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Coupled modelling and data assimilation of dynamics and chemistry of the atmosphere

The Session is dedicated to the memory of Prof. S. Zilitinkevich (13.04.1936 – 15.02.2021)

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.

Public information:
The Session is dedicated to the memory of Prof. S. Zilitinkevich (13.04.1936 – 15.02.2021)

Co-sponsored by WMO and CAMS
Convener: Alexander Baklanov | Co-conveners: Johannes Flemming, Georg Grell, Lu RenECSECS
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Fri, 30 Apr, 15:30–17:00

Chairpersons: Alexander Baklanov, Johannes Flemming, Georg Grell

5-minute convener introduction

Gregory R. Carmichael

Significant societal benefits come from services derived from environmental predication. Meeting the growing societal needs requires improved prediction skill at higher resolution and longer lead time.

To  meet societal needs the Seamless predictions of air quality, weather and climate are needed  to address many of the societal problems related to environmental hazards. Improving prediction capabilities via seamless coupling atmospheric composition modelling with weather and climate components via earth systems approaches is a key strategy of GAW to improve prediction capabilities and services. In this paper we describe efforts underway to advance atmospheric composition modelling in earth system models within the context of the recent WMO reforms.

How to cite: Carmichael, G. R.: The important role of atmospheric composition in advancing Earth System Predictions – a WMO Global Atmospheric Watch Perspective, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16496, https://doi.org/10.5194/egusphere-egu21-16496, 2021.

Ravan Ahmadov et al.

Since December, 2020 NOAA’s operational Rapid Refresh and High-Resolution Rapid Refresh (RAP/HRRR) numerical weather prediction modeling systems include smoke forecasting capability. In RAP/HRRR-Smoke primary aerosols (smoke) emissions from wildland fires are simulated by ingesting the fire radiative power data from the VIIRS (onboard S-NPP and NOAA-20) and MODIS (Terra and Aqua) satellite instruments in real time. I will describe the development and applications of the RAP and HRRR-Smoke models, which cover 3 domains – North America (at 13.5 km spatial gridding), CONUS and Alaska (3km resolution). I will present the applications of these models to forecast smoke distributions on regional and continental scales, and how adding the smoke direct feedback capability can improve weather and visibility forecasting. The RAP/HRRR-Smoke models are the first operational weather models in the US, which include the impact of the smoke aerosols on weather and visibility forecasting. The verification of the HRRR-Smoke model for July-August 2018 over the CONUS domain using various meteorological and aerosol measurements will be presented. For verification of the fire plume injection height simulations in HRRR-Smoke, we use the aircraft lidar and in-situ measurements from the FIREX-AQ campaign during August 6-8, 2019. Finally, I will discuss the future plans for improving forecasting of smoke-weather interactions in coupled air quality models.

How to cite: Ahmadov, R., James, E., Grell, G., Alexander, C., and McKeen, S.: Operational implementation of the smoke forecasting capability in the RAP/HRRR numerical weather prediction system, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14268, https://doi.org/10.5194/egusphere-egu21-14268, 2021.

Guy Brasseur and Rajesh Kumar

MAP-AQ (Modeling, Analysis and Predictions of Air Quality) is an international network that contributes to the development and implementation of global and regional air pollution monitoring, analysis, prediction and attribution systems with downscaling capability in areas of the world particularly affected by high levels of atmospheric pollutants, in particular in low and middle-income countries. The project supports the development of the science and software engineering needed to improve air quality forecasts from the global to the regional and local scales, and to develop reliable attribution systems for air pollution sources. Capacity development is another focus of the project sponsored by WMO/GAW and by IGAC. The paper will present a number of activities currently supported by MAP-AQ and will outline a strategy for future initiatives and cooperations.

How to cite: Brasseur, G. and Kumar, R.: MAP-AQ: An international network for air quality forecasts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8072, https://doi.org/10.5194/egusphere-egu21-8072, 2021.

Yang Zhang et al.


