Asian dust is a seasonal meteorological phenomenon which affects east Asia, particularly during spring. Dust is transported from the deserts of Taklimakan, Mongolia, northern China and Kazakhstan and can travel over long distances, affecting the air quality of China, North and South Korea and Japan. Occasionally, the dust can even be transported over the ocean over thousands of kilometers and reach the coasts of North America. Measurements at Mauna Loa, Hawaii, show the presence of dust of Asian origin .

Dust is becoming a more serious concern due to the increase of industrial pollutants contained in it, also in connection to intensified desertification in China causing longer and more frequent occurrences, both linked to climate factors and to land use change in west Asia . Areas affected by dust experience poor air quality and, as a consequence, dust is known to cause a variety of health problems, including sore throats and asthma in otherwise healthy people. Often, people are advised to avoid or minimize outdoor activities, depending on the severity of the storms. For those who already have asthma or respiratory infections, dust-related pollution can be fatal . The economic impact of dust storms connected to reduced air visibility, canceled flights and disrupted ground travel can also be high .

Because of its health, social and economical impacts, it is critical to understand the source strength, transport and deposition of dust and to establish sand and dust storms (SDS) forecasting and early warning capabilities. Under the auspices of the World Meteorological Organization (WMO), the need for a global SDS forecasting and early warning system was highlighted in 2005. More than 40 member countries expressed interest in participating in activities to improve capacities for more reliable SDS monitoring, forecasting and assessment. The SDS-WAS (Warning and Assessment System) was launched in 2007 with the mission to enhance the ability of countries to deliver timely and quality sand and dust storm forecasts, observations and information to users through an international partnership of research and operational communities. Towards this goal, three regional centers have been created: the Northern Africa–Middle East–Europe (NAMEE) node, the Pan-American node and the Asian node. The latter is hosted by the Chinese Meteorological Administration (CMA) and its focus is Asian dust prediction . Member countries of the Asian node include Japan, South Korea, Mongolia and Kazakhstan. All centers run their forecast models for the specific region . Some models such as that of the Japan Meteorological Agency run at the global scale . The European Centre for Medium-Range Weather Forecasts (ECMWF) also contributes its dust forecasts to the Asian node through a partnership between the Copernicus Atmosphere Monitoring Service (CAMS) and CMA

See http://eng.nmc.cn/sds_was.asian_rc/ (last access: 21 January 2019) for further reference.

Despite the recognized relevance, Asian dust monitoring and forecasting are still a challenge due to the poor characterization of the sources, which have strong spatial and temporal variability, and the paucity of observational data available to characterize both emissions and transport, especially in near-real time (NRT). A study by compared eight state-of-the-art dust models and showed that emission fluxes from the Taklimakan desert and Mongolia strongly differ among the models. They suggested that measurements of dust flux and accurate updated land use information are important to improve the models over these regions. They also found that modeling of dust transport and removal processes over key regions needs to be improved in order to obtain more skillful dust prediction that could underpin warning systems, improve preparedness and possibly lessen the impacts.

Although modeling problems need to be tackled at the level of the parameterization of the physical, dynamical and radiative processes, data assimilation of dust-related observations such as optical depth and lidar backscatter from satellite sensors can help alleviate problems and improve the forecast of dust events. This has been shown for example in a pioneering work by , in which the authors show that assimilation of data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar in an ensemble Kalman filter framework improves the model simulation of Aeolian dust events. Assimilation efforts to specifically improve dust prediction have been undertaken at the global and regional scale by several institutes, both using ensemble methods in which emissions are also perturbed and variational methods . All studies show the positive impact of satellite and in situ data assimilation in desert-dust-affected regions for high load episodes, with some impact lasting into the forecast range.

Progress has been made in recent years thanks to the deployment of accurate, multi-channel aerosol sensors on satellite platforms and the improvement of ground-based networks which are now offering stable observations of good quality over long periods and increasingly in NRT. Given these opportunities in this paper, we will investigate the added benefit of data assimilation for the desert dust analysis and short-range prediction using ECMWF's Integrated Forecasting System (IFS) and aerosol optical depth (AOD) observations from Moderate Resolution Imaging Spectroradiometer (MODIS), which are now also available on bright surfaces (Deep Blue collection) in the framework of the 4D-Var assimilation system extended to aerosol species . The novelty with respect to previous studies that also employed MODIS observations (i.e., ) rests in the use of a fully operational 4D-Var system in which the model dynamics serve as a constraint during the assimilation. For dust storms of synoptic extent, this constraint might imply a longer lasting impact of the assimilated data into the forecast range. The verification of the analysis and forecast benefits from the availability of data from the Aerosol Robotic Network (AERONET; ), as well from the China Aerosol Remote Sensing Network (CARSNET; ). Additionally, PM data from the China Environmental Protection Agency (CEPA) are also used to assess the value of AOD assimilation in surface PM forecasts.

