Paper to be submitted to Atmospheric Environment, IGAC Bologna issue
December 27, 1999
Distribution of Continental Surface Aerosol Extinction Based on Visual
Range Data.
Rudolf B. Husar, Janja D. Husar and Laurent Martin
Center for Air Pollution Impact and Trend Analysis (CAPITA)
Washington University, St. Louis, MO 63130-4899
Abstract
The global continental haze pattern was evaluated based on visibility data at 7000 surface weather
stations over five years, 1994-98. The data processing consisted of three broad categories of filters: (1)
validity of individual data points, (2) filters based on statistics for specific stations, and (3) filters based
on spatial analysis. The data are presented as the aerosol extinction coefficient (Bext or haze) at the
surface seasonally aggregated over five years. The data reveal that the continental haze is concentrated
over distinct aerosol regions of the world. The haziest regions of Asia are the Indian subcontinent,
eastern China, and Indochina where the 75 percentile seasonal Bext exceeds 0.4 km-1. In Africa the
highest year around extinction coefficient >0.4 km-1 is found over Mauritania, Mali and Niger. During
December, January, February, the savannah region of sub-Saharan Africa shows similar values. The
haziest region of South America is over Bolivia, adjacent to the Andes mountain range, and the highest
extinction levels occur during September, October, November (0.4-0.6 km-1). In North America and
Europe there are isolated haze pockets, such as the San Joaquin Valley in California and the Po River
Valley in the northern Italy. In many regions of the world the size, shape, and intensity of hazy pockets
is influenced by the topographic barriers.
Introduction
Atmospheric aerosols are major carriers in the biogeochemical cycle of sulfur, nitrogen, carbon and
trace metals, as well as crustal elements. As the substances carried by particles pass through air, land
and water, they cause many effects, including changes in climate and weather, fertilization of the
oceans and land, acidification of lakes and health effects to humans. Unfortunately, the quantification
of the linkage between aerosols and these effects is severely hampered by the lack of consistent global
scale aerosol data sets. Recently, satellite remote sensing allowed the construction of aerosol maps over
the oceans (e.g. Durkee et al., 1991; Husar et al., 1997), but corresponding aerosol maps over the
continents are currently not available. This paper presents the global pattern of horizontal extinction
coefficient over the land based on routine visibility observations at over 7000 synoptic weather stations.
Data Source and Processing Methodology
Global Summary of the Day (SOD) Database
This work uses the Global Summary of Day (SOD) database distributed by the National Climatic
Data Center (NCDC). The SOD data are derived from the data exchanged under the World
Meteorological Organization (WMO) World Weather Watch Program according to WMO Resolution
1
40 (Cg-XII) (WMO, 1996). Over 8000 stations' data are typically included each month. Data are
accessible through NCDC web server (NCDC, 1998).
The SOD data contain 18 surface meteorological parameters that are derived from the synoptic
hourly observations: mean temperature, mean dew point, mean sea level pressure, mean station
pressure, daily mean visibility, mean wind speed, maximum sustained wind speed, maximum wind
gust, maximum temperature, minimum temperature, precipitation amount, snow depth. The flags are
also included for the occurrence of fog, rain or drizzle, snow or ice pellets, hail, thunder, tornado/funnel
cloud. For the calculation of daily mean values it is required that at least four valid hourly readings are
available.
Surface Aerosol Extinction Coefficient from Visibility Observations
The primary goal of the data preparation was to derive a local aerosol extinction coefficient, Bext, as
an index of surface aerosol concentration. Bext is derived from the surface visual range observations.
The visual range, or visibility, is the maximum distance at which an observer can discern the outline of
an object against a horizon sky. The observational procedures are specified in the guidelines issued by
the World Meteorological Organization (WMO, 1996). Most visibility observations are made by human
observers in airport towers observing visual targets at known distance such as large buildings and hills.
