As the societal impacts of hazardous weather and other environmental pressures grow, the need for integrated predictions which can represent the numerous feedbacks and linkages between physical and chemical atmospheric processes is greater than ever. This has led to development of a new generation of high resolution multi-scale coupled prediction tools to represent the two-way interactions between aerosols, chemical composition, meteorological processes such as radiation and cloud microphysics.
Contributions are invited on different aspects of integrated model and data assimilation development, evaluation and understanding. A number of application areas of new integrated modelling developments are expected to be considered, including:
i) improved numerical weather prediction and chemical weather forecasting with feedbacks between aerosols, chemistry and meteorology,
ii) two-way interactions between atmospheric composition and climate variability.
This session aims to share experience and best practice in integrated prediction, including:
a) strategy and framework for online integrated meteorology-chemistry modelling;
b) progress on design and development of seamless coupled prediction systems;
c) improved parameterisation of weather-composition feedbacks;
d) data assimilation developments;
e) evaluation, validation, and applications of integrated systems.
This Section is organised in cooperation with the Copernicus Atmosphere Monitoring Service (CAMS) and the WMO Global Atmosphere Watch (GAW) Programme.
This year session is dedicated to the Global Air Quality Forecasting and Information Systems (GAFIS) - a new initiative of WMO and several international organizations - to enable and provide science-based air quality forecasting and information services in a globally harmonized and standardized way tailored to the needs of society.

Co-organized by NH1, co-sponsored by WMO and CAMS
Convener: Alexander Baklanov | Co-conveners: Johannes Flemming, Georg Grell
| Attendance Tue, 05 May, 16:15–18:00 (CEST)

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Chat time: Tuesday, 5 May 2020, 16:15–18:00

Chairperson: Alexander Baklanov
D3251 |
Johannes Flemming, Okasna Tarasova, Lu Ren, Alexander Baklanov, and Greg Carmichael

Air pollution is the single largest environmental risk factor to health globally; it contributes to climate change, is detrimental for ecosystems, damages property, impacts visibility and can threaten food and water security. A wide variety of Air Quality (AQ) systems operate at different spatial and temporal scales to provide information required to mitigate the impact of or to reduce air pollution. 

Recognising the importance to support the transition of scientific efforts into useful services, the Global Atmosphere Watch Programme (GAW) of the World Meteorological Organisation (WMO) has started an initiative on Global Air quality Forecast and Information Systems (GAFIS). GAFIS aims to become a network for the development of good practices for air quality forecasting and monitoring services using  diverse approaches. GAFIS will closely interact with existing GAW efforts on air pollution forecasting and dust strom prediction, and it intends to build strong links with the international health community. As a major first step, GAFIS will carry out and maintain a survey of AQ information systems and identify areas and regions with a lack of adequate AQ services. GAFIS aims to improve access to air quality observations and to encourage better quality control and meta-data provision.  GAFIS will initiate coordinated evaluation activities of air quality services using a harmonized evaluation protocol. Finally,  promoting operational applications of atmospheric composition feedbacks in Numerical Weather Prediction is a further objective of GAFIS.

In the presentation we will introduce GAFIS to the scientific community and invite collaboration within its framework. 

How to cite: Flemming, J., Tarasova, O., Ren, L., Baklanov, A., and Carmichael, G.: Global Air quality Forecast and Information Systems (GAFIS) - a new WMO - GAW initiative, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20058, https://doi.org/10.5194/egusphere-egu2020-20058, 2020

D3252 |
Augustin Colette, Gaelle Collin, and Jérôme Barré


The Copernicus Atmosphere Monitoring Service (CAMS) delivers a wealth of information on atmospheric composition change over short to long timescales. One of the core products of CAMS regards short term air quality forecasts with a three days lead time as well as reanalyses over the past years for the European region.

This service is covered by the CAMS_50 project which is now operational since 2015. It relies on a distributed production of 9 individual air quality models, consolidated by a centralised regional production unit at Météo-France before delivery to the European Centre on Medium Range Meteorological Forecasts, which implements the CAMS service.

