Inﬂuence of the Lake Erie Overlake Boundary Layer on Deep

Influence of the Lake Erie Overlake Boundary Layer on DeepConvective Storm Evolution

THOMAS E. WORKOFF*

Department of Atmospheric Sciences, and Center for Atmospheric Science, Illinois State Water Survey,

Prairie Research Institute, University of Illinois at Urbana–Champaign, Urbana, Illinois

DAVID A. R. KRISTOVICH

Center for Atmospheric Science, Illinois State Water Survey, Prairie Research Institute, University of

Illinois at Urbana–Champaign, Urbana, Illinois

NEIL F. LAIRD

Hobart and William Smith Colleges, Geneva, New York

ROBERT LAPLANTE

NOAA/National Weather Service Forecast Office, Cleveland, Ohio

DANIEL LEINS

NOAA/National Weather Service Forecast Office, Phoenix, Arizona

(Manuscript received 1 July 2011, in final form 6 February 2012)

ABSTRACT

The influence that the overlake boundary layer has on storm intensity and structure is not well understood.

To improve scientists’ understanding of the evolution of storms crossing Lake Erie, 111 events during 2001–09

were examined using observations from Weather Surveillance Radar-1988 Doppler (WSR-88D), surface,

buoy, and rawinsonde sites. It was found that on average, all storm modes tended to weaken over the lake;

however, considerable variability in changes of storm intensity existed, with some storms exhibiting steady-

state or increasing intensity in specific environments. Noteworthy changes in the storm maximum reflectivity

generally occurred within 60 min after storms crossed the upwind shoreline. Isolated and cluster storm modes

exhibited much greater weakening than those storms organized into lines or convective complexes. The at-

mospheric parameters having the greatest influence on storm intensity over Lake Erie varied by mode.

Isolated and cluster storms generally weakened more rapidly with increasingly cold overlake surface air

temperatures. Linear and complex systems, on the other hand, tended to exhibit constant or increasing

maximum reflectivity with cooler overlake surface air temperatures. It is suggested that strongly stable

conditions near the lake surface limit the amount of boundary layer air ingested into storms in these cases.

1. Introduction and background

It is well known that on climatic time scales the Great

Lakes suppress clouds and precipitation near the

coastlines during the warm season, particularly in re-

gions downwind of the lakes (e.g., Augustine et al. 1994;

Scott and Huff 1996). However, operational weather

forecasts for these regions must account for the rate of

* Current affiliation: Systems Research Group, Inc., NOAA/

Hydrometeorological Prediction Center, Camp Springs, Maryland.

Corresponding author address: David Kristovich, ISWS, PRI,

University of Illinois at Urbana–Champaign, 2204 Griffith Dr.,

Champaign, IL 61820.

E-mail: [email protected]

OCTOBER 2012 W O R K O F F E T A L . 1279

DOI: 10.1175/WAF-D-11-00076.1

� 2012 American Meteorological SocietyUnauthenticated | Downloaded 02/09/22 01:20 AM UTC

change of storm intensity as well as situations where the

storm evolution differs from climatology. Few published

studies have examined how the lakes and their associ-

ated overlake boundary layers (OLBLs)1 affect preex-

isting deep convective storms, particularly during the

warm season where the water surface is predominately

cooler than the surrounding land.

To develop better forecasting techniques for areas in

western lower Michigan, Graham et al. (2004) constructed

a 5-yr climatology of the behavior of severe mesoscale

convective systems (MCSs) crossing Lake Michigan. They

found that MCSs responded variably to passing over the

water surface, depending on time of year and structure.

For example, MCSs were more likely to weaken or dis-

sipate in the summer months (June–August, JJA) than

in the spring or fall. In addition, the stability of the OLBL

appeared to influence storm evolution over the lake. The

study showed that the convection tended to maintain

intensity when the OLBL was strongly stable (lake

surface $2.58C colder than the overlake air tempera-

ture), despite the reduction in surface-based instability.

However, their study did not examine the rate at which

storm intensity changes occurred.

Other studies have used numerical modeling tech-

niques to examine the effect of surface-based stable

layers on convective systems. Parker (2008) used an ide-

alized model to simulate squall-line interactions with

such layers. In his study, a surface-based isothermal cool

layer, approximately 1 km deep, was introduced to a ma-

ture squall line. He found that storm evolution is associ-

ated with the relative buoyancies of the cool layer and

the cold pool produced by the convective system. When

the cold pool is denser, lifting along the leading edge

of the cold pool can overcome a significant amount of

surface-based convective inhibition (CIN) if convective

available potential energy (CAPE) is present. This, in

turn, allows updrafts to maintain their intensity despite

interaction with convectively unfavorable near-surface

conditions. The results of Parker (2008) also showed

that in instances where the surface cool layer ahead of

the storm is cooler than the cold pool air, storms can

become elevated atop the surface layer, with continuous

updrafts being driven by bore propagation.

