An Exploration of the

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					 An Exploration of the NRL Global Surface Observation
                       Database

                                  Rudolf B. Husar
                                      CAPITA
                      Washington University, ST. Louis, MO 63130
                                     July, 2000

Background
Surface observations provide valuable data for the testing and verification of atmospheric
chemical transport models. In particular, the reported estimate of the surface visual range
provides an estimate of the horizontal extinction coefficient along the line of sight, which
in turn is related to the concentration of aerosols. Additional parameters in the surface
meteorological record include a coded description of the current weather. There are
specific codes for dust, smoke, and haze, as well as for a large variety of weather-related
obstructions to vision. The combination of the visual range and the weather code allows
the estimation of the surface aerosol concentration as well as its causes.
Given the successful operation of the NRL-NAPS global aerosol model it became
desirable to verify and/or augment the NAPS model using the available surface
observations over land. Such verification is particularly important since the current
operational satellite-based aerosol retrievals operate well over the oceans but do not
provide information over land.
A major problem with the visibility observations is that the obstructions to vision include
rain, fog, clouds, and other forms of hydrometeors. In order to detect the aerosol
contribution the weather related phenomena need to be filtered so that the aerosol signal
is not obscured by hydrometeors.
In 1999, NRL has contracted with R. Husar to implement the weather filters for the
calculation of the aerosol extinction coefficient, and to implement the ideal code in the
NAPS system that would perform the appropriate filtering and plotting algorithms. In
summer of 1999, these algorithms were developed and tested using a global data set for
one six hour period.
Since September 1999, the Bext filtering algorithm was incorporated in the production of
six hourly surface weather maps. The aerosol extinction coefficient was drawn as a
color-coded circle, with radius proportional to the extinction coefficient and color-coded
according to dust, smoke, and haze.
The experience of the past 9-month suggested that both the weather filter and the display
rendering should be re-examined to better represent the surface aerosol pattern. This
report summarizes this second effort which consisted of:
1. Assessment of the NRL surface observation over a longer period.
2. Evaluation of surface extinction coefficient data over Africa , March-June 2000.
3. Recommendation for additional activities that would improve the utility of the surface
   visibility data.

Assessment of the NRL surface observation over a longer period
The NRL global surface observations were collected over the period March 28-June8,
2000. The NRL data were plotted on the global map (Figure 1). On any particular day
(e.g. May 1, 2000), the station data are coded according to four different categories. Red
circles, represent stations for which valid Bext data were available. Blue circles represent
stations that are eliminated because of the weather flag, i.e. when the relative humidity
was (>90%). The yellow circles indicate stations that were filtered due to other weather
flags (ww code>13). The small black circles show the stations for which visibility data
were not available at that particular hour.
Since the weather filters are highly diurnal, the pattern of these stations is shown in
Figure 1 through AVI animation representing the 24-hours May 1, 2000.
This evaluation indicates that most of the global network provides data every 3 hours.
Eastern Europe, Japan and Australia have data for every hour. The majority of the U.S.
has data are in 6-hour increments at 00:00, 06:00, 12:00, 18:00.
The spatial pattern of the available data indicates that globally 30-40% of the stations
provides valid aerosol Bext values. Stations in Europe and Southeast Asia have low
fraction of valid Bext stations. However, the reason there is the high relative humidity,
particularly in the early morning hours.
The fraction of the valid stations is very low in Africa, between Sahara and South Africa,
where <20%?? of the available stations report valid values. The cause of the missing
Bext data in the mid-section of Africa is almost exclusively missing data. The spatial
pattern of the stations in North Africa for the 24-hour period is illustrated in Figure 2.

Evaluation of surface extinction coefficient data
The resulting bext values for Africa, north of the Equator, Europe, and Middle East are
shown in Figure 3. The yellow circles are proportional to the surface extinction
coefficient, 1/km. The animation represents daily pattern at noon (12:00 GMT)
The daily data over Africa indicate a rather consistent pattern in that high extinction
coefficients tend to occur over cluster of stations. This is consistent with the existence of
regional dust clouds that occur over 500-1000 km areas. The main problem in Africa is
the large number of missing values.
Recommendation for additional activities
Based on the above discussion it is concluded that the weather filter incorporated in the
current operational algorithm is not too stringent. The station data are eliminated due to
high humidity (RH>90%) and precipitation. A major problem, particularly in Africa is
lack of data.
In order to improve the station filters and derive meaningful Bext values for model
comparison it would also be necessary to evaluate each station whether it is suitable
based on additional statistical criteria. Many stations in Africa and elsewhere report
constant visibilities throughout the record. Since, it is impossible to have a constant
extinction coefficient in the atmosphere the invariant visibility data suggest that the
observers did not bother to report the actual visual range. This is troublesome for model
comparison since the existence of dust clouds do not show up in all of the data and the
model comparison would be meaningless.
Given sufficient length of a data record, say 3-month, it would be possible to identify and
reject those stations that report constant visibility. In fact, this latter approach was applied
in deriving a global continental haze climatology, published recently by Husar et al.,
2000.

				
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