1
1 Trajectory Analysis of Saudi Arabian Dust Storms 2 3 Michael Notaro 4 Nelson Center for Climatic Research, University of Wisconsin-Madison 5 1225 West Dayton Street, Madison, Wisconsin 53706 6 * [email protected], 608-261-1503 7 8 Fahad Alkolibi, Eyad Fadda, Fawzieh Bakhrjy 9 King Saud University 10 11 Revised Research Article to the Journal of Geophysical Research-Atmospheres 12 13 Abstract
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15 Temporal and spatial characteristics of Saudi Arabian dust storms, with focus on
16 associated air parcel trajectories, are investigated using station and gridded weather
17 observations and remotely-sensed aerosol optical depth (AOD). For 13 focal stations, an
18 extensive pool of 84-hour backward trajectories is developed for dust storm days, and the
19 trajectories are grouped into 3-5 representative clusters based on the K-means technique
20 and Silhouette Coefficients.
21 Saudi Arabian dust storms are most prominent during February-June, with a mid-
22 winter peak along the southern coast of the Red Sea, spring peak across northern Saudi
23 Arabia around the An Nafud Desert, and early summer peak in eastern Saudi Arabia around
24 the Ad Dahna Desert. Based on backward trajectories, the primary local dust source is the
25 Rub Al Khali Desert and the primary remote sources are the Saharan Desert, for western
26 Saudi Arabia, and Iraqi Deserts, for northern and eastern Saudi Arabia. During February-
27 April, the Mediterranean storm track is active, with passing cyclones and associated cold
28 fronts carrying Saharan dust to Saudi Arabian stations along the northern coast of the Red
29 Sea. Across Saudi Arabia, the highest AOD is achieved during dust storms that originate 2
30 from the Rub Al Khali and Iraqi Deserts. Most stations are dominated by local dust sources
31 (primarily Rub Al Khali), are characterized by three dominant trajectory paths, and achieve
32 AOD values exceeding 1. In contrast, for stations receiving predominantly remote dust
33 (particularly Saharan), 3-5 trajectory paths emerge and AOD values only reach
34 approximately 0.6 as dust is lost during transport.
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37 Key words: dust storms, Saudi Arabia, backward trajectories, aerosol optical depth, dust
38 sources, HYSPLIT
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52 3
53 1. Introduction
54
55 Global deserts supply roughly 5×108 tons of Aeolian dust to the atmosphere each
56 year (Peterson and Junge, 1971). Through substantial aerosol loading to the atmosphere,
57 dust storms can significantly alter the regional climate and physical environment (Park et
58 al., 2005; Goudie and Middleton, 2006). Dust can be transported over thousands of
59 kilometers, thereby influencing both the environment and society at great distances from
60 its source region (Goudie and Middleton, 2001, 2006; Middleton and Goudie, 2001). For
61 instance, the Saharan Desert contributes an estimated 40-66% of total Aeolian dust to the
62 global atmosphere (Junge, 1979; Morales, 1979; Schutz et al., 1981; Ganor and Mamane,
63 1982). In response to strong summertime heating of the land surface or gusty winds
64 associated with storms entering the Mediterranean Sea or North Africa, Saharan dust can
65 be lifted to heights in excess of 3-5 km above the ground (Escudero et al., 2005, 2011;
66 Dayan et al., 1991; Alpert and Ganor, 1993; Conte et al., 1996; Prospero, 1996). In extreme
67 cases, Saharan dust can reach China (Tanaka et al., 2005), the United States (Prospero,
68 1981; Gatz and Prospero, 1996), Barbados (Delany et al., 1967), and other remote locations,
69 even the Arctic (Barkan and Alpert, 2010).
70 Research on Aeolian dust is vitally important given its notable influence on
71 atmospheric and oceanic conditions, agriculture, soil, water quality, and human health. The
72 impact of dust on the atmosphere’s radiative balance (Hansen et al., 1998; Haywood and
73 Boucher, 2000) includes both direct effects on scattering and absorption (Tegen et al.,
74 1996; Haywood et al., 2003) and indirect effects related to the aerosols’ influence on cloud
75 microphysics (Rosenfeld et al., 1997). By altering the atmospheric temperature (Wong et 4
76 al., 2009) and concentration of condensation nuclei, dust storms can affect convective
77 activity, cloud formation, and precipitation efficiency (Bryson and Barreis, 1967; Maley,
78 1982; Lohmann and Feichter, 2005; Wong and Dessler, 2005). Dust aerosol radiative
79 heating can influence synoptic weather patterns, such as by strengthening the Saudi
80 Arabian heat low (Mohalfi et al., 1998). The impact of the dusty Saharan air layer on the
81 growth of easterly waves and tropical cyclones across the Atlantic Ocean continues to be
82 debated (Karyampudi and Carlson, 1988; Karyampudi and Pierce, 2002; Dunion and
83 Velden, 2004; Wu, 2007; Lau and Kim, 2007a,b; Sun et al., 2008). Iron is a key constituent
84 of aeolian dust (Zhu et al., 1997), and its deposition into the ocean enhances phytoplankton
85 blooms (Martin et al., 1991; Gruber and Sarmineto, 1997; Jickells et al., 1998; Sarthou et al.,
86 2003) and potentially leads to ocean cooling (Schollaert and Merrill, 1998). Dust can affect
87 atmospheric chemistry, including sulfur dioxide concentrations through physical
88 adsorption and heterogeneous reactions (Adams et al., 2005). Dust storms can damage
89 crops and reduce soil fertility (Fryrear, 1981; Thiagarajan and Lee, 2004). Furthermore,
90 dust storms dramatically affect human society. Reduced visibility can lead to traffic
91 accidents and vertigo in aircraft pilots (Morales, 1979; Hagen and Woodruff, 1973;
92 Middleton and Chaudhary, 1988; Dayan et al., 1991; Yong-Seung and Ma-Beong, 1996).
93 Dust storms can transport allergens and disease-spreading spores (Leathers, 1981; Shinn
94 et al., 2000; Pope et al., 2002; Kampa and Castanas, 2008), trigger asthma and respiratory
95 ailments (Kar and Takeuchi, 2004; Chen et al., 2004; Gyan et al., 2005; Thalib and Al-Taiar,
96 2012), and contaminate drinking water (Clements et al., 1963).