The effect of changing low-level conditions near shore-

lines on mature squall lines was examined in an ideali-

zed modeling study by Lericos et al. (2007). Their study

focused on squall lines moving eastward from the Gulf

of Mexico onto the northern Florida coast. In cases of

moderate to strong ambient low-level vertical wind shear,

land breezes were found to reduce the magnitude of

ambient shear. This, in turn, caused the updraft of the

squall line to tilt in an upshear direction, resulting in

weakening of the squall line.

While the Graham et al. (2004), Parker (2008), and

Lericos et al. (2007) studies provide important insights

into the evolution of convection interacting with varying

near-surface conditions in the vicinity of coastlines, they

consider only organized convective systems (e.g., MCSs),

leaving the potential effect of a large lake on other con-

vective modes unknown. In addition, the rate of change

in the storm system, critical in an area of widely varying

lake sizes, was not examined. The current study uses

a climatological approach incorporating observations

commonly available to forecasters (e.g., radar, hourly

surface observations, rawinsonde observations) to ex-

amine how large lakes influence the intensity of the wide

range of convective modes.

2. Data and methodology

Statistical analyses were performed by season, time of

day, and storm mode to identify common storm re-

sponses and the potential causes. Weather Surveillance

Radar-1988 Doppler (WSR-88D) data from Cleveland,

Ohio (KCLE) (Fig. 1), were used for this study. The area

of study chosen was south of 42.258N over Lake Erie;

this was selected to include storms within a range of

;100 km of the radar when crossing the lake. This is an

area where convective storms frequently pass over Lake

Erie (Laing and Fritsch 1997) and are routinely moni-

tored by the Cleveland National Weather Service

Forecast Office.

a. Identification of events

Appropriate events (preexisting convective storms

that advected from land to over Lake Erie) were identi-

fied by a three-step process: 1) surface observations were

used to identify dates when significant rain occurred,

2) composite radar reflectivity imagery was used to re-

move events where the observed precipitation was clearly

not associated with convective storms moving over Lake

Erie, and 3) more detailed analyses of WSR-88D Level II

data were conducted to identify which of the remaining

events were appropriate for this study.

Precipitation observations at sites around Lake Erie

(Fig. 1, appendix A) were used to identify potentially

appropriate events. The azimuth range of 1208–3408 with

respect to the location of KCLE was chosen to capture

the most likely path of storms crossing the lake while

staying inside the study area, despite the lack of suitable

1 Overlake boundary layers are often identified in the scientific

literature and forecast discussions as marine internal boundary

layers. OLBL is used in this article to emphasize the more limited

spatial extent of lakes relative to oceans.

1280 W E A T H E R A N D F O R E C A S T I N G VOLUME 27

Unauthenticated | Downloaded 02/09/22 01:20 AM UTC

precipitation observation stations on the southern and

eastern shores of Lake Erie. Days when snow occurred

or surface air temperatures were ,08C were excluded.

Over the period 2001–09, a total of 504 dates were

identified with $1.27 cm (0.5 in.) of precipitation and

above freezing temperatures.

National composite radar reflectivity imagery obtained

from the National Center for Atmospheric Research

(NCAR) was examined for each of the 504 dates to

identify those with convective storms developing up-

wind and moving over Lake Erie. While the national

composite lacks the desired spatial and temporal reso-

lutions, it allows for identification of precipitation sys-

tems that are clearly not convective. Any date that had

a maximum base reflectivity ,35 dBZ on the national

composite was eliminated. These criteria reduced the list

of dates with possible convective storms moving over the

Lake Erie study region to 280. It should be noted that

an individual date may have included multiple convective

storm events.

More detailed analysis of Level II radar data from

KCLE was conducted for each of the remaining 280

potential events using GR2Analyst (Gibson Ridge,

version 1.60). These data were obtained from the Na-

tional Climatic Data Center (NCDC). In order for the

event to qualify for this study, the base reflectivity ob-

served at the 0.58 elevation level with values .45 dBZ,

a common threshold for convective rainfall, needed

to be observed during at least three consecutive radar

scans (’10–15 min) prior to moving over the lake. If

a reflectivity value of $45 dBZ was not observed at least

15 min before the storm crossed the shoreline, the event

was not included in the study. This was done to ensure

storms included in the study were not experiencing large

changes in intensity as they approached the lake; there-

fore, the evolution of intensity observed over the water

was likely the result of OLBL interaction. Note that this

criterion would remove cases where the reflectivity in-

creased to .45 dBZ over the lake; however, this was an

infrequent occurrence and is not thought to change the

findings of this study.

