Trajectory Cluster Analysis
An air parcel trajectory shows the path of an air parcel
as it is transported through the atmosphere by the wind.
HYSPLIT is
one such model that computes trajectories. One application
of a forward in time trajectory, i.e. computed using a
meteorology forecast, is to estimate where a hazardous
substance in the air, such as radioactivity,
will be at a later time. One example of a backward in time
trajectory is to estimate the source region(s) of atmospheric
pollutants occuring at a given location (see NARE,
the North Atlantic Regional Experiment). An individual
trajectory gives only a general description of the wind
field because it does not account for atmospheric processes
such as vertical and horizontal mixing and diffusion. Dispersion
models, which include atmospheric mixing processes and
may have the same advection algorithm as trajectory models,
are usually run to provide a more complete representation
of atmospheric transport.
Given a large set of trajectories, say one back-trajectory
beginning each day precipitation occurred at an AIRMoN-wet
site over the period of a year, one method to analyze the
atmospheric transport associated with the AIRMoN samples
is to cluster the trajectories. Trajectory clustering is
a process of grouping similar trajectories together whereby
differences among individual trajectories in a cluster
are minimized and differences among clusters are maximized.
Ideally, each cluster represents different classes of synoptic
regimes over the duration of the trajectories.
Trajectory clustering at ARL (Silver Spring) has been
done for the following projects.
- To assess the quality of forecast
trajectories (the attached example shows the trajectories
in 5 distinct clusters),
Forward trajectories
(36-h duration) in each of five clusters resulting from a
trajectory cluster analysis. The origin is in east-central
Oklahoma 500 m above ground level.
- To form the basis of the criteria used to select case
studies to evaluate fine scale Eulerian mesoscale models
(e.g. Chesapeake
Bay study). Models such as the Regional Atmospheric
Modeling System, (RAMS),
and the Regional Acid Deposition Model, (RADM),
can be used to predict or analyze deposition velocities
for anthropogenic substances known to contribute to eutrophication,
the most serious threat to ecosystem habitat in the Chesapeake
Bay and other regions.
- To investigate effects of the 1990 Clean Air Act Amendments-mandated
sulfate emissions reductions (see below).
Research Summary
AIRMoN
data from 1995 show generally lower sulfate concentrations
in precipitation than in 1993, presumably because of
the 1990 Clean Air Act Amendment-mandated emissions reductions.
However, meteorological and chemical factors, among others,
also contribute to the sulfate concentrations. An investigation
of the meteorological factors affecting sulfate concentration
in precipitation is currently underway. In a preliminary
study, HYSPLIT back-trajectories
from one AIRMoN site (State College, PA) for two years,
1993 and 1995, were computed beginning at the midpoint
of the 24-h AIRMoN precipitation sample and at an elevation
of 2000 m, assumed to be representative of atmospheric
transport leading up to a precipitation event. Trajectories
associated with small precipitation amounts (less than
5 mm) were removed from further analysis because very
high sulfate concentrations typically occurred with the
very low precipitation amounts, an artifact of dilution.
Results from a cluster analysis found generally lower
sulfate concentrations, by cluster, in 1995 as compared
to 1993 (see figures).
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Precipitation volume-weighted
mean SO4 concentration by cluster for 1993 (left) and 1995
(right).
Presentation
Stunder, B.J.B. and R.S. Artz, A comparison of 1993 and
1995 AIRMoN precipitation chemistry measurements using
HYSPLIT trajectories, 1996 NADP Technical Committee Meeting,
October 21-24, 1996, Williamsburg, VA.
Stunder, B.J.B., 1996: An assessment of the quality of forecast
trajectories, J. Appl. Meteor., 35, 1319-1331.
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