97 Saudi Arabia is a region of complex topography and extensive deserts (Fig. 1). Its
98 three primary desert regions are the Rub Al Khali (“Empty Quarter”, ≈600,000 km2) in the 5
99 southeast, An Nafud (≈65,000 km2) in the northwest, and Ad Dahna sand corridor (≈40,000
100 km2) in the east, connecting the previous two deserts. Remote desert regions that can
101 potentially serve as dust source regions to Saudi Arabia include the vast Saharan Desert to
102 the west and Syrian and Iraqi (Al-Hajarah and Al-Dibdibah) Deserts to the north. The
103 Arabian Peninsula is bordered by the Mediterranean Sea to the northwest, Red Sea to the
104 west, Gulf of Aden and Arabian Sea to the south-southeast, and Persian (Arabian) Gulf to
105 the east, with the Sarawat Mountains (up to 3.3 km in elevation) along the peninsula’s west
106 coast.
107 According to Total Ozone Mapping Spectrometer (TOMS) data, the most prolific dust
108 source regions in the world are the Sahara Desert, particularly the Bodélé Depression in
109 Chad (most active in spring), and the Rub Al Khali along the Saudi Arabia-Oman border
110 (Goudie and Middleton, 2001, 2006; Giles, 2005). Within the Middle East, the TOMS
111 aerosol index peaks over the Rub Al Khali and Ad Dahna Deserts, and dust storms typically
112 occur in areas with a mean annual precipitation less than 100 mm and a mean annual
113 potential evapotranspiration greater than 1140 mm (Goudie and Middleton, 2002, 2006).
114 Middle Eastern dust storms are most frequent across Sudan, Iraq, Saudi Arabia, and the
115 Persian (Arabian) Gulf (Kutiel and Furman, 2003).
116 An extensive discussion of the causes of Middle Eastern dust storms is provided by
117 Goudie and Middleton (2006). According to Vishkaee et al. (2011), dust storms are
118 primarily triggered through dynamical lifting in the cool season, related to cold fronts and
119 their associated mid-latitude troughs, or diurnal vertical mixing in the warm season,
120 related to solar heating. The most frequent trigger for dust storms is a frontal passage,
121 with strong winds associated with intense baroclinicity. The concentration of atmospheric 6
122 dust is tightly correlated with wind velocity (Kutiel and Furman, 2003). Strong surface
123 cyclones can also stir up dust clouds. In monsoon regions, dust may be lifted into the
124 atmosphere along convergence zones between cold air masses, associated with cyclones,
125 and tropical anticyclonic air masses. In areas of complex terrain, katabatic winds can
126 trigger localized dust storms. Dust can be delivered into the atmosphere through
127 convective plumes and vortices (Koch and Renno, 2005). Haboobs and dust devils are local
128 causes of dust-raising and transport. A haboob is a convection-generated dust storm
129 associated with the cool outflow from a thunderstorm downdraft. Middle Eastern dust
130 storm activity usually peaks during the daylight hours, when intense solar heating of the
131 ground generates turbulence and local pressure gradients (Middleton, 1986). Dust activity
132 and the remotely-sensed aerosol index generally peak during May-August across the
133 Arabian Peninsula (Prospero et al., 2002; Washington et al., 2003; Barkan et al., 2004;
134 Goudie and Middleton, 2006), when solar heating and climatological wind speeds are
135 greatest. However, Sharav (Saharan) cyclones from the Mediterranean Sea (Trigo et al.,
136 1999) are responsible for the winter-spring peak in dust activity that characterizes
137 northern Saudi Arabia (Ganor et al., 1991; Herut and Krom, 1996; Kubilay et al., 2000,
138 2005; Shao, 2001; Kubilay et al., 2003).
139 A strong northerly Shamal wind can lift up dust from the Tigris-Euphrates Basin of
140 Iran/Iraq and transport it to the Persian (Arabian) Gulf and Arabian Peninsula (Middleton,
141 1986a,b), with severe Arabian dust storms often associated with the summer Shamal (Shao,
142 2001). The Shamal wind is usually generated by a strong baroclinic gradient between a
143 semi-permanent anticyclone over northern Saudi Arabia and a transient cyclones over
144 southern Iran, with strong turbulent winds along the convergence zone that are ideal for 7
145 lifting dust into the atmosphere (Membery, 1983; Goudie and Middleton, 2006). The
146 summer Shamal in JJA blows nearly continuously. In contrast, the rare winter Shamal
147 episodes during NDJFM persists for only one to five days and are characterized by gusty
148 northwesterly winds on the back side of a cold front, associated with an eastward or
149 southward-propagating mid-latitude disturbance from the Mediterranean Sea or Turkey
150 (Perrone, 1979; Vishkaee et al., 2012).
151 In the present study, we investigate the temporal and spatial characteristics of dust
152 storms across Saudi Arabia, with particular focus on the responsible air parcel trajectories
153 and dust source regions, using station and gridded weather observations and remotely-
154 sensed aerosol optical depth (AOD). Based on station observations, we formulate a list of
155 dust storm days for 13 unique locations across Saudi Arabia (Table 1, Figs. 1-2) and
156 examine the seasonal cycle of the dust storm activity. For each station, we develop a series
157 of 84-hour backward trajectories for these dust storm days. From these trajectories, we
158 determine the likely dust source regions. Next, we apply cluster analysis to the trajectories
159 to identify preferred air mass routes associated with the dust storms. Using a reanalysis
160 product and remotely-sensed AOD, we assess the time-evolving synoptic weather pattern
161 that generates the dust storms and their related AOD pattern.
162
163 2. Data and Methods
164
165 2.1 Datasets
166 Present weather observations are retrieved from the National Climatic Data Center
167 (NCDC) Hourly Global and U.S. Integrated Surface Hourly Dataset for 2005-2012 at 13 8
168 stations across Saudi Arabia (Figs. 1-2). Here, modest dust activities are characterized by
169 station observations of “widespread dust in suspension in air”, “dust or sand raised by
170 wind”, “well-developed dust/sand whirl”, or “duststorm/sandstorm within sight”, while
171 intense dust activities are characterized by “slight/moderate dust/sandstorm” or “severe
172 duststorm/sandstorm”. For each day in a station’s record, a dust index is computed by
173 weighting hourly reports of modest dust activity by one and intense dust activity by three
174 and then summing the values for each day; while the weight of 3:1 is relatively arbitrary,
175 the results are largely insensitive to the weight selection. For each station, the top 100 dust
176 days are identified during 2005-2012 based on this index, with a dust index criterion that
177 ranges from 4 in Jeddah to 19 in Riyadh; dust storms are less common at the former
178 location, along the Red Sea, than the latter location, near the Ad Dahna Desert. Based on
179 hourly dust reports, the most active time of each day is determined (0Z, 3Z, …, 21Z), and
180 backward trajectories are developed using this list of 100 dust days and their central times
181 for each station.