According the Koschmieder (1924) theory, the visual range of an object viewed against the horizon
sky, VR[km], is inversely proportional to the horizontal extinction coefficient, Bext [km-1],
Bext=K/VR. The Koschmieder constant, K, depends on the contrast threshold sensitivity (2-5%) of the
human eye as well as on the inherent contrast of the visible objects against the horizon sky (Middleton,
1953). The limitations in visual range estimates include the observers’ visual acuity, the number,
configuration, and physical and optical properties of the visible targets. Observer’s subjectivity
imposes a random component on the observed signal. The lower contrast of real targets compared to
black objects imposes a systematic underestimate of visual range. In addition, visibility is reported in
quantized units, depending on the availability of visible targets. Thus, an observation of 10 miles
means that the visual range is greater than 10 miles. The reported visual range is always an
underestimate of the actual visual range compared to ideal black target conditions. In this report, we
have taken K=1.9 in accordance with the data of Griffing (1980) which is about half of the standard
value of 3.92. The factor of two reduction of the Koschmieder constant incorporates the fact that real
visual targets are not black, they are frequently too small in angular size, and are located only at
quantized distances away from the observer. All non-ideal conditions tend to reduce the apparent visual
range and increase the Koschmieder constant.
In the absence of particles, the visual range of a Raleigh atmosphere would be over 200 km, due to
scattering by air molecules (Bext=0.005 km-1). In fact, under those conditions, the visibility of most
distant objects near the surface would be limited by the curvature of the earth. The visual range in the
atmosphere is reduced mainly by the presence of aerosol particles (dust, smoke, and haze) and
hydrometeors. Hydrometeors are large droplets or crystals of water (>5m) and they occur as rain, fog,
clouds, and snow.
The major goal of the visibility data processing is to determine the magnitude of the haze by
eliminating the influence of naturally occurring hydrometeors, such as rain, snow, and fog. This is
accomplished by the application of a rather elaborate set of filtering algorithms described below. The
resulting weather-filtered extinction coefficient is referred to as FBext (km-1). In what follows, FBext
will be used interchangeably with the words haze and haziness.
Data Processing
The global SOD data undergo extensive automated quality control by the Air Weather Service
(AWS), and over 400 algorithms are applied automatically to correctly 'decode' the synoptic data, and
to eliminate many of the random and systematic errors found in the original data. The details of the
2
algorithms are unknown to us. However, an evaluation of the SOD data revealed that many visibility
data points remained in the SOD data that were unsuitable from the present analysis. Additional filters
were developed for this work, consisting of three broad categories of filters: (1) validity of individual
data points, (2) filters based on statistics for specific stations, and (3) filters based on spatial analysis of
the data.
Single data point filters. A missing Bext value in the SOD database arises when less than 4 valid
hourly observations are recorded for a day. In addition, observations were eliminated when either the
temperature, dew point or the precipitation data were not available. These variables are used in the
weather filter and their absence would prevent the identification of weather related obstructions to
vision.
The weather filter eliminated visibility records when the obstruction to vision could be attributed to
weather, i.e. hydrometeors associated meteorological phenomena. Records that contain flags for rain,
fog, or precipitation >0.25 cm throughout the day were eliminated. Furthermore, the daily record when
the difference between temperature and dew point was <2.2 C were also eliminated. This temperature
spread corresponds to about 90% relative humidity. It is to be recalled that, both temperature and dew
point are daily averages reported in the SOD database. Finally, an “ice fog” and “blowing snow” filter
was applied that eliminated extreme cold and windy conditions (temperature <-29 C and wind speed
>16 km/hr). The latter filter was applied to eliminate low visibility conditions that occur frequently at
monitoring sites above the Arctic Circle and do not affect any observations at mid and low latitudes.
Evidently, the fog flag is somewhat ambiguous in the SOD database. Sometimes it refers to high
humidity, fog situation, in other circumstances the fog flag is applied when the visibility is less than a
few miles, regardless of the humidity. “Dry fog” (<75% RH) often occurs in tropical regions such as
Indonesia when smoke from biomass burning obscure the vision. Hence, when the temperature-dew
point difference exceeded 4 C (RH<75%), the fog flag was overridden and the visibility observations
were retained.