Online-coupled meteorology-chemistry models provide powerful tools for more realistically simulation of current and future air quality with feedbacks between atmospheric composition and meteorology that cannot be considered in offline-coupled models. In this work, several state-of-science online-coupled models are applied to generate the best possible predictions of surface ozone (O3) and fine particulate matter (PM2.5) concentrations under current emission and climate conditions. Two ensemble methods are used to further reduce the model biases and errors including a simple ensemble mean based on an average of ensemble members, and a weighted ensemble mean based on the multi-linear regression. The skills of individual models and their ensembles are evaluated using available surface network data.  Compared to individual models and the simple ensemble mean, the weighted ensemble predictions based on the multi-linear regression perform the best overall for both O3 and PM2.5. The model with best performance is selected to apply for future years to project the changes in air quality under various energy transition scenarios to support the development of emission control strategies. These results illustrate the current capability of the online-coupled models and the potential of weighted ensemble in generating the best possible estimates of air pollutant concentrations under current and future atmospheric conditions. 

How to cite: Zhang, Y., Wang, K., and Schuch, D.: Projecting Future Air Quality Under Energy Transition Scenarios over the U.S. using Online-Coupled Models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16488, https://doi.org/10.5194/egusphere-egu21-16488, 2021.

Peter Huszar et al.

Urban canopies impact the meteorological conditions in the planetary boundary layer (PBL) and above in many ways: apart from urban heat island effect, the urban breeze circulation can form. Further, the enhanced drag causes intensification of the turbulent diffusion leading to elevated PBL height and this drag, at the same time causes lower windspeeds. These changes together act as a 'meteorological forcing' for the chemical processes involing transport, diffusion and chemical transformation of urban pollutants in the urban canopy and over larger scales, therefor we use the term urban canopy meteorological forcing (UCMF). Using regional scale coupled chemistry-climate models over central Europe (involving models RegCM, CAMx and WRF-Chem),  we investigate here how the UCMF influences the urban emissions and their dispersion into regional scales. The analysis covers key pollutants as O3, NO2 and PM2.5 and the 2015-2016 period.

While urban emissions contribute by about 60-80% to the total NO2 and PM2.5 concentrations in cities, for ozone, they cause decrease in the urban cores and slight increase over sourrounding rural areas. More importantly, we found that if UCMF is considered, the impacts on all three pollutants are reduced, by about 20-30%. This is caused by the fact that vertical turbulence is greatly enhanced in urban areas leading to reduced fingerprint of the urban emissions (the case of NO2 and PM2.5) while in case of O3, reduction of the NO2 impact means smaller first-order titraltion therefor higher ozone concentrations - i.e. the ozone fingerprint of urban emissions is smaller. Our analysis showed that for evaluating the impact of urban emissions over regional scales, the meterological effects caused by the urban canopy have to be considered in modeling studies.

How to cite: Huszar, P., Karlicky, J., Markova, J., Novakova, T., Liaskoni, M., Bartik, L., and Belda, M.: Impact of urban emission on local and regional air-quality: investigating the role of the urban canopy meteorological forcing, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1992, https://doi.org/10.5194/egusphere-egu21-1992, 2021.

Vincent Huijnen et al.

The Integrated Forecasting System (IFS) of ECMWF is the core of the Copernicus Atmosphere Monitoring Service (CAMS) which provides global analyses and forecasts of atmospheric composition, namely reactive gases, aerosol and greenhouse gases. With respect to the atmospheric chemistry component, the operational system currently relies on a modified version of the CB05 chemistry scheme for the troposphere, combined with the Cariolle scheme to describe stratospheric ozone. In an alternative, more recent configuration also stratospheric ozone chemistry is included based on the BASCOE chemistry module. Alternative atmospheric chemistry modules which can be employed are based on MOZART and MOCAGE chemistry. 
Recently, further revisions to the modified CB05 tropospheric chemistry scheme have been developed, focusing both on inorganic and organic chemistry, with the aim of improving the quality of existing air-quality products, and the development of new products. On major update is a revision of the isoprene oxidation scheme based on those employed in existing chemistry transport models, as well as inclusion of the basic chemistry describing C8 and C9 aromatics degradation. 
An example of a new product derived from these updates include a description of global distribution of glyoxal, while this also resulted in an improved modeling of OH recycling particularly over tropical forests. Also we support improved secondary organic aerosol formation due to gaseous anthropogenic, biogenic and biomass burning sources.
In this contribution we provide an overview of these revisions, and provide a first quantification of their uncertainties, by comparing products to observations and to those from alternative chemistry modules.