182 Backward trajectories are computed based on six-hourly, three-dimensional wind
183 fields on a 1° x 1° grid from the National Centers for Environmental Prediction (NCEP)
184 Global Data Assimilation System (GDAS) (Kanamitsu, 1989), which applies the spectral
185 Medium Range Forecast (MRF) model. Numerous prior studies have applied GDAS in
186 developing backward air trajectories (e.g. Jorba et al., 2004; Brimelow and Reuter, 2005;
187 Moore et al., 2012). Time-evolving composites of daily sea-level pressure and 500-hPa
188 geopotential height anomalies are created using the NCEP-National Center for Atmospheric
189 Research (NCAR) Reanalysis on a 2.5° x 2.5° grid (Kalnay et al., 1996). 9
190 Daily Deep Blue aerosol optical depth (AOD) at 550 nm for 2005-2012, on a 1° x 1°
191 grid, is obtained from the National Aeronautics and Space Administration (NASA) as part of
192 the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Daily Level-
193 3 dataset (Hsu et al., 2004). The MODIS instruments are onboard the NASA Earth
194 Observing System (EOS) Terra and Aqua satellites (Salomonson et al., 1989; Barnes et al.,
195 1998). The Deep Blue algorithm uses radiances from blue channels on the MODIS
196 instruments. The surface reflectance is minimal at these wavelengths, so aerosols may be
197 detected by an increase in total reflectance and spectral contrast (Hsu et al., 2004, 2006;
198 Ginoux et al., 2012). Estimates of AOD are only considered reliable over bright land
199 surfaces (Marey et al., 2011; Ginoux et al., 2012) and are characterized by substantial
200 uncertainties up to 25-30% (Hsu et al., 2006). More specifically, MODIS-derived AODs have
201 expected errors of ±(0.05+0.15*AOD) over land (Remer et al., 2005, 2008).
202
203 2.2 Trajectory model
204 A Lagrangian trajectory model describes the paths of individual, infinitesimally
205 small air parcels as they travel through space and time (Dutton, 1986). The resulting
206 backward trajectories contain information on wind direction, wind speed, and atmospheric
207 stability (Dorling et al., 1992). Here, we apply the Hybrid Single Particle Lagrangian
208 Integrated Trajectory Model from the National Oceanic and Atmospheric Administration
209 (NOAA) Air Resources Laboratory (ARL) (Draxler and Hess, 1998; Rolph, 2003; Draxler,
210 2006; Draxler and Rolph, 2012). Prior studies have applied HYSPLIT backward trajectories
211 to examine the sources and pathways of dust events in China (Lee et al., 2010; Logan et al.,
212 2010), Iran (Vishkaee et al., 2011), Spain (Cabello et al., 2012; Valenzuela et al., 2012), the 10
213 Nile Delta (Prasad et al., 2010), and Indo-Gangetic Plains (Prasad and Singh, 2007), among
214 other regions.
215 Backward trajectories are computed using the Real-time Environmental
216 Applications and Display System (READY) website (Rolph, 2012). HYSPLIT uses the u- and
217 v-component of the wind, temperature, height, and pressure at different atmospheric levels,
218 typically from a reanalysis product (Banacos and Ekster, 2010) or operational model runs
219 (Moore et al., 2012), to compute backward trajectories for air parcels at specified heights above
220 the ground. For the current study, GDAS output is provided to the HYSPLIT model to compute
221 84-hour backward trajectories, using a dust arrival height of 500-m. Backward air trajectories
222 are typically computed for a maximum duration of three to four days, since excessive errors can
223 develop over longer time scales (Stohl, 1998). For all trajectories, hourly data is saved for height
224 above the ground, relative humidity, and atmospheric pressure (used to compute vertical motion).
225 For each of 13 Saudi Arabian stations, backward trajectories are computed from that station’s set
226 of 100 dust days during 2005-2012, and the trajectory ensemble option of HYSPLIT is applied
227 which results in 27 trajectories per day, or a total of 2700 backward trajectories per station. By
228 computing backward trajectories as ensembles, the inherent uncertainty of trajectory calculations
229 is addressed (Vishkaee et al., 2012). Each member of a trajectory ensemble is computed by
230 offsetting the meteorological GDAS data by a pre-determined grid factor, consisting of one grid
231 cell in the horizontal and 0.01 sigma units in the vertical, resulting in 27 ensemble members
232 based on all potential offsets in the X, Y, and Z directions.
233 For each of the six major desert regions noted in Section 1 (Rub Al Khali, An Nafud,
234 Ad Dahna, Sahara, Iraqi, and Syrian Deserts), a mask file is created with its approximate
235 spatial extent (Fig. 1). For all 2700 backward trajectories for each of 13 stations, the 11
236 percentage of trajectories passing over each desert is computed, along with the time spent
237 over the desert region during the 84-hour trajectories, in order to identify potential dust
238 source regions. In a similar fashion, Estellés et al. (2007) and Toledano et al. (2009)
239 identified air mass origin sectors by considering the amount of time spent over source
240 regions through trajectory analysis.
241 Each trajectory is classified as anticyclonic, neutral, or cyclonic, roughly following
242 the approach of Dong et al. (2011). First, the angle (α84) is computed between the latitude
243 and longitude at t0 (time of the dust storm event) and the latitude and longitude at t-84
244 (84-hours before the event). The Euclidean vector is determined between the former point
245 and latter point and its angle is computed, relative to true north. This is repeated for each
246 subsequent hour (e.g. t0 versus t-83 for α83). Then, the differences between neighboring
247 angles (e.g. α83-α84, α82-α83, …, α0-α1) are summed. If the sum is greater than 30 or less than
248 -30, then the trajectory is classified as anticyclonic or cyclonic, respectively; otherwise, it is
249 classified as neutral.