The intent of the spike filter was to eliminate observations that constitute a large one-day drop of
visibility (spike in extinction). Such short-term spikes in extinction coefficient are attributed to
meteorological obstructions to vision that did not get eliminated by the previous filters. A spike is
defined when the extinction coefficient on a given day is three times higher than the previous and next
day. This filter does not eliminate sudden but persistent changes in extinction.
Statistical filters. The statistical properties of a station accumulated over longer periods of time
allow the identification of unsuitable stations. The minimum number of valid observations per 3-month
season was set to be 10, i.e. stations having less than 10 valid data points were not accepted.
A major filter is the threshold filter. The significance of this filter arises from the fact that at many
remote locations all the good visibilities are reported as >12 km or >20 km. Hence, there is a threshold
visual range above which the visibility is not resolved. Stations that have visibility threshold <12 km
(Bext<0.16 km-1) were eliminated.
There are some monitoring sites where the visibility is reported to be low and constant from one day
to another, for example at 6 km. These stations were judged to be unacceptable because they do not
reflect the normal day-to-day fluctuations of aerosol induced horizontal extinction coefficients. An
indication for a time invariant extinction coefficient is when all the percentile values are identical over
a season. The temporal variability filter eliminated a station when the ratio of 75th and 50th percentile
was less than 1.07 or if the ratio of the 90th to 75th percentile was less than 1.1.
Spatial filters. Additional 29 stations out of 9,731 total were removed manually from the data set.
These stations were identified subjectively as “outliers” because they differed greatly from their
surrounding stations. Thus, there were spatial “spikes” on geographic maps. Once an outlier station
3
was identified the daily time series over the four-year period was visually examined. All 29 stations
have exhibited anomalous behavior, including sudden but persistent jumps of extinction coefficient, or
high and changing threshold value. It is conceivable that some of the anomalous pattern was the result
of actual aerosol concentration peaks. However, these were considered less relevant to the present
global analysis. The above-described filters have eliminated 2611 stations from the data set and the
remaining 7120 valid visibility stations were used for the following global continental haze pattern
analysis.
Spatial Distribution of Qualified Stations
The global distribution of remaining visibility-monitoring sites is shown in Figure 1. The spatial
coverage is highest in Europe, former Soviet Union, China and US, where the meteorological stations
are about 100-300 km apart. Throughout much of the remaining continents, the average station
distance is on the order of 200-400 km. The visibility monitoring data have low spatial coverage over
northern Canada, northern Siberia, western China, as well as over the central portions of South
America, Africa, and Australia. The low station density in the Sahara region and over northern Brazil
and south Peru also constitute a major limitation of the surface synoptic data set.
For some countries data are not available due to restrictions or “communication” problems. Most
notably, data are missing from large African countries, Nigeria, Zambia, Angola, Somalia, and
Botswana, as well as from Liberia and Sierra Leone. The data loss from Zambia, Angola, and Nigeria is
particularly unfortunate since these areas include major sources of biomass burning areas. In Asia, data
are missing from Iraq and Afghanistan.
Statistical Measure of Bext at each Station
In this climatological analysis, the aerosol extinction coefficient was aggregated for four seasons.
For each season, e.g. December, January and February, the data were aggregated over five years
between 1994-98. Due to inherent limitations of the data set (visibility threshold) the aggregation was
performed using non-parametric statistics (percentiles) rather than averaging. The specific parameter
that is plotted for the haze maps is the 75th percentile of the extinction coefficient. While this is
unconventional, it constitutes the safest approach in that it does not require any extrapolation or other
adjustments to the data. More conventional statistical measures, e.g. the mean, can be estimated as
follows: from previous research, e.g. Husar et al. (1979), the extinction coefficient is roughly log
normally distributed with the typical logarithmic standard deviation ranging between 1.6 and 3.4. For a
distribution with g=2.5, the 50th percentile is 0.5 times the 75th percentile, and the mean is 0.76 times
the 75th percentile.
4
Figure 1. Visibility measurement station location density.