In some instances, areas of .45-dBZ reflectivity

were embedded in areas of widespread rainfall associ-

ated with synoptic disturbances. Since the presence and

FIG. 1. Locations of observation sites used in this study. Surface observation sites over

land are denoted by black diamonds, precipitation observation sites denoted by numbered

circles, buoys denoted by black ovals, radar locations denoted by , and raob sounding sites

by . Radar ranges for KCLE and Detroit, MI (KDTX), are denoted by a dashed circle

(50 km) and a solid circle (100 km), respectively. Dashed line denotes 42.258N, the northern

boundary of the study. Names and locations of precipitation observation sites are located in

appendix A.



organization of convective storms is not always obvious

in such situations, these events were also eliminated

from the database. Finally, events when the convective

storm moved north of the 42.258N boundary were re-

moved. This resulted in a total of 111 events (on 101 days)

used for analysis of storm evolution over Lake Erie.

b. Determination of convective mode

Each storm was classified by mode to investigate its

role in convective evolution over the lake. During events

when the storm mode underwent a change over the lake,

the storm was classified by the mode at the time it moved

over the upwind shoreline. Previous studies (e.g., Fowle

and Roebber 2003; Gallus et al. 2008) tended to use three

dominate convective modes (linear, isolated, multicellu-

lar); a fourth mode (complex) was added for this study

due to the propensity for nonlinear mesoscale convective

complex (MCC) development in the study area, as well

as to allow for a more direct comparison with the findings

from Graham et al. (2004).

Examples of the four convective storm modes used in

this study are shown in Fig. 2 and were defined as follows:

Isolated (I)—individual convective storms, including

supercells, with a continuous area of reflectivity

.45 dBZ that was ,500 km2 (Fowle and Roebber

2003); storms must have been separated from other

reflectivity maxima by greater than two diameters of

the area containing reflectivity values of .45 dBZ

(Fig. 2a);

Cluster (CL)—areas of unorganized convection, with

several (three or more) reflectivity maxima located

within a distance equivalent of two diameters of the

45-dBZ reflectivity area of each individual storm

(Fig. 2b); areas of .45-dBZ reflectivity were gener-

ally small (,40 km2) for individual storms and were

separated by reflectivities ,35 dBZ;

Complex (C)—nonlinear, organized storm structure

having an area of .500 km2 with continuous reflec-

tivity values of .45 dBZ (Fig. 2c); and

Linear (L)—an area with reflectivity values .45 dBZ

organized in a curvilinear manner (Fig. 2d); storms

were considered linear if they were organized in

a line ,50 km wide, exhibited a length–width ratio

of at least 3:1 (Fowle and Roebber 2003), and areas

of reflectivity .45 dBZ were separated by less than

two of their diameters.

c. Analysis of storm evolution

Radar reflectivity changes were used as an indication

of storm intensity variations. The maximum base

reflectivity, recorded to the nearest whole decibel, at the

FIG. 2. Base reflectivity images from the Cleveland WSR-88D radar showing examples of (a) isolated, (b) cluster,

(c) complex, and (d) linear storm modes used in this study.



0.58 elevation angle was determined from KCLE Level II

data for each volume scan (approximate time interval

of 5 min). Maximum reflectivity was recorded from

30 min before (230 min) the time the storm moved

over the water (TMOW) to at least 60 min following

the TMOW (160 min), or until the storm arrived at the

downwind shoreline. In events when the storm had not

developed at 230 min, the maximum reflectivity was

determined from the volume scan at the time when

the maximum reflectivity exceeded 35 dBZ prior to

TMOW.

The storm evolution over Lake Erie was determined

by comparing the maximum reflectivity at TMOW with

both the maximum reflectivity at 30 (130 min) and at

60 min (160 min) over the lake. For simplification of

discussion, five categories were used to describe the

evolution of an individual storm:

major strengthening, with $ 18 dBZ maximum reflec-

tivity change,

moderate strengthening, with 13 to 17 dBZ maximum

reflectivity change,

no change, with 2 to 12 dBZ maximum reflectivity

change,

moderate weakening, with 23 to 27 dBZ maximum

reflectivity change, and

major weakening, with # 28 dBZ maximum reflec-

tivity change.

d. Environmental and storm parameters

To examine the influence of environmental conditions

on storm evolution over Lake Erie, several parameters

were developed to represent the atmospheric conditions

that storms encountered. These parameters and their

associated definitions are given in appendix B. The data

used to calculate these parameters for each storm are

described below.