Each model is operated by its own development team across Europe, all of them deliver air quality forecasts covering the whole continent at 10km resolution. The modelling team currently operational are at present: CHIMERE (France), DEHM (Denmark), EMEP/MSC-W (Norway), EURAD-IM (Germany), GEM-AQ (Poland), LOTOS-EUROS (The Netherlands), MATCH (Sweden), MOCAGE (France), SILAM (Finland). Two additional models are now applying to join the ensemble: MINNI (Italy), and MONARCH (Spain).

Such an ensemble of different models offers excellent complementarity in model capabilities as demonstrated by the performances of the ENSEMBLE product. It also leads to substantial challenges in coordinated model development. We will present the main recent achievements, status, and future plans for the validation and development of models underlying the service.


How to cite: Colette, A., Collin, G., and Barré, J.: Update on European Regional Air Quality Forecast in the Copernicus Atmosphere Monitoring Service (CAMS), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3487, https://doi.org/10.5194/egusphere-egu2020-3487, 2020

D3253 |
Raffaele Montuoro, Georg Grell, Li Zhang, Stuart McKeen, Gregory Frost, Ravan Ahmadov, Judy Henderson, Jeff McQueen, Li Pan, Partha Bhattacharjee, Jack Kain, Barry Baker, Ivanka Stajner, Jun Wang, Cecelia DeLuca, Jon Pleim, and David Wong

Significant progress has been made within the last couple of years towards developing online coupled systems aimed at providing more accurate descriptions of atmospheric chemistry processes to improve performance of global aerosol and air quality forecasts. Operating within the U.S. National Weather Service (NWS) research-to-operation initiative to implement the fully-coupled Next Generation Global Prediction System (NGGPS), cooperative development efforts have delivered two integrated online global prediction systems for aerosols (GEFS-Aerosols) and air quality (FV3GFS-AQM). These systems include recent advances in aerosol convective transport and wet deposition processes introduced into the SAS scheme of the National Center for Environmental Prediction’s (NCEP) latest Global Forecast System (GFS) based on the Finite-Volume cubed-sphere dynamical core (FV3). GEFS-Aerosols is slated to become the new control member of the NWS Global Ensemble Forecast System (GEFS). The model features an online-coupled version of the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model with a biomass-burning, plume-rise model and recent advances from NOAA Earth System Research Laboratory (ESRL), along with a state-of-the-art FENGSHA dust scheme from NOAA Air Resource Laboratory (ARL). FV3GFS-AQM incorporates a coupled, single-column adaptation of the U.S. Environmental Protection Agency’s (EPA) Community Multiscale Air Quality (CMAQ) model to improve NOAA’s current National Air Quality Forecast Capability (NAQFC). Both coupled systems’ design and development benefited from the use of the National Unified Operational Prediction Capability (NUOPC) Layer, which provided a common model architecture for interoperable, coupled model components within the framework of NOAA’s Environmental Modeling System (NEMS). Results from each of the described coupled systems will be discussed.

How to cite: Montuoro, R., Grell, G., Zhang, L., McKeen, S., Frost, G., Ahmadov, R., Henderson, J., McQueen, J., Pan, L., Bhattacharjee, P., Kain, J., Baker, B., Stajner, I., Wang, J., DeLuca, C., Pleim, J., and Wong, D.: Improving global chemical weather forecast with modern online-coupled models for the U.S. Next Generation Global Prediction System (NGGPS), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11807, https://doi.org/10.5194/egusphere-egu2020-11807, 2020

D3254 |
Gabriele Pfister, Andrew Conley, Mary Barth, Louisa Emmons, Forrest Lacey, and Rebecca Schwantes