Spatial Distribution of Extinction Coefficient by Region
The global haze patterns are presented in four seasonal maps of extinction coefficient for five of the
haziest regions of the world (Figures 3-7). The three-month seasons are centered in January, April, July,
and October. The main feature of the extinction coefficient maps is that aerosol pockets with high light
extinction are scattered over all continents. As an example of hazy regions, Figure 2 illustrates
Southeast Asia on a hazy winter day using data from the SeaWiFS satellite (McClain et al., 1998).
5
Figure 2. Hazy regions of southeast Asia from the SeaWiFS satellite data on December 28, 1999. The spectral
reflectance data were rendered as a "true color" digital image by combining the blue (442 nm), green (550 nm),
and red (670 nm) channels. The scattering by air molecules was removed. Bluish haze covers northern India and
eastern China.
The extinction coefficient data (Figures 3-7) show that within each continent there is at least factor
of 2-5 variation in seasonal average aerosol extinction. It is also evident that the concentration in the
various pockets is highly seasonal. However, the months of the year of peak extinction coefficient, the
duration of the peak season, as well as the seasonal amplitude varies strongly from region to region.
In the following analysis the aerosol pattern for each of the continental region is examined in more
detail. The evaluation consists of spatial pattern analysis, including extinction levels and gradients, and
identification of peak seasons. Attention is also given to the relationship between haze aerosol and
topography since many of the high concentration aerosol pockets are adjacent to mountain ranges.
Haze over Asia
The ground level extinction coefficient over Asia exhibits extreme variations between the pristine
clean air over the Tibet Plateau and the hazy global region in the low-lying valleys of the Indian
subcontinent, China, and Indochina (Figure 3). The region of most intense surface haze is found just
south of the Himalayas stretching from Northern Pakistan through India to Northern Bangladesh. The
highest seasonal extinction coefficient in that region is recorded during December, January, and
February (DJF), while the lowest values occur in September, October, and November (SON).
Throughout the year the 75 percentile Bext exceededs 0.5 km-1, which corresponds to <4 km visibility.
There is a strong gradient of extinction coefficient at the Himalayas mountain range. The high
elevation sites have much lower extinction coefficients, indicating that these sites are generally above
the shallow haze layer that covers the northern region of the Indian subcontinent.
6
Another hazy region exists over the low-lying valleys of northern Thailand and Laos, The peak
extinction levels in excess of 0.5 km-1 occur in the cold season, between December and May. Between
June and November the extinction level is <0.25 km-1. Closer inspection of the spatial pattern reveals
that the hazy regions of Indochina are also confined to the low-lying valleys, while higher elevation
mountain sites are above the haze layers throughout the year.
A unique region of elevated surface extinction coefficient is found over Indonesia and Malaysia.
During SON of 1994-98, the 75 percentile had the highest seasonal value in the world. Six stations in
the region had seasonal value in excess of 1 km -1, which correspond to visual range of about 2 km.
According the weather records, the extreme haze levels in the region are attributable to smoke due to
major forest fires that occur mainly during SON season and less frequently during March, April, May
(MAM).
Within China the highest extinction coefficients are recorded in the Sichuan Basin in South-central
China. During December-May the extinction levels exceed 0.4 km-1 (5 km visual range). This circular
500 km wide basin is completely surrounded by mountain ranges, where the extinction coefficient is
<0.1 km-1. The intense cold season haze is probably attributable to the emissions and poor ventilation
in the confined basin.
Another confined basin of elevated extinction coefficient, is found over the Xinjiang Autonomous
Region of extreme western China. The highest seasonal values (>0.3 km-1) are found during March
through August. This arid region is dominated by frequent springtime dust storms. The spatial pattern
of the surface extinction coefficients indicates that climatologically the dust events are confined to the
Tarim Basin, while the adjacent monitoring sites in the surrounding mountains are outside the dust
layer.
The coastal zone of Eastern China stretching from Northern China to Vietnam is covered by diffuse
haze with moderate extinction coefficients between 0.25 km-1 in winter and 0.2 km-1 in the summer.
This region of the China-Korea seaboard is mostly flat and is bounded to the west by mountains.
Throughout the region elevated extinction levels are recorded, mostly in the vicinity of urban-industrial
centers.