How to cite: Huijnen, V., Williams, J., Bouarar, I., Belamari, S., Chabrillat, S., Remy, S., and Flemming, J.: Recent updates to the atmospheric chemistry modeling of the ECMWF IFS in support to CAMS, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2481, https://doi.org/10.5194/egusphere-egu21-2481, 2021.

Rajesh Kumar et al.

We present a research system for regional air quality forecasting over  the contiguous United States (CONUS). This system has been developed at the National Center for Atmospheric Research (NCAR) to support community model development, allow early identification of model errors and biases, and support the atmospheric science community in their research. At the same time, it assists field campaign planning and air quality decision-making. The forecasts aim to complement the operational air quality forecasts produced by the National Oceanic and Atmospheric Administration (NOAA) and not to replace them. A publicly available information dissemination system has been established that displays various air quality products including a near-real-time evaluation of the model forecasts. Our forecasting system has been producing a 48-h forecast every day at 12 km x 12 km grid spacing over the entire CONUS since June 2019 and at 4 km x 4 km grid spacing in Colorado since June 2020. Here, we will report on the performance of our air quality forecasting system in simulating meteorology, PM2.5, ozone, and NOx for the period of 1 June 2019 to 31 December 2020. Our system showed excellent skill in capturing hourly to daily variations in temperature, surface pressure, relative humidity, water vapor mixing ratios, and wind direction but showed, in parts, relatively larger errors in wind speed. The model captured the seasonal cycle of surface PM2.5 and ozone very well in different regions of CONUS and at different types of sites (urban, suburban, and rural) but generally overestimates summertime surface ozone and fails to capture very high surface PM2.5 events. These shortcomings are being addressed in current work. The skill of the air quality forecasts remains fairly stable between the first and second days of the forecasts. Our air quality forecast products are publicly available at https://www.acom.ucar.edu/firex-aq/forecast.shtml and we invite the community to use our forecasting products for their research, as input for urban scale (< 4 km) air quality forecasts, or the co-development of customized products just to name a few applications.

How to cite: Kumar, R., Pfister, G., and Bhardwaj, P.: A quasi-operational air quality forecasting system for the contiguous United States (CONUS) , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3221, https://doi.org/10.5194/egusphere-egu21-3221, 2021.

Jason Williams et al.

CO is an abundant tropospheric pollutant that originates from numerous emission sources. Large CO fluxes are emitted during intense Biomass Burning (BB) events over relatively short periods of a few days, which combined with the tropospheric lifetime of a month, act as a marker for air-masses influenced by burning events. Once lifted out the boundary layer, air masses influenced by BB undergo chemical processing which can be assessed by subsequent changes in tropospheric ozone. Increases in ozone and aerosol in the Free Troposphere influence photolysis at lower levels impact surface air-quality. Therefore, capturing this feedback is a necessary step towards determining tropospheric lifetimes of greenhouse gases and pollutants, which affects the fraction of transport out of the burning regions.

Here we present results from the Integrated Forecasting System (IFS) of ECMWF, which is the core of the Copernicus Atmosphere Monitoring Service (CAMS). We perform simulations with three independent chemistry modules (modified CB05, MOZART, MOCAGE), including variable photolysis schemes and variable approaches for coupling tropospheric aerosol. We choose the simulation year of 2019 corresponding with the FIREXAQ measurement campaign which occurred over California. We subsequently assess the ability of IFS in terms of (i) the representation of the transport of air masses effected by large BB emissions, (ii) the ability towards capturing chemical processing which occurs in such plumes and (iii) using large discrepancies in the simulated tropospheric profiles to imply deficiencies in BB emission estimates.


How to cite: Williams, J., Huijnen, V., Bouarar, I., Belamari, S., Remy, S., and Flemming, J.: Assessing pyro-convective uplift and chemical processing of Biomass Burning plumes in the ECMWF IFS, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4067, https://doi.org/10.5194/egusphere-egu21-4067, 2021.

Farahnaz Khosrawi et al.

The strong coupling between atmospheric circulation, moisture pathways and atmospheric diabatic heating is responsible for most climate feedback mechanisms and controls the evolution of severe weather events. However, diabatic heating rates obtained from current meteorological reanalyses show significant inconsistencies. Water isotopologue observations (e.g. H2O and HDO) assimilated into meteorological reanalyses can make an invaluable contribution since the isotopologue composition depends on the history of phase transition. Therefore, isotopologue observations can provide information that is closely linked to latent heating processes. Using the retrieval recipe of MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water), the free tropospheric water vapour isotopologue composition can be retrieved from IASI spectra measured for cloud free conditions.

