IDENTIFICATION OF FLORIDA COASTAL SHOWERS
Brian C. Zachry and Steven M. Lazarus
Florida Institute of Technology, Melbourne, Florida
ABSTRACT
Showers that occur along the east coast of Florida during the fall months (Sept.,
Oct. and Nov.) are often referred to as “coastal showers”. Four years of hourly
surface data were sampled to construct two distinct data sets in an effort to
identify these events. The first data set consisted of data in which hourly
observations (events) were selected using specific criteria for time, wind
speed/direction and precipitation amount. A second data set is produced using a
linear regression of the along-coast pressure gradient to select days in which
onshore flow is assumed strong enough to produce coastal showers. Results
indicate that the number of ‘identified’ events are sensitive to the selection criteria
(and station location). These data sets will be used to better understand the
nature of the phenomenon, including their propensity for a nocturnal maximum,
their inter-seasonal variability, and precipitation distribution and amount.
1.
INTRODUCTION
Relatively significant precipitation events that occur along the east coast of Florida
during the fall months are often referred to as “coastal showers”. These easterly flow events are
associated with the easterly low-level fetch from a surface high pressure system moving off or
remaining anchored along the coast of the east/central United States mainland. Observations
indicate that the coastal shower events range from ephemeral (i.e. on the order of a few hours)
to extended periods of up to several days in some cases. In part, coastal showers appear to be
a result of air mass modification as relatively cool/dry air is advected over the Atlantic Ocean
(along the eastern/southern periphery of the surface high pressure) towards the east coast of
Florida. The impact of the Gulf Stream, although not totally understood, appears to plays a role
in coastal shower development along the Florida east coast. Observations suggest that Gulf
Stream SSTs, being a few to several degrees warmer than surrounding SSTs during fall
months, acts as an energy source to help initiate showers (provided sufficient moisture
availability). Once formed, the coastal showers are typically advected towards the coast by
relatively strong low-level easterly winds that accompany these events. The inland land mass,
usually cooler than adjacent ocean, often causes nocturnal coastal showers to dissipate rapidly
with increasing inland penetration. For the most part, coastal locations tend to observe more
precipitation than do inland stations - the latter of which often observe little or no precipitation.
The shower events appear to have a nocturnal maximum intensity prior to sunrise and have
often been observed to propagate inland shortly after sunrise. Ultimately, little is known
concerning the specific forcing (initiation) mechanisms responsible for coastal showers but
given their relationship with the large-scale flow, coastline, SSTs, etc., they appear to involve
complex interactions between the synoptic and mesoscale.
2.
Data
Integrated Surface Hourly Weather Observation data were downloaded from the
National Climatic Data Center (NCDC) for 19 stations along the east coast of Florida (Table 1).
The data currently ranges from 1 January 2000 to 9 October 2004 with anticipation that it will
eventually be extended back to 1994. The data contains the typical variables included in all
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hourly surface weather observations. However, in order to identify coastal shower events, the
parameters of interest include: wind direction (degrees), wind speed (m/s) and precipitation total
(mm).
Integrated Surface Hourly Weather Observation data were mined to construct two
distinct data sets in an effort to identify coastal shower events. The first (initial) data set
consisted of hours in which hourly observations met specific criteria to denote a precipitation
event as a coastal shower (see next section for details). This data set, clearly not completely
robust, used the variables above (and time of day), to construct a data set of hourly coastal
shower events. The second (alternative) data set is comprised of days in which the pressure
gradient from JAX to PBI is greater than 3 mb. This set acts as a surrogate for the low level
wind flow since light and variable winds are often observed during the morning hours at land
stations. The idea is to identify potentially significant easterly flow wind events, for the initial data
base, that might otherwise be overlooked due to nocturnal decoupling effects on the 10 m wind.
The pressure gradient is an arbitrary minimum threshold chosen to represent an onshore (i.e.
easterly) geostrophic wind, for which to identify coastal shower events. Methods used to
construct the data sets are shown in the following section.
3.
Methods
a.
Weather Observing Stations
Weather observing stations nearest the east coast were selected for the initial data set.
These coastline stations were chosen to ensure the highest probability that coastal showers
would be identified by reported precipitation amounts. Ultimately, the study will be enhanced to
include inland stations in order to quantify what appears to be the dissipation of showers with
increasing inland penetration.
Figure 1: Weather observation station map of Florida where station locations are
depicted by a black dot with corresponding station IDs to the upper-right.
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b.