7
Figure 3. Extinction coefficient for Asia a) March, April, May; b) June, July, August; c) September, October,
November; and d) December, January, February.
Haze over Africa
Africa has several hazy regions, Sahara being the most prominent (Figure 4). Seasonally the highest
extinction coefficient over Sahara is recorded during Jun, July, and August (JJA), with 75% percentile
extinction coefficient in excess of 0.4 km-1 over Mauritania, Mali, and Niger. Unfortunately, the details
of the spatial pattern in this important aerosol region cannot be established since large portions of the
8
Sahara Desert are void of monitoring sites. However, the data indicate a clear decline of extinction
coefficients toward the Mediterran and toward the East Africa. Seasonally, the extinction coefficient
over Sahara is highest during spring and summer and lowest in the fall. In this region the weather
records indicate the cause of the obstruction to vision to be windblown dust.
Another haze region is located just south of Sahara is the sub-Saharan African region that stretches
from Senegal to Sudan. The magnitude of the extinction coefficient shows a sharp peak during DJF
with average values exceeding 0.4-0.6 km-1. In the summer season, JJA, the regional average haze is
<0.2 km-1. The data coverage of this region is rather complete with the exception of Nigeria. It
indicates a rather uniform distribution of wintertime haziness throughout the sub-Saharan Sahel region.
The region is free from major topographic features such that aerosol dispersion is unhindered by
topography.
Haze South America
The spatial pattern of extinction coefficient over most of South America is between 0.1-0.2 km-1
throughout the year (Figure 5). The highest haziness occurs over central South America covering
western Brazil and Bolivia. The Andes mountain range to the west presents a sharp boundary to the
haze. Toward north, east, and south there is a gradual decay of haze. The haze is highest during
September, October, November (0.4-0.6 km-1) and declines to 0.2 km-1 throughout the rest of the year.
There is no evidence of local hot spots where the extinction level is significantly higher than its
neighborhood. Unfortunately, the spatial pattern of haze in South America can not be fully assessed
because of poor spatial coverage over much of Amazonian basin.
In Bolivia several adjacent stations show declining haze with altitude. The low elevation station
(Camir, elevation 792 m) show highest Bext, while the station above 2,900 m (Potosi/Rojas, elevation
3940 m) are the clearest in South America.
Haze over North America
Compared to other continents, continents North America (Figure 6) has low levels of haziness
throughout the year. Only Australia has lower levels of extinction coefficients. Increased haze is found
in Central America from Guatemala to southern Mexico during the spring season, March, April, May.
High elevation sites above 1,500 m show low or moderate extinction coefficients, including in Mexico
City.
Haziness is also observed covering most of eastern US. The extinction coefficient in that region is
relatively moderate (0.1-0.2 km-1). However, during all seasons, the haze is remarkably uniform over
the 2,000 km size area. This includes the major metropolitan areas of the Washington-Boston corridor,
as well as the industrial Ohio River Valley. Seasonally, the JJA period has the highest haze values
(0.25 km-1), while the transition seasons have the lowest levels. Throughout eastern US and
southeastern Canada the terrain is relatively flat, and the surface based haze layers cover the entire
territory. Possible exception is the crest of the Appalachian Mountains (above 1,500 m) which extrudes
from the haze during the cold season.
Haze is also evident in California throughout the San Joaquin Valley, and in the Los Angeles basin.
The monitoring sites at the adjacent mountains show low extinction coefficient (<0.1 km-1) indicating
that the haze layers in these air basins are confined to the low lying areas, while the mountains extrude
from the boundary layer haze. These are relatively small pockets of haze and do not have global
significance compared to the hazy regions of Asia, Africa and S. America.
9
Figure 4. Extinction coefficient for Africa a) March, April, May; b) June, July, August; c) September, October,
November; and d) December, January, February.
10
Figure 5. Extinction coefficient for South America a) March, April, May; b) June, July, August; c) September,
October, November; and d) December, January, February.
11
Figure 6. Extinction coefficient for North America a) March, April, May; b) June, July, August; c) September,
October, November; and d) December, January, February.