Here, we theoretically assess with an Observation Simulation Experiment (OSSE) the potential of the MUSICA IASI isotopologue data for constraining uncertainties in analyses fields. For this purpose, we use the isotopes-incorporated General Spectral Model (IsoGSM) and mock MUSICA IASI isotopologue observations. We use the Local Transform Ensemble Kalman Filter (LETKF) data assimilation method and perform two different experiments. In a first experiment we assimilate temperature, humidity and wind profiles obtained from radiosonde and satellite data. In a second experiment we assimilate additionally the mocked IASI isotopologue data. When mocked isotopologue data are additionally assimilated, we find reduced ensemble spreads with respect to meteorological variables and rain rates. This indicates that IASI isotopologue observations can indeed reduce the uncertainties of latent heating rates and meteorological analysis fields and in consequence offer potential for improving weather forecasts.

How to cite: Khosrawi, F., Toride, K., Yoshimura, K., Diekmann, C., Ertl, B., Hase, F., and Schneider, M.: Can the assimilation of IASI water isotopologue observations improve the quality of meteorological analyses fields?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7369, https://doi.org/10.5194/egusphere-egu21-7369, 2021.

Dina Gubanova et al.

The data of intensive complex experiment carried out by A.M. Obukhov Institute of Atmospheric Physics RAS to study the atmospheric composition in Moscow gave some new results on seasonal and daily variations in the elemental composition of surface aerosol in Moscow. The elemental composition of daily aerosol samples includes 65 chemical elements from Li to U, measured by ICPM spectrometer (during about 40 days in each of four seasons from summer 2019 to spring 2020). The enrichment factors (EFs) of element concentrations in relation to earth’s crust allowed us to distinguish terrigenous (Mn, Mg, Zn, Fe, Al, etc.) and anthropogenic (for example, Cd, Sb, Pb) elements.

The correlations between temporal variations in element concentrations and EFs helped us to divide all elements into 4 groups: elements of global distribution, heavy metals and metalloids of predominantly terrigenous or anthropogenic origin, radioactive elements. Heavy metals and sulfur, the main sources of which in Moscow are engines, are the elements of anthropogenic/local origin. In winter and summer seasons, the EFs of the most of these elements reach their highest values, which indicates an increase of anthropogenic emissions (heating and energetics, road transport) in cold season and soil/dust contributions in summer. Elements of terrigenous/global origin have small seasonal variations in EFs. In winter, coefficients of aerosol accumulation for a number of anthropogenic elements are high because of the low values of their deposition rates onto the cold or also snow-covered surface.

The spatial distribution of anthropogenic/local origin elements in surface aerosol in Moscow is not uniform, which is associated with the specificity of the sources, the features of the underlying surfaces and the wind regimes in different regions of the metropolis. The maximal ​​element concentrations are in the central region of the city, in densely built-up areas and near highways with high traffic loads.

Meteorological and synoptic conditions have a strong influence on the composition of the surface aerosol and its variability in Moscow. In the spring of 2020, weekly cycle of element concentrations corresponded to weather parameters. Under anticyclonic conditions, aerosol particles accumulate in the surface layer of the atmosphere. With pressure drop and humidity increase, cleaning of the atmosphere from aerosol particles occurs by washout with precipitation or by coagulation and deposition onto the surface.

We tried to identify in Moscow aerosol element composition any specific features due to the restrictive measures to prevent the spread of coronavirus infection from 26 March 2020. On the one hand, during the lockdown, there is a decrease in anthropogenic (especially transport) emissions to the city atmosphere. On the other hand, a number of chemical elements should be added into the environment during the disinfection of soils and streets. So far, insufficient data does not allow us to make any determined conclusions. To detect defined changes in aerosol composition, it is necessary to compare with measurement data in other seasons (to take into account intra-annual variations) and in other years (without restrictive and disinfection measures).

The work has financial support from RFBR, projects 19-05-00352 and 19-05-50088.

