PII
In order to derive the aerosol extinction, the raw visibility data from the instrument (and/or
observer) needs to filtered and corrected for a number of factors. That part of the processing and
extra QA/QC we intend to do here, at least until it becomes routine. The processed current and
historical visibility data would pe posted on the CAPITA website. The results of our processing is
- total extinction coefficient due to weather and aerosol
- aerosol extinction coefficient at ambient humidity
- aerosol extinction coefficient normalized to 60% RH
- spatially interpolated daily (possibly X-hourly) maps of the above 3 extinction
parameters
Task 1: Evaluation of ASOS Visibility Data as a PM Surrogate
Weather Filters and Humidity Correction
The ASOS data undergo extensive automated quality control by the Weather Service.
However, many interferences, largely due to weather makes the raw ASOS data
unsuitable as a surrogate for PM2.5. For this reason, additional filters need to be applied
to the ASOS data.
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 need to be 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.
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Finally, an “ice fog” and “blowing snow” filter need to be applied that eliminates extreme
cold and windy conditions (temperature <-29 C and wind speed >16 km/hr).
Our past experience is with the weather-filters developed for the human-gathered weather
data. As part of this work, the weather filters will be re-examined and adopted for the
automated ASOS data.
Aggregation into Hourly and Daily Averages
The ASOS monitoring data are transmitted at one-minute resolution. For some
applications, including fast changing weather the one-minute resolution is highly
desirable for the interpretation of the phenomenon involved. Example applications
include the identification of dust fronts; smoke plumes near major fires or pollution
plumes near urban-industrial centers. The fine-scale temporal resolution of ASOS should
allow separation of aerosol plumes from the background aerosol based on temporal
structure of the 1-minute data. Since the spatial and temporal pattern of pollutant
concentrations are related through the wind speed, the ASOS data should allow
estimating the size of aerosol puffs passing over the sensor.
As part of the proposed analysis the fine time resolution of the ASOS data will be
evaluated with the goal of characterizing the aerosol fine structure.
For many applications the “noisy” one-minute resolution ASOS data are not appropriate.
This is the case for regional scale analysis as well as for seasonal and long-term pattern
analysis. The aggregation of the ASOS light scattering data into hourly and daily
aggregates is made difficult by the strong non-linearity associated with the humidity
effects. As part of the proposed work alternative aggregation schemes will be evaluated
along with recommendations for routine processing.
The ASOS visibility sensor is a forward scattering instrument
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ASOS Stations from FAA, NWS and Archived at NCDCFor this analysis data for 220
stations were available from the NCDC archive
These ASOS sites are mostly NWS sites, uniformly distributed over the country
(Imagine if we could get the entire set, including the DOD sites, not listed)
June 2002 Fires in CO, NM, UT
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Data Quality Assessment•Co-located ASOS Sensors – absolute calibration
•Lower detection limit (0.05 km-1, 50 km visual range)
•Random sensor malfunctions
•File format problems
Comparison of Sites with Duplicate ASOS Sensors
•Co-located ASOS sensors are installed at different runways of the same airport.
•Dual ASOS sensors (55) are distributed uniformly over the 800+ station network
•Triple sensors are particularly useful for sensor calibration and consistency checking•At several duplicate sites the 2-sensor
correlation is excellent and the absolute values also match.
•This indicates that the scattering sensor per se has high precision and temporal stability.
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•At several duplicate sites the 2-sensor correlation is excellent and the absolute values also match.
•This indicates that the scattering sensor per se has high precision and temporal stability.
Duplicate Sensors:
Mediocre
Sites
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•S
ome sites (e.g. Tulsa, OK) show very high correlation between the sensors but they are off by a factor.
••Other sites indicate poorer correlation and a significant offset.
Duplicate Sensors:
Poor
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Sites
•Duplicate
sensors at some sites show significant deviation in scale and offset.
•••The nature of these deviations indicate poor instrument calibration maintenance for the ASOS visibility sensors.
Three Sensor Comparison
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•At 7 NWS Sites, there are 3 ASOS visibility sensors which allow more detailed sensor evaluation.
••Both at Cleveland, OH and Hartford, CT Bext1 and Bext3 show excellent correlation, R 2=0.99
••On the other hand, the Bext1 and Bext2 correlation is poor. This indicates that the Bext2 sensor either
–produces bad data or
–it is located at a site with significantly different Bext
Three Sensor Comparison
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Comparison of Sites with Duplicate ASOS Sensors
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Comparison
of Sites with Duplicate ASOS Sensors
At the St. Louis Lambert airport, the Bext from the two ASOS sensors track well (right,
top)
However, the absolute magnitude of the Bext values differ by a factor
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When the ASOS sensor values are multiplied by a 1.66, the two signals are virtually
identical (right, bottom)
This indicates that at this site, the absolute calibration of the sensors differs by a factor of
1.66
ASOS Bext Threshold: 0.05 km(-1)•The Bext values below 0.05 km-1 are reported
as 0.05.
•For Koschmieder coeff K=3.9, this threshold VR=78km(~ 50 mile); for K=2
VR=40km(~25mi)
•In the pristine SW US, the ASOS threshold distorts the data
•Over the East and West, the ASOS signal is well over the threshold most of the time
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Evidence of ASOS Data ProblemsOccasionally, the Bext values are replaced the threshold value of
0,05 km-1.The ASOS data for Temperature and Dewpoint appear to be erratic for some stations
The problems include constant values, spikes and rapid step changes.
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Data Problems: Bad data records•The main data reading problems are due to bad records
•Some records for some stations are not fixed length
•Cause of the bad data records need to be identified
ASOS Data Pattern Analysis•Diurnal Cycle
•RH Dependence of Bext
•ASOS Bext – PM2.5 Relationship
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Typical
Diurnal Pattern of Bext, Temperature and Dewpoint
•Typically, Bext shows a strong nighttime peak due to high relative humidity.
••Most of the increase is due to water absorption by hygroscopic aerosols. At RH >90% , the aerosol is mostly water
••At RH < 90%, the Bext is mostly influenced by the dry aerosol content; the RH effect can be corrected.
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Diurnal Cycle of Relative Humidity and Bext The diurnal RH cycle causes the high Bext values in
the misty morning hours
The shape of the RH-dependence is site (aerosol) dependent – needs work
Adopted
RH
Correction
Curve
(To be validated for different locations/seasons)
RH is calculated from T – Temperature, deg C and D – Dewpoint, deg CRH = 100*((112-(0.1*T)+D)/(112+(0.9*T)))8-The ASOS
Bext value are filtered for high humidity
-Values at RH >= 80% is not used
-Later we will try to push the RH correction to 90%)
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-The
Bext
is
also
corrected
for
RH:
RHCorrBext
=
S
Bext/RHFactor
easonal Average Diurnal Bext Pattern•For each minute of the day, the data were averaged over
June, July and August, 2000
••Average Bext was calculated for
–Raw, as reported
–For data with RH < 90%
–RH < 90% and RH Corrected
••Based on the three values, the role of water can be estimated for each location
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