1. Introduction and background
The region east of Lake Ontario (Fig. 1a) receives some of the largest seasonal snowfall totals in eastern North America. Annual average snowfall exceeds 450 cm (e.g., Eichenlaub and Hodler 1979; Burt 2007; Hartnett et al. 2014; Veals and Steenburgh 2015). Much of this snowfall is produced by lake-effect storms. These storms can produce snowfall rates that are among the most intense in the world, including 30.5 cm in 1 h at Copenhagen, New York, and 129.5 cm in 16 h at Bennett’s Bridge, New York (Burt 2007).
Overview of the study area, showing (a) major geographic features and (b) measurement locations. Values in (b) indicate 13-yr annual average snowfall on days with lake-effect snow from COOP stations (from Veals and Steenburgh 2015). Terrain elevation is color shaded in both (a) and (b).
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Overview of the study area, showing (a) major geographic features and (b) measurement locations. Values in (b) indicate 13-yr annual average snowfall on days with lake-effect snow from COOP stations (from Veals and Steenburgh 2015). Terrain elevation is color shaded in both (a) and (b).
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Overview of the study area, showing (a) major geographic features and (b) measurement locations. Values in (b) indicate 13-yr annual average snowfall on days with lake-effect snow from COOP stations (from Veals and Steenburgh 2015). Terrain elevation is color shaded in both (a) and (b).
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Annual average (and frequently storm total) snowfall in this region has a distinct maximum over the 600-m-high Tug Hill Plateau (hereafter Tug Hill; Fig. 1) 20–30 km east of Lake Ontario (Eichenlaub and Hodler 1979; Reinking et al. 1993; Burt 2007; Hartnett et al. 2014; Veals and Steenburgh 2015). This study uses observations from the Ontario Winter Lake-effect Systems (OWLeS) field campaign to better understand how the inland evolution of lake-effect clouds produces the Tug Hill snowfall maximum.
a. Lake-effect snow: Environmental controls and mesoscale morphologies
Lake-effect snow is produced when cold air is advected over a relatively warm lake surface. If the lake–air temperature difference is sufficient, turbulent sensible and latent heat fluxes moisten and destabilize the boundary layer, leading to the formation of shallow, but often intense, convective clouds and snowfall. Lake-effect convection exhibits multiple modes of mesoscale organization, including widespread cellular convection, widespread wind-parallel linear roll circulations, localized wind-parallel bands, and localized mesoscale vortices (Hjelmfelt 1990; Niziol et al. 1995; Kristovich and Steve 1995). The crucial environmental factors determining the mode of organization include the lake-induced convective available potential energy (CAPE), the lake–land temperature difference, the height of the boundary layer capping inversion, the low-level wind speed and direction, the wind-relative geometry of the lake, and the geometry of nearby topography (e.g., Hjelmfelt 1990; Niziol et al. 1995; Onton and Steenburgh 2001; Laird et al. 2003a,b; Laird and Kristovich 2004; Alcott and Steenburgh 2013).
The most intense lake-effect snowfall usually occurs when the prevailing winds are oriented along the long axis of an elongated lake. In this case, land-breeze circulations can organize convection into a single wind-parallel band of strong horizontal convergence and intense snowfall (Peace and Sykes 1966; Passarelli and Braham 1981; Hjelmfelt and Braham 1983; Hjelmfelt 1990; Reinking et al. 1993; Niziol et al. 1995; Steiger et al. 2013). We refer to these as long-lake-axis-parallel (LLAP) bands, following Steiger et al. (2013).1 LLAP bands generally have a horizontal width of 5–50 km and a length of 50–200 km (Peace and Sykes 1966; Reinking et al. 1993; Niziol et al. 1995; Steiger et al. 2013). The line of horizontal convergence and vertical motion associated with LLAP bands is often less than 5 km wide, much narrower than the band of snowfall (Peace and Sykes 1966; Reinking et al. 1993; Steiger et al. 2013).