How to cite: Gubanova, D., Skorokhod, A., Iordanskii, M., Vinogradova, A., Elansky, N., and Minashkin, V.: Variability of atmospheric aerosol element composition in Moscow in 2019 and 2020, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7771, https://doi.org/10.5194/egusphere-egu21-7771, 2021.

Athanasios Tsikerdekis et al.

A top-down approach for aerosol emission estimation from polarimetric retrievals of aerosol amount, size, and absorption is employed . The method uses a fixed-lag ensemble Kalman smoother (LETKF-Smoother) under the framework of Observing System Simulation Experiments (OSSEs), in order to evaluate the observational capabilities of a satellite with near perfect global coverage as well as of the future multi-angle polarimeter instrument, SPEXone. ECHAM-HAM is used for the nature runs (NATs), the control (CTL) and the data assimilation (DAS) experiments. The ensemble is composed by 32 simulations where the default aerosol emissions for all species are perturbed with factors taken from a Gaussian distribution. Synthetic observations, specifically Aerosol Optical Depth at 550nm (AOD550), Angstrom Exponent 550nm to 865nm (AE550-865) and Single Scattering Albedo at 550nm (SSA550) are assimilated in order to estimate the aerosol emission fluxes of desert dust (DU), sea salt (SS), organic carbon (OC), black carbon (BC) and sulphates (SO4), along with the emission fluxes of two SO4 precursor gases (SO2, DMS). The synthetic observations are sampled from the NATs according to two satellite observing systems, with different spatial coverage capabilities. The first, is an idealized sensor that retrieves observations over the whole globe in 2days even under cloudy conditions, hence is named PERFECT. The second, is the sensor SPEXone, a hyperspectral multi-angle polarimeter with a narrow swath (100km), that will be a part of the NASA PACE mission. The assimilated observations sampled using the PERFECT sensor, estimate the emission of all aerosol species with a global relative Mean Absolute Error (MAE) equal or lower than 5% (except SO4). Despite its limited coverage, the SPEXone sampling bares similar results, although MAE is a bit larger for Dust and Sea Salt. Further, experiments show that doubling the measurement error on the assimilated observations, increases additionally the global relative MAE by less than 10%. In addition, the role of biased meteorology on emission estimation was quantified by using two different datasets (ERA5 and ERAi) to nudge the U and V wind components of the model. The results reveal that when the wind of NAT and DAS are nudged to different datasets the global relative MAE of SS grows by 24%, while the estimated emissions of DU, OC, BC and SO2 are negatively affected to a smaller extent (~10%). The upcoming SPEXone sensor will provide observations related to aerosol amount size and absorption, with sufficient coverage and accuracy, in order to estimate aerosol emission accurately.

How to cite: Tsikerdekis, A., Schutgens, N., Fu, G., and Hasekamp, O.: Aerosol emission estimation using SPEXone observational capabilities and Observing System Simulation Experiments (OSSEs) , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8896, https://doi.org/10.5194/egusphere-egu21-8896, 2021.

Min Huang et al.

We use intense observations during the 2016 Korea-US Air Quality Field Study (KORUS-AQ) and a WRF-Chem modeling system configured over East Asia, which includes land-atmosphere and aerosol-chemistry-meteorology interactions, to investigate the benefits of satellite (land surface, weather, and atmospheric composition) data for understanding and enhancing WRF-Chem performance at process level via evaluating and/or constraining the model’s chemical initial/boundary conditions, meteorological fields, and sectoral emissions. Applications are conducted to address the following topics: 1) impacts of transboundary transport from outside of the East Asia domain and regional transport from China to the Korean Peninsula on carbon monoxide pollution in South Korea during selected episodes; 2) impacts of anthropogenic emissions of nitrogen oxides from urban and shipping sources on aerosol fields as well as their feedbacks to meteorological conditions and terrestrial ecosystem productivity over East Asia; and 3) soil moisture controls on spatiotemporal variability of nitrous acid and reactive nitrogen via directly regulating soil emissions as well as indirectly adjusting homogeneous/heterogeneous reactions and other processes. These applications all utilize products or/and modeling tools (e.g., CAMS, WRF-Chem) related to the new WMO initiative Global Air Quality Forecasting and Information System. They are in line with carbon-cycle- and nitrogen-cycle-related “ecosystem service” specified in the “WMO Global Atmosphere Watch Implementation Plan: 2016-2023”. Their implications for future applications will be discussed in relation to the recently launched GEMS which monitors East Asia as well as anticipated missions focusing on North America such as TEMPO and TRACER-AQ.