Observations of temperature, wind speed, and wind

direction collected at surface sites over land near the

upwind (downwind) shore of Lake Erie were used to

represent the upwind (downwind) conditions encoun-

tered by each storm (Fig. 1). The upwind and down-

wind observation sites were relative to individual storm

motion as determined from radar data. The majority

(about 75%) of the storms examined had mean motions

in the range from southwest (2258) through northwest

(3158C). Observation sites used for each storm were

those deemed closest to the storm path. Only obser-

vations taken before the storm appeared to influence

conditions at each site were used for this purpose.

Overlake conditions were derived from air and water

temperature data collected at National Oceanic Atmo-

spheric Administration (NOAA) buoy 45005 (Fig. 1) for

each storm when the buoy was operational (generally

April–November). All buoy data were acquired from

the National Data Buoy Center. Storms for which

buoy data were not available were excluded from any

analyses that included overlake conditions. Velocity

azimuth display (VAD) wind analyses provided in

the KCLE WSR-88D Level III dataset were used to

represent surface to 3-km and surface to 6-km wind

velocities.

3. Results

a. Temporal distribution of storms

The largest frequency (76%) of storms moving over

Lake Erie occurred in the months of May–August, with

the maximum in July (Fig. 3). Isolated, complex, and

linear storms showed similar monthly trends while

cluster storms exhibited a peak in May. As expected,

storms were more frequent during the afternoon and

evening hours (Fig. 4) with differences in timing

FIG. 3. Number of occurrences (111 total) of each storm mode by month.



between modes. Cluster and isolated storms had a rela-

tive maximum during the late afternoon and evening

[1400–2200 local standard time (LST)], indicative of

the importance of surface buoyancy to their develop-

ment. Linear storms showed a broad maximum during

the late morning to late evening (1000–0200 LST) and

minimum near sunrise (0600–1000 LST). The relative

maximum for complex storms occurred during the

overnight (2200–0200 LST) and morning (0600–1000

LST) hours, similar to the pattern of initial convective

development in the late evening and maximum extent

in the overnight hours observed by Ashley et al. (2003).

This is consistent with the findings of Graham et al.

(2004) and Parker (2008) that MCS events can maintain

their evolution over shallow stable layers.

b. Evolution of storms over Lake Erie

The evolution of storm intensity, estimated by changes

in the maximum base reflectivity, as a function of time

over Lake Erie and storm mode are shown in Table 1.

Regardless of storm mode, increases of $3 dBZ are rare

(5% of events) and no storms exhibited major strength-

ening ($8 dBZ).

One of the most notable features is the difference in

storm evolution at 130 min after TMOW and 160 min

after TMOW (Table 1). At 130 min, the majority of

storms experienced no change in intensity (22/12 dBZ),

with smaller percentages of all storms experiencing

moderate weakening (23/27 dBZ) and significant

weakening (#28 dBZ). This was consistent for all storm

modes. On average, the maximum base reflectivity only

changed by 21 to 22 dBZ from the time the storm

crossed the upwind shore. In contrast, excluding storms

that moved onshore, by 160 min only about a third of

all storms experienced no change in reflectivity and 28%

of the storms experienced major weakening. Average

reflectivity changes ranged from 22 to 29 dBZ at 160

min from TMOW.

Differences in storm evolution over Lake Erie were

noted between storm modes at 160 min (Table 1).

Linear and complex storms tended to weaken less than

isolated and cluster storms. For example, average re-

flectivity changes were only 22 to 23 dBZ for linear and

complex storms, while for isolated and cluster storms the

average reflectivity changes were about 27 to 29 dBZ.

Additionally, only 10%–15% of linear and complex

storms experienced major weakening at 160 min,

compared to 40% of isolated and cluster storms.

c. Relationships with environmental parameters

Graham et al. (2004), Lericos et al. (2007), and Parker

(2008) found that changes in environmental conditions

associated with a land–water boundary can play im-

portant roles in subsequent storm evolution. While

some factors affecting the role of the boundary slowly

change over long time scales (e.g., surface friction changes

over land due to plant growth), forecasting applications

focus on shorter time-scale changes due to variations in

atmospheric conditions. This study focuses on how

short-term changes in atmospheric conditions influence

storm evolution over Lake Erie.

To investigate which parameters or combinations of

parameters are most closely related to storm evolu-

tion, stepwise linear multivariable regression (SLMR)

analysis was conducted. SLMR selects only the most

statistically significant parameters for inclusion in the

linear regression model, which makes it useful for

identifying parameters that exhibit the largest influence

on storm evolution. The regression model takes the

form

DdBZ 5 C 1 boVo 1 b1V1 1 � � � ,

FIG. 4. Same as Fig. 3 except by time of day (using TMOW). Time of day shown in local

standard time (LST 5 UTC 2 4/5 h).



where DdBZ is the change in reflectivity at 160 min, C is

a constant, Vi are the predictor parameters, and bi are

the coefficients of Vi. Only parameters that have a sta-

tistical significance level (a) of a # 0.10 are retained. In

events where no parameters met the statistical signifi-

cance criteria, the parameter with the highest statistical

significance is shown.