12
Figure 7. Extinction coefficient for Europe a) March, April, May; b) June, July, August; c) September, October,
November; and d) December, January, February.
Haze over Europe
Europe is a small continent but it exhibits extreme variations in haziness. The highest levels of
haziness are found in the Po River Valley in northern Italy (Figure 7). Throughout the year the
extinction coefficients there exceed 0.2 km-1. The haze peak is at 0.35 km-1 during the cold season and
0.25 km-1 in the warm season. The Po River Valley is confined by the Alps and the prevailing winds
tend to accumulate the haze in the basin. Virtually all of the high elevation sites in the Alps show low
seasonal extinction values.
The remaining part of Europe including the Iberian Peninsula and the British Isles show moderate
levels of haziness. Seasonally, the highest levels of haziness throughout Europe are observed during the
cold months, October-March. The gradient of haze is relatively mild and declines toward Scandinavia,
and toward southern Europe.
Discussion
The interpretation of the above presented global haze maps can benefit from further explanations
and qualifications.
The relationship between horizontal extinction coefficient and fine particle mass.
The extinction coefficient derived from visual range observations can be related to the
concentration of fine particles. For dry conditions, i.e. relative humidity below 60%, the extinction to
mass relationship depends on characteristic particle size and to some extent the particle refractive
index.
13
A review of the extensive literature on the light extinction per unit particle mass (mass extinction
efficiency) yields the relatively coherent relationship as shown in Table 1.
Table 1. Extinction efficiency per unit particle mass for different locations and aerosol types.
Location
Remarks
m2/
References
g
Tenerife
0.5
Maring et al., 1999
DUST
Barbados
dust
0.8
Li et al., 1996
Seoul, Korea
*
0.8
Chung and Yoon, 1996
AVERAGE
0.7
SMOKE
HAZE
Australia
South America
South America
Porto Velho, Brazil
Ciuba, Brasil
Ciuba, Brasil
Maraba, Brasil
AVERAGE
Fire
Fire
Fire
Abbeyville, LA
Luray, VA
Lewes, DE
Lewes, DE
Lenox, MA
K-Puszta
AVERAGE
summer
summer
summer
winter
summer
summer
local
aged
4.2
2.9
3.0
3.9
3.1
4.1
3.2
3.5
Eccelston et al., 1974
Reid and Hobbs, 1998
Kaufman et al., 1998
Reid et al., 1998
Reid et al., 1998
Reid et al., 1998
Reid et al., 1998
3.9
5.0
4.8
3.7
5.8
6.0
4.9
NAPAP, 1990
NAPAP, 1990
NAPAP, 1990
NAPAP, 1990
NAPAP, 1990
Meszaros et al., 1998
The lowest mass extinction efficiency of 0.5-0.8 m2/g is associated with windblown dust since dust
particles are 1-5 m in diameter and they do not scatter light efficiently. Smoke from open fires is
reported to have mass extinction efficiency in the 3-5 m2/g range. Evidently, haze particles scatter most
efficiently at 4-6 m2/g. These efficiency factors allow the estimation of aerosol mass concentration
from the measured extinction coefficient. For example in India under winter hazy conditions (Bext =
0.5 km-1, mass extinction efficiency = 4 m2/g) the approximate aerosol concentration would be
125g/m3. For similar surface extinction in the dusty West African desert (0.7 m 2/g) the estimated
aerosol concentration would be 700 g/m3.
Haze, smoke and to some extent dust particles are hygroscopic, i.e. they absorb an increasing
amount of water with increasing relative humidity. The role of the hygroscopicity is most pronounced
during the rainy cold seasons when the relative humidity is high and over regions where the haze is
composed of hygroscopic sulfates, nitrates, and condensed organic substances, rather than nonhygroscopic soil dust. As a reference, all the extinction data presented here were taken below 90% RH.
While the role of humidity in surface extinction is extremely important, a detailed evaluation of the role
of hygroscopicity is beyond the scope of this report.
The Role of Daily Averaging in the SOD Database.
