Numerical Simulation of Tehran Dust Storm on 2 June 2014: a Case Study of Agricultural Abandoned Lands As Emission Sources

atmosphere

Article Numerical Simulation of Tehran Dust Storm on 2 June 2014: A Case Study of Agricultural Abandoned Lands as Emission Sources

Ana Vukovic Vimic 1,*, Bojan Cvetkovic 2, Theodore M. Giannaros 3 , Reza Shahbazi 4, Saviz Sehat Kashani 5, Jose Prieto 6, Vassiliki Kotroni 3 , Konstantinos Lagouvardos 3, Goran Pejanovic 2, Slavko Petkovic 2, Slobodan Nickovic 2 , Mirjam Vujadinovic Mandic 1, Sara Basart 7, Ali Darvishi Boloorani 8,9 and Enric Terradellas 10

1 Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11080 Belgrade, Serbia; [email protected] 2 Republic Hydrometeorological Service of Serbia (RHMSS), Bulevar Oslobodjenja 8, 11000 Belgrade, Serbia; [email protected] (B.C.); [email protected] (G.P.); [email protected] (S.P.); [email protected] (S.N.) 3 National Observatory of Athens (NOA), Institute for Environmental Research and Sustainable Development, Vas. Pavlou & I. Metaxa, 15236 Penteli, Greece; [email protected] (T.M.G.); [email protected] (V.K.); [email protected] (K.L.) 4 Geological Survey of Iran (GSI), Meraj Blvd, Azadi Square, Tehran 1387835841, Iran; [email protected] 5 Atmospheric Science and Meteorological Research Center (ASMERC), Pajoohesh Blvd, Shahid Kharrazi Highway, Tehran 1493845161, Iran; [email protected] 6 Citation: Vukovic Vimic, A.; EUMETSAT, Eumetsat Allee 1, D-64295 Darmstadt, Germany; [email protected] 7 Earth Sciences Department, Barcelona Supercomputing Center-Centro Nacional de Cvetkovic, B.; Giannaros, T.M.; Supercomputación (BSC-CNS), Plaça Eusebi Güell 1-3, 08034 Barcelona, Spain; [email protected] Shahbazi, R.; Sehat Kashani, S.; Prieto, 8 Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Azin Alley. 50, J.; Kotroni, V.; Lagouvardos, K.; Vesal Str., Tehran 1417853933, Iran; [email protected] or [email protected] Pejanovic, G.; Petkovic, S.; et al. 9 Key Laboratory of Digital Land and Resources, East China University of Technology, Nanchang 330013, China Numerical Simulation of Tehran Dust 10 State Meteorological Agency (AEMET), Arquitecte Sert, 1, 08005 Barcelona, Spain; Storm on 2 June 2014: A Case Study [email protected] of Agricultural Abandoned Lands as * Correspondence: [email protected] Emission Sources. Atmosphere 2021, 12, 1054. https://doi.org/10.3390/ Abstract: On 2 June 2014, at about 13 UTC, a dust storm arrived in Tehran as a severe hazard that atmos12081054 caused injures, deaths, failures in power supply, and trafﬁc disruption. Such an extreme event is not considered as common for the Tehran area, which has raised the question of the dust storm’s Academic Editors: Zoran Mijic and origin and the need for increasing citizens’ preparedness during such events. The analysis of the Stavros Solomos observational data and numerical simulations using coupled dust-atmospheric models showed that intensive convective activity occurred over the south and southwest of Tehran, which produced cold Received: 14 July 2021 downdrafts and, consequently, high-velocity surface winds. Different dust source masks were used Accepted: 10 August 2021 Published: 17 August 2021 as an input for model hindcasts of the event (forecasts of the past event) to show the capability of the numerical models to perform high-quality forecasts in such events and to expand the knowledge on

Publisher’s Note: MDPI stays neutral the storm’s formation and progression. In addition to the proven capability of the models, if engaged with regard to jurisdictional claims in in operational use to contribute to the establishment of an early warning system for dust storms, published maps and institutional afﬁl- another conclusion appeared as a highlight of this research: abandoned agricultural areas south of iations. Tehran were responsible for over 50% of the airborne dust concentration within the dust storm that surged through Tehran. Such a dust source in the numerical simulation produced a PM10 surface dust concentration of several thousand µm/m3, which classiﬁes it as a dust source hot-spot. The produced evidence indivisibly links issues of land degradation, extreme weather, environmental Copyright: © 2021 by the authors. protection, and health and safety. Licensee MDPI, Basel, Switzerland. This article is an open access article Keywords: dust storm; dust source mask; Tehran; forecast; agriculture; early warning distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Atmosphere 2021, 12, 1054. https://doi.org/10.3390/atmos12081054 https://www.mdpi.com/journal/atmosphere Atmosphere 2021, 12, 1054 2 of 26

1. Introduction Increasing demand for food production has led to increased land consumption and the implementation of cultivation practices targeting higher gains and lower costs of land management. Unsustainable land management of agricultural land reduces the fertility of the soil and thereby its productive capacity. While soil restoration demands time and additional investment, the abandonment of cultivation and its migration to another area is a faster and more productive solution in short-term planned agricultural production. Abandoned agricultural surfaces are left as bare lands with poor topsoil structure, which are severely exposed to wind erosion during lower topsoil moisture conditions [1]. Such surfaces act as sources of dust storms in cases of high-velocity winds. Agricultural lands are considered to be anthropogenic (man-made) dust sources and can generate airborne dust hazards. Among others, dust storms have a significant impact on human health and safety [2–4]. The scale of airborne dust transport ranges from close-to-surface processes that last a few seconds to the global scale with significant climate impacts [5,6]. Dust storms which last several hours have impacts over several hundred kilometers far from the sources. During such dust storms, intensive emission of dust particles occurs, and the measured PM10 values are on the order of magnitude of 10000 µg/m3, and within urban areas, where most of the observation sites are situated; recorded values in such cases are on the order of magnitude of 1000 s µg/m3 [7]. Due to the relatively small spatial scales of agricultural sources, the highest impacts are usually restricted to areas in the vicinity of the sources. Since agricultural lands are usually relatively near populated areas or roads, compared to desert dust sources, their impacts can have large socio-economic impacts. Dust storms originating from local agricultural sources during dry periods impose very high risks to human safety, especially in highway transport, as presented by the example of one impressive case of a pileup involving 164 vehicles on Interstate 5 in California [8]. Severe dust emission from dried-up farmlands can travel far from the sources and cause high PM10 concentrations in remote areas. Such an event happened in 2007 when dust emitted from southern Ukraine reached Central Europe (Slovakia, the Czech Republic, Poland, and Germany) with maximum PM10 concentrations over 1000 µg/m3 [9]. To improve the understanding of this dust storm, a modeling approach was used to fill the knowledge gaps and reconstruct the dust pathway [10]. Nevertheless, the most recognizable case of man-made land degradation impact on the air quality, health, and weather conditions on a large scale is that of the American dust bowl in the 1930s. In addition to scarce available observations and information during this period, the GCM (General Circulation Model) was used to reconstruct the atmospheric conditions during this period [11]. The results showed that land degradation induced by humans caused a severe dust storm period and, most likely, also amplified the drought. These two major consequences of land degradation probably caused modest drought (if no human factor was involved) to turn into one of the worst environmental disasters in USA. Iran suffers significant impacts from dust storms in the western, south-western, and southern parts from the intrusion of dust storms of larger scales formed from dust sources outside of the country, with some contribution by local sources. Two more areas (Tabas and Sistan, located in the eastern parts of the Iran) have been shown to suffer from a larger impact of dust storms, but they are formed from sources in the interior of the Iran [12]. Land degradation has a large impact on the transformation of land surfaces into dust-producing areas. In the area of southwest Iran and southeast Iraq during the period 2005–2015, 12% of the land cover classes were transformed into barren land, which act as dust sources. Such change in land cover caused higher airborne dust production and impacted atmospheric conditions in this area by changing radiative balance (by about 10%) and consequently temperature conditions [13]. Another study for southwest Iran provided a methodology for land degradation modeling and found that the major land cover changes contributing to land degradation were wetland to barren, wetland to cropland, wetland to grassland, and cropland to barren. Severe land cover changes detected in the study area Atmosphere 2021, 12, 1054 3 of 26

were recognized as being hot-spots, where human activities have significantly contributed to the degradation process. Such surfaces should be considered as surfaces with a high risk of acting as or becoming dust storm sources in the future [14]. Tehran Province, located in the north of Iran (south of the Caspian Sea) is not considered an area with high vulnerability to dust storms because of the relatively rare or weak airborne dust events compared to other areas analyzed in [13,14]. The capital of Iran, Tehran city, is located in Tehran Province, which is a highly populated area of about 15.5 million citizens in the metropolitan area (https://www.citypopulation.de/en/world/agglomerations/, accessed on 10 July 2021). Thereby, this province has a high exposure level and, consequently, dust storm risk can be high, despite being a lower frequency of dust events. The case of a Tehran dust storm presented in this study can serve as an example in which high dust storm risk was evident in Tehran. A dust storm on 2 June 2014 that surged through Tehran was a kind of short-lived hazard that reduced visibility to zero, disabled traffic, caused power shutdowns, and caused deaths and injuries. The unpreparedness of the citizens and emergency response teams has raised the issue of making improvements to early warning systems since, for this event, a warning was issued just prior to its arrival [15]. From an extensive observational study [16], the main features of the atmospheric conditions that led to the Tehran dust storm formation were defined, and this dust storm was classified as a ‘haboob’. However, this analysis of the storm relying on the limited observations left uncertainties in understanding the main causes of such extreme weather events, such as the origins of the airborne dust, as indicated by the authors, highlighting the need for high-resolution numerical dust modeling of this event. Haboobs, as intensive, relatively local and short-lived dust storms, are considered a challenge for forecasting systems with embedded dust-related processes since a high resolution is required that is able to recognize dust-source hot-spots and has the ability to simulate strong convective activity. For high-quality dust storm forecasts that can be used for warning announcements, the expected level of accuracy should take into account good representation of the dust wall’s movement, its timing and spatial scale, and dust concentration levels. Due to the large spatial variability of the PM10 concentrations during such events [17], accurate in-point forecasts of PM10 are not possible to be reproduced by the model, and quantitative verification of the PM10 dust concentration forecast for this reason is avoided. Obstacles for currently available dust forecasts within the WMO SDS-WAS (World Meteorological Organization Sand and Dust Storms Warning Advisory and Assessment Sys- tem; https://sds-was.aemet.es/forecast-products/dust-forecasts, accessed on 10 July 2021) to predict dust storms, such as those that occurred in Tehran, in general, include their relatively coarse resolution and a lack of information on dust sources. The occurrence of Tehran dust storm was recognized as a good case study to assess the necessary capacities for operational forecast and warning system developments on national or sub-national levels. This work was initiated by the SDS-WAS, indicating the need for the development of such dust warning systems on national and sub-national levels [18]. Two modeling groups responded to such initiative, and simulation experiments were carried out with coupled dust–atmospheric regional non-hydrostatic numerical models: DREAM (Dust REgional Atmospheric Model) and WRF-Chem (Weather Research and Forecasting with Chemistry model). Both models performed well in simulation of the Phoenix 2 July 2011 haboob type of dust storm [17,19]. The models were set for the same domain and resolution, initial and boundary conditions, forecast initialization, and duration. The difference was in the input data on dust source masks. Additional DREAM experiments were performed to show the necessity of model resolution requirements and to explore origin of the airborne dust which swept through Tehran city. Atmosphere 2021, 12, x FOR PEER REVIEW 4 of 25