The SLMR used 160-min dBZ change as the pre-

dictand and five predictor parameters: 1) the near-

surface air temperature over the lake (LAT); 2) OLBL

stability, estimated by the difference between the water

temperature and the overlake near-surface air temper-

ature (LT-LAT, negative values imply stable condi-

tions); 3) spatial variations in near-surface conditions,

estimated by the difference between the overlake near-

surface air temperature and the upwind (relative to

storm motion) overland near-surface temperature

(LAT-UWT); 4) larger-scale spatial variations in near-

surface conditions not associated with the lake, esti-

mated by the difference between the downwind and

upwind near-surface equivalent potential temperature

(ue); and 5) 3-km VAD wind velocities, which may also

give some indication of the ambient low-level vertical

wind shear. Changes in surface temperature, low-level

stability, and low-level wind speed were included, as

they are factors generally cited as influencing convec-

tive structure and evolution.

Table 2 shows which atmospheric parameters had

the most influence on storm evolution determined by

the SLMR analysis: overlake air temperature (LAT)

for cluster and complex storms, horizontal tempera-

ture differences (LAT-UWT) for isolated storms, and

3-km wind speed for linear storms. The results suggest

that near-surface temperature changes associated

with the OLBL are the most influential parameters for

all storm modes, with the exception of linear. How-

ever, the differing signs of the b1 coefficients suggest

the effect of lake-induced temperature differences

depends on storm mode. The positive b1 found for

cluster and isolated modes shows a direct relation-

ship between reduced overlake air temperature and

storm intensity decrease. However, the negative b1

value for complex storms shows that cooler overlake

temperatures are associated with increasing storm

intensity.

It should be noted that very few events exhibited en-

vironmental or storm evolution changes far removed

from all of the other events. Such events can have large

influences on the relationships determined by SMLR. If

statistical outliers (defined as greater than three stan-

dard deviations) are removed, the same parameters

were chosen by SMLR. However, the significance of the

regression model was greatly improved for the isolated

(a 5 0.250) mode.

Another method of illustrating the effect of multiple

parameters on storm evolution over the lake is flowchart

analysis. Flowcharts (Fig. 5) were created to demon-

strate how maximum reflectivity changed, at 60 min

over the water, with regard to combinations of two pa-

rameters: overland–lake temperature difference (LAT-

UWT1/2) and overlake air temperature (LAT greater

than or less than 228C). The value of 228C for the overlake

air temperature was chosen to approximately denote

transitions between cool and warm seasons. For this

analysis, cluster and isolated storms were combined

into an unorganized mode and linear and complex

TABLE 1. Percentage of events of each storm mode (rows) that experienced associated changes in maximum reflectivity at (top) 30 and

(bottom) 60 min over water. The bold italic values denote the highest percentage for each mode. The ‘‘onshore’’ column denotes per-

centage of events that moved over the downwind shoreline. The numbers in parentheses below the convective mode represent the number

of events in each category. Far-right column shows the average maximum reflectivity change of each storm mode over water.

#28 dBZ 23/27 dBZ 12/22 dBZ 13/17 dBZ $18 dBZ Onshore Avg DdBZ

130 min

Total (111) 6 29 59 5 0 1 21.68

Cluster (30) 7 30 60 3 0 0 22.10

Isolated (25) 4 36 52 4 0 4 22.08

Linear (34) 6 24 67 3 0 0 21.06

Complex (22) 9 27 55 9 0 0 21.64

160 min

Total (111) 26 23 37 5 0 8 25.03

Cluster (30) 40 23 23 3 0 10 27.30

Isolated (25) 40 24 28 0 0 8 28.83

Linear (34) 12 24 47 12 0 6 22.00

Complex (22) 14 23 50 4 0 9 22.45



storms were combined as organized modes due to their

similar evolutionary patterns while traversing the lake

(Table 1).

Each row of the flowchart represents a way of catego-

rizing the storms: 1) convective mode, 2) overland–lake

temperature difference, and 3) overlake air temperature.

For example, the first two rows in Fig. 5a illustrate that

cluster and isolated storms were divided into 7 events

with conditions over the lake warmer than those over the

upwind shore area (LAT-UWT . 0) and 36 events

occurred when the overlake air temperature was cooler

than at the upwind shore (LAT-UWT , 0).

Each box in the flowchart gives information on the

parameter used to categorize the events and the number

of events (above the horizontal line) and convective

trends (below the horizontal line). Convective trends are

summarized by two categories: no change/moderate

strengthening or major weakening (refer to section 2 for

evolution definitions).