Although the dynamical cause of lake-effect snow is convective instability, lake-effect cloud morphologies can be stratiform or convective in character. Broadly, clouds classified as convective have updrafts strong enough to loft ice and snow particles (exceeding ~1 m s−1), high spatial and temporal variability, and supercooled water droplets that facilitate hydrometeor growth by collection. In contrast, stratiform clouds have weaker updrafts, reduced horizontal and temporal variability, and a dominance of depositional growth of ice crystals [see Houze (1997, 2014, chapter 6)]. Lake-effect convection often contains convective regions of localized strong horizontal convergence and high reflectivity adjacent to stratiform regions with weaker convergence and more homogenous weaker echoes (Reinking et al. 1993; Steiger et al. 2013). Presumably, these stratiform regions are generated similarly to the deep convective case: ice crystals initiated in strong convective updrafts are spread laterally and slowly precipitate to the surface, growing by diffusion in a surrounding region of weak ascent (Biggerstaff and Houze 1991; Yuter and Houze 1995b; Houze 1997).
b. Snowfall characteristics east of Lake Ontario
The region immediately east of Lake Ontario lies downwind of the long axis of the lake under prevailing westerly flow (Fig. 1), an ideal location for frequent and intense lake-effect snowfall. On average, over 900 h of lake-effect snow occurs per cold season in this region (Veals and Steenburgh 2015). The highest frequency of occurrence is in December–February (Kristovich and Steve 1995; Veals and Steenburgh 2015), when lake–air temperature differences are large and temperatures are cold enough for snow to occur (Holroyd 1971; Niziol et al. 1995).
Numerous studies indicate a local maximum in lake-effect snowfall over Tug Hill, centered on its western slopes and upper elevations (Eichenlaub and Hodler 1979; Reinking et al. 1993; Burt 2007; Hartnett et al. 2014; Veals and Steenburgh 2015). Recently, Veals and Steenburgh (2015) analyzed 13 cold seasons of daily COOP surface observations and radar imagery (from the KTYX WSR-88D; Fig. 1b) over the Tug Hill region. They found a maximum in annual mean snowfall on days with lake-effect snow of 539 cm in north Redfield (NR), more than double the values just north of Tug Hill and on the Lake Ontario coast to the southwest (Fig. 1b). Additionally, they found a local maximum in the frequency of radar echoes exceeding given equivalent radar reflectivity, 
The physical mechanisms responsible for this localized snowfall maximum over Tug Hill have not been clearly discerned. All else being equal, one might expect a monotonic decrease in snowfall downwind of the lake, since the loss of surface heat and moisture fluxes should cause convection to weaken as it moves inland. Several authors have suggested that the topography of Tug Hill plays an important role in locally enhancing lake-effect snowfall (Reinking et al. 1993; Niziol et al. 1995; Veals and Steenburgh 2015). Modeling studies have diagnosed orographic enhancement of lake-effect snowfall over the modest topography (200–500-m relief) adjacent to Lakes Michigan and Erie and the much taller topography of the Wasatch Mountains (1–2-km relief) adjacent to the Great Salt Lake (Lavoie 1972; Hjelmfelt 1992; Onton and Steenburgh 2001; Alcott and Steenburgh 2013). Other simulations indicate that the mountains of Japan enhance snowfall from convective sea-effect clouds generated over the Sea of Japan (Murakami et al. 1994; Saito et al. 1996). Our analysis of snowfall over Tug Hill contrasts with previous studies of orographic effects on lake-effect snow in that it relies on detailed field observations (in contrast with the above-mentioned numerical studies) and focuses on a location with modest topography (in contrast to the mountains of Japan and Utah) that is frequently affected by well-organized LLAP storms (in contrast to locations south of Lake Erie and east of Lake Michigan).
c. Possible mechanisms for the Tug Hill snowfall maximum
In this study, we use observations to clarify the mechanisms controlling the inland gradient in lake-effect snowfall from the Ontario shoreline onto Tug Hill. Broadly, we are motivated to better understand these mechanisms, both as a fundamental science question regarding the response of shallow convection to surface forcing, and also to aid in the critical evaluation of conceptual and numerical models used to forecast these high-impact storms. Here, we focus specifically on how convective clouds evolve as they transition onto land and rise over Tug Hill. Our primary observations come from an east–west transect (black dots in Fig. 1b) of four vertically pointing Doppler radars (described in section 2a) that measure vertical profiles of 
Hypothetical changes in lake-effect convection and snowfall associated with landfall and orographic forcing. Size of snowflakes represents snowfall rate. Vertical arrows represent updraft speed in clouds. Mean winds are assumed to blow from left to right. Depicted mechanisms include the following: (a) orographic invigoration of convection, (b) orographically forced increases in snowfall frequency, (c) orographic suppression of sublimation, (d) seeder–feeder enhancement, (e) convective-to-stratiform transition, and (f) hydrometeor advection. Elevated terrain is omitted from (e) and (f) to emphasize that these mechanisms do not rely on orographic forcing.