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2. Materials and Methods 2. MaterialsData used and in Methods this study include meteorological and air quality observations, as well as theData results used of in numerical this study simulations include meteorological and input data and required air quality by observations,the models. Analys as wellis asof thethe resultsdata in ofthis numerical study includes: simulations the analysis and input of collected data required observational by the models. data for Analysis defining ofthe the main data characteristics in this study of includes: the dust the storm analysis (reconstructing of collected the observational main features data of atmospheric for deﬁning theconditions main characteristics and the dust ofstorm), the dust and storm analysis (reconstructing of the data obtained the main b featuresy numerical of atmospheric simulation conditionsof the dust and storm the to dust better storm), understand and analysis the event of the and data to obtained assess the by required numerical model simulation set-up ofto theobtain dust a stormhigh-quality to better forecast understand of such the events. eventand to assess the required model set-up to obtain a high-quality forecast of such events. 2.1. Case Study Area 2.1. CaseFigure Study 1 presents Area the models’ domain of the numerical simulation of the dust storm (31° FigureN–39° 1N, presents 46° E–56° the models’E) and the domain case study of the area numerical for which simulation analysis of of the the dust results storm is ◦ ◦ ◦ ◦ (31presented.N–39 TheN, 46 models’E–56 domainE) and is the much case larger study than area the for area which of analysisinterest because of the results thermo- is presented.dynamic atmospheric The models’ processes domain is that much occur larger over than a larger the area area, of interestwhich could because have thermody- contrib- namicuted to atmospheric the formation processes of such that storm, occur need over to a largerbe covered area, whichby a high could-resolution have contributed numerical to thesimulation. formation of such storm, need to be covered by a high-resolution numerical simulation.

FigureFigure 1.1. DomainDomain ofof thethe numerical numerical simulations simulations (model (model domain), domain), the the case case study study area, area, and and locations locations of Tehranof Tehran Province Province and and Tehran Tehran city; city; background background data data are are the the orography orography of the of the domain domain represented represented on on the models’ high-resolution set-up (0.025° × 0.025°). the models’ high-resolution set-up (0.025◦ × 0.025◦).

2.2.2.2. ObservedObserved DataData TheThe initialinitial analysis analysis of of the the Tehran Tehran dust dust storm storm on 2on June 2 June 2014 2014 was publishedwas published in the in news the andnews later and in later [15], in providing [15], providing a qualitative a qualitative description description of the storm, of the its storm, timing, its duration,timing, dura- and impacts,tion, and which impacts, will which be given will in be the given following in the sectionfollowing where section analysis where is carriedanalysis out. is carried out. Measurements collected later provided more detailed information on the weather and air qualityMeasurements conditions, collected which gave later a quantitativeprovided more measure detailed of the information storm parameters on the and weather justi- fiedand itsair classification quality conditions, as a ‘haboob’ which storm. gave a The quantitative stations from measure which of the the meteorological storm parameters data wereand justified collected its are classification presented in as Table a ‘haboob’1 and Figure storm.2a. The The stations relevant from available which data the analyzed meteoro- forlogical this data event were were: collected 2 m air temperature,are presented dewin Table point, 1 and atmospheric Figure 2a. pressure, The relevant visibility, available and winddata analyzed direction. for Regular this event observations were: 2 werem air availabletemperature, every dew 3 h, butpoint, some atmospheric of the stations pressure, have missingvisibility, data and during wind direction. the period Regular of interest. observations were available every 3 h, but some of the stations have missing data during the period of interest. Observations from the airports (METAR data) have data available for every half hour, which are more usable for the analysis of such sudden and short-lived dust storms, or any severe local weather hazard with a duration of less than the data frequency of the

Atmosphere 2021, 12, x FOR PEER REVIEW 5 of 25

collected observations in meteorological stations. They also provide information on the nature of the atmospheric condition that caused the visibility reduction, which is the observational proof, as well as public photos and other public evidence, that a high dust concentration was present. Data from two airports were collected: OIIE (Imam Khomeini) and OIII (Mehrabad). Both locations are listed in the list of meteorological stations, but METAR data will provide added values for this analysis.

Table 1. Meteorological stations in Tehran province, with 3-h data frequency: station code, name, acronym, location (latitude, longitude and elevation), and data availability Atmosphere 2021, 12, 1054 5 of 26 Code Name Acr. Lat. (°N) Lon. (°E) El.(m) Data 40751 Tehran (Shemiran) TETG 35.78 51.62 1549 every 3 h 40754 Tehran (Mehrabad) * OIII 35.68 51.32 1191 every 3 h Table 1. Meteorological stations in Tehran province, with 3-h data frequency: station code, name, acronym, location (latitude, 40755 Abali TETB 35.75 51.88 2465 every 3 h longitude and elevation), and data availability. 40756 Firoozkooh TETF 35.92 52.83 1976 available 03–15 UTC Code Name40777 Imam Acr. Khomeini Lat. (◦ N)Airport Lon. * (◦OIIEE) El. (m)35.42 51.17 990 Data every 3 h 40751 Tehran (Shemiran) TETG 35.78 51.62 1549 every 3 h missing 15 h (2 40754 Tehran (Mehrabad)99320 * OIII Chitgar35.68 51.32TETC 119135.70 51.13 1305 every 3 hJune)–06 h (3 June) 40755 Abali TETB 35.75 51.88 2465 every 3 h UTC 40756 Firoozkooh99331 TETFTehran (Geophysics) 35.92 52.83TETO 197635.73 51.38 available1419 03–15available UTC 03–15 UTC 40777 Imam Khomeini Airport * OIIE 35.42 51.17 990 every 3 hevery 3 h; visibility 99320 Chitgar99366 TETCLavasan 35.70 51.13TETA 130535.83 missing51.6415 h (21863 June)–06 havailable (3 June) 03 UTC–15 h 99331 Tehran (Geophysics) TETO 35.73 51.38 1419 available 03–15 UTC UTC 99366 Lavasan TETA 35.83 51.64 1863 every 3 h; visibility available 03–15 h UTC 99369 Damavand99369 TETDDamavand 35.72 50.83TETD 205135.72 50.83 available2051 03–15available UTC 03–15 UTC Firoozkooh Aminabad Firoozkooh Aminabad 99370 99370 TETL 35.72 52.40TETL 298635.72 52.40 available2986 03–15available UTC 03–15 UTC (GAW station) (GAW station) 99372 Saveh99372 OIIVSaveh 35.05 50.33OIIV 111235.05 50.33 available1112 03–15available UTC 03–15 UTC 99375 Shahryar99375 TETSShahryar 35.67 51.03TETS 116335.67 51.03 available1163 03–15available UTC 03–15 UTC 99406 Varamin99406 TETVVaramin 35.32 51.65TETV 97335.32 51.65 available973 03–15available UTC 03–15 UTC * METAR data available.* METAR data available.

FigureFigure 2. 2.LocationsLocations of of the the stations stations from from which the groundground measurementsmeasurements were were collected: collected: (a ()a meteorological) meteorological stations stations in in TehranTehran province, province, listed inin TableTable1, divided1, divided into into three three groups groups (purple, (purple, blue, pink)blue, for pink) later for analysis later andanalysis ( b) air and quality (b) air stations quality stationsin Tehran in Tehran city, from city, which from the which PM10 the measurements PM10 measurements were collected; were locations collected; of airportslocations (OIIE of airports and OIII) (OIIE and theand approximate OIII) and the approximatedistance between distance them between are also them marked. are also marked.

Observations from the airports (METAR data) have data available for every half hour, which are more usable for the analysis of such sudden and short-lived dust storms, or any severe local weather hazard with a duration of less than the data frequency of the collected observations in meteorological stations. They also provide information on the nature of the atmospheric condition that caused the visibility reduction, which is the observational proof, as well as public photos and other public evidence, that a high dust concentration was present. Data from two airports were collected: OIIE (Imam Khomeini) and OIII (Mehrabad). Both locations are listed in the list of meteorological stations, but METAR data will provide added values for this analysis. Atmosphere 2021, 12, 1054 6 of 26

PM10 measurements can be useful for the quantiﬁcation of dust concentration, which cannot be carried out from visibility or satellite data. They include all airborne particles with diameter up to 10 µm, but, in the case of severe local dust storms, an immediate signiﬁcant increase in PM10 occurs (up to several 1000 µm/m3 or 10000 µm/m3 near sources), and it can be assumed this is actually the PM10 concentration of the dust particles. Unfortunately, PM measurement stations are mostly in highly populated urban areas, far from the source regions, where dust storms are rare or where they weaken. Another problem is the frequency of the data. For this study, the available PM10 data provided are hourly averaged values. Averaging the values for one hour does not represent peaking PM10 values during the passing of the dust storm, which lasts about 10 min. Table2 and Figure2b present the locations of the stations with the collected PM10 measures and information on data availability for 2 June 2014. Table2 includes all PM10 measurement stations that were available to the authors (besides the fact that data are missing for the period of interest) to show the availability of PM10 measurements for this case study compared to the available measurement sites.

Table 2. Air quality stations with PM10 measurements in Tehran city: station name, acronym, location (latitude, longitude), and data availability.

Name Acr. Lat. (◦ N) Lon. (◦ E) Data Aghdasyeh AG 35.79587 51.48414 data available Darous DA 35.77000 51.45416 no data Fath FA 35.67882 51.33753 data available Golbarg GO 35.73103 51.50613 no data Poonak PO 35.76230 51.33168 no data Region 2 RE 35.77709 51.36818 data available Shahre Rey SR 35.60363 51.42571 no data Sharif University SU 35.70227 51.35094 data available Rose Park RO 35.73989 51.26789 no data after 17 h l.t.