How to cite: Huang, M., Crawford, J., and Carmichael, G.: Coupled modeling studies over East Asia during the KORUS-AQ field campaign, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9002, https://doi.org/10.5194/egusphere-egu21-9002, 2021.

Jeronimo Escribano et al.

The generation of the ensemble forecast is a key step in the design of an ensemble-based data assimilation scheme as it bears a significant impact on the assimilation outcome. The ensemble of model states is used within the assimilation algorithm to derive a flow-dependent background error covariance which is used to express prior information uncertainty. The covariance matrix of the background errors is associated to both, the quality of the prior, and the relations between the elements of the control vector. This matrix drives the spread of the observational information through the control variables determining, in part, the quality of the analyses. Only a handful of studies have focused on investigating the generation of ensembles for aerosol data assimilation which might be compromising the optimal integration of model simulations and observations when it comes to the use of ensemble-based assimilation schemes. 

This work presents a series of design methodologies and approaches to create regional dust aerosol ensembles. We include in our experiments ensembles with and without perturbed meteorological boundary and initial conditions, spatially random source strength perturbations, perturbations of the size distribution at emission, and random perturbation of a linear combination of dust emission schemes. We compute analyses of dust optical depth by assimilating satellite dust optical depth retrievals and present our results qualitatively through the inspection of the prior correlation matrices structure, and quantitatively with a comparison against independent measurements of aerosol optical depth.

This work is in the framework of the next upgrade of the operational forecast for the WMO Barcelona Dust Forecast Center (http://dust.aemet.es/) as well as of a contributing model to the WMO Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS, http://sds-was.aemet.es/ ), both services hosted by the Spanish Meteorological Agency (AEMET) and the Barcelona Supercomputing Center (BSC).

How to cite: Escribano, J., Pérez García-Pando, C., Di Tomaso, E., Jorba, O., Klose, M., Macchia, F., and Montané, G.: Ensemble generation for the assimilation of dust aerosol observations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10328, https://doi.org/10.5194/egusphere-egu21-10328, 2021.

Hannah Barnes et al.

The Grell-Freitas (GF) cumulus parameterization is an aerosol-aware, scale-aware convective parameterization. This presentation will focus one of the several developmental activities ongoing in GF: the continued development of its aerosol-aware capabilities and the impact in global forecast models.

Previous versions of GF initialized aerosols based on an assumed value of aerosol-optical depth (AOD) that was applied uniformly across the entire globe. Observations of AOD indicate that AOD varies substantially across the globe. Recently, the constant AOD value assumed in GF has been replaced by global AOD data from NASA’s MERRA2 reanalysis. Thus, the distribution of aerosols at initialization more physically reasonable and geographically appropriate. This is important since the treatment of aerosols in GF should be most sensitive in regions with either very high or very low AOD. This method is extremely efficient, but can be adapted so that other aerosol and AOD products can be used in GF. Other products that could be used for initialization include the aerosol climatology used by the Thompson Aerosol-Aware Microphysical Parameterization or predicted aerosols using NOAA’s aerosol prediction model, which is currently one ensemble in the Global Ensemble Forecast System – Aerosols (GEFS-Aerosols).   

GF includes three aerosol related cloud processes: aerosol-influenced evaporation, aerosol-influenced auto-conversion of cloud water to rain water, and aerosol wet scavenging based on memory. As in Wang (2013) the treatment of wet scavenging has been modified so that the aerosol wet scavenging efficiency is proportional to precipitation efficiency. Additionally, aerosols in GF are now allowed to slowly return to their original concentrations during precipitation-free periods. These changes are important since they allow the aerosols in GF to evolve over time in a physically realistic manner.

The impact of these changes to GF will be shown in a version of NOAA’s operational global prediction model.   

How to cite: Barnes, H., Grell, G., Freitas, S., Li, H., Henderson, J., and Sun, S.: Aerosol impacts for convective parameterizations: Recent changes to the Grell-Freitas Convective Parameterization, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13352, https://doi.org/10.5194/egusphere-egu21-13352, 2021.

Haiqin Li et al.