Identifying Potential Coastal Shower Events
1)
Initial Data Set
Specific criteria were chosen so as to maximize the probability of identifying a coastal
shower event using the NCDC data. Events were identified based on the following: (1) events
are limited to occurrence in September, October and November; (2) stations are limited to DAB,
VRB, MLB, TTS and COF (3) precipitation totals must be reported (i.e. not 0 mm or ‘missing’);
(4) a wind speeds must be reported (i.e. not 0 mph or ‘missing’); (5) wind directions are limited
to 345° to 180° and can be ‘missing’; (6) times are limited to 2340 to 1220 UTC (roughly 8PM –
8AM EDT or 7PM – 7AM EST). An observation in which precipitation total was ‘missing’ was
discarded as it’s the fundamental metric that defines an event. An event in which a ‘missing’
wind speed and/or direction occurred was retained since discarding such data could result in the
exclusion of a coastal shower event where the weather observing station failed. It is possible
that the missing wind speed and/or direction can be inferred from nearby stations, an earlier
observation or the low-level synoptic conditions.
2)
Alternative Data Set
Florida coastal showers are related to easterly low-level synoptic-scale wind flow events
over the region. The data that constitute this portion of the project reflect cases where a strong
onshore geostrophic wind component, as determined from the pressure gradient over Florida,
exists. A sequence of steps was taken in order to ensure the quality and consistency of the
selected data. The first of these was to reduce the data such that only one observation per hour
per station remained (provided the data was not ‘missing’). The most recent (i.e. latest)
observation from 30 minutes before the hour to on the hour (e.g., the latest observation from
1230 and 1300 was used for the 1300 observation time) was taken for each observation time
(i.e. 00 UTC – 23 UTC).
Secondly, an alongshore n̂ coordinate axis was defined as positive north (Figure 2, left).
This natural coordinate yields a relatively clear delineation between an onshore and offshore
wind, with its orientation determined so as to compensate for Florida’s offset from true north.
The coordinate origin, Jacksonville (JAX) was defined to be located at 0 km with all remaining
stations (south of JAX) defined in terms of a negative distance (i.e. in the - n̂ direction). Since
not all the stations used in the calculation lie directly on n̂ , the distances to these stations from
JAX were determined by the method shown in Figure 2. The negative distance along n̂ (red),
where the normal (blue) line intersected n̂ , was used to determine the station distance from the
coordinate origin (see distance determination for station SGJ shown in Figure 2, right).
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Figure 2: (Left) Defined n̂ axis (red) used to determine the distance from origin (JAX) (i.e.
along n̂ ) to each station via the intersection of the normal line (blue) drawn from the
station location and the n̂ axis. Weather observing station locations are depicted by a
black dot with corresponding station IDs to the upper-right. (Right) Modified zoom image
from (Left) showing the determined distance (green) along n̂ to station SGJ.
The distance determination was used in combination with reported surface pressure
values to obtain an average pressure gradient across the stations for each day. To accomplish
this, a Perl statistics regression module was used to calculate a ‘best-fit’ line (the straight line
that best represents the trend that the points in a scatter plot follow) to pressure using the
method of “least-squares”. The least-squares method minimizes the difference between the
‘estimate’ y (here, assumed to be linear), i.e.
y  mx  b
(1)
and observations yo (here, surface pressure). Here, ‘x’ represents the station distance as
measured from the coordinate origin (JAX), the coefficient b (intercept) is the ‘estimated’
pressure at zero distance (i.e., JAX) and m (slope) is the (best-fit) pressure gradient ( dp dn ) along
the normal. Minimization of the quadratic [y – yo]2 yields the slope (m) and intercept (b),
respectively
m
n  xy   x y
 
(2)
n x 2   x 
2
4
b
 n  m x
(3)
n
where n is number of stations. A correlation coefficient r is defined as
r
n xy   x y
 
 
(4)
n x 2   x  * n y 2   y 
2
2
The statistics regression module returns R² (coefficient of determination), dp dn , n and b
(although not important here) for each hour. Estimates of the pressure gradient are restricted to
cases where more than 8 stations (n) were used and R² was greater than or equal to 0.65.
Here, a ‘significant’ correlation indicates a value greater than 0.8 (e.g., Tyrrell et al. 2004, MBJ
2004, Mathbits 2004). This corresponds to an R² value of 0.65 or greater. The coefficient of
determination represents the percent of the data that is closest to the line of best fit or, in other
words, a measure of how well the independent variable (here, distance) predicts the dependant
variable (here, pressure). The cutoff-value selected for R² means that 65% (at minimum) of the
total variation in y can be explained by the linear relationship between x and y (as described by
the regression equation). The other 35% (at maximum) of the total variation in y remains
unexplained by the regression.
An average pressure gradient (and coefficient of determination) was calculated by
averaging the relevant regression outputs over each hour. The average pressure gradient and
coefficient of determination were output only if dp dn was greater than a pre-determined cutoff
value (3 mb). The distance and orientation of the n̂ axis indicates that the pressure gradient
must be positive (i.e., pressure increasing in the +n direction, a surface high pressure to the
north) in order to result in an onshore geostrophic wind.