EUMETSAT RGB composites built from three infrared channels of the SEVIRI (Spin- ning Enhanced Visible and InfraRed Imager) radiometer travelling onboard METEOSAT satellites are the most common tool used to detect and monitor dust plumes. However, in this case, such a product did not allow observation of the dust storm that passed through Tehran since it occurred under convective clouds, but the detected convective activity visible from satellite data provided proof that the region was under high convective activity that was able to produce cold downdrafts, high-velocity surface winds, and high concentration of airborne dust along the frontal zone. The development and movement of the convective cells provided information about the area of convective cell formation.

2.3. Numerical Simulation Hindcasts (forecasts of the past event) of the 2 June 2014 Tehran dust storm event were performed over the domain presented in Figure1. The start time of the forecast was 12 UTC 1 June, and it ended at 00 UTC 3 June 2014 (forecast time: 36 h). The initial and boundary conditions for the regional forecast were used from the ECMWF IFS (Integrated Forecasting System) global forecast. Forecast global ﬁelds, rather than reanalysis, were chosen in order to have better assessment of regional forecast quality in case it is employed in an operational forecast system, and its potential for the use in warning systems. The resolution of the models was 0.025◦ (about 3 km) with 60 vertical levels. The model used for this case study was DREAM (Dust REgional Atmospheric Model), which performed well in another haboob case study in Phoenix, Arizona, on 5 July 2011 [17]. It is a fully coupled atmospheric-dust non-hydrostatic numerical weather prediction model including prediction of dust concentrations (for example, [20,21]). In this version, dust transport is driven in-line with the NOAA/NCEP (National Centers for Environmental Prediction) atmospheric numerical weather prediction model NMME (Non-hydrostatic Atmosphere 2021, 12, x FOR PEER REVIEW 7 of 25

Atmosphere 2021, 12, 1054 version, dust transport is driven in-line with the NOAA/NCEP (National Centers7 of for 26 En- vironmental Prediction) atmospheric numerical weather prediction model NMME (Non- hydrostatic Mesoscale Model on E-grid). The dust particle size distribution contains eight size categories of dust particle sizes (radii of 0.15, 0.25, 0.45, 0.78, 1.3, 2.2, 3.8, and 7.1 µm). DustMesoscale transport Model includes on E-grid). a viscous The dust sublayer particle between size distribution the surface contains and eightthe lowest size cate- model gories of dust particle sizes (radii of 0.15, 0.25, 0.45, 0.78, 1.3, 2.2, 3.8, and 7.1 µm). Dust layer [22], which is the main feature that distinguishes DREAM from other dust-atmos- transport includes a viscous sublayer between the surface and the lowest model layer [22], phericwhich models. is the main More feature details that on distinguishes the DREAM DREAM version from used other in this dust-atmospheric study can be mod-found in [17]els. and More the details references on the within. DREAM version used in this study can be found in [17] and the referencesThe mask within. of the potential dust source areas (referred to below as ‘dust source mask— DSM’)The represents mask of thethe potential map of dust the sourcebare land areas fractions (referred toover below the as model ‘dust source domain mask—DSM’) and is a nec- essaryrepresents input the for map dust of-atmo the barespheric land models. fractions It over indicates the model the areas domain where and issoil a necessary particles are availableinput for for dust-atmospheric emission in the models. case of a It drier indicates topsoil the areaslayer whereand higher soil particles surface are velocity available winds. Forfor this emission case study, in the such case a of mask a drier was topsoil obtained layer using and higherthe approach surface from velocity [17], winds. whereFor NDVI (Normalizethis case study,d Difference such a maskVegetation was obtained Index) MODIS using the data approach representative from [17], for where the beginning NDVI of(Normalized the June 2014 Difference and MODIS Vegetation land cover Index) data MODIS were dataused representative to parametrize for the the bare beginning land frac- of the June 2014 and MODIS land cover data were used to parametrize the bare land tion. Soil texture is information already included in numerical weather prediction models fraction. Soil texture is information already included in numerical weather prediction (in this case from hybrid database STATSGO-FAO: State soil geographic database of US models (in this case from hybrid database STATSGO-FAO: State soil geographic database of DepartmentUS Department of Agriculture of Agriculture (STATSGO) (STATSGO) and and Food Food andand AgricultureAgriculture Organization Organization database database (FAO))(FAO)) and and contains contains data data on thethe soilsoil particleparticle size size distribution. distribution. In In this this case case study, study, forecast forecast ofof PM10 PM10 (contribution (contribution from from PM10 dustdust concentration) concentration) is is presented, presented, meaning meaning that that clay clay and and siltsilt size size particles particles are are contributors contributors toto thethe PM10 PM10 concentration. concentration. In In addition addition to theto the already already mentionedmentioned surface surface conditions and and bare bare land land fraction fraction information information needed needed for dust for emission, dust emission,dust dust emission emission rates alsorates depend also depend on the clayon the and clay silt content and silt in content the topsoil. in theFigure topsoil.3 presents Figure 3 presentssoil texture soil datatexture from data DREAM from DREAM and a potential and a dustpotential source dust mask source designed mask from designed MODIS from MODISdata (indicated data (indicated as DSM1) as for DSM1) this case for study.this case study.

FigureFigure 3. Soil 3. Soil texture texture data: data: clay clay (a ()a and) and silt silt (b (b) )content content in in the the topsoil, andand bare bare land land fraction fraction (c ),(c considered), considered as a as dust a dust source source mask,mask, defined deﬁned from from MODIS MODIS data data—DSM1;—DSM1; data data are are presented presented overover thethe model model domain domain and and case case study study area area is approximately is approximately markedmarked with with black black-lined-lined squa square.re.

◦ TheThe DREAM DREAM forecast forecast was alsoalso carriedcarried out out with with a coarsera coarser resolution resolution of 0.1of 0.1°,, using using DSM1,DSM1, to to compare compare the the quality quality ofof thethe resultsresults with with a a high-resolution high-resolution run run and and to assess to assess the the necessity of the dynamical downscaling of such event forecasts to a high resolution. necessity of the dynamical downscaling of such event forecasts to a high resolution. DREAM dust forecasts were carried out with two more versions of DSM. The map of DREAM dust forecasts were carried out with two more versions of DSM. The map of dust sources in Iran, provided by Geological Survey of Iran, was implemented as DSM2 in dustDREAM, sources instead in Iran, of DSM1, provided and theby simulationGeological was Survey carried of outIran, with was the implemented same model set-up. as DSM2 inFinally, DREAM, from instead DSM2, of we DSM1, selected and only the agricultural simulation surfaces was carried (dry farming out with and the abandoned same model setagricultural-up. Finally, surfaces), from DSM2, and DSM3 we selected was created only as agricultural an input for anothersurfaces simulation (dry farming experiment. and aban- donedThe source agricultural map and surfaces), derived DSM2and DSM3 and DSM3 was arecreated presented as an in input Figure for4. another simulation experiment.Another The contribution source map to and the modelingderived DSM2 assessments and DSM3 was performedare presented with in the Figure widely 4. usedAnother WRF-Chem contribution model version to the 3.7.1, modeling which hasassessments embedded was dust performed transport and with other the dust- widely usedrelated WRF processes.-Chem model WRF-Chem version has 3.7.1, been which validated has forembedded its ability dust to realistically transport forecast and other dust dust- relatedAOD inprocesses. the broader WRF region-Chem of northern has been Africa, validated the Middle for its East,ability and to therealistically Mediterranean forecast [23]. dust AODIt is operationallyin the broader used region by the of METEO northern unit Africa, at the Nationalthe Middle Observatory East, and of the Athens Mediterranean (NOA),

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Atmosphere 2021, 12, 1054 8 of 26 [23]. It is operationally used by the METEO unit at the National Observatory of Athens (NOA), and its model outputs are provided to the WMO SDS-WAS initiative. The forecast carrandied its out model for outputsthe case are study provided of the to theTehran WMO dust SDS-WAS storm initiative.was performed The forecast with carried the same modelout for set the-up case as studyDREAM of the (domain, Tehran dustinitial storm and was boundary performed input with fields, the same resolution, model set-up forecast startas DREAMtime, and (domain, resolution). initial andA potential boundary dust input source fields, mask resolution, was forecastdefined start using time, the and infor- resolution). A potential dust source mask was defined using the information on land mation on land cover already embedded in the model, selecting bare land, grassland, open cover already embedded in the model, selecting bare land, grassland, open shrubland, and shrubland, and cropland as potential emission areas. The selected land cover types can be cropland as potential emission areas. The selected land cover types can be dust sources dustin thesources case thatin the they case are that bare they or sparsely are bare vegetated, or sparsely with vegetated, low soil moisture. with low In soil the modelmoisture. In domain,the model they domain, cover the they majority cover ofthe the majority area. This of choicethe area. was This made choice in order was to made assess in the order to abilityassess ofthe the ability WRF-Chem of the WRF model-Chem performance model performance in forecasting in atmospheric forecasting conditions atmospheric and con- ditionsdust forecastingand dust forecasting performance performance in general if usingin general only alreadyif using availableonly alread informationy available from infor- mationthe model from itself.the model The GOCART itself. The (Goddard GOCART Chemistry (Goddard Aerosol Chemistry Radiation Aerosol and Radiation Transport and Transportmodel) dust model) emission dust scheme emission was scheme used in thiswas study used [ 24in] this modified study as [24] in [ 23modified]. This emission as in [23]. Thisscheme emission is used scheme in an operationalis used in an model operational version inmodel NOA. version in NOA.

FigureFigure 4. Dust 4. Dust source source masks masks for for additional additional high high-resolution-resolution DREAM simulationsimulation experiments: experiments: (a )(a source) source mask, mask, (b) ( derivedb) derived DSM2,DSM2, which which represents represents the the source source mask mask prepared as as an an input input for for the the model, model, and and (c) DSM3, (c) DSM3, which which includes includes only agricultural only agricul- turalsources sources from from the th sourcee source mask; mask; the the model model domain domain in (a )in and (a) the and case the study case areastudy in area (a–c )in are (a approximately–c) are approximately marked withmarked withblack-lined black-lined squares; squares; original original source source mask mask in (a in): Shahbazi,(a): Shahbazi, R.; Sheikh, R.; Sheikh, M.; Ahmadi, M.; Ahmadi, N. Potential N. Potential Sources Sources of Aerosols of Aerosols and and Dust,Dust, NationalNational Project Project Ref. Ref. Code: Code: 140031213307), 140031213307), 2017, 2017, GSI, GSI, Tehran, Tehran, Iran Iran (http://webgis.ngdir.ir/management/Guest/ (http://webgis.ngdir.ir/management/Guest/,, accessedaccessed on 10 on July 10 July 2021). 2021).