Online atmosphere-chemistry coupled models have been rapidly developed in recent years. In online models, the atmospheric model can impact air quality and atmospheric composition, while the aerosol feedbacks also impact the atmosphere through direct, semi-direct and indirect effects. At NOAA GSL, in collaboration with scientists from the Chemical Science Laboratory (CSL) and Air Resource Laboratory (ARL), we developed an atmospheric composition suite (based on WRF-Chem) and coupled it online with FV3GFS through the National Unified Operational Prediction Capability (NUOPC)-based NOAA Environmental Modeling System (NEMS) software. This modeling system has been operational since September 24th, 2020 as an ensemble member of the Global Ensemble Forecast System (named as GEFS-aerosols) for global aerosol predictions. When using the NUOPC coupler, there are two independent components for atmosphere and chemistry that communicate via the NUOPC coupler every time-step. Because of the interactive and strongly couple nature of chemistry and physics, it is natural to allow for some of the atmospheric composition modules to be called directly from inside the physics suite. This can be accomplished through the use of the Common Community Physics Package (CCPP). CCPP, designed to facilitate a host-model agnostic implementation of physics parameterizations, is a community development and will be used by many different organizations. All the physics parameterizations in the NOAA Unified Forecast System (UFS) Weather Model are CCPP-compliant. Here we broke up the chemistry suite used in GEFS-aerosols, and all the chemical modules were embedded into UFS Weather Model using CCPP as subroutines of physics. This newly developed model with CCPP has been running in real-time starting in the middle of November, 2020. Because of this development we were able to include the CCPP-compliant modules of sea salt, dust, and wild-fire emissions into the NWP model to provide input for the double moment Thompson microphysics parameterization. The inclusion of smoke and aerosol emission modules into the Rapid Refresh Forecast System (RRFS) with CCPP is also ongoing. We will show results from real-time experiments for medium range weather forecasting and compare results with runs that do not include aerosol impacts.

How to cite: Li, H., Grell, G., Zhang, L., Ahmadov, R., Mckeen, S., Henderson, J., Trahan, S., Barnes, H., Sun, S., Schnell, J., and Heinzeller, D.: The Inclusion of chemistry modules into the NOAA UFS Weather Model with the Common Community Physics Package (CCPP), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13401, https://doi.org/10.5194/egusphere-egu21-13401, 2021.

K. Emma Knowland et al.

NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution global forecasts for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke. The assimilation system is being expanded to include chemically reactive trace gases. We discuss current capabilities of the GEOS Constituent Data Assimilation System (CoDAS) to improve atmospheric composition modeling and possible future directions, notably incorporating new observations (TROPOMI, geostationary satellites) and machine learning techniques. We show how machine learning techniques can be used to correct for sub-grid-scale variability, which further improves model estimates at a given observation site.

How to cite: Knowland, K. E., Keller, C., Wargan, K., Weir, B., Wales, P., Ott, L., and Pawson, S.: Near real-time air quality forecasts using the NASA GEOS model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13587, https://doi.org/10.5194/egusphere-egu21-13587, 2021.

Samuel Remy et al.

The Integrated Forecasting System (IFS) of ECMWF is used within the Copernicus Atmosphere Monitoring Service (CAMS) to provide global analyses and forecasts of atmospheric composition, including aerosols as well as reactive trace gases and greenhouse gases.

The aerosol model of the IFS, IFS-AER, is a simple sectional-bulk scheme that forecasts seven species:  dust, sea-salt, black carbon, organic matter, sulfate, and  since July 2019, nitrate and ammonium.  The main developments that have been recently carried out, tested and are now contemplated for implementation in the next operational version (known as cycle 48r1) are presented here.

The dry deposition velocities are computed as a function of roughness length, particle size and surface friction velocity, while wet deposition depends mainly on the precipitation fluxes. The parameterizations of both dry and wet deposition have been upgraded with more recent schemes, which have been shown to improve the simulated deposition fluxes for several aerosol species. The impact of this upgrade on the skill scores of simulated aerosol optical depth (AOD) and surface particulate matter concentrations against a range of observations is very positive.