250
251 2.3 Cluster analysis
252 Cluster analysis is a multivariate statistical method for exploring structure within a
253 dataset (Anderberg, 1973; Everitt, 1980). In particular, non-hierarchical partitional
254 clustering techniques, such as the K-means clustering algorithm (Moody and Galloway,
255 1988; Kaufman and Rousseeuw, 1989; Dorling et al., 1992), are well designed for analyzing
256 large datasets (Čolović-Daul, 2007). Moody (1986) and Moody and Galloway (1988)
257 introduced the application of cluster analysis to air trajectories, leading to a series of
258 subsequent studies of trajectory clustering (e.g. Čolović-Daul, 2007; Žabkar et al., 2008; 12
259 Markou and Kassomenos, 2010). Briefly stated, the K-means method iteratively allocates a
260 set of objects, such as trajectories, among a predetermined number of clusters until the
261 sum of distances from each object to its cluster centroid over all clusters is minimized
262 (Žabkar et al., 2008).
263 According to the K-means algorithm, a set of trajectories is grouped into K clusters
264 according to a distance measure of metric space. Two common measures, also known as
265 metrics or distance functions, are the sum of square distance on the Euclidean plane
266 between end points (Moody and Galloway, 1988) and sums of the great-circle distance on a
267 Euclidean sphere between trajectory end points (Lin et al., 2001; Jorba et al., 2004). A
268 metric space is defined for quantifying similarities or differences between trajectories. For
269 each cluster, its cluster centre is determined as the mean trajectory for that cluster. Based
270 on the cluster centres, the distance between trajectories is computed. Trajectories are
271 grouped so as to minimize the variance of the distance between trajectories in the same
272 cluster and maximize the variance for trajectories belonging to different clusters (Crawford
273 et al., 2009).
274 One of the challenges with the K-means clustering technique is to determine the
275 appropriate number of clusters. The silhouette coefficient, which is a dimensionless
276 measure of both internal cluster cohesion and external cluster separation, can be used to
277 determine the appropriate number of clusters (Anderberg, 1973; Rousseaw, 1987). When
278 applied to trajectories, this method assesses how well the trajectories are clustered around
279 centroids into unique groupings and how distinct these clusters are from each other
280 (Anderberg, 1973). For each ith trajectory, the silhouette value is computed as:
281 = − ( ) ( , ) . 13
282 If we assume that the ith trajectory is assigned to cluster A, then a(i) is the average
283 dissimilarity of the ith trajectory to all other trajectories in cluster A and b(i) is the average
284 dissimilarity of the ith trajectory to all trajectories in neighboring clusters, B. The silhouette
285 value ranges from -1 to +1. If S(i) is close to -1, then the trajectory is misclassified, but if
286 S(i) is close to +1, then the trajectory is well classified. The average Silhouette Coefficient,
287 computed as the mean value of S for all trajectories, is a measure of how well the cluster
288 structure fits the dataset and can be used to identify the ideal number of clusters to assign
289 to the dataset (Kaufman and Rousseeuw, 2005). The silhouette method generally leads to
290 clearer results than the traditional approach of identifying an elbow in a curve of the
291 number of clusters versus the within-cluster variance, particularly given that many curves
292 lack a distinct elbow. As pointed out by Salvador and Chan (2004), most studies that apply
293 the elbow approach to determine the appropriate number of clusters lack a clear statistical
294 justification.
295 In the current study, HYSPLIT backward trajectories (100 dust storm days * 27
296 ensemble members/day = 2700 trajectories per station) are clustered in Matlab using the
297 K-means technique based on the squared Euclidean distance metric, with 20 replicates.
298 Mean Silhouette Coefficients are then computed for cluster sizes ranging from three to ten,
299 with the highest coefficient determining the optimal number of clusters to apply. Any two
300 84-hour trajectories can be compared by computing the root-mean-square-difference
301 (RMSD) between their 84 latitudes and against between their 84 longitudes, and these two
302 values are then averaged for a net RMSD. The mean RMSD can be compared within-cluster
303 and across-cluster to assess the consistency within a cluster and uniqueness from other
304 clusters. 14
305
306 3. Results
307
308 3.1 Seasonal cycle of dust storms
309 Based on NCDC data for 13 Saudi Arabian stations (Table 1), the mean seasonal
310 cycle of dust storm activity is computed (Fig. 2). Averaged among these stations, Saudi
311 Arabian dust storms are most frequent from mid-winter to early summer, with 77% of dust
312 storms occurring during February-June. Dust storms are most active during March (19%)
313 and least active during November (1%). The pattern of preferred timing for dust storms is
314 heterogeneous across the country. Dust storm frequency peaks in mid-winter (February)
315 in proximity to the lower Sarawat Mountains (e.g. Jeddah and Al-Baha), in early spring
316 (April-May) over northern Saudi Arabia around the An Nafud and Syrian Deserts (e.g.
317 Turaif, Arar), and in early summer (June) over eastern Saudi Arabia around the Ad Dahna
318 Desert (e.g. Riyadh, Al-Ahsa). The station located at the highest elevation (1652 m in the
319 Sarawat Mountains), Al-Baha, exhibits a distinct bimodal peak in dust storm activity in
320 February-March and June-July; this is later attributed to south-southeasterly and
321 northwesterly trajectories, respectively.
322
323 3.2 Silhouette Coefficients
324 By computing the mean Silhouette Coefficients for a range from three to ten clusters
325 at each station, the optimal number of clusters for representing the pool of 2700 backward
326 trajectories at each station is determined (Fig. 3). In general, as the number of clusters
327 increases, the mean Silhouette Coefficient declines rather linearly, from 0.52±0.03 (mean ± 15
328 one standard deviation) at three clusters to 0.41±0.03 at ten clusters. For most stations,
329 three clusters are the best fit, with the exception of four clusters for Arar, Turaif, and
330 Jeddah and five clusters for Hafr Al-Batin. In agreement with Philipp et al. (2007), the
331 Silhouette Coefficient (along with other metrics) does not always provide a distinct
332 determination of the optimal number of clusters, but the selection of three to five clusters
333 is considered a manageable number for analysis and successfully leads to clear,
334 independent groupings of backward trajectories.