3. 3.Results Results TheThe analysis analysis of of the collectedcollected observations observations provided provided the necessarythe necessary information information to assess to as- sessthe the atmospheric atmospheric conditions conditions during during the dust the storm dust event. storm The event. dust forecastThe dust with forecast the coupled with the coupleddust-atmospheric dust-atmospheric numerical numerical models provided models more provided information more information about the atmospheric about the at- conditions in a wider area that led to the formation of the dust storm, the dust storm’s mospheric conditions in a wider area that led to the formation of the dust storm, the dust origins, and its progression. Model data analysis also provided reasoning for the necessity storm’s origins, and its progression. Model data analysis also provided reasoning for the necessity of high-resolution modeling implementation in the forecast of such events and

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the capacities of the models to give high-quality dust forecasts that may serve as inputs for improvementsof high-resolutionof dust warning modeling systems. implementation in the forecast of such events and the capacities of the models to give high-quality dust forecasts that may serve as inputs for improvements 3.1. Description ofof the dust Dust warning Storm systems. from Public Evidence According 3.1.to public Description evidence, of the Dustthe dust Storm storm from reached Public Evidence Tehran at 5:30 p.m., local time (13:00 UTC). The passingAccording of the to dust public wall evidence, over the the fixed dust stormsite lasted reached about Tehran 15 min, at 5:30 which p.m., local time indicates the speed(13:00 of UTC).dust storm The passing movement. of the The dust estimated wall over theduration fixed site of the lasted whole about event 15 min, which was about 2 h. Theindicates dust thestorm speed caused of dust a stormreduction movement. in visibility The estimated to about duration10 m in ofTehran the whole event city; wind gustswas reached about 110 2 h. km/h The dust(30.5 storm m/s), causedand measured a reduction temperature in visibility dropped to about from 10 m in Tehran 33 °C to 18 °C. Thecity; damage wind gusts the storm reached caused 110 km/h in Tehran, (30.5 m/s), besides and demolition, measured temperature was 5 deaths, dropped from 82 injured, multiple33 ◦C vehicle to 18 ◦ C.collisions, The damage and theabout storm 50,000 caused residential in Tehran, units besides lost demolition, power. Over was 5 deaths, 7000 emergency82 workers injured, multiple were deployed vehicle collisions, within the and hour about for 50,000 field residential interventions units lost and power. Over transport of the 7000injured emergency to hospitals workers [15]. were A deployed photo of within the dust the hourstorm for when field interventions it entered the and transport Tehran city is givenof the in injured Figure to 5. hospitals The estimated [15]. A photo height of theof the dust visible storm whendust wall it entered was about the Tehran city is given in Figure5. The estimated height of the visible dust wall was about 1500–2000 m. 1500–2000 m.

Figure 5.FigurePhoto of5. thePhoto Tehran of the dust Tehran storm ondust 2 June storm 2014; on the2 June photo 2014; was the taken photo by Alireza was taken Naseri by (at Alireza the time, Naseri a photography (at student) fromthe time, Tehran’s a photography Aghdasieh neighborhood student) from (northern Tehran’s part Aghdasieh of Tehran neighborhood at a higher altitude (northern area of part the city);of Tehran the image is from: https://news.yahoo.com/deadly-wall-dust-devours-tehran-photo-182346241.htmlat a higher altitude area of the city); the image is from: https://news.yahoo.com/deadly, accessed on 10-wall July-dust 2021).- devours-tehran-photo-182346241.html, accessed on 10 July 2021). 3.2. Analysis of the Observed Data 3.2. Analysis of the3.2.1. Observed Analysis Data of the Meteorological Stations Data 3.2.1. Analysis of theThe Meteorological availability of Stations meteorological Data station data is given in Table1. The period 03 UTC The availability2 June of—00 meteorological UTC 4 June 2014 station is presented data is in given Figure in6 (with Table surface 1. The air perio temperature,d 03 UTC atmospheric pressure, wind direction). Visibility data are not presented, only commented upon. Due to 2 June—00 UTC 4 June 2014 is presented in Figure 6 (with surface air temperature, atmos- the low frequency of data, some visibility reduction was only noted in few stations. pheric pressure, wind direction). Visibility data are not presented, only commented upon. At the OIIV station located in the southwest, a drop in temperature of over 10 ◦C was Due to the low frequencydetected at of 12 data, UTC some 2 June visibility Year, and reduction the next measurement was only noted at 15 in UTC few showedstations. an increase, At the OIIVwhich station means located the in storm the southwest, had passed. a Thedrop other in temperature two stations of in over the south 10 °C of was Tehran, OIIE detected at 12 UTCand 2 TETV, June Year, detected and a the drop next in temperaturemeasurement of at about 15 UTC 15 ◦ Cshowed at 15 UTC. an increase, The other stations which means thealso storm showed had a passed. drop in temperatureThe other two at 15 stations UTC, and in afterwardsthe south of the Tehran, temperature OIIE stayed low and TETV, detectedsince a it drop was the in nighttime.temperature Going of furtherabout 15 to the°C northeast,at 15 UTC. the The signal other of the stations storm weakened also showed a dropand in did temperature not reach the at TETF 15 UTC, station. and In afterwards the visibility the data temperature (not shown stayed here), low the ﬁrst signal since it was the ofnighttime. the storm Going was detected further at to 12 the UTC, northeast when visibility, the signal at OIIV of the dropped stormto weak- 3000 m (during ened and did notother reach hours the when TETF no station. visibility In reductionthe visibility was noted,data (not the valuesshown were here), equal the to first 10000 m), and signal of the stormstayed was somewhat detected lower at 12 (7000 UTC, m) when at 15 UTC.visibility At OIIE at OIIV station, dropped a decrease to in3000 visibility m in these data was not detected. Another station, TETS, detected a drop in visibility at 12 UTC, and (during other hours when no visibility reduction was noted, the values were equal to most of the others at 15 UTC, but none of them showed a decrease in visibility near zero, as 10000 m), and stayed somewhat lower (7000 m) at 15 UTC. At OIIE station, a decrease in reported during the dust storm. In the surface atmospheric pressure data, the values at visibility in these data was not detected. Another station, TETS, detected a drop in visibility at 12 UTC, and most of the others at 15 UTC, but none of them showed a decrease in visibility near zero, as reported during the dust storm. In the surface atmospheric pressure

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mostdata, ofthe the values stations at most showed of the an increasestations inshowed pressure an atincrease 15 UTC. in The pressure most pronounced at 15 UTC. wasThe atmost TETD, pronounced over 10 mb.was Theat TETD, measurements over 10 mb. of dewThe measurements point (not shown of dew here) point also (not showed shown an increasehere) also in showed values atan 15increase UTC, exceptin values in OIIV,at 15 UTC, where except an increase in OIIV, was where detected an increase at 12 UTC. was Winddetected direction at 12 UTC. data Wind at the direction southern data stations, at the OIIV southern and TETV, stations, showed OIIV similar and TETV, values, showed with thesimilar wind values, changing with from the w 9ind to 12 changing UTC from from south 9 to to 12 west, UTC andfrom later south at 15to UTCwest, toand the later north at wind.15 UTC The to the stations north in wind. Tehran, The OIII, stations and in TETO Tehran, showed OIII, thatand theTETO wind showed direction that remained the wind withdirection a dominant remained south with direction a dominant until south 12 UTC, direction and anuntil observation 12 UTC, and at 15 an UTC observation showed at a dominant15 UTC showed north direction.a dominant The north stations direction. in the northeastThe stations largely in the did northeast not observe largely this did type not of directionobserve this change type duringof direction these change hours. during these hours.

Figure 6.6. Observed data in meteorological stations listed in Table 1 1,, divideddivided intointo threethree groupsgroups (purple,(purple, blue,blue, andand pink)pink) accordingaccording toto theirtheir location, location, as as shown shown in in Figure Figure2a. 2a. Available Available data data are are given given for thefor periodthe period 2–3 June2–3 June in a UTCin a timeUTC scaletime (andscale (and for wind from 6–18 UTC 2 June): air surface temperature (a–c), surface atmospheric pressure (d–f), and wind direction for wind from 6–18 UTC 2 June): air surface temperature (a–c), surface atmospheric pressure (d–f), and wind direction (g–i); (g–i); in the wind direction graphs (g–i), the stations have the same markers as in the temperature and pressure graphs, in the wind direction graphs (g–i), the stations have the same markers as in the temperature and pressure graphs, and time and time (UTC) is given at the radii of the polar diagrams. (UTC) is given at the radii of the polar diagrams. Considering the presented meteorological observations with the available data in 3- Considering the presented meteorological observations with the available data in h intervals, it can be concluded that the event’s main characteristics were a significant 3-h intervals, it can be concluded that the event’s main characteristics were a signiﬁcant drop in temperature, a change in wind direction, a rise in air humidity and atmospheric drop in temperature, a change in wind direction, a rise in air humidity and atmospheric pressure, and a drop in visibility. TheThe listedlisted featuresfeatures indicateindicate that the nature of thethe eventevent was a haboob as reported in [[16].16]. The frequency of the observations does not allow a clear depiction of all features, but the data point out thatthat thethe stormstorm originatedoriginated somewheresomewhere in the south or southwest of the Tehran overover thethe drydry and barren areasareas beforebefore 1212 UTC,UTC, andand itit reached Tehran betweenbetween 1212 andand 1515 UTC.UTC.

