Introduction:

1
Determining the Analogue City for Predicting
Syracuse's 60 Hour Meteogram Louis Mejias and
Dr. Ted Endreny FOR 338 Meteorology Course, 207
Marshall Hall, SUNY College of Environmental
Science and Forestry, Syracuse, NY 13210
Results After compiling the seven comparisons,
the R squared value of each was determined, and
then the mean was calculated. According to these
figures, shown in Table 1, the city to best
predict Syracuses temperatures would be Albany,
with an R2 value of 0.7121. Since this is the
closest value to 1, we would say that Albanys
meteogram is best suited for comparison with
regard to Syracuse weather. However, we must note
that both Binghamton and Buffalos R2 vales were
0.5373 and 0.5114, respectively. As for the dew
point prediction assessment, Buffalo has the
highest R2 value, 0.3767, but Albany and
Binghamton were close behind at 0.3412 and
0.2542, respectively. Table 1. Data results
from the meteogram analysis Discussion and
Conclusions Syracuse received relatively
significant statistical results, but without
absolute statistical significance, since our R2
values were not extremely, or notably, close to
1. There is more statistical significance to not
support the hypothesis, that Syracuse weather can
be predicted by surrounding cities. However, the
temperature correlation between Syracuse and
Albany is the most prominent statistically
significant result in our experiment, but is
probably not good enough to use in order to
predict weather in Syracuse yet it is
significant enough to reject the null hypothesis.
The results are insufficient to fail to
reject the null hypothesis on many aspects. One
major flaw of the experiment is duration over
which data was collected was much too short we
used seven-six hour periods out of a possible
1460-six hour period in a year, which equates
into less than 0.5. This extremely small sample
size is definitely inadequate to produce
confident results. In order for this system of
trend forecasting to work there needs to be one
of two conditions slow moving stable pressure
systems, or storms moving through the area. This
technique does not work well when there are open
wave systems or converging fronts in the area,
because these systems have too much variability.
However, singularly moving fronts, cold or warm,
bring more predictable weather patterns that may
be used to predict Syracuse weather. This
technique would be best implemented when severe
weather systems are approaching Syracuse, to warn
the public early. Even with current advances
in technology in the meteorological field,
predicting weather, even into the near future, is
still difficult. The best forecasting remains on
a daily basis, with models particular to your
area. Although, these techniques can be
successfully used to track storms and generate
warnings and watches, but to plan a lakeside
picnic, at Onondaga Lake, based on Albany weather
is still quite a gamble. Other Sources
Byers, John A. Surface Distance between
two Points of Latitude and Longitude.
http//www.vsv.slu.se/johnb/java/lat-long.htm.
USGS. Query form for the United States and
its territories. http//geonames.usgs.gov/pls/gni
s/
Introduction Predicting the weather is more than
a fascination, its motives are larger than mans
need to survive weather ultimately drives our
lives, and our economics from slowing road
traffic on our daily commutes, to grounding
planes to tropical vacation getaways, even
determining crop yields for the food we eat.
Predicting weather has since become a full time
occupation. To aid meteorologists,
entrepreneurships like the UNISYS corporation
have started. These companies take synoptic data
gathered throughout a region, and create
meteograms, which are are predictions of surface
conditions, created by entering data into a
user-defined numerical weather modeling
algorithm, complied into a series of easily
understood graphics. Below is a common example of
a meteogram.1 Figure 1.
Example Meteogram showing weather
variables. Objective The goal of this
experiment is to determine if meteograms of
predicted surface conditions from a surrounding
city will be reflective of the weather received
here in Syracuse, NY, and can subsequently be
used for trend forecasting to predict weather in
Syracuse. The surrounding cities that have
meteograms generated for them, and are used in
this experiment are Binghamton, Buffalo, and
Albany. Null Hypothesis Syracuse weather will
be predictable by Binghamtons meteogram.