Current chemical transport models inadequately account for the two-way coupling of atmospheric chemistry with other Earth System components over the range of urban/local to regional to global scales and from the surface up to the top of the atmosphere.  To meet future challenges, future modeling systems need to have the ability to (1) change spatial scales in a consistent manner, (2) resolve multiple spatial scales in a single simulation, (3) couple model components which represent different Earth system processes, and (4) easily mix-and-match model components. This is the motivation behind MUSICA - the Multi-Scale Infrastructure for Chemistry and Aerosols, which we develop together with the atmospheric chemistry community. MUSICA will allow simulation of large-scale atmospheric phenomena while still resolving chemistry at scales relevant for representing societal and scientific critical phenomena (e.g. urban air quality, or convection in monsoon regions) and also enable connections to other components of the earth system by fully coupling to land and ocean models. MUSICA objectives will be achieved through development of a global modeling system capable of regional refinement and the new Model Independent Chemistry Module (MICM). We will discuss the infrastructure and show preliminary results of atmospheric chemistry simulations being conducted in a global model with regional refinement: the Community Atmosphere Model with chemistry using spectral element grids that refine from one-degree resolution to ~14 km resolution over the conterminous United States. These early results confirm that model resolution does matter for representing regional air quality and that the two-way feedback between the local and global scale can play an important role.

How to cite: Pfister, G., Conley, A., Barth, M., Emmons, L., Lacey, F., and Schwantes, R.: MUSICA - Modeling for Chemistry, Weather and Climate, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6160, https://doi.org/10.5194/egusphere-egu2020-6160, 2020

D3255 |
Radenko Pavlovic, Jacinthe Racine, Marika Egyed, Serge Lamy, and Pierre Boucher

Canadian Air Quality Forecasting and Information Systems

Environment and Climate Change Canada (ECCC) has been in charge of the national air quality program for more than 20 years. As of today, air pollution remains one of the most important environmental risk factors to health, in addition to hazardous effects on climate change, ecosystems, properties, and food and water chain.

Currently, Canadian air quality forecasting and information systems with observational and modeling components are a key element for policy and mitigation measures, which are used to reduce the negative impacts of air pollution. The operational ECCC’s air quality program provides immediate adaptive measures based on early warning services. In addition to this operational service, the air quality scenario and policy modelling is essential for implementing cost-effective emission reduction strategies and local planning to ensure compliance with air quality standards.

Canadian air quality forecasting and information systems also enable access to air quality data at different temporal and spatial scales. This is done through coordination of national activities to facilitate seamless provision of atmospheric composition information at various scales. This work will present Canadian air quality forecasting and information systems, components, collaboration, application and data streaming, as an example that can be helpful in building the WMO GAFIS initiative.

How to cite: Pavlovic, R., Racine, J., Egyed, M., Lamy, S., and Boucher, P.: Canadian Air Quality Forecasting and Information Systems, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20982, https://doi.org/10.5194/egusphere-egu2020-20982, 2020

D3256 |
Dimitris Akritidis, Eleni Katragkou, Aristeidis K. Georgoulias, Prodromos Zanis, Stergios Kartsios, Johannes Flemming, Antje Inness, and Henk Eskes

Within the framework of the Copernicus Atmosphere Monitoring Service (CAMS) element CAMS-84 (Global and regional a posteriori evaluation and quality assurance), we analyze and evaluate the performance of CAMS forecast systems during the passage of ex-hurricane Ophelia in mid-October 2017, carrying Saharan dust and Iberian fire smoke over several Western European regions. To this end, day-1 forecasts from CAMS-global (ECMWF Integrated Forecast System; IFS) and CAMS-regional (ensemble of seven regional air quality models) products are compared against satellite retrievals (MODIS/Terra and Aqua, CALIPSO) and ground-based measurements. The analysis indicates that dust and smoke are injected into the warm sector of Ophelia, lying in the vicinity of the warm and cold front, respectively, gradually affecting the air quality and atmospheric composition over France, the Netherlands and Great Britain. The distinct pattern of enhanced aerosol optical depth (AOD) over Western coastal Europe seen in satellite retrievals is well reproduced by the CAMS near-real time forecast. The observed implications for air quality (PM10 and PM2.5) are satisfactorily forecasted in qualitative terms by both CAMS-global and CAMS-regional systems, while in quantitative terms, the CAMS-regional system exhibits a better performance in predicting surface PM concentrations (higher correlation and lower bias) compared to the global.