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3.2.2.3.2.2. AnalysisAnalysis of the Airport METAR METAR Data Data AirportAirport measurementsmeasurements are are available available in in half half-hour-hour intervals, intervals, which which additionally additionally con- con- tributedtributed toto the determination determination of of the the dust dust storm’s storm’s progression. progression. METAR METAR data f datarom two from air- two airportsports were were considered: considered: Imam Imam Khomeini Khomeini (OIIE) (OIIE) and Mehrabad and Mehrabad (OIII). (OIII).The location The location of these of thesestations stations and their and theirdistance distance are shown are shown in Figure in Figure 2. In2 addition. In addition to the to higher the higher frequency frequency of ofdata, data, compared compared to toalready already discussed discussed data data from from the the meteorological meteorological sta stations,tions, the the METAR METAR datadata includeinclude informationinformation on the cause cause of of visibility visibility reduction; reduction; in in both both stations, stations, the the dust dust stormstorm waswas markedmarked asas the cause. FigureFigure7 7 presents presents thethe 22 mm airair temperature,temperature, dew point, point, mean mean sea sea level level pressure, pressure, and and visibility,visibility, andand FigureFigure8 8 presents presents windwind directiondirection and velocity data data for for 2 2 June June 2014 2014 for for both both airports (some of the presented graphs were made to be the same or similar to those pre- airports (some of the presented graphs were made to be the same or similar to those sented in [16]). The main evidence from these data is that the passage of the dust storm presented in [16]). The main evidence from these data is that the passage of the dust storm was registered at 12:30 UTC at OIIE, and at OIII at 13:00 UTC according to the visibility was registered at 12:30 UTC at OIIE, and at OIII at 13:00 UTC according to the visibility data, data, with a significant drop in temperature, rise in humidity, and rise in atmospheric with a significant drop in temperature, rise in humidity, and rise in atmospheric pressure. pressure. More pronounced changes were recorded at the southern airport, OIIE, where More pronounced changes were recorded at the southern airport, OIIE, where visibility visibility dropped to 20 m. The distance between the observations is about 30 km, which dropped to 20 m. The distance between the observations is about 30 km, which gives gives information on the speed of the dust storm’s movement. The storm impacted the information on the speed of the dust storm’s movement. The storm impacted the conditions conditions for about an hour to an hour and a half over one station. The wind speed infor about an hour to an hour and a half over one station. The wind speed increased up to creased up to 50 knots (~25 m/s, ~93 km/h) at 12:30 UTC at OIIE and at 13:00 UTC. The 50 knots (~25 m/s, ~93 km/h) at 12:30 UTC at OIIE and at 13:00 UTC. The wind direction wind direction changed from a south direction until 12 UTC at OIIE, and 12:30 at OIII, to changed from a south direction until 12 UTC at OIIE, and 12:30 at OIII, to a west direction a west direction (half an hour to an hour later), and after, it changed to winds blowing (half an hour to an hour later), and after, it changed to winds blowing from the north. from the north.

FigureFigure 7. 7.METAR METAR data data at at the the OIIE OIIE (Imam (Imam Khomeini,Khomeini, left)left) and OIIE (Mehrabad, right) right) airports airports on on 2 2June June 2014: 2014: (a (,ba,)b 2) m 2 mair air temperature, dew point, mean sea level pressure, and (c,d) visibility. temperature, dew point, mean sea level pressure, and (c,d) visibility.

TheseThese datadata conﬁrmconfirm that the storm arrived arrived from from drylands drylands south/southwest south/southwest of ofTehran Tehran andandreached reached the the southern southern airport airport at at about about 12:30, 12:30, travelled travelled toward toward the the center center of of the the town town for halffor half an hour, an hour, and and weakened. weakened. When When the dustthe dust storm storm reached reached the the town, town, at about at about 13:00–13:30, 13:00– the13:30, wind the changed wind changed to a west to a direction, west direction, which probablywhich probably pushed pushed the dust the toward dust toward the east the and cleared the air in the town. After, north winds prevailed and fully cleared the city air of the

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east and cleared the air in the town. After, north winds prevailed and fully cleared the city airdust. of Thisthe dust. evidence This additionallyevidence additionally conﬁrms itconfirms was a haboob-like it was a haboob storm,-like but storm, raisesthe but idea raises of thea multicell idea of storm,a multicell with storm, some originatingwith some inoriginating the south in and the some south in and the west,some accordingin the west, to accordingthe wind direction to the wind change. direction change.

Figure 8. METAR data at the OIIE (Imam(Imam Khomeini, left) and OIIE (Mehrabad, right) airports on 2 June 2014: (a,,b) windwind direction and (c,,d)) windwind speedspeed (knots).(knots).

3.2.3. Analysis Analysis of EUMETSAT Images The satellite satellite images images for for the the day day of the of event the event were were provided provided by the byEUMETSAT the EUMETSAT SEVIRI, SEVIRI,as a wider as a domain wider domain than ground than ground observations observations was collected was collected (Figure (Figure9). Within 9). Within the case the casestudy study area, area, high high convective convective activity activity (in red (in color) red color) is visible, is visible, forming forming in the in southwest the southwest of the ofarea, the continuing area, continuing toward toward the northeast, the northeast, and, after and, 13 after UTC, 13 moving UTC, moving toward toward the east. the A localeast. Adust local storm dust should storm beshould detected be detected as a strong as a pink strong color, pink but color, it was but beneath it was beneath the clouds the and clouds not andvisible not in visible this case in this within case the within case the study case area. study area. The satellite data complement the conclusions derived derived from from the the meteorological ob- ob- servations, providing evidence that there was strong convective activity in the south and southwest of of Tehran, Tehran, related to the strong heating of the soil during the the day day in in the the drydry- lands.lands. This This multicell multicell storm, storm, which which was was moving moving toward the north and northeast, had cold downdrafts followed by by the strong surface winds, which swept over the dry and barren soil picking up dust and carrying it to towardward Tehran. In the northern parts of Tehran, the storm intensity was probably reduced because the wind changed toto aa westerlywesterly direction.direction.

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FigureFigure 9.9. EUMETSAT RGBRGB compositescomposites built fromfrom threethree infraredinfrared channelschannels ofof thethe SEVIRISEVIRI instrumentinstrument travelingtraveling onboardonboard METEOSAT-10;METEOSAT-10; (a–f) are the the images images for for the the period period 10 10–15–15 UTC UTC 2 June 2 June 2014 2014 with with 1 hour 1 hour interval; interval; red color red color represents represents convective activity, and pink should represent airborne particles. convective activity, and pink should represent airborne particles.

3.2.4. Analysis of PM Data The list of stations from which PM data were collectedcollected are given in Table2 2,, and and their their locations inin Tehran areare shownshown inin FigureFigure2 2.. The The datadata areare hourlyhourly averagedaveraged valuesvalues andand PM10,PM10, presented inin FigureFigure 1010.. The highest highest values values of of the the hourly hourly averaged averaged PM10 PM10 at 18 at h 18 local h local time time(13:30 (13:30 UTC) UTC)were weredetected detected in the in stations the stations in northern in northern part of part Tehran of Tehran (stations (stations RE— RE—RegionRegion 2 and 2 AG and— AG—Agh- Aghdasyeh).dasyeh). The values The values were were over over400 μm/m 400 µ3m/m in the3 inRE the station RE station and over and 250 over μm/m 250 3µ inm/m the 3AGin thestation. AG station.The values The in values FA (Fath) in FA and (Fath) SU and (Sharif SU (Sharif University) University) were about were about100 μm/m 100 µ3m/m, while3, whilefor the for other theother stations stations during during and andafter after the thestorm, storm, data data are are not not available. available. At At 19 19 h, h, higher PM10 was also detecteddetected inin thethe RERE stationstation andand waswas aboutabout 150150 µμm/mm/m3 in the FA andand SUSU stations. In [16], [16], it is reported thatthat thethe PM10PM10 measurementsmeasurements fromfrom MehrabadMehrabad airportairport (not(not available here)here) were were over over 900 900µm/m μm/m3 at3 at the the time, time, which which took took into into account account the measurements the measure- duringments during the dust the storm dust passing. storm passing. Since the Since FA and the SU FA stations and SU are stations near the are airport near butthe showed airport significantlybut showed significantly lower values, lower the measurements’ values, the measurements’ quality during quality this event during may this be questioned.event may Nevertheless,be questioned. the Nevertheless, overall conclusion the overall derived conclusion from the derived PM10 from data isthe that PM10 the data dust is storm that passedthe dust through storm passed the city through and caused the city a sudden and caused increase a sudden in PM10. increase After in its PM10. passing, After a fast its decreasepassing, a was fast recorded. decrease Thiswas indicatesrecorded. thatThis the indicates dust storm that wasthe dust short-lived storm was event short but-lived with veryevent high but with dust very concentrations, high dust concentrations, which also confirms which the also sudden confirms and the short-lived sudden and visibility short- reductionlived visibility over thereduction airports. over This the conclusion, airports. This derived conclusion, from the derived PM10 data,from willthe bePM10 adopted data, forwill a be comparison adopted for with a comparison the models’ resultswith the in models’ a qualitative results (descriptive) in a qualitative way, rather(descripti thanve) a quantitativeway, rather than way becausea quantitative the values way (hourlybecause averaged) the values are (hourly not comparable averaged) withare not the compa- results fromrable thewith models the results (values from simulated the models for a (values specific simulated time moment), for a specific and in-point time comparisonmoment), and of stationin-point and comparison model data of (duestation to theand high model temporal data (due and to spatial the high variability temporal of and dust spatial concentra- var- tionsiability in theseof dust events) concentrations can cause ain double these events) penalty can problem cause (even a double with penalty the better problem performance (even ofwith high-resolution the better performance model simulations of high and-resolution scores derived model simulationsfrom in-point and verification scores derived drops, discussedfrom in-point in [17 verification]); the quality drops, of the discussed PM10 measurements in [17]); the quality is questioned of the PM10 and themeasurements data are, in largeis questioned part, missing and the for data the time are, periodin large of part, the event.missing for the time period of the event.

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FigureFigure 10. 10.Hourly Hourly averaged averaged PM10 PM10 measured measured in air in qualityair quality stations stations in Tehran; in Tehran; sites insites which in which data are data are notnot available available for for 2 June2 June 2014 2014 are are marked marked with with line. line.