335
336 3.3 Trajectory clusters
337 According to the K-means clustering of the backward trajectories for 13 Saudi
338 Arabian stations and resulting mean Silhoutte Coefficients, the preferred 84-hour
339 trajectories for dust storms are grouped into three to five clusters and presented in Figs. 4-
340 6. In these figures, trajectories are assigned a color of red, green, blue, orange, or yellow in
341 order from the greatest AOD (red) to least AOD (yellow) measured at the station at time t0,
342 according to MODIS (as discussed later in Section 3.4). Furthermore, Table 1 includes the
343 percentage of trajectories for each station that pass over specified deserts, along with the
344 mean number of hours spent over the deserts; these statistics hint at potential dust source
345 regions. Based on the percentage of backward trajectories that pass over certain desert
346 regions in Saudi Arabia, the desert regions, in order from most to least important, are Rub
347 Al Khali (32%), Sahara (26%), Iraqi (25%), Ah Dahna (20%), Syrian (15%), and An Nafud
348 (13%). For Saudi Arabia, the Rub Al Khali is the most prominent local source of dust, while
349 the Saharan Desert is the greatest remote dust source. Further analysis indicates that the 16
350 Iranian Deserts are not substantial contributors of dust to Saudi Arabia, since few back
351 trajectories display a northeast to southwest path.
352 The individual trajectories for six select stations are shown in Fig. 7 to illustrate how
353 the K-means clustering technique grouped similar trajectories. For instance, the three
354 primary trajectories for dust storms in Sharorah (Figs. 4a, 7a) are northerly over the Rub Al
355 Khali Desert (39%, green), southeasterly from the Gulf of Aden and Yemen (33%, blue), and
356 northwesterly across the Mediterranean Sea, Iraqi Deserts, and Rub Al Khali Desert (28%,
357 red). From Fig. 7, it is evident that there is spread among the trajectories within a single
358 cluster but they generally follow a similar track in terms of distance and rotation. For
359 Sharorah, air parcels travel a longer distance on the northwesterly Mediterranean track
360 than the northerly Rub Al Khali track, while the southeasterly trajectories are largely
361 tropical maritime in nature. Based on Fig. 7, it is evident that the individual trajectories are
362 reasonable clustered, thereby supporting the relatively high Silhouette Coefficients. For
363 the 13 stations, the mean within-cluster RMSD and across-cluster RMSD are 4.67 and 9.61,
364 respectively, with a ratio between the two calculations of 2.06:1 (ranging from 1.73:1 for
365 Hail to 2.27:1 for Turaif). Clearly, trajectories within a single cluster are self-consistent and
366 unique from those in other clusters.
367 The vast majority of dust storm trajectories for Saudi Arabia are anticyclonic. On
368 average among the 13 stations, 56% of the 35,100 analyzed backward trajectories are
369 anticyclonic, 27% are neutral, and 17% are cyclonic. For Wadi Al-Dawasser and Riyadh, in
370 central Saudi Arabia, the dominant trajectory rotates clockwise out of Iraq (Fig. 4b,c), such
371 that 76% and 73% of their total trajectories are anticyclonic in nature, respectively. For 17
372 Tabuk (and other locations in northwestern Saudi Arabia), 33% of total trajectories are
373 cyclonic in nature, primarily those originating over the Mediterranean Sea (Fig. 5b).
374 Stations in Fig. 4, labeled here as Type I stations, generally have local dust sources
375 within Saudi Arabia, namely the Rub Al Khali (Sharorah, Wadi Al-Dawasser, Najran, and Al-
376 Baha), Ad Dahna (Riyadh), and An Nafud (Hail) Deserts (Table 1). The position of Najran,
377 Sharorah, and Wadi Al-Dawasser along the western fringe of the Rub Al Khali Desert makes
378 this desert a dominant source of dust to those locations. For Al-Baha, along the coast of the
379 Red Sea, the two dominant trajectories are south-southeasterly from Yemen and
380 northwesterly from the northern Sahara Desert (Fig. 4e), with preferred peaks in February-
381 March and June-July, respectively. These two preferred paths explain the bimodal seasonal
382 cycle of dust activity at Al-Baha as noted in Fig. 2. The northwesterly trajectory is
383 associated with the summer Shamal.
384 In contrast, stations in Figs. 5-6 generally receive their dust loadings from remote
385 sources outside of the country (Table 1). The primary remote dust sources are the Saharan
386 Desert, particularly for the “Type II” stations of Yenbo, Tabuk, Jeddah, and Turaif (Fig. 5),
387 and the Iraqi and Syrian Deserts, particularly for the “Type III” stations of Arar, Hafr Al-
388 Batin, and Al-Ahsa (Fig. 6). The Saharan Desert likely contributes the most dust to stations
389 in west-northwestern Saudi Arabia. For example, 68% of Yenbo’s trajectories cross the
390 Saharan Desert for a mean of 26.1 hours (Table 1). In eastern Saudi Arabia, close to the
391 Persian (Arabian) Gulf, the Iraqi Deserts are critical sources of dust, with 60% and 50% of
392 backward trajectories crossing these deserts for Hafr Al-Batin and Al-Ahsa, respectively
393 (Table 1). For stations bordering the northern Red Sea, such as Yenbo and Tabuk (Fig.
394 5a,b), the Mediterranean Sea track is quite active, with roughly three-fourths of total 18
395 trajectories crossing the sea with a preferred timing in mid-winter to early spring
396 (February-April). These dust events are associated with mid-latitude cold season cyclones
397 and their associated cold fronts, carrying Saharan dust to the region.
398
399 3.4 Aerosol Optical Depth statistics
400 The intensity of dust storms, in terms of aerosol loading, is assessed by quantifying
401 the mean AOD on dust storm days at each of 13 locations (Table 2). The mean AOD ranges
402 substantially from 0.43 at Tabuk, in northwest Saudi Arabia, to 1.10 at Najran, in southwest
403 Saudi Arabia. The highest AOD, on average close to 1.00, is associated with stations largely
404 impacted by the Rub Al Khali and Iraqi Deserts, namely Najran, Sharorah, Wadi Al-
405 Dawasser, and Hafr Al-Batin. In particular, the anticyclonic trajectory clusters that cross
406 the Rub Al Khali Desert into Sharorah (Fig. 4a, red), Wadi Al-Dawasser (Fig. 4b, red), and
407 Najran (Fig. 4d, red) generate an AOD in excess of 1.10, making these dust paths the most
408 efficient. In contrast, stations with trajectories that largely come off the Sahara Desert are
409 characterized by low AOD on dust storm days, including Yenbo and Tabuk with a mean