How to cite: Akritidis, D., Katragkou, E., Georgoulias, A. K., Zanis, P., Kartsios, S., Flemming, J., Inness, A., and Eskes, H.: Ex-hurricane Ophelia and air quality impacts over Europe in CAMS forecast systems , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4061, https://doi.org/10.5194/egusphere-egu2020-4061, 2020

How to cite: Akritidis, D., Katragkou, E., Georgoulias, A. K., Zanis, P., Kartsios, S., Flemming, J., Inness, A., and Eskes, H.: Ex-hurricane Ophelia and air quality impacts over Europe in CAMS forecast systems , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4061, https://doi.org/10.5194/egusphere-egu2020-4061, 2020

How to cite: Akritidis, D., Katragkou, E., Georgoulias, A. K., Zanis, P., Kartsios, S., Flemming, J., Inness, A., and Eskes, H.: Ex-hurricane Ophelia and air quality impacts over Europe in CAMS forecast systems , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4061, https://doi.org/10.5194/egusphere-egu2020-4061, 2020

D3257 |
Xu Feng, Haipeng Lin, and Tzung-May Fu

We developed the two-way version of the WRF-GC model, which is an online coupling of the Weather Research and Forecasting (WRF) mesoscale meteorological model and the GEOS-Chem chemical transport model, for regional air quality and atmospheric chemistry modeling. WRF-GC allows the two parent models to be updated independently, such that WRF-GC can stay state-of-the-science. The meteorological fields and chemical variables are transferred between the two models in the coupler to simulate the feedback of gases and aerosols to meteorological processes via interactions with radiation and cloud microphysics. We used the WRF-GC model to simulate surface PM2.5 concentrations over China during January 22 to 27, 2015 and compared the results to the outcomes from classic GEOS-Chem nested-grid simulations as well as the surface observations. For PM2.5 simulations, both models were able to reproduce the spatiotemporal variations, but the WRF-GC (r = 0.68, bias = 29%) performing better than GEOS-Chem (r = 0.72, bias = 55%) especially over Eastern China. For ozone simulations, we found that including aerosol-chemistry-cloud-radiation interactions reduced the mean bias of simulated surface ozone concentrations from 34% to 29% compared to observed afternoon ozone concentrations. WRF-GC is computationally efficient, with the physical and chemical variables managed in distributed memory. At similar resolutions, WRF-GC simulations were three times faster than the classic GEOS-Chem nested-grid simulations, due to the more efficient transport algorithm and the MPI-based parallelization provided by the WRF software framework. We envision WRF-GC to become a powerful tool for advancing science, serving the public, and informing policy-making.

How to cite: Feng, X., Lin, H., and Fu, T.-M.: WRF-GC: online two-way coupling of WRF and GEOS-Chem for regional atmospheric chemistry modeling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5165, https://doi.org/10.5194/egusphere-egu2020-5165, 2020

D3258 |
Shan Zhang, Xiangjun Tian, Hongqin Zhang, Xiao Han, and Meigen Zhang

        While complete atmospheric chemical transport models have been developed to understanding the complex interactions of atmospheric chemistry and physics, there are large uncertainties in numerical approaches. Data assimilation is an efficient method to improve model forecast of aerosols with optimized initial conditions. We have developed a new framework for assimilating surface fine particulate matter (PM2.5) observations in coupled Weather Research and Forecasting (WRF) model and Community Multiscale Air Quality (CMAQ) model, based on nonlinear least squares four-dimensional variational (NLS-4DVar) data assimilation method. The NLS-4DVar approach, which does not require the tangent and adjoint models, has been extensive used in meteorological and environmental areas due to the low computational complexity. Two parallel experiments were designed in the observing system simulation experiments (OSSEs) to evaluate the effectiveness of this system. Hourly PM2.5 observations over China be assimilated in WRF-CMAQ model with 6-h assimilation window, while the background state without data assimilation is conducted as control experiment. The results show that the assimilation significantly reduced the uncertainties of initial conditions (ICs) for WRF-CMAQ model and leads to better forecast. The newly developed PM2.5 data assimilation system can improve PM2.5 prediction effectively and easily. In the future, we expect emission to be optimized together with concentrations, and integrate meteorological assimilation into aerosol assimilation system.