Results shown in Fig. 5 suggest that cluster/isolated

storms tend to be sensitive to overlake changes in near-

surface air temperature, and a greater percentage weak-

ened as the air temperature decreased from land to

lake (LAT-UWT , 0). This is the opposite of the re-

sults for linear/complex storms, which showed less of

a decrease in intensity when the overlake environment

was cooler.

4. Discussion and conclusions

Results of a climatological study examining the evo-

lution of convective storms crossing over Lake Erie

suggest that storm evolution is strongly related to storm

mode, amount of time the convection has spent over the

water, low-level wind speed, and near-surface temper-

atures over and near the lake. Most observed storms

weakened after crossing the upwind shore of Lake Erie;

storms rarely exhibited an increase in intensity of .3 dBZ

over the lake. While minor weakening of the convec-

tive storms was observed at 30 min after crossing the

upwind shore, most substantial decreases in intensity

were not observed until convection had spent 60 min

TABLE 2. Summary of the results from SLMR using atmospheric

parameters to predict storm intensity changes over Lake Erie (at

60 min over the water), for each storm mode. Columns 1–3 (itali-

cized) display information about the linear regression model de-

veloped with the selected parameters. Columns 4 and 5 display

information about the chosen parameters inside the model. Here,

N is the number of storms included in the regression, R2 represents

the coefficient of determination (goodness of fit) of the regression

model, and significance displays the statistical significance level (a)

of the model.

Parameters N R2

b

coefficient

(standardized) Significance

Mode: Cluster

A. LAT 20 0.120 0.347 0.134

Mode: Isolated

A. LAT-UWT 16 0.043 0.207 0.441

Mode: Linear

A. 3-km speed 23 0.136 0.368 0.084

Mode: Complex

A. LAT 15 .282 20.531 0.042

FIG. 5. Flowcharts for (a) cluster and isolated, and (b) linear and complex storms. Numbers

given in parentheses are the number of observed storm occurrences for each given parameter

combination. The numbers below each parameter give the percentage of events that exhibited,

after 60 min over the water, no change or moderate strengthening/major weakening. Per-

centages are not shown for conditions that had less than five storm occurrences.



over the water. This may reflect a common time scale for

atmospheric circulations associated with the convection

to incorporate surface-influenced air parcels and in-

fluence precipitation rates.

An important finding is that storm mode plays

a critical role on the amount of weakening that storms

experience while over Lake Erie. Cluster and isolated

storms tended to decrease in intensity much more

rapidly than linear and complex storms. This decrease

in intensity was found to be most strongly correlated

with the overlake near-surface air temperature (LAT)

and its relation to the overland air temperature (LAT-

UWT). Overlake air temperature and horizontal land–

lake temperature differences were diagnosed as the

most influential parameters by stepwise linear multi-

variate regression (SLMR). It is thought that these

relationships reflect the dependence of cluster and

isolated convection on local near-surface stability, in

agreement with their peak occurrence during times of

greatest surface heating: late afternoon and evening.

As a result, the majority of these storms were observed

to undergo substantial weakening while over the lake,

particularly when the overlake air temperature was

cooler than the upwind overland air temperature

(LAT-UWT , 0).

Linear and complex storms were shown to be less

sensitive to local lake-induced atmospheric changes and

more influenced by the 3-km wind speed. This finding

appears to be consistent with previous studies (Weisman

et al. 1988; Rotunno et al. 1988; Weisman and Rotunno

2004; Bryan et al. 2006) that suggest dependence be-

tween linear storm intensity and low-level vertical wind

shear.

While the relationship between surface temperatures

and intensity changes of the storms tended to be weaker

than 3-km wind speed for linear and complex storm

modes, surface temperatures did exhibit an interesting

influence. The rate of decrease of reflectivity within

linear and complex storms was inversely correlated

with land–lake air temperature differences. SLMR

found this relationship to be nonstatistically signifi-

cant. However, this relationship is supported by the

flowchart analyses, which showed that organized (lin-

ear and complex) storms maintained intensity more

often in events where the OLBL was cooler (relative to

land). These results are in agreement with the earlier

observational results of Graham et al. (2004) for Lake

Michigan and the idealized modeling results of Parker

(2008).

It is important to note that the data used in this study

do not capture potentially important features in the

synoptic environment associated with the storms. Features

such as elevated or midlevel instability, or the location of

the convection relative to the original convective forcing,

could not always be determined. Indeed, preliminary

examination of storm environments suggested that

complex-mode storms were generally found to form

north of surface warm fronts or during the local over-

night/morning hours, suggesting that a majority of them

are elevated. This would likely result in minimal in-

teractions with the OLBL. This is a topic of that should

be explored in more detail in future studies. It would

also be anticipated that the thermodynamic structure

within the OLBL, as well as its depth, which could not

be determined in this study, play significant roles in

storm evolution over Lake Erie.