410 AOD of 0.52 and 0.43, respectively. As dust clouds travel a long distance from the Sahara
411 Desert to Saudi Arabia, much of the aerosols are lost through wet and dry deposition.
412 Trajectories that originate over the Mediterranean Sea and cross the Sahara Desert are the
413 least efficient dust paths, as evident by low AOD values at Tabuk (Fig. 5b, blue) and Yenbo
414 (Fig, 5a, blue) of 0.36 and 0.39, respectively. In general, stations with local dust sources
415 within Saudi Arabia (Type I) are characterized by greater AOD values during dust storms
416 than stations with remote dust sources from outside of the country, especially Type II. 19
417 For stations dominated by dust from the Rub Al Khali Desert, namely Najran,
418 Sharorah, Wadi Al-Dawasser, and Al-Baha, the trajectory cluster with the lowest mean
419 relative humidity in the final 24-hours is associated with the highest AOD, as the air mass
420 passes over favorable desert conditions. For example, the red and green trajectory clusters
421 for Sharorah (Fig. 4a) result in a mean AOD of 1.32 and 1.02 (Table 2), respectively. Their
422 paths are similar, yet the mean AOD is substantially different. Air masses associated with
423 the red trajectory are characterized by 10% lower relative humidity, favoring dust
424 production.
425
426 3.5 Synoptic and AOD characteristics of dust storms
427 The synoptic pattern (Fig. 9) that generates dust storms and the associated
428 characteristics of AOD (Fig. 8) are examined for three select stations, Najran (Type I), Al-
429 Ahsa (Type III), and Tabuk (Type II), which have unique dominant dust source regions of
430 the Rub Al Khali, Iraqi, and Sahara Deserts, respectively (Table 1). In particular, the
431 analysis focuses on the northerly trajectories at Najran (Fig. 4d, red), the short-distance
432 northwesterly trajectories at Al-Ahsa (Fig. 6c, green), and the long-distance northwesterly
433 trajectories at Tabuk (Fig. 5b, blue). The synoptic environment responsible for dust storms
434 in each of these three locations is found to be notably unique.
435 In the case of Najran, the anticyclonic trajectory initiates at 84-hours prior to the
436 dust events over southern Iraq, generating modest dust lifting, but as the trajectory passes
437 over the Rub Al Khali Desert, the mean AOD increases dramatically, indicating that this
438 desert is the primary source of dust (Fig. 8a-d). Following this specific trajectory cluster,
439 the mean AOD increases substantially from 0.72 on d-2 (2 days prior) to 1.02 on d-1 as the 20
440 air mass crosses the Rub Al Khali Desert (Fig. 10j). The anticyclonic trajectory is attributed
441 to a surface high-pressure system that propagates eastward from the eastern
442 Mediterranean Sea on d-2 (two days prior to the dust event) to the Caspian Sea at d0 (day
443 of the dust event) (Fig. 9a-c), while intensifying in the mid-troposphere (Fig. 9d-f). Winds
444 on the eastern and southern sides of this anticyclone stir up dust over the Rub Al Khali
445 Desert.
446 For Al-Ahsa, the northwesterly flow associated with dust storms is generated by the
447 pressure gradient between a surface anticyclone over North Africa and the Mediterranean
448 Sea and a surface cyclone east of the Caspian Sea (Fig. 9g-i). As these systems propagate
449 eastward, dust is channeled out of Iraq into eastern and southern Saudi Arabia (Fig. 8e-h).
450 The upper-level height anomalies are quite weak and dampen over time, leading up to the
451 dust event (Fig. 9j-l). As the air mass passes over the Iraqi Deserts, the mean AOD along
452 this trajectory cluster increases dramatically from 0.41 to 0.71 from d-2 to d-1 (Fig. 10a).
453 The mechanism for dust storms in Tabuk, generated by the long-range
454 northwesterly trajectory that originates in the Mediterranean and crosses the northern
455 Saharan Desert, is substantially different than that of Najran or Al-Ahsa. During the mid-
456 winter, the active Mediterranean storm track supports deep cyclones (-10 hPa anomalies)
457 passing over the Mediterranean and Black Seas, with an associated cold front that lifts dust
458 from the northern Sahara and carries it into northern Saudi Arabia, including Tabuk (Figs.
459 8i-k, 9m-o). A deep upper-level trough (anomalies < -70 m) shifts slightly eastward over
460 the Mediterranean Sea as its positive tilt on d-2 becomes neutral on d0 at the time of the
461 dust storm (Fig. 9p-r), indicative of a strengthening surface cyclone.
462 21
463 3.6 Along-trajectory meteorological conditions
464 Temporal changes in height above the ground of the air parcels, relative humidity,
465 and vertical motion are examined along the 84-hour backward trajectory clusters for four
466 select stations: Al-Ahsa, Riyadh, Arar, and Najran (Fig. 10). In general, air parcels along
467 trajectories that originate over the Mediterranean Sea are found at altitudes of 2-4 km at t-
468 84. This is most evident for the long-range northwesterly trajectories at Al-Ahsa (Fig. 6c,
469 blue), which initiate over the Mediterranean Sea at altitudes in excess of 4 km (Fig. 10a,
470 blue). The parcels rapidly descend during the 84 hours, particularly in the final 24 hours as
471 they cross the An Nafud and Iraqi Deserts. Dust storms at Al-Ahsa are characterized by the
472 greatest trajectory-mean subsidence of +2.45 hPa/hour (Fig. 10c) of any station. Typically,
473 air parcels remain at low altitudes of 400-1200 m, within the atmospheric planetary
474 boundary layer, for south-southeasterly trajectories originating over the Gulf of Aden,
475 Arabian Sea, Yemen, and Oman. For dust storms reaching Najran, tracked air parcels
476 remain at or below 1 km altitude during the entire 84-hour period (Fig. 10j) for all three
477 trajectory clusters (Fig. 4d), particularly the southeasterly trajectory from the Gulf of Aden
478 (blue). The southern Sarawat Mountains contribute towards a lifting of the air parcels in
479 the final 24 hours for dust storms in southwest Saudi Arabia, such as Najran (Fig. 10j,l),
480 Sharorah, and Wadi. Likewise, stations with primarily local dust sources, within Saudi
481 Arabia (Fig. 4), usually have trajectories at low altitudes, unlike dust events that originate
482 from the Mediterranean storm track.