How to cite: Zhang, S., Tian, X., Zhang, H., Han, X., and Zhang, M.: A NLS-4Dvar Assimilation System of Surface PM2.5 with WRF-CMAQ Model : Observing System Simulation Experiments, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4019, https://doi.org/10.5194/egusphere-egu2020-4019, 2020

D3259 |
Tao Niu, Xiaoye Zhang, Shanling Gong, Yaqiang Wang, Hongli Liu, and Chunhong Zhou

A data assimilation system (DAS) was developed for the Chinese Unified Atmospheric Chemistry Environment– Dust (CUACE/Dust) forecast system and applied in the operational forecasts of sand and dust storm (SDS) in spring in Asia. The system is based on a three dimensional variational method (3D-Var) and uses extensively the measurements of surface visibility (phenomena) and dust loading retrieval from the Chinese geostationary satellite FY-2C. By a number of case studies, the DAS was found to provide corrections to both under- and over-estimates of SDS, presenting a major improvement to the forecasting capability of CUACE/Dust in the short-term variability in the spatial distribution and intensity of dust concentrations in both source regions and downwind areas.  By now The DAS was upgrade to assimilate FY-4A dust aerosol observations. The seasonal mean Threat Score (TS) over the East Asia in spring increased when DAS was used. The forecast results with DAS usually agree with the dust loading retrieved from FY and visibility distribution from surface meteorological stations, which indicates that the 3D-Var method is very powerful by the unification of observation and numerical model to improve the performance of forecast model.

How to cite: Niu, T., Zhang, X., Gong, S., Wang, Y., Liu, H., and Zhou, C.: Data assimilation of FY-4A dust aerosol observations for the CUACE/dust forecasting system, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9449, https://doi.org/10.5194/egusphere-egu2020-9449, 2020

D3260 |
Virginie Buchard, Arlindo da Silva, Dan Holdaway, and Ricardo Todling

In the GEOS near real-time system, as well as in MERRA-2 which is the latest reanalysis produced at NASA’s Global Modeling Assimilation Office (GMAO), the assimilation of aerosol observations is performed by means of a so-called analysis splitting method. The prognostic model is based on the GEOS model radiatively coupled to GOCART aerosol module and includes assimilation of bias-corrected Aerosol Optical Depth (AOD) at 550 nm from various space-based remote sensing platforms.

Along with the progress made in the JCSDA-Joint Effort for Data Assimilation Integration (JEDI) framework, we have developed a prototype including GEOS aerosols as a component of the JEDI framework. Using members produced by the GEOS hybrid meteorological data assimilation system, we are updating the aerosol component of our assimilation system to a variational ensemble type of scheme. In this talk we will examine the impact of replacing the current analysis splitting scheme with this new approach. By including the assimilation of satellite-based single and multi-channel retrievals; we will discuss the impact of this aerosol data assimilation technique on the 3D aerosol distributions by means of innovation statistics and verification against independent datasets such as the Aerosol Robotic Network (AERONET) and surface PM2.5.