The influences of the OLBL on convective storms are

anticipated to be different depending on the size of the

water body. For the Great Lakes it is possible that in

events where the OLBL is highly stable, unstable air

from nearby land areas may move over the OLBL and

help maintain the convective storm. This seems likely

for organized convective storms, where bore propaga-

tion atop a highly stable OLBL would work to lift un-

stable air atop the OLBL. For an oceanic coastal region,

no downwind shore is available to maintain convective

buoyancy. Further observational and numerical model-

ing studies of convective changes over lakes of various

sizes and convective types are needed. To fully un-

derstand the mechanisms influencing convective storm

evolution over a water body, further investigation of the

relative importance of the sources and sinks of buoyant

energy would be highly beneficial.

Finally, smaller-scale variations in surface conditions

can play important roles in storm evolution. For exam-

ple, lake surface temperatures can vary rapidly with

time and space over a lake. Likewise, spatial variations

in atmospheric conditions, such as stability and wind

characteristics, could not be investigated with currently

available observational datasets. Future studies should

seek to quantify the influences of such surface and at-

mospheric conditions on storm evolution over the Great

Lakes.

Acknowledgments. This research was supported by

the National Science Foundation Mesoscale and Dy-

namic Meteorology Program (Grant ATM 07-11033)

and the Cooperative Meteorology, Education, and

Training (COMET) program (Grant COM UCAR S09-

71437). We appreciate the comments and suggestions

from Michael Timlin and James Angel from the Uni-

versity of Illinois at Urbana–Champaign, the anonymous

reviewers, and the editor. This manuscript represents

the opinions of the authors and does not necessarily

reflect the views of the funding agencies or the authors’

affiliations.



APPENDIX A

Observation Stations

APPENDIX B

Study Parameters

TABLE A1. The station location, station ID, and type of data collected for each of the observation stations used in this study. Stations

where ‘‘precip’’ data were used are considered to be ‘‘upwind’’ of Cleveland for this analysis.

Station ID in Fig. 2 Data

Buffalo, NY KBUF Surface obs, raob, radar, VAD

Dunkirk, NY KDKK Surface obs

Erie, PA KERI Surface obs

Ashtabula, OH KHZY Surface obs

Cleveland, OH KCLE, 10 Surface obs, raob, radar, precip, VAD

Mansfield, OH KMFD Surface obs

Toledo, OH KTOL Surface obs

Ypsilanti, MI KYPI, 3 Surface obs, precip

Findlay, OH KFDY Surface obs

Defiance, OH KDFI Surface obs

Lambertville, MI KDUH Surface obs

Adrian, MI KADG Surface obs

Ann Arbor, MI 2 Precip

Detroit, MI (metropolitan airport) KDTW, 4 Surface obs, precip

Detroit, MI KDTX Surface obs, raob, radar, precip, VAD

Windsor Riverside, ON 7 Precip

Amherstburg, MI 5 Precip

Windsor, ON 6 Precip

Kingsville, ON 8 Precip

Chatham, ON 9 Precip

Erieau, ON CWAJ Surface obs

NOAA buoy 45132 45132 Surface obs

NOAA buoy 45005 45005 Surface obs

TABLE B1. Definitions and abbreviations of each of the parameters used for analyses in this study.

Parameter Abbreviation Definition

Time moved over water TMOW Time of radar scan when the geographical center of convection

moved over lakeshore

Starting reflectivity Start dBZ Maximum base reflectivity of the convection 30 min prior to

TMOW; in events where convection did not exist 30 min

prior, the first radar scan with maximum reflectivity

.35 dBZ was used

Reflectivity at the time the

convection moved over water

TMOW dBZ Maximum base reflectivity of the convection at TMOW

Reflectivity at 30 min after

TMOW

130 min dBZ Maximum base reflectivity of the convection at the time scan

closest to 30 min after TMOW

Reflectivity at 60 min after

TMOW

160 min dBZ Maximum base reflectivity of the convection at the time scan

closest to 60 min after TMOW

Delta 30-min reflectivity D30 min dBZ Change in max reflectivity 30 min after TMOW: 30 min

dBZ 2 TMOW dBZ

Delta 60-min reflectivity D60 min dBZ Change in max reflectivity 60 min after TMOW: 60 min

dBZ 2 TMOW dBZ; events where the convection died or

moved over the downwind shore are not included



REFERENCES

Ashley, W. S., T. L. Mote, P. G. Dixon, S. L. Trotter, E. J. Powell,

J. D. Durkee, and A. J. Grundstein, 2003: Distribution of

mesoscale convective complex rainfall in the United States.

Mon. Wea. Rev., 131, 3003–3017.