The simulated surface concentration of nitrate and ammonium are frequently strongly overestimated over Europe and the  United States in the current version of the IFS. Nitrate, ammonium, and their precursors nitric acid and ammonia, were evaluated against a range of ground and remote data and it was found that the recently-implemented gas-particle partitioning scheme is too efficient in producing nitrate and ammonium particles.

A series of small-scale changes, such as adjusting nitrate dry deposition velocity, direct particulate sulphate emission, and limiting nitrate/ammonium production by the concentration of mineral cations, have been implemented and shown to be effective in improving the simulated surface concentration of  nitrate and ammonium.

The representation of secondary organic aerosol (SOA) in the IFS has been overhauled with the introduction of a new SOA species, distinct from primary organic matter, with anthropogenic and biogenic components. The implementation of this new species leads to a significant improvement of the simulated surface concentration of organic carbon. An evaluation of simulated SOA concentrations at the surface against climatological values derived from observations using Positive Matrix Factorisation (PMF) techniques also shows a reasonable agreement.

How to cite: Remy, S., Kipling, Z., Huijnen, V., Flemming, J., Metzger, S., and Engelen, R.: Recent updates to the atmospheric aerosol modelling of the ECMWF IFS in support to CAMS, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14367, https://doi.org/10.5194/egusphere-egu21-14367, 2021.

Lucia Mona et al.

In December 2019, a contract between CNR and ECMWF was signed for a pilot ACTRIS/EARLINET data provision to the Copernicus Atmosphere Monitoring Service (CAMS). Such pilot contract (CAMS21b) aims to put in place a first data provision for a set of selected stations and it will demonstrate the feasibility of fully traceable and quality-controlled data provision for the whole network.

In CAMS21b, the main effort is devoted to design, test and set up the provision of quality-controlled ACTRIS/EARLINET products in Real Real Time (RRT) and/or Near Real Time (NRT) to CAMS. The activities are focused on the automatic centralized data processing and data provision, ensuring the full traceability of the products from the data acquisition level up to the final quality-controlled data level. Most of the activities are done at ARES, the EARLINET/ACTRIS data center node at CNR, for assuring the centralized, harmonized and quality-controlled processing in compliance with FAIR principles.

New modules and submodules of the ACTRIS/EARLINET Single Calculus Chain (SCC) as well as optimized algorithms for cloud screening have been designed. Additional procedures were implemented for improving the quality of the data provided in NRT, but also for the quality control of the Level 2 products which are delivered with a time delay.

The release of a new version of SCC and of QC procedure is planned for mid-February.

The data provision started in October 2020 at the test site of Potenza. A system has been set up for measurement reporting and monitoring of KPIs (Key Performance Indicators). After 3 months of measurements, the overall data provision system showed no critical points.

In January 2021, the provision started for a group of 9 stations which are seen as representative for the whole network in terms of instrumental capability, but also ensuring a good geographical coverage of the European continent.

In order to accommodate also measurements from non-continuous operation systems, a measurement schedule has been set up, compromising between the need of a large number of measurements and costs/efforts at each station. The measurement schedule has been designed through a representativeness study and foresees 6 slots of measurements per week, 3 in daytime and 3 in nighttime conditions.

The successful implementation of the pilot allows the provision of aerosol optical property profiles to the CAMS services. from a set of observational sites distributed over the different European regions. These profiles is expected to be of interest for the assimilation, near real time evaluation and re-analysis evaluation of several CAMS products, including the aerosol load over Europe for air quality issues, atmospheric composition, climate forcing and solar and UV products. This allows for having a systematic solution for looking into specific events as they develop (e.g. the dust plume that you investigated earlier this month or the Californian fires in September), supporting or contradicting model forecasts. This pilot is the first provision of aerosol profiles from a high-quality ground-based network in NRT for this kind of applications. It is expected that these efforts will be continued in the next phase of CAMS/Copernicus (2021-2027).

How to cite: Mona, L., D'Amico, G., Gagliardi, S., Amato, F., Amodeo, A., Ciamprone, S., De Rosa, B., Ripepi, E., Summa, D., Alados-Arboledas, L., Amiridis, V., Baars, H., Kompula, M., Mattis, I., Nicolae, D., Pietras, C., Stachlewska, I. S., and Peuch, V. H.: Pilot provision of EARLINET/ACTRIS lidar profiles to CAMS, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14943, https://doi.org/10.5194/egusphere-egu21-14943, 2021.

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