor Systems and Networks to Enhance Air Quality Applications, 2024, https://library.wmo.int/idurl/4/68924
How to cite: Basart, S., Malings, C., Huneeus, N., and Flemming, J.: Integrating Low-cost Sensor Systems and Networks to Enhance Air Quality Forecasting and Reanalysis Applications , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16136, https://doi.org/10.5194/egusphere-egu25-16136, 2025.
Aerosols are an important component of the Earth’s atmosphere, where they influence radiative forcing, cloud microphysics, and air quality. Accurate modelling of their spatiotemporal evolution is needed for producing reliable simulations of climate and air quality impacts. Thus far the aerosol description of the ECMWF IFS (Integrated Forecasting System) has relied on a bulk-bin scheme, which provides limited information on the aerosol size distributions. For more accurate calculation of the climate effects and air quality detailed simulations of both mass and number concentrations of aerosols are required. For this work we have utilised OpenIFS-AC, which is a portable and easy-to-use version of the IFS which was recently extended with online chemistry calculation. In the OpenIFS model we replaced the bulk-bin description with the HAM-M7 (Hamburg Aerosol Model M7) modal aerosol scheme. The HAM-M7 module describes aerosol processes such as emissions, transport, deposition, and microphysical interactions across seven log-normal modes, including both mass and number concentrations as size-resolved properties for key aerosol species, including sulfate, black carbon, organic matter, sea salt, and dust. Furthermore, the current implementation within OpenIFS Cy48r1 includes aerosol interactions with radiation and cloud microphysics.
We used the model to simulate the global evolution of the different aerosol components and evaluate the performance against observational data. The model is run for one year for 2010 with CMIP6 emissions and 2024 with CAMS emissions with one year of spinup. The simulated aerosol fields are compared with observed number and mass concentrations of aerosols for the observational sites in the ACTRIS network, Furthermore, the simulated surface concentrations are compared with those provided by the aerosol models within the AeroCom project. Moreover, as the modal aerosol module is computationally more expensive than the bulk-bin module we will discuss the computational cost of running the new aerosol module.
This work was supported by the European Union’s Horizon Europe projects CAMAERA - CAMS AERosol Advancement (number 101134927) and FOCI, Non-CO2 Forcers and Their Climate, Weather, Air Quality and Health Impacts (number 101056783).
How to cite: Bergman, T., Holopainen, E., Wu, L., Kokkola, H., Laakso, A., Halonen, H., Juurikkala, K., Le Sager, P., Huijnen, V., van Noije, T., Checa-Garcia, R., Hill, A., and Köhler, M.: Implementation of the Aerosol Module HAM-M7 within OpenIFS: Evaluation of Surface Concentrations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17295, https://doi.org/10.5194/egusphere-egu25-17295, 2025.
Within the Copernicus Atmosphere Monitoring Service (CAMS), ECMWF operates the Integrated Forecasting System with atmospheric composition extension (IFS-COMPO) to provide global forecasts and reanalysis of aerosols and trace gases. Emissions of sea-salt aerosols in IFS-COMPO are estimated by first computing the whitecap fraction, using a polynomial fit between a dataset of retrieved whitecap fraction from remote sensing and wind speed and sea-surface temperature (SST), and applying a shape function on the whitecap fraction to derive sea-salt aerosol emissions.
In the context of the Horizon Europe CAMAERA (CAMS AERosol Advancement) project, we apply a range of deep learning and machine learning algorithms to estimate whitecap fraction offline, using a two-year long dataset of whitecap fraction derived from remote sensing observations. Meteorological and oceanic predictors are used, including wind speed and direction, sea-surface temperature, significant wave height from wind- and total-sea, as well as the turbulent energy of breaking waves. The latter two parameters are provided by the wave model (WAM) that is included in IFS-COMPO. For some of the deep-learning and machine learning methods, the correlation and error of the estimated whitecap fraction are much improved as compared to the usual physical models used in the atmospheric composition and remote sensing communities.
This work can be seen as a benchmark of machine learning/deep learning methods for the simulation of atmospheric composition processes. This expertise will be used for other processes such as desert dust emissions, in the CAMAERA project.
How to cite: Capon, N., Meyer, R.-C., Remy, S., Anguelova, M., Bidlot, J., Kousal, J., Elias, T., and Bonanni, A.: Harnessing machine learning and deep learning methods to forecast whitecap fraction and sea-salt aerosol emissions in the ECMWF Integrated Forecast System (IFS-COMPO), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17696, https://doi.org/10.5194/egusphere-egu25-17696, 2025.