Augustine, J. A., W. L. Woodley, R. W. Scott, and S. A. Changnon,

1994: Using geosynchronous satellite imagery to estimate

summer-season rainfall over the Great Lakes. J. Great Lakes

Res., 20, 683–700.

Bryan, G. H., J. C. Knievel, and M. D. Parker, 2006: A multimodel

assessment of RKW theory’s relevance to squall-line charac-

teristics. Mon. Wea. Rev., 134, 2772–2792.

Fowle, M. A., and P. J. Roebber, 2003: Short-range (0–48 h) nu-

merical prediction of convective occurrence, mode, and loca-

tion. Wea. Forecasting, 18, 782–794.

Gallus, W. A., Jr., N. A. Snook, and E. V. Johnson, 2008: Spring

and summer severe weather reports over the Midwest as

a function of convective mode: A preliminary study. Wea.

Forecasting, 23, 101–113.

Graham, R., M. Bentley, J. Sparks, B. Dukesherer, and J. Evans,

2004: Lower Michigan MCS climatology: Trends, pattern

types, and marine layer impacts. Preprints, 22nd Conf. on

Severe Local Storms, Hyannis, MA, Amer. Meteor. Soc., 7B.6.

[Available online at http://ams.confex.com/ams/pdfpapers/

81343.pdf.]

Laing, A. G., and J. M. Fritsch, 1997: The global population of

mesoscale convective complexes. Quart. J. Roy. Meteor. Soc.,

123, 389–405.

Lericos, T. P., H. E. Fuelberg, M. L. Weisman, and A. I. Watson,

2007: Numerical simulations of the effects of coastlines on the

evolution of strong, long-lived squall lines. Mon. Wea. Rev.,

135, 1710–1731.

Parker, M. D., 2008: Response of simulated squall lines to low-level

cooling. J. Atmos. Sci., 65, 1323–1341.

Rotunno, R., J. B. Klemp, and M. L. Weisman, 1988: A theory for

strong, long-lived squall lines. J. Atmos. Sci., 45, 463–485.

Scott, R. W., and F. A. Huff, 1996: Impacts of the Great Lakes

on regional climate conditions. J. Great Lakes Res., 22, 845–

863.

Weisman, M. L., and R. Rotunno, 2004: ‘‘A theory for strong,

long-lived squall lines’’ revisited. J. Atmos. Sci., 61, 361–

382.

——, J. B. Klemp, and R. Rotunno, 1988: Structure and evolution

of numerically simulated squall lines. J. Atmos. Sci., 45, 1990–

2013.

TABLE B1. (Continued)

Parameter Abbreviation Definition

Upwind equivalent potential

temperature

UWTe (t) Upwind equivalent potential temperature (K) as measured

from the surface observation station nearest the storm path;

data were collected from at least 24 to 12 h of time

convection crossed the lakeshore

Downwind equivalent potential

temperature

DWTe (t) Downwind equivalent potential temperature (K) as measured

from the surface observation station nearest the storm path;

data were collected from at least 22 to 14 h of time

convection crossed the lakeshore

Lake temperature LT (t) Temperature (8C) of Lake Erie taken from buoy 45005, when

available; data were collected from 24 to 14 h of TMOW

Lake air temperature LAT (t) Temperature (8C) of air 4 m above Lake Erie taken from buoy

4505, when available; data were collected from 24 to 14 h of

TMOW

Temperature difference

LT 2 LAT

LT-LAT The difference between the water temperature and air

temperature over the lake, taken at TMOW (precold pool)

Temperature Difference

LAT 2 UWT

LAT-UWT The difference between the air temperature over the lake at

TMOW and the ambient upwind temperature over land

(precold pool)

Temperature difference

DWTe 2 UWTe

DWTe-UWTe The difference between the downwind equivalent potential

temperature and the upwind equivalent potential

temperature

3-km wind speed 3-km speed The wind speed (m s21) determined from the VAD profile

nearest the 3-km height 2 h prior to TMOW; if no 3-km VAD

data were available at this time, the first 3-km wind speed

value available prior to 2 h, but no longer than 4 h prior to

TMOW, was used; zero surface winds were assumed

6-km wind speed 6-km speed The wind speed (m s21) determined from the VAD profile

nearest the 6-km height; if no 6-km VAD data were available

at this time, the first 6-km wind speed value available prior to

2 h, but no longer than 4 h prior to TMOW, was used; zero

surface winds were assumed

Stability Various Several methods of estimating stability were investigated, such

as calculating CAPE or most unstable CAPE (MUCAPE)

from evening soundings, soundings closest in time to storm

occurrence, and modified morning soundings



Documents

Inﬂuence of the Lake Erie Overlake Boundary Layer on Deep