The Summary of the Day (SOD) database consists of one record of meteorological observations per
day. For most meteorological parameters the summary parameter is a daily average calculated from the
hourly or 3-hourly observations provided that there were at least four hourly data per day available.
Most importantly, the visibility is arithmetically averaged throughout the day. Both temperature and
dewpoint are also given as daily average values. On the other hand, the 6 reported weather flags (fog,
14
rain, snow, hail, thunder, tornado) indicate the occurrence of these weather events during any part of
the day.
Figure 8. Diurnal visibility pattern for Milano, Italy and Nouakchott, Mauritania. The two stations have the same
daily average visibility, but distinctly different diurnal cycle.
It is evident that replacing the full diurnal cycle of meteorological parameters with a single daily
value causes significant loss of information that is relevant to the interpretation of the global visibility
data. At most geographic locations there is a strong diurnal cycle of visual range, as well as of relative
humidity, and the relationship between the two is highly non-linear.
The consequences of the daily averaging were examined for different regions of the world. For
example, in Milan Italy (Figure 8) during the wintertime in wet and humid conditions the visual range
decreases from 10-20 km during the day, to <2 km during the night when the relative humidity
approaches 100%. The resulting daily average visibility reported in SOD is 6.2 km, which is less than
half of the daytime values. An opposite example is Nouakchott on the Atlantic coast of Mauritania,
where the reported visibility is high (10 km) throughout the night hours but declines throughout the day
to 3.5 km, due to wind blown dust. The resulting daily average visibility reported in SOD is also 6.2
km, which is twice as high as the daytime values. In this example of extreme cases, for identical day
average SOD visibility values, the daytime visibility may differ by a factor of three.
Another disadvantage of averaging visibility throughout the day arises from the existence of
visibility thresholds. For example, at Nouakchott the maximum reported visibility is 10 km, while in
reality the nighttime visibility could have been 20 km or higher, as illustrated schematically in Figure 8.
Accordingly, the true day average visibility could have been 8-10 km compared to the reported 6.2 km.
Finally, the SOD reporting of the weather flags necessitates the elimination of all of the days that have
fog or precipitation during any part of the day.
From these observations it is evident that for those regions that have nighttime peak in extinction
coefficient (lowest visibility), the SOD values of Bext will be higher than the daytime only values. On
the other hand, in regions that have a daytime peak of extinction coefficient, the SOD reported Bext
will be lower compared to the daytime only values. Hence, in regions dominated by daytime dust such
as Sahara, Arabia, and western China, the SOD derived extinction maps will underestimate the daytime
extinction levels up to a factor of two. Regrettably, hourly data such as used in the illustration Figure 8,
are only available to a 1,500 station subset of the global synoptic database. The detailed exploration
and analysis of the hourly global visibility data is currently in progress.
The combination of these limitations is that the reported metric for the seasonal aggregate extinction
coefficient is strongly influenced by these aggregation procedures. Alternative seasonal extinction
metrics, would use daytime observations only, or average the extinction coefficients rather than
15
visibilities. These alternative metrics could produce extinction estimates that may differ by factor of
two from the currently presented metric. Accordingly, the aggregated station values and the maps
presented here can only be used as semi quantitative indices of haze pattern rather than accurate
absolute values. Nevertheless, the 7,000 station data set is meaningful to delineate the spatial extent
and the seasonal variation of atmospheric haze over the continents.
Future Work
Full quantification of the global continental haze pattern will require instrumental measurement,
most probably from remote sensing satellites. In fact, a limited global aerosol optical thickness
climatology over the oceans was reported using the NOAA AVHRR sensor data (Husar et al., 1997).
However routine quantitative techniques for the retrieval of aerosol properties over land from satellite
data are not yet available, but it is an area of active research (King et al., 1999]. Even when such
techniques will be developed, the surface-based visibility data will complement the satellite
observations since it represents the horizontal extinction coefficient at the surface, while the satellite
sensors respond to the vertical integral of the extinction coefficient. Proper fusion of surface visibility
and satellite remote sensing data could yield a global estimate of the aerosol scale height.
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