3.3.3.3. Analysis Analysis of of the the Numerical Numerical Simulations Simulations Data Data InIn total, total, five five experiments experiments were were carried carried out out for thefor Tehranthe Tehran dust dust storm storm case: case: 1. Experiment DHR1: DREAM high-resolution (0.025◦, ~3 km) forecast with dust source 1. maskExperiment defined DHR1: using MODIS DREAM data high (DSM1);-resolution (0.025°, ~3 km) forecast with dust source 2. Experimentmask defined DLR1: using DREAM MODIS low-resolution data (DSM1); (0.1◦) forecast with dust source mask 2. definedExperiment using DLR1: MODIS DREAM data (DSM1); low-resolution (0.1°) forecast with dust source mask de- 3. Experimentfined using DHR2: MODIS DREAM data (DSM1); high-resolution (~3 km) forecast with dust source mask 3. definedExperiment from dustDHR2: source DREAM map createdhigh-resolution by Iranian (~3 institutions km) forecast (DSM2); with dust source mask 4. Experimentdefined from DHR3: dust DREAM source ma high-resolutionp created by (~3Iranian km) forecastinstitutions with (DSM2); dust source mask 4. definedExperiment from dustDHR3: source DREAM map created high-resolution by Iranian institutions(~3 km) fore butcast with with selected dust source agricul- mask turaldefined surfaces from only dust such source as dry ma farmingp created and by abandoned Iranian institutions agricultural but surfaces with selected (DSM3); agri- 5. Experimentcultural surfaces WHR: WRF-Chem only such high-resolution as dry farming (~3 and km) abandoned forecast using agricultural information surfaces on land(DSM3); cover from the model in defining dust source areas. 5. AllExperime experimentsnt WHR: have WRF a cold-Chem start high (no airborne-resolution dust (~3 in km) the initialforecast fields) using and information passive on dust transportland cover (no from impact the of model the airborne in defining dust ondust the source atmospheric areas. conditions). 3.3.1. AnalysisAll experiments of DREAM have High-Resolution a cold start (no Forecasts airborne Using dust Three in the Versions initial offields) Dust and Source passive Masksdust transport (Experiments (no impact DHR1, DHR2,of the airborne and DHR3) dust on the atmospheric conditions). The data analysis presented in this section is the core of this study. We aimed to assess the3.3.1. model Analysis performance of DREAM in forecasting High-Resolution atmospheric Forecasts conditions Using which Three led toVersions the formation of Dust of theSource Tehran Masks dust storm(Experiments and the dust DHR1, forecast DHR2, quality, and as DHR3) well as the contribution of agricultural surfaces to the airborne dust concentration in this event. The data analysis presented in this section is the core of this study. We aimed to as- Figure 11 presents wind velocity and direction over the case study area from 10 UTC to sess the model performance in forecasting atmospheric conditions which led to the for- 13 UTC. The frontal area formed from several downdrafts from convective clouds is marked withmation a black of the line. Tehran In the frontal dust storm area, the and wind the direction dust forecast changed, quality, and after as itswell passing, as the the contribution wind velocityof agricultural significantly surfaces increased to the and airborne had values dust over concentration 20 m/s. The movement in this event. of the frontal zone was fromFigure the southwest11 presents to wind the northeast velocity and and passed direction through over Tehran the case at 12–13 study UTC. area from 10 UTC to 13To UTC. additionally The frontal validate area the formed wind patterns from several in the southwestern downdrafts part from of convective Tehran Province clouds is overmarked the Imam with Khomeinia black line. airport In the (OIIE frontal station) area, where the wind the storm direction signal changed, was the strongest and after its (presentedpassing, the in Figure wind8 velocity), in Figure significantly 12, wind data increased are presented and had only values for this over area 20 at 12,m/s. 13, The and move- 14ment UTC. of At the 12 frontal UTC, the zone wind was direction from the over southwest OIIE was theto the strongest northeast and hadand apassed southwest through direction;Tehran at at 12 14– UTC,13 UTC. the wind was blowing from the west, and at 14 UTC from the northern side. ComparedTo additionally to the observations,validate the wind the model patterns well in represented the southwestern the wind part patterns. of Tehran Prov- ince over the Imam Khomeini airport (OIIE station) where the storm signal was the strongest (presented in Figure 8), in Figure 12, wind data are presented only for this area at 12, 13, and 14 UTC. At 12 UTC, the wind direction over OIIE was the strongest and had a

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Atmosphere 2021, 12, 1054 southwest direction; at 14 UTC, the wind was blowing from the west, and at 14 UTC15 of from 26 the northern side. Compared to the observations, the model well represented the wind patterns.southwest direction; at 14 UTC, the wind was blowing from the west, and at 14 UTC from the northern side. Compared to the observations, the model well represented the wind patterns.

FigureFigure 11. 11. DREAMDREAM wind wind velocity velocity (m/s) (m/s) and and direction direction ov overer the case study areaarea andand lineline ofof the the front front (black (black line) line) during during the the periodFigure 10 11.–13 DREAM UTC, at wind (a–d )velocity with one (m/s) hour and interval. direction over the case study area and line of the front (black line) during the period 10–1310–13 UTC, atat ((aa––dd)) withwith oneone hourhour interval.interval.

Figure 12. DREAM high-resolution simulation: wind patterns during the period 12–14 UTC, (a–c) with one hour interval, over Tehran Province; approximate location of the OIIE station is marked with a grey square.

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Atmosphere 2021, 12, 1054 16 of 26 Figure 12. DREAM high-resolution simulation: wind patterns during the period 12–14 UTC, (a–c) with one hour interval, over Tehran Province; approximate location of the OIIE station is marked with a grey square.

High winds on their path provoke the emission of dust particles from the bare frac- High winds on their path provoke the emission of dust particles from the bare fractions tions of the soil surface. From the experiment DHR1, the PM10 dust surface concentration of the soil surface. From the experiment DHR1, the PM10 dust surface concentration for the for the period 10–13 UTC is presented in Figure 13. The formation of the three main period 10–13 UTC is presented in Figure 13. The formation of the three main downdrafts downdrafts that seemed to be responsible for the majority of dust emissions and transport that seemed to be responsible for the majority of dust emissions and transport are marked are marked with squares. The formation of these downdrafts happened consecutively at with squares. The formation of these downdrafts happened consecutively at 10 UTC and 10 UTC and 11 UTC. Two of them happened south of Tehran Province, and the frontal 11 UTC. Two of them happened south of Tehran Province, and the frontal area and dust area and dust movement were predominantly from south to north. At 12 UTC, a strong movement were predominantly from south to north. At 12 UTC, a strong downdraft downdraft occurred in the southwest from Tehran and impacted the movement of the occurred in the southwest from Tehran and impacted the movement of the storm in a way storm in a way to change its direction to SW–NE. Maximum PM10 values were near the to change its direction to SW–NE. Maximum PM10 values were near the southern border southern border of Tehran Province (over 8000 μm/m3), but a change in dust movement of Tehran Province (over 8000 µm/m3), but a change in dust movement and dominant and dominant westerly winds at 13 UTC caused the southern part of Tehran Province to westerly winds at 13 UTC caused the southern part of Tehran Province to be most affected be most affected by the dust storm, while the northern part was relatively protected. After by the dust storm, while the northern part was relatively protected. After 13 UTC, it started 13 UTC, it started to clear from the area. North winds started to blow and cleared the area to clear from the area. North winds started to blow and cleared the area of dust. of dust.

Figure 13. DREAM (experiment DHR1) PM10 dust surface concentration (µm/m3) and wind arrows over the case study area during the period 10–13 UTC, at (a–d) with one hour interval; areas where the three major downdrafts which happened

are framed with a black square. Atmosphere 2021, 12, x FOR PEER REVIEW 17 of 25

Figure 13. DREAM (experiment DHR1) PM10 dust surface concentration (μm/m3) and wind arrows over the case study Atmospherearea during2021, 12the, 1054 period 10–13 UTC, at (a–d) with one hour interval; areas where the three major downdrafts which hap-17 of 26 pened are framed with a black square.

To show the intensity of the cold downdraft from a supercell simulated with DREAM,To show a vertical the intensity cross-section of the cold that downdraft approximately from a supercellcuts through simulated the area with of DREAM, the third a vertical cross-section that approximately cuts through the area of the third marked down- marked downdraft and that goes over the southern side of Tehran is given. Figure 14 pre- draft and that goes over the southern side of Tehran is given. Figure 14 presents vertical sents vertical cross-sections of the temperature field and the vertical component of the cross-sections of the temperature ﬁeld and the vertical component of the wind velocity windwith velocity streamlines. with The streamlines. timing of The the imagestiming isof 12 the UTC. images In the is temperature12 UTC. In the cross-section, temperature crossit can-section, be seen it that can in be the seen ﬁrst that couple in the of hundredfirst couple meters, of hundred the near-surface meters, temperaturethe near-surface is temperatureover 35 ◦C, but is over where 35 the °C, downdraft but where happened, the downdraft cold air fell happened, from the cold heights air and fell caused from the heightsthe temperatures and caused near the temperatures the surface to near be about the surface 15–20 ◦ toC, be which about corresponds 15–20 °C, which to the corre- air spondstemperature to the atair about temperature a 1500–2000 at about m altitude a 1500 in– the2000 surrounding m altitude area. in the The surrounding velocity of the area. Thecold velocity air downdraft of the cold reached air downdraft over 10 m/s reached (locally over over 10 20m/s m/s), (locall andy over when 20 them/s), air and with when a thestrong air with vertical a strong velocity vertical hit the velocity ground, hit it producedthe ground, a high-velocity it produced horizontala high-velocity component horizon- talof component the surface winds.of the surface winds.

FigureFigure 14. 14. VerticalVertical cross cross-section-section of of temperature temperature field ﬁeld ( a) and verticalvertical componentcomponent of of wind wind velocity velocity with with streamlines streamlines (b ),(b), obtainedobtained by by DREAM DREAM high high-resolution-resolution run run at at 12 12 UTC; UTC; on the x-axisx-axis isis thethe latitude;latitude; on on the the y-axis y-axis are are the the model model levels; levels; black black lineslines are are the the heights heights above above land land surface. surface.

UsingUsing the the same modelmodel set-up, set-up, which which gave gave the the same same atmospheric atmospheric conditions condition durings during the theforecast forecast but but with with implemented implemented dust maskdust versionsmask versions DSM2 (experimentDSM2 (experiment DHR2) and DHR2) DSM3 and DSM3(experiment (experiment DHR3), DHR3), the PM10 the dust PM10 surface dust concentrationssurface concentrations are presented are presented in Figure 15 in. TheFigure 15.data The are data presented are presented for 12 UTC for 12 and UTC 13 UTC. and 13 UTC. ImplementingImplementing DSM2 DSM2 (experiment (experiment DHR2),DHR2), whichwhich has has lesser lesser area area coverage coverage with with dust dust sources,sources, produced produced a a smaller smaller area affectedaffected byby the the airborne airborne dust. dust. Near Near and and over over the the Tehran Tehran area, the concentrations were up to about 4000 µm/m3. area, the concentrations were up to about 4000 μm/m3. Using only abandoned agricultural areas and dry farming as a dust sources (dust Using only abandoned agricultural areas and dry farming as a dust sources (dust source mask DSM3, experiment DHR3) where the abandoned agricultural surfaces are source mask DSM3, experiment DHR3) where the abandoned agricultural surfaces are located south of Tehran (in this version of simulation, the only source area which emitted locateddust which south came of Tehran to Tehran), (in this the version results of for simulation, 13 UTC show the only that source it produced area which PM10 emitted dust dustsurface which concentrations came to Tehran), in the range the results of 2000–4000 for 13µ UTCm/m 3show. This that means it thatprodu thisced source PM10 was dust surfaceresponsible concentrations for over 50% in the of the range dust of within 2000– the4000 dust μm/m storm3. This that means reached that Tehran this city.source The was responsibleintrusion of for the over airborne 50% dustof the from dust abandoned within the agricultural dust storm areas that inreached the city Tehran maybe city. had aThe intrusionsigniﬁcantly of the greater airborne contribution dust from locally. abandoned agricultural areas in the city maybe had a significantly greater contribution locally.