483 The along-trajectory mean relative humidity ranges from 27.9% in Riyadh to 40.4%
484 in Najran, with the lowest relative humidity for locations in central Saudi Arabia, close to
485 the Ad Dahna Desert (e.g. Riyadh, Wadi Al-Dawasser, and Hail), and the highest for 22
486 locations in western Saudi Arabia, in close proximity to the Red and Mediterranean Seas
487 (e.g. Najran, Jeddah, and Turaif) (Fig. 10). For air parcels following a trajectory from the
488 Mediterranean Sea, once they pass over Saudi Arabia, the relative humidity abruptly
489 declines, as noted in Riyadh (Fig. 10e, blue). For Arar, trajectories from a maritime
490 environment, like the Mediterranean Sea (Fig. 6a, blue), or cooler environment, like Eastern
491 Europe (Fig. 6a, orange), exhibit relatively high relative humidity, in contrast to trajectories
492 that mostly pass over the Syrian (Fig. 6a, red) and Iraqi Deserts (Fig. 6a, green) (Fig. 10h).
493
494 4. Discussion and Conclusions
495 In the current study, backward trajectories are generated and clustered through the
496 K-means technique, based on mean Silhouette Coefficients, for dust storm days at 13
497 stations across Saudi Arabia. Furthermore, temporal and spatial patterns of remotely-
498 sensed AOD and meteorological conditions are assessed for these dust events to
499 understand the mechanisms and sources of the dust loading. Dust storms are most
500 common in Saudi Arabia during February-June, with a peak in March. Their activity
501 reaches a maximum during the mid-winter along the southern coast of the Red Sea (with
502 trajectories originating over Yemen and the Gulf of Aden), spring in northern Saudi Arabia,
503 and early summer in eastern Saudi Arabia, in the vicinity of the Ad Dahna Desert.
504 During February-April, cold fronts associated with Sharav cyclones from the
505 Mediterranean Sea can transport Saharan dust to Saudi Arabian stations along the northern
506 coast of the Red Sea, as verified from remotely-sensed AOD. Our trajectory-based finding of
507 the importance of cool-season Mediterranean cyclones to Saudi Arabian dust storms is
508 consistent with prior studies by Ganor et al. (1991), Herut and Krom (1996), Kubilay et al. 23
509 (2000, 2005), Shao (2001), and Kubilay et al. (2003). Likewise, Vishkaee et al. (2011)
510 indicated that dynamical lifting during the cool season from cold fronts is a primary trigger
511 of dust storms. The Mediterranean trajectories that support Saudi Arabian dust storms
512 have initial parcel altitudes of 2-4 km, in support of prior studies that Saharan dust can be
513 lifted to heights of several kilometers above the ground due to strong winds (Escudero et
514 al., 2005, 2011; Dayan et al., 1991; Alpert and Ganor, 1993; Conte et al., 1996; Prospero,
515 1996). Type II stations, such as Tabuk, generally experience a peak in dust activity during
516 April, associated with Mediterranean cyclones and transported Saharan Dust. Diurnal
517 heating and resulting turbulence are likely critical for the early summer dust storms in
518 eastern Saudi Arabia around the Ad Dahna Desert (Middleton, 1986; Vishkaee et al., 2011).
519 Strong heating over the Iraqi and Syrian Deserts lead to active dust periods over Type III
520 stations (e.g. Al-Ahsa) in June.
521 According to the backward trajectory analysis, the primary dust source regions,
522 from most to least important, are the Rub Al Khali, Saharan, Iraqi, Ad Dahna, Syrian, and An
523 Nafud Deserts. The primary local dust source, within the country, is the Rub Al Khali
524 Desert, while the primary remote dust sources are the Saharan Desert, for western Saudi
525 Arabia, and Iraqi Deserts, for northern / eastern Saudi Arabia. These findings agree with
526 studies of TOMS data by Goudie and Middleton (2001, 2006) and Giles (2005), which
527 identify the Saharan Desert and Rub Al Khali as the most prolific dust source regions in the
528 world. Based on MODIS data, the highest AOD is achieved during Saudi Arabian dust
529 storms that originate from the Rub Al Khali and Iraqi Deserts. The Saharan Desert is an
530 important remote source of dust, but Saudi Arabian dust storms associated with Saharan
531 dust rarely achieve large values of AOD. It is acknowledged that MODIS AOD has 24
532 substantial expected errors (Remer et al., 2005, 2008; Hsu et al., 2006), and in coastal
533 regions bordering the Red Sea, the satellite might also be detecting marine aerosols in
534 addition to Aeolian dust (Yu et al., 2012).
535
536 Acknowledgements
537 This study was funded by the King Saud University. The authors gratefully
538 acknowledge the NOAA Air Resources Laboratory for the READY website
539 (http://ready.arl.noaa.gov) for generating HYSPLIT trajectories used in this publication.
540 The authors are thankful for helpful discussions with Ms. Yan Yu, Dr. Zhengyu Liu, and Dr.
541 Guangshan Chen and comments from three anonymous reviewers. Nelson Center for
542 Climatic Research publication #.
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554 25
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848
849
850
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853 38
854 Figure Captions
855
856 Figure 1. Map of the Middle East and North Africa, with shaded elevation in meters. The
857 source of the 0.25° x 0.25° elevation data is TerrainBase (TBASE) from the National Center
858 for Atmospheric Research (NCAR), obtained through the University of Washington. The 13
859 Saudi Arabian stations analyzed in this study are identified by large dots. Red, green, and
860 yellow dots indicate stations with dust sources primarily from the Rub Al Khali, Sahara, and
861 Iraqi Deserts, respectively. The six primary desert regions (Sahara, Rub Al Khali, An Nafud,
862 Ad Dahna, Syrian, and Iraqi) are labeled and identified with small dots.
863
864 Figure 2. Mean seasonal cycle of the percentage frequency of dust storms at 13 Saudi
865 Arabian stations during 2005-2012. The dark bars indicate the most active month for each
866 station. On the map, the stations’ locations are shown, along with the most active month.