Desert dust forecasts are provided by the ECMWF CAMS system, which is a comprehensive modeling and assimilation system based on the IFS developed at ECMWF with the extended capabilities of including atmospheric composition tracers in the transport model and in the analysis . The system has been developed in the CAMS precursor projects, Global and Regional Earth System Monitoring Using Satellite and In Situ Data (GEMS; ) and Monitoring Atmospheric Composition and Climate (MACC; ). Aerosols are forecast within the ECMWF CAMS global system by an aerosol model , based on earlier work by and that uses five species: dust, sea salt, black carbon, organic carbon and sulfates. In the version of the model used in this study, nitrates were not included. More recent versions of the model do include this important aerosol component.

Dust aerosols specifically are represented by three prognostic variables that correspond to three size bins, with bin limits of 0.03, 0.55, 0.9 and 20 µm radius. Specific dust processes that are parameterized are the production of dust through saltation and removal by wet and dry deposition and sedimentation. Various areas are marked as having the potential to emit dust based on surface albedo, moisture of the top soil level and bare soil fraction. Dust emissions are parameterized following as a function of the cubic power of 10 m wind speed. A correction is also applied to account for gustiness as described in . Dry deposition depends on a prescribed deposition velocity and on the aerosol concentration in the model level closer to the surface.

For sea salt, a bin representation is used and emissions are also parameterized as a function of surface winds. For all other tropospheric aerosols (carbonaceous aerosols and sulfates), a bulk parameterization is used and emission sources are defined according to established inventories . Biomass burning emissions contributing to black carbon and organic matter loads are prescribed from the Global Fire Assimilation System (GFAS; ). Removal processes include sedimentation for dust and sea salt only, and wet and dry deposition and in-cloud and below-cloud scavenging for all particles. A simplified representation of the sulfur cycle is also included with only two variables, sulfur dioxide (SO2) acting as a precursor for sulfate (SO4) in particulate form. Overall, a total of 12 additional prognostic variables for the mass mixing ratio of the different components (bins or types) of the various aerosols are used in this configuration. The ECMWF CAMS system also includes chemically reactive tracers such as ozone, carbon monoxide, NOx and formaldehyde, as well as greenhouse gases.

The ECMWF CAMS system is used operationally to provide global forecasts of aerosols, reactive gases and greenhouse gases, as well as boundary conditions for higher resolution regional models proving air quality forecasts over Europe. Data are freely available from the CAMS website (https://atmosphere.copernicus.eu/; last access: 21 January 2019).

The ECMWF CAMS system also runs a 4D-Var assimilation of satellite- and ground-based data for the meteorological variables as well as the atmospheric composition variables. The aerosol assimilation uses total aerosol mixing ratio as a control variable. Each aerosol species is assumed to contribute to the total aerosol mixing ratio according to a fractional contribution which is assumed constant over the 12 h assimilation window. At the end of the minimization, the increments in total aerosol mixing ratio are distributed to the individual species according to their fractional contribution to the total mass. A forward model is used to calculate the aerosol optical depth (AOD) from the individual components using a lookup table approach. The aerosol optical properties are prescribed according to . During the minimization, the tabulated optical properties of the individual species are retrieved and used in the calculation of the total extinction. The vertical integral of the extinction profile provided the total AOD, which is the observed quantity, at every observation location. The vertical profile of the aerosol mixing ratio is not modified by the assimilation as only AOD is used as observation. Thus, the vertical profile is largely dictated by the model and the distribution of the increments in the vertical is due to the vertical structure function in the background error.

AOD data at 550 nm from the MODIS sensors on board the Terra and Aqua satellites have been assimilated in the ECMWF 4D-Var system since 2008 . Initially, only the Dark Target product was used . Since 2015, both MODIS Dark Target and Deep Blue data have been used. Deep Blue offers the advantage of providing aerosol optical depth (AOD) retrievals over bright surfaces, which is of particular interest for getting insight on dust sources by means of data assimilation. It can be assumed that over deserts the biggest contribution to AOD comes from dust aerosols, even if the analysis uses total AOD and the other aerosol species are also included. Recently, MODIS Collection (Col.) 6 has become available . This is the collection that has been used in this study.