Atmosphere 2021, 12, 1054 18 of 26 Atmosphere 2021, 12, x FOR PEER REVIEW 18 of 25

3 FigureFigure 15. 15. DMDM PM10 PM10 dust surfacesurface concentration concentration (µm/m (μm/m3) at) 12 at UTC 12 UTC using using DSM2 DSM2 (experiment (experiment DHR2) (DHR2)a) and DSM3 (a) and (experiment DSM3 (ex- perimentDHR3) (DHR3)b); (c,d) ( sameb); (c as,d) ( asame,b), respectively, as (a,b), respectively, but for 13 UTC. but for 13 UTC.

AA ver verticaltical cross cross-section-section ofof thethe dustdust datadata from from the the DREAM DREAM forecast forecast DHR2 DHR2 is is presented presented inin Figure Figure 16. 16. The The dust, dust, which which went throughthrough thethe Tehran Tehran area, area, is is visible visible here here as as a relativelya relatively narrownarrow dust dust wall withwith PM10 PM10 dust dust concentrations concentrations in thein the range range of 1000–4000 of 1000–4000µm/m μm/m3 reaching3 reach- inga height a height of about of about 2000–3000 2000– m3000 (Figure m (Figure 16a). Due 16a). to Due its narrow to its structure,narrow structure, it passed quicklyit passed quicklyover the over Tehran the (accordingTehran (according to public to evidence, public evidence, about 15 min about over 15 the min ﬁxed over point). the fixed Another point). Anotherproduct product generated generated by the model by the is model the dust is numberthe dust concentration number concentration (DNC) presented (DNC) inpre- sentedFigure in 16 Figureb. This 16b. product This couldproduct be could more be suitable more insuitable relation in torelation visibility to visibility and a useful and a usefulalternative alternative to PM10 to forPM10 dust for forecast dust forecast presentation. presentation.

Atmosphere 2021, 12, x FOR PEER REVIEW 19 of 25 Atmosphere 2021, 12, 1054 19 of 26 Atmosphere 2021, 12, x FOR PEER REVIEW 19 of 25

Figure 16. Vertical cross-section of (a) PM10 dust concentration (μm/m3) and (b) dust number concentration (number of dust particles in cm3) from DREAM forecast (experiment DHR2) at 13 3UTC; x- and y-axis have the same meaning as in FigureFigure 16. 16.VerticalVertical cross cross-section-section of of (a (a) )PM10 PM10 dust dust concentration (µ(μm/mm/m3)) andand ( b(b)) dust dust number number concentration concentration (number (number of of Figure 14. 3 dustdust particles particles in incm cm) from3) from DREAM DREAM forecast forecast (experiment (experiment DHR2) DHR2) at 1313 UTC;UTC; x-x- and and y-axis y-axis have have the the same same meaning meaning as inas in FigureFigure 14. 14. 3.3.2. Analysis of DREAM Low-Resolution Forecast (Experiment DLR1) 3.3.2.3.3.2.The Analysis Analysis DREAM of of DREAM DREAM low-resolution Low Low-Resolution-Resolution forecast ForecastForecast in this (Experimentstudy (Experiment was performed DLR1) DLR1) with the same modelTheThe set DREAM-up DREAM as the low low-resolution high-resolution-resolution forecast forecasts, in in this thisbut study studywith wasthe was model performed performed resolution with with the set the same to same0.1°, ◦ modelusingmodel the set set-up dust-up as source as the the high high-resolutionmask-resolution DSM1. This forecasts, resolution butbut withwas with chosen the the model model as the resolution resolution highest setresolution set to 0.1to 0.1°,, of usingtheusing operational the the dust dust source dust source models mask mask DSM1. DSM1.forecast This This available resolution resolution in SDS was was- WAS.chosen chosen The as thePM10 highest dust resolution resolutionsurface con- of of the operational dust models forecast available in SDS-WAS. The PM10 dust surface thecentration operational for the dust case models study forecastarea is presented available inin FigureSDS-WAS. 17, including The PM10 arrows dust surface which indi-con- concentration for the case study area is presented in Figure 17, including arrows which centrationcate wind directionfor the case and study velocity area (the is presented same arrow in Figure size set 17,-up including as in figures arrows related which to highindi-- indicate wind direction and velocity (the same arrow size set-up as in ﬁgures related to cateresolutionhigh-resolution wind directionruns). runs).The and low The velocity-resolution low-resolution (the runsame d run idarrow not did managenotsize manage set- upto reproduce as to reproducein figures the therelated event; event; tono nohigh cold- resolutiondowndraftscold downdrafts runs). in the The instudy the low study area-resolution occurred, area occurred, run and did and windnot windmanage velocities velocities to reproducewere were below below the 3 m/s.event; 3 m/s. The no The pres- cold downdraftsencepresence of the of airbornein the the airborne study dust dustarea of ofseveral occurred, several hundred hundred and wind μm/mµm/m velocities33 occurred were becausebecause below of of3 the m/s. the dominant dominantThe pres- enceeasteast winds,of winds, the whichairborne which occurred occurred dust of before before several the hundred strongstrong convective convective μm/m3 occurred activity activity over because over the the case of case the study study dominant area, area, eastmeaningmeaning winds, it itwas which was not not occurred produced produced before by the the atmospheric strong convective conditions conditions activity related related over to to event theevent case of theof studythe Tehran Tehran area, meaningdustdust storm. storm. it was not produced by the atmospheric conditions related to event of the Tehran dust storm.

FigureFigure 17. 17.DREAMDREAM (experiment (experiment DLR1) DLR1) PM10 PM10 dust dust surface concentrationconcentration ( µ(μm/mm/m3)3) at at 11 11 UTC UTC (a (),a), 12 12 UTC UTC (b ),(b and), and 13 UTC13 UTC Figure(c) for(c) forthe 17. the caseDREAM case study study (experiment area. area. DLR1) PM10 dust surface concentration (μm/m3) at 11 UTC (a), 12 UTC (b), and 13 UTC (c) for the case study area. Vertical cross-sections for the low-resolution run (DLR1), similar to the one presented for theVertical high-resolution cross-sections forecast, for the are low presented-resolution in Figure run (DLR1), 18. From similar the vertical to the one cross presented-section forof the the temperaturehigh-resolution field, forecast, cold down are presenteddraft did innot Figure occur, 18. and From the the vertical vertical velocities cross-section were oflow. the The temperature vertical cross field,-section cold ofdown the dustdraft concentration did not occur, one and hour the later vertical shows velocities the presence were low. The vertical cross-section of the dust concentration one hour later shows the presence

Atmosphere 2021, 12, 1054 20 of 26

Vertical cross-sections for the low-resolution run (DLR1), similar to the one presented Atmosphere 2021, 12, x FOR PEER REVIEW 20 of 25 for the high-resolution forecast, are presented in Figure 18. From the vertical cross-section of the temperature ﬁeld, cold downdraft did not occur, and the vertical velocities were low. The vertical cross-section of the dust concentration one hour later shows the presence of ofthe the dust dust on on the the eastern eastern side side of theof the case case study study area area with with relatively relatively similar similar values values to those to those of ofthe the ground ground up up to to 3000–5000 3000–5000 m m and and over over the the Tehran Tehran area area below below 1000 1000 (µm/m (μm/m3). A3). wallA wall of of highhigh dust dust concentrations concentrations isis notnot visible visible in in this this cross-section, cross-section, and and the the dust dust present present here here is of ais of a differentdifferent origin, origin, as as already already explained. explained. DueDue to to the the lack lack of strong winds,winds, whichwhich in in reality reality impacted impacted the the faster faster movement movement of of thethe dust, dust, and and of of opposite opposite direction toto thethe observedobserved winds winds in in the the case case study study area, area, in thisin this simulationsimulation the the dust dust coming coming from thethe easteast wentwent over over the the case case study study area area and and remained remained for for longer period of time. longer period of time.

FigureFigure 18. 18. VerticalVertical cross cross-section-section of of temperature temperature ﬁeldfield at at 12 12 UTC UTC (a (),a vertical), vertical component component of wind of wind velocity velocity with with streamlines streamlines at at 12 12 UTCUTC ( b(),b), and and PM10 PM10 dust dust concentration concentration at 13 UTCat 13 (cUTC), obtained (c), obtained by DREAM by DREAM low-resolution low-resolution forecast (experiment forecast (experiment DLR1); x- DLR1);and y-axis x- and have y-axis the have same the meaning same asmeaning in Figure as 14in. Figure 14.

Additionally,Additionally, the model setset toto thisthis lowlow resolution resolution is is not not capable capable to to properly properly see see small- small- scalescale dust dust sources sources (hot (hot-spots)-spots) suchsuch asas agricultureagriculture farms, farms, or or it it could could see see them them as as weaker weaker sourcessources because because ofof thethe grid grid box box averaging averaging of theof inputthe input information. information. Thus, Thus, besides besides the absence the absence of high-velocity surface winds, as shown here, additional dumping for the reproduction of high airborne dust concentrations would reduce the potential of hot-spots for dust emission. In the Supplementary Material, the DREAM results for surface temperature, wind and dust concentration for the whole model domain are provided (Video S1), as well as surface dust concentrations from all three high-resolution runs (Video S2). The results are

Atmosphere 2021, 12, 1054 21 of 26

of high-velocity surface winds, as shown here, additional dumping for the reproduction of Atmosphere 2021, 12, x FOR PEER REVIEWhigh airborne dust concentrations would reduce the potential of hot-spots for dust emission.21 of 25

In the Supplementary Material, the DREAM results for surface temperature, wind and dust concentration for the whole model domain are provided (Video S1), as well as surface dust concentrations from all three high-resolution runs (Video S2). The results are given in the form of animated gifs, which show the formation, progression, and dissipa- given in the form of animated gifs, which show the formation, progression, and dissipation tion of the storm events on 2 July 2014. of the storm events on 2 July 2014.