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Hypothetical changes in lake-effect convection and snowfall associated with landfall and orographic forcing. Size of snowflakes represents snowfall rate. Vertical arrows represent updraft speed in clouds. Mean winds are assumed to blow from left to right. Depicted mechanisms include the following: (a) orographic invigoration of convection, (b) orographically forced increases in snowfall frequency, (c) orographic suppression of sublimation, (d) seeder–feeder enhancement, (e) convective-to-stratiform transition, and (f) hydrometeor advection. Elevated terrain is omitted from (e) and (f) to emphasize that these mechanisms do not rely on orographic forcing.
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Hypothetical changes in lake-effect convection and snowfall associated with landfall and orographic forcing. Size of snowflakes represents snowfall rate. Vertical arrows represent updraft speed in clouds. Mean winds are assumed to blow from left to right. Depicted mechanisms include the following: (a) orographic invigoration of convection, (b) orographically forced increases in snowfall frequency, (c) orographic suppression of sublimation, (d) seeder–feeder enhancement, (e) convective-to-stratiform transition, and (f) hydrometeor advection. Elevated terrain is omitted from (e) and (f) to emphasize that these mechanisms do not rely on orographic forcing.
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Predicted profiling radar signatures expected for each of the various snowfall enhancement mechanisms discussed in section 1c.
1) Orographic invigoration of convection
Orographic lifting by Tug Hill may invigorate lake-effect convection, increasing updraft speeds and/or cloud depths (Fig. 2a). This could occur if orographic lifting of the inversion layer allows convection to deepen [as illustrated in Lackmann (2011), his Fig. 9.21]. Idealized model simulations support this mechanism for lake-effect snow alongside Lake Erie (Lavoie 1972) and sea-effect snow in Japan (Murakami et al. 1994). Alternatively, orographic invigoration can take place in the absence of any change in inversion height. This occurs over the mountains of the Caribbean island Dominica, where shallow trade wind cumuli are invigorated because of increases in cloud buoyancy associated with layer lifting and because of decreases in buoyancy dilution from entrainment associated with changes in cloud size (Kirshbaum and Smith 2009; Kirshbaum and Grant 2012; Minder et al. 2013). If orographic invigoration is important, then with increasing inland distance from Lake Ontario, profiling radar data should show an increase in echo depth (deepening of convection), an increase in the variability of 
2) Orographically forced increases in snowfall frequency
Orographic lifting by Tug Hill may increase the frequency or horizontal size of precipitating convective cells (Fig. 2b). If the flow is sufficiently moist and unstable, orographic lifting can trigger new convective cells and precipitation (e.g., Browning et al. 1974; Colle et al. 2008; Smith et al. 2009). Additionally, orographic lifting can increase the horizontal scale of clouds (Kirshbaum and Grant 2012). A role for this mechanism is suggested by radar observations collected during the Lake Ontario Winter Storms project that indicated an increase in frequency but not intensity of echoes over Tug Hill relative to upwind locations (Reinking et al. 1993). Furthermore, multiyear statistics show a locally enhanced frequency of radar echoes exceeding 10 dB
3) Orographic suppression of sublimation
The orography of Tug Hill may enhance surface snowfall by limiting subcloud sublimation over the terrain relative to upwind locations. Sublimation may be suppressed over Tug Hill by an increase in the relative humidity of subcloud air caused by orographic lifting or by a decrease in the depth of the subcloud layer due to elevated terrain (Fig. 2c). Such orographic effects on sublimation are thought to be important for enhancing sea-effect snow over the mountains of Japan (Murakami et al. 1994) and synoptically generated precipitation over the mountains of Utah (Schultz and Trapp 2003). If orographic suppression of sublimation is important, profiling radar data should indicate a subcloud reduction in snowfall (
4) Seeder–feeder enhancement
Orographic lifting over Tug Hill may produce shallow stratiform orographic clouds that enhance precipitation because of the seeder–feeder mechanism (Fig. 2d). In this mechanism, seed precipitation generated aloft grows via collection of liquid water as it falls through an orographic feeder cloud (Bergeron 1965). Model simulations suggest that this process is important in the enhancement of sea-effect snow over Japan (Saito et al. 1996; Murakami et al. 1994). In profiling radar data, seeder–feeder enhancement should produce a low-level enhancement of 
5) Convective–stratiform transition
Since landfall of lake-effect convection isolates it from the surface heat and moisture fluxes that sustain it, there should be an inland reduction in the vigor of convective motions. This may produce clouds that have characteristics intermediate between convective and stratiform, analogous to the later stages of the life cycle of deep convection (Yuter and Houze 1995a). These less-convective clouds may be more efficient at producing surface snowfall (Fig. 2e). For instance, a reduction in updraft strength associated with decay of convection could allow snow to fall through a weak updraft to the surface while growing within the moist core of a cloud instead of being lofted by a strong updraft and ejected into dry environmental air where it is more vulnerable to sublimation. Alternatively, increases in cloud width associated with a stratiform transition could increase snowfall frequency. The expected profiling radar signature of a convective–stratiform transition is largely the opposite of the convective invigoration, with the added expectation of increased echo frequency because of wider stratiform echoes.