How to cite: Buchard, V., da Silva, A., Holdaway, D., and Todling, R.: Assimilation of Aerosol Observations in the NASA GEOS model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11090, https://doi.org/10.5194/egusphere-egu2020-11090, 2020

D3261 |
Soyoung Ha and Zhiquan Liu

The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal and spatial resolution every day for the last decade, providing unprecedented information on air pollutants over the upstream region of the Korean peninsula. In this study, the GOCI Aerosol optical depth (AOD), retrieved at 550 nm wavelength, is assimilated to ameliorate the analysis quality, thereby making systematic improvements on air quality forecasting in South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics. The preprocessed data are then assimilated in the three-dimensional variational data assimilation (3DVAR) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Over the Korea-United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of air quality forecasting is examined by comparing with other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and fine particulate matter (PM2.5) observations at the surface. Consistent with previous studies, the assimilation of surface PM2.5 concentrations alone systematically underestimates surface PM2.5 and its positive impact lasts mainly for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecasts are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea. The talk will be finished with an introduction of our ongoing efforts on developing the assimilation capability for more sophisticated aerosol schemes such as Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) and the Modal Aerosol Dynamics Model for Europe (MADE)-Volatility basis set (VBS).

How to cite: Ha, S. and Liu, Z.: Improving air quality forecasting with the assimilation of GOCI AOD retrievals, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2804, https://doi.org/10.5194/egusphere-egu2020-2804, 2020

D3262 |
Sachin D Ghude, Chinmay Jena, Rajesh Kumar, Sreayshi Debnath, Vijay Soni, Ravi S Nanjundiah, and Madhavan Rajeevan

Managing air quality levels in the National Capital Region (NCR), especially Delhi, India has emerged as a complicated task. It is now a matter of top priority to develop meaningful policy options. Short-term air quality forecasts can provide timely information about forthcoming air pollution episodes that the decision-makers can use to implement temporary emission control measures and reduce public exposure to extreme air pollution events. Although India has developed air quality forecasting systems for NCR, it was challenging to predict acute air pollution episodes during which hourly PM2.5 concentrations exceed 300 µg/m3. In this perspective, a very high-resolution (400 m) operational air quality prediction system has been developed to predict extreme air pollution events in Delhi and issue timely warnings. This modeling framework consists of a high-resolution fully coupled state-of-the-science Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and three-dimensional Variational (3DVAR) framework of the community Gridpoint Statistical Interpolation (GSI) system. The system assimilates satellite aerosol optical depth (AOD) retrievals at 10 km resolution, real-time crop residue burring at 1km resolution, surface PM2.5 data from 43 air quality monitoring stations, and uses high-resolution dynamical emissions (400 m) from various anthropogenic sources. The chemical data assimilation is further integrated with dynamical downscaling to obtain improved chemical conditions for the 400 m resolution domain. This paper summarizes the performance of the model forecasts for the winter season 2019-2020 and the evaluation of the model against the observations. Here, we demonstrate that the assimilation of chemical data in a coupled weather-air quality model improved the overall accuracy of PM2.5  forecasts in New Delhi by about 70 % during the winter season 2019-2020. Results show that the skill score for the poor (AQI 200-300), very-poor (AQI 300-400) and sever pollution (AQI 400-500) days is relatively promising for the hit rate with a value of 0.74 for (very-poor). This indicates that the model has reasonable predictive accuracy for air quality events. False Alarm rate (0.19), missing rate (0.32) are low, and the probability of detection is relatively high (0.67), indicating that the performance of the real-time forecast is better for both very poor events and no-very poor events.

How to cite: Ghude, S. D., Jena, C., Kumar, R., Debnath, S., Soni, V., Nanjundiah, R. S., and Rajeevan, M.: Development of a high-resolution (400 m) operational air quality early warning system for Delhi, India through integrated chemical data assimilation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12876, https://doi.org/10.5194/egusphere-egu2020-12876, 2020

D3263 |
Juan Pedro Montavez, Antonio Juarez-Martinez, Alejandro García-López, Amar Halifa-Marin, Enrique Pravia-Sarabia, and Pedro Jimenez-Guerrero

Air pollution forecasting can be used to alert about dangerous health effects caused by airborne pollutants and, in consequence, to take  actions to reduce pollutant concentrations (i.e reducing traffic, control industrial activities, etc..). Therefore, the development of reliable  air quality forecast systems is a of great interest.