3.3.3.3.3.3. Analysis Analysis of of WRF WRF-Chem-Chem High High-Resolution-Resolution Forecast Forecast SinceSince the the WRF WRF-Chem-Chem is is widely widely used used and and accessible accessible to interested to interested users, users, with the with provided the pro- videdguidance guidance and support, and sup aport, WHR a experimentWHR experiment was carried was out carried to assess out itsto assess capacity its to capacity act as a to actdust as forecasta dust forecast model for model early warningfor early purposes. warning The purposes. surface windThe surface velocity wind and direction velocity for and directionthe case studyfor the area case are study presented area are in Figure presented 19. The in frontFigure line 19. generated The front by line strong generated surface by strongwinds surface that were winds produced that were by the produced cold downdrafts by the cold was downdrafts moving across was the moving area from across the the areasouthwest from the toward southwest the northeast, toward and the after northeast, the front and passed, after windthe front velocities passed, increased wind tovelocities over increased20 m/s. This to over model 20 m/s. was setThis to model produce was outputs set to everyproduce half outputs an hour every so as tohalf allow an hour a better so as torepresentation allow a better of thisrepresentation fast-changing of event. this fast At-changing 11:30 UTC, event. the front At reached 11:30 UTC, Tehran. the At front 12 UTC, the highest winds hit Tehran city. reached Tehran. At 12 UTC, the highest winds hit Tehran city.

FigureFigure 19. 19. WRFWRF wind wind velocity velocity (m/s) and directiondirection over over the the case case study study area area during during the period the period 10 UTC 10 to UTC 13 UTC to 13 (a– UTCf); black (a–f); blackdots dots markers markers in Tehran in Tehran province province mark mark the location the location of two of airports two airports (OIIIE and(OIIIE OIII). and OIII).

TheThe results results obtained obtained for the PM10 dustdust surfacesurface concentration concentration for for the the case case study study area area areare presented presented in in Figure Figure 20.20. This This model, usingusing anan approximation approximation of of dust dust sources sources from from ﬁxed fixed datadata on on land land cover, cover, managed managed to reproducereproduce the the atmospheric atmospheric conditions conditions before before and and during during thethe Tehran Tehran dust dust storm. storm. The The arrival arrival ofof thethe frontalfrontal line line and and increasing increasing wind wind velocity velocity blowing blowing fromfrom the the southwest southwest to to the the northeast overover TehranTehran Province Province in in the the southern southern and and western western parts were somewhat earlier than observed (about an hour early), but the signal of the approaching storm was strong and clear. Since the sources are widely distributed over the model domain, higher concentrations of dust are present over the case study area, bring- ing dust into the Tehran region even after the passing of the dust storm, the timing and duration of which is evidenced from the observations. Such false dust signals can be avoided by incorporating more representative dust source mask as input information for

Atmosphere 2021, 12, 1054 22 of 26

parts were somewhat earlier than observed (about an hour early), but the signal of the approaching storm was strong and clear. Since the sources are widely distributed over the model domain, higher concentrations of dust are present over the case study area, Atmosphere 2021, 12, x FOR PEER REVIEWbringing dust into the Tehran region even after the passing of the dust storm, the timing22 of 25

and duration of which is evidenced from the observations. Such false dust signals can be avoided by incorporating more representative dust source mask as input information forthe themodel, model, as was as performed was performed for the for DREAM the DREAM model model simulation. simulation. Nevertheless, Nevertheless, the numer- the numericalical performance performance of WRF of-Chem WRF-Chem in forecasting in forecasting the Tehran the Tehran dust dust storm storm showed showed that that the themodel model is capable is capable of forecasting of forecasting intensive, intensive, short short-lived-lived dust duststorms storms and of and providing of providing early early warnings for such events. warnings for such events.

3 Figure 20. WRF-ChemWRF-Chem (experiment WHR)WHR) PM10PM10 dustdust surfacesurface concentrationconcentration ((µμm/mm/m3) and wind arrows over the case study areaarea duringduring thethe periodperiod 11–1311–13 UTCUTC ((aa––dd);); blackblack dotsdots inin TehranTehran ProvinceProvince markmark thethe locationlocation ofof twotwo airportsairports (OIIIE(OIIIE andand OIII).OIII).

4. Discussion The Tehran dustdust stormstorm onon 22 JuneJune 20142014 hadhad thethe characteristicscharacteristics ofof aa haboob.haboob. StrongStrong convective activity occurred in the south and southwest of Tehran Province becausebecause ofof thethe intensive heating during the day.day. Multiple convective cells formed with an intensive coldcold downdraft, which produced strong surface winds. The frontal area of thethe high-velocityhigh-velocity winds merged and and formed formed a astorm storm front front line, line, which which swept swept through through Tehran Tehran Province. Province. In- Intensivetensive emission emission of of dust dust par particlesticles occurred occurred along along the the path path of of the high-velocityhigh-velocity surface surface winds and created the fast-moving dust storm (approximately 60 km/h). The most affected areas were the southern and southwestern parts of Tehran Province and Tehran city. The modeled and observed PM10 dust surface concentrations were up to 8000 μm/m3. A clearly visible dust wall arrived in Tehran about 13 UTC as a hazard that endangered the lives of the citizens and caused many structural damages. Public and emergency units were not warned sufficiently prior to its arrival.

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winds and created the fast-moving dust storm (approximately 60 km/h). The most affected areas were the southern and southwestern parts of Tehran Province and Tehran city. The modeled and observed PM10 dust surface concentrations were up to 8000 µm/m3.A clearly visible dust wall arrived in Tehran about 13 UTC as a hazard that endangered the lives of the citizens and caused many structural damages. Public and emergency units were not warned sufficiently prior to its arrival. The forecasts of the dust storm performed with the DREAM and WRF-Chem models in this study gave high-quality results and proved that operational dust forecast can provide information on such events at least 24 h before they happen. Both of these models and any other coupled dust-atmospheric non-hydrostatic model—with the capability of providing high-quality forecasts of atmospheric conditions that lead to severe local dust storm formation, the capacity to simulate sudden emissions of high dust concentrations, and a properly defined dust source mask—are very likely capable of being employed in early warning systems. This finding confirms that a large contribution to the efficiency of early warning systems is possible if a dust operational forecast is employed. Lessons learned from the numerical simulation experiments provide guidance for the operational model set-up if the forecast of such a local, short-lived, and intensive dust storm is required: (a) The resolution of the coupled dust-atmospheric model must be of several kilometers to be able to reproduce strong convective activity, which is the essential atmospheric initiator of such storms. (b) The model domain should be wider than the area of interest because atmospheric processes that lead to the formation of the event happen over a larger domain. (c) The dust source mask should be designed based on the current information (the bare land fraction should be defined from the latest available information on surface coverage, derived from satellite data, and updated according to the dynamic of land cover change within the domain); information from national (or sub-national, depending on the domain) data on the dust sources and/or soil texture, if available, should be included in defining the dust source masks. (d) The dust forecast employed over a local domain will not contain information on the intrusion of the dust outside of the model domain, and, if such intrusion is possible, the model boundary conditions must include dust concentrations of some operational dust model in a coarser resolution and of larger domain (for example, the SDS-WAS operational dust forecast); an alternative is to use both forecasts as a complementary material for early warnings. The conclusion derived from the performed experiments that stands out is the evidence for the consequences of misuse of land surface and its over-exploitation and how that can adversely affect human livelihoods. Abandoned agricultural lands at the southern side of Tehran city, recognized as dust sources by the relevant governmental and scientific institutions of Iran (small-scale and highly dust-productive source, i.e., dust source hot- spot), were responsible for the major portion of the airborne dust within the dust wall that swept through Tehran city. While this severe impact of abandoned agricultural surfaces is only one of the findings derived from the presented simulation experiments, we consider it to be the most original and significant indicator for setting priorities in emergency re- sponses for the mitigation of dust sources or for preventing the formation of man-made dust sources. Interventions of such kind are targeted by the “land degradation neutrality” actions, which are defined, studied, and promoted world-wide by the UNCCD (United Nations Convention to Combat Desertification) [25]. In addition to the overexploitation of soil surfaces by crop production, water scarcity, grazing, and deforestation can have a direct impact on soil degradation [26] and can thereby result in the formation of SDS sources. In this way, affected surfaces should also be included in dust source mapping (depending on the area) because dried lakes and riverbeds, overexploited pastures, and the removal of self-preserved vegetation coverage are surfaces potentially vulnerable to wind erosion. Atmosphere 2021, 12, 1054 24 of 26

Dust storms are globally represented in all latitudes as part of the natural dust cycle with positive environmental impacts or as the consequence of disturbed land surface and climate conditions [5,6,27–31]. As future socio-economic pathways indicate for the possible futures, in the case of increasing population and food and water demands, land degradation may increase [1], and, under climate change impacts, such man-made dust sources may increase. Existing and potentially increasing problems in the future related to dust storms are linked to the responsibilities of many UN agencies. For this reason, the UN Sand and Dust Storm Coali- tion was created, including representatives from 15 UN agencies (https://unemg.org/our- work/emerging-issues/sand-and-dust-storms/, accessed on 10 July 2021). Coordinated work by UN agencies (which is responsible for forecast and warning system developments, for resolving the problem of land degradation, for environmental protection, and for the protection of human health and safety) is expected to contribute to the restoration of the natural dust cycle and to the mitigation of negative dust storm impacts and will guide countries and regions in resolving priority issues related to dust storms.

Supplementary Materials: The following are available online at https://www.mdpi.com/article/ 10.3390/atmos12081054/s1. In the ppt file, the following are given: Video S1: Animated gif of the DREAM forecast for the experiment DHR1 for the whole model domain for the period 06-20 UTC 2 June 2014: surface (2 m) air temperature (left), surface (10 m) wind direction and velocity (middle), and PM10 surface dust concentration (right); and Video S2: Animated gif of the DREAM forecasts of PM10 surface dust concentration, for the case study area for the period 09-18 UTC 2 June 2014: experiment DHR1 (left), experiment DHR2 (middle), and experiment DHR3 (right). Author Contributions: Conceptualization and methodology A.V.V.; Software (DREAM simulations) and visualization A.V.V., B.C., S.N. and G.P.; Software (WRF-Chem simulations) and visualization T.M.G., V.K. and K.L.; Software input data preparation A.V.V., B.C., M.V.M. and R.S.; Ground observations data and advisory S.S.K., A.D.B. and S.P.; Satellite observations data J.P.; Communication and information mining S.B. and E.T. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data available on request due to their robustness and restrictions on public sharing. Acknowledgments: The study was initially proposed within the framework of the WMO Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS). For the conversion of the dust source mask of Iran into the geo-referenced, we thank Bojan Spasojevic and Dimitrije Kostic. The communications that allowed the interconnectivity of the researchers who contributed to this study were supported by InDust CA16202. The authors from Faculty of Agriculture, University of Belgrade, were supported for their scientific work in 2021 by the contract on financing between the Faculty of Agriculture and the Ministry of Education, Science and Technological Development of RS (451-03-9/2021-14/200116). Conflicts of Interest: The authors declare no conflict of interest.

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