A back-of-the-envelope calculation of the geostrophic wind using a pressure gradient of
3 mb yields a significant onshore geostrophic wind component (on the order of 19 kts, assuming
air density of 1 kg/m³, dn of -442.4 km and Φ of 28.52°N).
3.
RESULTS
Preliminary Florida coastal shower data sets have been identified in two ways via the
methods discussed above. The first (initial) data set identifies hourly coastal shower events
based on the criteria specified above. The number of hourly ‘hits’ for the stations of focus are
reported in Table 2 which indicates that the number of ‘identified’ events are sensitive to the
station location (and to the selection criteria used to pinpoint an event). With hourly ‘hits’ it is
true that more than one event can occur at each station for each day. However, coastal showers
are defined on a daily scale. Hence, the data set will eventually be broken into event days with
multiple hours in each.
The second (alternative) data set identifies daily easterly flow events across the
designated region of Florida in Figure 2 (i.e. length of n̂ ). This data set was produced using a
linear regression of the along-coast pressure gradient to select days in which onshore flow is
assumed strong enough to produce coastal showers. A pressure gradient equal to or greater
than 3 mb over the area resulted in 94 identified events. As with the first data set, the number of
potential events are sensitive to the selection criteria. An example regression plot of a costal
shower event using Microsoft Excel is shown in Figure 3 from the 11:00 PM EST observation on
27 September 2000 which includes: the line of ‘best-fit’ and its corresponding regression
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equation and the coefficient of determination (R²). The corresponding data and the regression
parameters output from the statistics module are shown in Table 3 – the latter of which are in
good agreement with the statistical parameters determined using Microsoft Excel.
4.
COMMENTS
Florida coastal showers have been identified using two methods. The robust methods
above are believed to have produced two data sets which capture coastal showers based on: 1)
nighttime showers generating precipitation and an onshore component to the wind and 2) a
situation where a strong pressure gradient exists over the region with high pressure to the north
and low pressure to the south. These data sets will be used to better understand the nature
(both dynamic and thermodynamic) of the phenomenon, including their propensity for a
nocturnal maximum, their inter-seasonal variability, and precipitation distribution and amount.
5.
REFERENCES
Mathbits, cited 2004. Correlation Coefficient. [Available online at
http://mathbits.com/MathBits/TISection/Statistics2/correlation.htm.]
Tyrrell, Sidney and Nicholson, James, cited 2004. Correlation Coefficient. [Available online at
http://www.mis.coventry.ac.uk/~nhunt/regress/good4.html.]
British Medical Journal (BMJ), cited 2004. Statistics at Square One. [Available online at
http://bmj.bmjjournals.com/collections/statsbk/11.shtml.]
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Table 1: Station IDs and corresponding names used for the initial data set of Integrated
Surface Hourly Weather Observation data from NCDC.
Station ID
COF
CRG
DAB
FLL
FXE
HST
HWO
JAX
MFL
MIA
MLB
NIP
PBI
PMP
SFB
SGJ
TMB
TTS
VRB
Station Name
Patrick Air Force Base
Craig Municipal Airport
Daytona Beach International Airport
Fort Lauderdale/Hollywood International Airport
Fort Lauderdale Executive Airport
Homestead Air Force Base
Hollywood/North Perry Airport
Jacksonville International Airport
Miami
Miami International Airport
Melbourne International Airport
Jacksonville Naval Air Station
West Palm Beach International Airport
Pompano Beach Airpark
Sanford/Orlando International Airport
St. Augustine Airport
Miami Kendall-Tamiami Executive Airport
NASA Shuttle Facility
Vero Beach Municipal Airport
Table 2: Station IDs and the number of hourly ‘hits’ for stations of focus for the selection
criteria specified in section 3b.
Station ID
DAB
VRB
TTS
MLB
COF
# of 'hits'
378
359
207
204
171
Table 3: (Left) Regression parameters output from the regression module in Perl. (Right)
Data used to calculate the regression parameters using Microsoft Excel (and in
regression module).
Regression Module
intercept
1017.0243
dp/dn
0.0071
R²
0.93
n
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Excel Calculation
Distance (km)
Pressure (mb)
-265.8
1015
-16
1017
-152.4
1016.1
0
1017.4
-22
1016.8
-442.4
1014.3
-185.7
1015.2
-218.3
1015.2
-333.6
1014.6
7
PresGrad Regression for 11:00 PM EST Observation
on 09/27/2000
1018
y = 0.0071x + 1017
1017.5
2
R = 0.9302
Pressure (mb)
1017
1016.5
1016
1015.5
1015
1014.5
1014
1013.5
-500
-400
-300
-200
-100
0
Distance (km)
Figure 3: Example regression plot of a costal shower event from the 11:00 PM EST
observation on 27 September 2000 – showing the line of ‘best-fit’ and its corresponding
regression equation and the coefficient of determination (R²).
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