The system consist of two main branchs. A statistical method based on  Neural Networks is used to forecast (10 days) several dayily air quality
index at the sites were historical data is available (i.e. pollution  measurement stations). A dynamical method based on WRF-CHEM to forecast hourly (48h) values of a large variety of species in a high resolution  domain (2km). Both subsystems use GFS and ECMWF forecasts as driving  conditions. The  dynamical subsystem incorporates 4DVAR data assimilation  of meteorological data (first 12 hours of forecast), and dynamical  emissions. The dynamical  emissions consist in changing the emissions of  large factories and trafficc. The emissions data are obtained by machine  learning methods based on historical series and meteorological conditions (mainly big energy factories). The WRF-CHEM configuration consist of several domains one way nested. The mother domain covers the entire Saharian desert in order to incorporante the dust transport contribution to particulate matter concentration. In addition, the base emission data is continuously updated.    The system also incorporates a module for automatic verification by comparing forecast with observed data, and analysis runs (in order to minimize meteorological forecast uncertainty). This verification process permit us to construct a MOS (Model Output statistics) in order to correct
possible model bias.

How to cite: Montavez, J. P., Juarez-Martinez, A., García-López, A., Halifa-Marin, A., Pravia-Sarabia, E., and Jimenez-Guerrero, P.: A full forecast system of air quality for the South East of the Iberian Peninsula., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19221, https://doi.org/10.5194/egusphere-egu2020-19221, 2020

D3264 |
Guangqiang Zhou

Air pollution is severely focused due to its distinct effect on climate change and adverse effect on human health, ecological system, etc. Eastern China is one of the most polluted areas in the world and many actions were taken to reduce air pollution. Numerical forecast of air quality was proved to be one of the effective ways to help to deal with air pollution. This abstract will present the advance, uncertainty and thinking about the future of the numerical air quality forecast emphasized in eastern China region. Brief history of numerical air quality modeling in Shanghai Meteorological Serveice (SMS) will be reviewed. The operational regional atmospheric environmental modeling system for eastern China (RAEMS) and its performance on forecasting the major air pollutants over eastern China region will be introduced. And uncertainty will be analyzed meanwhile challenges and actions to be done in the future are to be suggested for better service of numerical air quality forecast.

How to cite: Zhou, G.: Numerical Air Quality Forecast over East China: Advance, Uncertainty and Future, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1711, https://doi.org/10.5194/egusphere-egu2020-1711, 2019

D3265 |
Ariane Frassoni

The biomass burning season in South America is mainly concentrated between July and October, period characterized by dry conditions associated with the decay phase of the South American monsoon system. The dry season in South America starts at the end of March and beginning of April when the maximum convection starts its shift to northward South America. The climatological dryness condition over central South America during July to October increases the occurrence of vegetation fires. The number of active fires detected by the AQUA satellite from 1998 to November 2019 in South America indicate fires abruptly increase from July to August, reaching a peak in September. Fires convert vegetation used as fuel into a series of combustion products that can remain in burned places or can be transported to other places by the atmospheric circulation. The 2019 dry season in South America was characterized by an abnormal high occurrence of intense and persistent fire episodes that injected tons of aerosols into the atmosphere. The present study aims to perform a comparative assessment of the four last South American biomass burning seasons. To compare the 2019 biomass burning season with 2016, 2017 and 2018 season, in this paper it is presented the fire active data compiled by the National Institute for Space Research (INPE) for the periods of analysis, the climatological aspects associated with each season and finally the validation of the operational integrated meteorology/air quality forecasting system Brazilian developments on the Regional Atmospheric Modeling System of the Center for Weather Forecasting and Climate Studies (CPTEC/INPE), for the considered periods.

How to cite: Frassoni, A.: The 2019 biomass burning season in South America: climate diagnostics, fire monitoring and air quality forecasting, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20086, https://doi.org/10.5194/egusphere-egu2020-20086, 2020