The U.S. National Oceanic and Atmospheric Administration (NOAA) provides operational air quality (AQ) predictions over the United States and global aerosol forecasts. The current operational model, the National Air Quality Forecast Capability (NAQFC) at NOAA, has undergone a fundamental paradigm shift through its integration into the Earth system modeling Unified Forecast System (UFS) as a coupled component, the Air Quality Modeling component (AQMv7). AQMv7 embeds the U.S. EPA Community Multiscale Air Quality Model (CMAQ) and it has been operational at the National Weather Service (NWS) since May 2024. The model was also updated with a larger domain size and new emissions, including the development of the NOAA Emission and eXchange Unified System (NEXUS) along with dynamic processes such as using Model of Emissions of Gases and Aerosols from Nature (MEGAN) v2.1 and the FENGSHA dust scheme, and the Regional Advanced Baseline Imager (ABI)-Visible Infrared Imaging Radiometer Suite (VIIRS) Emissions (RAVE) biomass burning algorithm.
Operational global aerosol modeling is performed through the NOAA Global Ensemble Forecast System with Aerosols and has been operational in this manner since 2020. Planned future advances include upgrading to a fully coupled atmosphere/land/ocean/sea-ice/wave/aerosols systems developed within the Unified Forecast System (UFS) framework. In its final configuration, the coupled UFS system will consist of: (1) FV3 dynamical core and CCPP atmospheric physics package using the Noah-MP land model, (2) MOM6 ocean model, (3) CICE6 sea ice model, (4) WAVEWATCH III wave model, and (5) the UFS-Aerosol component, based on NASA’s 2nd generation GOCART aerosol model. GOCART is a simplified chemistry and aerosol component that predicts the major aerosol species including dust, organic and black carbon, sea salt, and sulfate aerosols.
In this presentation we will discuss NOAA’s current and future AQ and air composition forecasting capabilities and the performance of each system.
How to cite: Baker, B., Yang, F., Huang, J., Campbell, P., Tang, Y., Li, W., Wang, K., Montuoro, R., Bhattacharjee, P., Pan, L., Barton, N., Martin, C., Tangborn, A., Curtis, B., Zhang, L., Kondragunta, S., and Fu, B.: Current and Future Advances in NOAA’s Air Quality Predictions from a regional to global perspective, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13618, https://doi.org/10.5194/egusphere-egu25-13618, 2025.
Posters virtual: Wed, 30 Apr, 14:00–15:45 | vPoster spot 5
EGU25-12277 | ECS | Posters virtual | VPS3
Comparison of Differently Parameterized SARIMA Models using CERES-Derived Aerosol Optical Depth over Indo-Gangetic PlainWed, 30 Apr, 14:00–15:45 (CEST) | vP5.36
The Indo-Gangetic Plain (IGP) is a globally recognized hotspot for high aerosol loading, necessitating precise modelling to understand its spatial and temporal dynamics. This study evaluates the performance of differently parameterized Seasonal Autoregressive Integrated Moving Average (SARIMA) models in forecasting the Aerosol Optical Depth (AOD) at 550 nm retrieved from the CERES (Clouds and the Earth's Radiant Energy System) satellite platform across eight locations: Delhi, Dhaka, Jaipur, Kanpur, Karachi, Kolkata, Lahore, and Varanasi in the IGP. Using long-term AOD datasets from CERES during the period of 2005 to 2020, we tested various SARIMA configurations to capture seasonal trends and irregular variations specific to urban environments. The SARIMA configurations tested include configure_1: (1,0,1)(1,0,1)₁₂, configure_2: (1,1,1)(1,1,1)₁₂, configure_3: (2,0,1)(2,0,1)₁₂, and configure_4: (2,1,1)(2,1,1)₁₂ These configure models were compared with CERES-derived observations for AOD at the study sites for the next two years, that is, Jan, 2021 to Dec, 2022. Each configuration was assessed for data stationarity using the Augmented Dickey-Fuller (ADF) test and if not follows, then the differentiation method has been used to stationaries the series. The Model performance was evaluated using multiple statistical metrics, including normalized Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Squared Error (RMSE), Mean Bias Error (MBE), and Mean Absolute Percentage Error (MAPE) for every configuration showed the low metric values. The result indicates high correlation coefficients, ranging from 0.54 to 0.91, and R-squared values, varying between 0.31 and 0.81 for all configurations that significantly determined the best-suited models for each location. Every modelled configuration has been checked with 95% and 99% confidence interval (with alpha=0.05 and 0.01, respectively) showing the p-value <0.001. These results emphasize the models' ability to replicate observed AOD patterns effectively. It reveal that parameter sensitivity plays a critical role in predictive accuracy, with optimal configurations varying across locations due to heterogeneity in aerosol sources and meteorological conditions. The present study underlines the importance of site-specific model tuning for reliable aerosol forecasting in densely populated and pollution-prone regions. These insights provide a foundation to enhance air quality prediction studies and address health, and climate impacts associated with aerosols in the IGP.
How to cite: Mall, A. and Singh, S.: Comparison of Differently Parameterized SARIMA Models using CERES-Derived Aerosol Optical Depth over Indo-Gangetic Plain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12277, https://doi.org/10.5194/egusphere-egu25-12277, 2025.