867
868 Figure 3. Mean silhouette coefficients for each of 13 Saudi Arabian stations for trajectory
869 cluster sizes ranging from three to ten. Peaks in these silhouette curves indicate the
870 optimum cluster size for stations, including three (red lines) for Sharorah, Wadi Al-
871 Dawasser, Riyadh, Najran, Al-Baha, Hail, Yenbo, Al-Ahsa, and Tabuk; four (blue lines) for
872 Arar, Turaif, and Jeddah; and five (green lines) for Hafr Al-Batin.
873
874 Figure 4. K-means cluster analysis of 84-hour backward trajectories (at 500-m) for 100
875 dust storm days during 2005-2012 at (a) Sharorah, (b) Wadi Al-Dawasser, (c) Riyadh, (d)
876 Najran, (e) Al-Baha, and (f) Hail. These stations primarily have local dust sources within 39
877 Saudi Arabia. For these stations, the trajectories are grouped into three clusters, shown in
878 red, green, and blue, with the percent frequency and dominant month of each trajectory
879 cluster shown in the small black boxes. For each cluster, a representative trajectory is
880 shown based on the maximum within-cluster silhouette coefficient. The grey shading
881 indicates the percentage of total backward trajectories passing through each grid cell.
882
883 Figure 5. K-means cluster analysis of 84-hour backward trajectories (at 500-m) for 100
884 dust storm days during 2005-2012 at (a) Yenbo, (b) Tabuk, (c) Jeddah, and (d) Turaif.
885 These stations primarily have remote dust sources outside of Saudi Arabia, with a
886 significant component from the Sahara Desert. For these stations, the trajectories are
887 grouped into three or four clusters, shown in red, green, blue, and orange, with the percent
888 frequency and dominant month of each trajectory cluster shown in the small black boxes.
889 For each cluster, a representative trajectory is shown based on the maximum within-
890 cluster silhouette coefficient. The grey shading indicates the percentage of total backward
891 trajectories passing through each grid cell.
892
893 Figure 6. K-means cluster analysis of 84-hour backward trajectories (at 500-m) for 100
894 dust storm days during 2005-2012 at (a) Arar, (b) Hafr Al-Batin, and (c) Al-Ahsa. These
895 stations primarily have remote dust sources outside of Saudi Arabia, primarily from the
896 Iraqi and Syrian Deserts. For these stations, the trajectories are grouped into three or four
897 clusters, shown in red, green, blue, and orange, with the percent frequency and dominant
898 month of each trajectory cluster shown in the small black boxes. For each cluster, a
899 representative trajectory is shown based on the maximum within-cluster silhouette 40
900 coefficient. The grey shading indicates the percentage of total backward trajectories
901 passing through each grid cell.
902
903 Figure 7. Based on the trajectory clusters from Figs. 4-6, individual backward trajectories
904 are shown for (a) Sharorah, (b) Riyadh, (c) Hail, (d) Hafr Al-Batin, (e) Arar, and (f) Yenbo
905 using the same color scheme as in Figs. 4-6. For each station, every third trajectory from
906 the pool of 2700 is shown to limit clutter.
907
908 Figure 8. (a-d) Composite for Najran of daily-mean MODIS AOD for dust storm days during
909 2005-2012 for the northerly trajectory cluster (red, Fig. 4d) over the Ad Dahna and Rub Al
910 Khali on (a) d-3 (three days prior to dust storm), (b) d-2, (c) d-1, and (d) d0 (day of dust
911 storm). (e-h) Composite for Al-Ahsa of daily-mean MODIS AOD for dust storm days for the
912 northwesterly trajectory cluster (green, Fig. 6c) over the Iraqi Desert on (e) d-3, (f) d-2, (g)
913 d-1, and (h) d0. (i-k) Difference in composited daily-mean MODIS AOD for dust storm days
914 at Tabuk for its northwesterly trajectory cluster (blue, Fig. 5b) over the Mediterranean Sea
915 and Saharan Desert, consisting of (i) [d-2] minus [d-3], (j) [d-1] minus [d-2], and (k) [d0]
916 minus [d-1]. The upper and lower color bars pertain to (a-h) and (i-k), respectively. For
917 Tabuk, changes in AOD are shown to clearly illustrate their link to a passing cold front.
918
919 Figure 9. Composite of daily (a-c, g-i, m-o) sea-level pressure (hPa) and (d-f, j-l, p-r) 500-
920 hPa height (m) anomalies on dust days. Results are shown for (a-f) Najran’s northerly
921 trajectory cluster (red, Fig. 4d), (g-l) Al-Ahsa’s northwesterly trajectory cluster (green, Fig.
922 6c), and (m-r) Tabuk’s northwesterly trajectory cluster (blue, Fig. 5b). Anomalies, 41
923 computed from the NCEP-NCAR Reanalysis, are shown for d-2 (two days prior to the dust
924 event), d-1, and d0 (day of the dust event) in the first, second, and third columns,
925 respectively. The number of composited dust days is 28, 44, and 15 for Najran, Al-Ahsa,
926 and Tabuk, respectively. Hatching in the sea-level pressure anomaly panels indicates
927 MODIS AOD≥0.6 for Najran and Al-Ahsa and ≥0.45 for Tabuk. Surface cyclone and
928 anticyclone centers and associated fronts are also displayed in the sea-level pressure
929 anomaly panels.
930
931 Figure 10. Mean height above the ground (m), relative humidity (%), and vertical motion
932 (hPa/hour) of the tracked air parcels for (a-c) Al-Ahsa, (d-f) Riyadh, (g-i) Arar, and (j-l)
933 Najran, according to the 84-hour backward trajectories in Figs. 4-6 (using the same
934 trajectory colors). Data is shown hourly from 84 hours prior to the dust storm to the time
935 of the dust events (time 0). In (a,d,g,i), color dots indicate mean AOD following the relevant
936 trajectory cluster, with dot sizes increasing for intervals of 0.00-0.35, 0.36-0.55, 0.56-0.75,
937 and 0.76-1.20 (see legend in panel f).
938
939 Table Captions
940
941 Table 1. List of the 13 Saudi Arabian stations used in this study and their latitude,
942 longitude, and elevation (m). For each station, the percentage of total dust storms that
943 cross over specific deserts, along with the mean number of hours over those deserts, from
944 the 84-hour backward trajectories in Figs. 4-5 is given; italics indicate the highest values
945 per station. 42
946
947 Table 2. Mean MODIS AOD at each of 13 stations on dust storm days, for each trajectory
948 cluster shown in Figs. 4-5 and weighted across all clusters. Trajectory clusters that result
949 in a mean AOD ≥ 1.10 are identified in bold. Below each AOD value, two percentages are
950 provided, which represent the percentage of trajectories that achieve an AOD≥1.0 and ≥0.8,
951 respectively.