The operational ECMWF CAMS system also uses AOD data from the MetOp Polar Multi-sensor Aerosol product (PMAp), operationally provided by EUMETSAT after February 2014, and hence not available for the experiments outlined in this paper. Assimilation of this product for the purpose of the application outlined here would need an independent assessment.

To investigate the impact of the assimilation on the dust prediction, three experiments were set up for the whole of 2013: one experiment with 4D-Var data assimilation of MODIS Col. 6 with the exclusion of Deep Blue data (indicated hereafter as DT), one experiment with 4D-Var assimilation of MODIS Col. 6 data with the inclusion of Deep Blue data (indicated as DTDB) and one control experiment with free-running aerosols (indicated as CONTROL). In the control experiment, no aerosol data are assimilated in the analysis. In this paper, we focus on results during the spring season (March–April–May 2013), when several marked events occurred.

The horizontal resolution of the operational version run in the context of CAMS is currently 40 km with 60 vertical levels and model top at 0.1 hPa. The experiments shown here were run with a previous version of the system, comparable with the CAMS configuration for 2013, with a horizontal resolution of 80 km and the same number of vertical levels.

The operational ECMWF CAMS system was used to demonstrate the impact of assimilating satellite AOD data for an improved characterization of Asian dust storms. Three experiments were conducted: one control experiment with no aerosol data assimilated, one experiment with only Collection 6 MODIS Dark Target AOD retrievals, which do not offer coverage over bright surfaces, and one experiment with all available MODIS data including Deep Blue AOD retrievals.

Several concluding points can be learned from these experiments. Results confirm that assimilation of advanced satellite data is effective for improving the representation of desert dust distributions over Asia and for monitoring and forecasting dust episodes (in the short range). In particular, MODIS Deep Blue data are beneficial both in source regions (i.e., Tazhong) and downwind of desert areas (i.e., Xilinhot and Shangri-La). The fractional bias and the gross error with respect to independent AOD observations from the AERONET and CARSNET ground-based networks are lower in the assimilation experiments than in the control forecast with no assimilated aerosol data. Time series of FGE averaged over central–northern China reveal that in some extreme cases the FGE is reduced by as much as 60 %–80 % in the experiment which includes all MODIS data (DTDB) with respect to the experiment which has no aerosol data (CONTROL). The impact of the assimilated data is mainly felt at the analysis time but remains visible in the 48 h forecast.

Comparisons with PM10 data from the CEPA network for March 2013 in the Beijing area show that the model has skill in predicting the passage of a dust front. However there is a mismatch in PM10 values with large biases (up to 2 orders of magnitude in some cases) displayed by the model, even for the assimilation experiments. The assimilation of satellite AOD in this case can only make small adjustments to PM10 but is unable to improve the quality of forecast fundamentally. Despite this, the passage of the storm is captured, indicating that the global model is somewhat able to capture the regional pollution flow, even at coarse resolution. This indicates that the global model analyses may be used as boundary conditions for regional air quality models at higher resolution, enhancing their performance in situations when part of the pollution may have originated via synoptic flow mechanisms. However, the skill of the global model for PM10 is poor. This is likely due to model biases, coarse resolution, unresolved topography, a lack of resolved local emissions and a lack of observations to constrain the aerosol speciation and vertical structure. It is worth mentioning that AOD is less affected by these factors as it is a column-integrated variable. Also, spatial patterns of AOD do not always match with those of surface PM due to variability in aerosol hygroscopicity and relative humidity which can affect AOD greatly but leave PM10 completely unchanged.

These results highlight the crucial importance of the verifying datasets to assess model skill and assimilation impact. CARSNET data offer unprecedented coverage over remote Asian dust regions which are otherwise poorly observed. From the regional distribution maps of Fig.  it is possible to see that CARSNET and AERONET stations over Asia are largely complementary. Data availability in these regions both for assimilation and model evaluation and development is key to progress in further addressing the challenges of Asian dust prediction.

As part of CAMS, ECMWF is providing twice-daily forecasts of atmospheric composition (including desert dust) up to 5 days ahead, which are publicly available. These forecasts rely on assimilation of MODIS Collection 6.1 AOD (both Dark Target and Deep Blue data) as well as PMAp data. They have been shown to provide useful information and reliable boundary conditions for local air quality applications in Europe, China and east Asia, provided they are used in conjunction with high-resolution regional air quality models.