Theories The jet stream plays an important role
in delivering weather. Jet streams can be caused
by horizontal variations in temperature and
pressure along the polar front, a boundary
between cold polar air to the north and warm
subtropical air to the south. Since, the greatest
contrast in air temperature occurs along this
zone, a steep pressure gradient develops, which
intensifies the wind speed, thus causing the jet
steam. The westerly flow aloft is due to other
factors other than steep temperature gradients,
like the conservation of angular momentum.
Angular momentum, like linear momentum, is equal
to the product of mass (m) and velocity (v),
except angular momentum includes a radius (r)
parameter. Angular momentum is classically
illustrated by the ice skater example, where an
they spin faster with their arms tucked in,
rather than outstretched as the radius decreases
the momentum increases. In the absence of
torques, twisting forces, acting on the system,
the angular momentum of the system does not
change, thus it is conserved.2 If we
consider a warm air mass rising from the equator
encountering the tropopause and dispersing
laterally. As the air mass continues toward pole,
the curvature of the earth causes the radius to
decrease, since angular momentum is conserved,
this decrease is compensated by an increase in
speed. To us on the surface of the earth this
results in a strong westerly wind, termed the jet
stream. 1 http//www.weather.unisys.com/. 2 Rose,
Morris Edgar. Elementary Theory of Angular
Momentum. New York. Wiley. 1957
These atmospheric jet streams are swiftly moving
air currents thousands of kilometers long, and a
few hundred kilometers wide, and a few kilometers
thick.Average wind speeds in the core of the jet
stream often exceed 100 knots, and are usually
found at the tropopause, which occurs at
elevations of 10 to 15 kilometers, although not
exclusively.3 Due to this phenomenon of jet
streams, forecasters have developed a technique
to predict weather according to this dominant
upper atmospheric wind pattern, called trend
forecasting. This technique predicts the movement
of surface weather systems according to the
ideology that they in the same direction and at
the same speed as they have been. Analysis
Method and Metric Although it may be evident
that weather moves from west to east, the primary
objective of this experiment is to determine
which city can best predict Syracuses weather.
The importance of the westerly trend becomes
apparent when we are determining the accuracy of
a meteograms of the surrounding cities, we must
compensate for travel time between the cities.
The time differential to be applied to each city
was based on the theoretical average speed of the
jet stream, 100 knots, or 115 miles per hour,
divided by the distance of each city from
Syracuse. The distance from each city to Syracuse
was determined by converting latitude and
longitude coordinates into surface mileage.
Coordinates were recovered from the United States
Geological Survey (USGS), and converted into
mileage using a program designed by John.
Boyers.. To determine if prediction of Syracuse
weather is possible using a surrounding
meteogram, each meteogram generated from the
surrounding stations, from a given date, will be
compared to a current conditions from Syracuse
generated from data gathered by NOAA at the
Syracuse Hancock International Airport. Of the
weather parameters displayed on the meteograms,
temperature and dew point will be regarded as the
most important factors, since these weather
attributes ultimately steer the weather for which
most of us are concerned with, they drive basic
daily decisions like what clothes to wear and how
sunny will it be. XY scatter plots of both
series, temperature and dew point, were created
by taking seven samples of randomly selected
dates and times, with replacement, and fitted
with a regression line, or best fit line,
computed by Microsoft Excel as shown below.
Figure 2. Plot of meteogram data
showing goodness of fit. This regression
approach, produces an R squared (R2 ) value,
those that approach 1 are of statistical
significance, whereas those values that approach
0 are not. The XY scatter plots were generated
for each city, subject to the randomly chosen
date and time, using Microsoft Excel. The linear
regression analysis uses the least squares
method to fit a line through a set of
observations. You can analyze how a single
dependent variable is affected by the values of
one or more independent variables.4  
3Ahrens, C. Donald.
Meterology Today An Introduction to Weather,
Climate, and the Environment. New York.
Brooks/Cole. 6th edition. 2000. 4 Christ, C. F.
Econometric Models and Methods. John Wiley Sons
Inc., 1966.