6) Hydrometeor advection
Horizontal advection of hydrometeors may play an important role in determining the location of the snowfall maximum because of the finite time required for convection to respond to changes in surface fluxes and the finite time required for hydrometeor fallout. For instance, in the time it takes a snowflake falling at 1 m s−1 to fall 2 km, a 10 m s−1 horizontal wind could advect it 20 km inland. Accordingly, the location of maximum surface snowfall may be displaced tens of kilometers downwind of the location of maximum surface heat and moisture fluxes or the location of maximum updraft strength (Fig. 2e). Thus, even if convective instability or intensity maximizes near the coast, an inland precipitation maximum may occur. The expected signature of hydrometeor advection in profiling radar data is unclear, since it will depend on the details of hydrometeor trajectories and, in turn, the details of airflow kinematics and hydrometeor terminal fall speeds.
This study uses observations to better constrain the role of the above mechanisms in producing inland variations in snowfall rate. Section 2 describes the profiling radar datasets and other supplementary datasets used. Section 3 presents a case study highlighting the inland transition of lake-effect convection. Section 4 places the observations in a broader context using statistics from 29 separate events. Section 5 provides discussion of how the observations relate to the above-described mechanisms. Section 6 provides a summary and conclusions.
2. Methods
From December 2013 to January 2014 the OWLeS field campaign (http://www.eol.ucar.edu/field_projects/owles) studied lake-effect systems to the east and south of Lake Ontario. This multi-investigator campaign included the deployment of an instrumented aircraft, mobile X-band scanning radars, precipitation and wind profilers, surface observations, and sounding systems (Kristovich 2014). A manuscript describing the field project and its goals is currently in review. Here, we draw on a subset of the OWLeS data to investigate the inland evolution of lake-effect snowfall in the Tug Hill region. All OWLeS-related field observations utilized here were attained from the OWLeS dataset repository maintained by the NCAR Earth Observing Laboratory (EOL) (available online at http://data.eol.ucar.edu/master_list/?project=OWLeS.)
This study utilizes observations collected along an east–west-oriented transect of four sites, stretching from the Lake Ontario shoreline to the upper reaches of Tug Hill (Fig. 1b). The sites, from west to east, include the following: Sandy Island Beach (SIB, 75 m MSL), Sandy Creek (SC, 175 m MSL), north Redfield (385 m MSL), and upper plateau (UP; 530 m MSL). Throughout OWLeS, profiling radar observations were collected at all four sites, while, at two sites (SC and NR), surface meteorology and manual snow observations were also collected. The transect was designed to align with the axis of maximum frequency of lake-effect snowfall and sample the pronounced climatological gradient in snow accumulation that occurs from the shoreline onto Tug Hill (Veals and Steenburgh 2015).
a. Micro Rain Radars
At each transect site, a vertically pointing Micro Rain Radar 2 (MRR; manufactured by Metek) was deployed. The MRR is a frequency-modulated continuous-wave Doppler radar with a transmit frequency of 24 GHz (wavelength of 1.24 cm; K-band) and a beamwidth of 2°. Each MRR measured spectral reflectivity at 32 evenly spaced range gates, with a range resolution of 200 m.
The standard processing software provided by Metek is designed for measurement of rain (e.g., Klugmann et al. 1996; Löffler-Mang et al. 1999; Peters et al. 2002). Maahn and Kollias (2012) developed a postprocessing algorithm for the MRR raw data that improves noise removal, velocity dealiasing, and sensitivity. Importantly, this algorithm allows for the collection of high-quality profiles of equivalent radar reflectivity factor 
The duration of MRR deployment at each site is given in Table 2. Data were collected at all sites from 6 November 2013 to 19 January 2014, a time period that includes nearly all of the OWLeS intensive observing periods (IOPs). Data were collected at SIB and NR over an extended period, allowing for a more robust statistical comparison between these sites. From 25 to 28 January 2014, three of the MRRs were collocated at NR for an intercomparison during a period that included both synoptic and lake-effect snow. The intercomparison revealed only modest (<3 dB
Range of dates corresponding to the MRR deployments at each site shown in Fig. 1b. Radars were relocated in late January to conduct an intercomparison; this period is omitted from the table.
b. X-band profiling radar
During all or part of four different IOPs, the University of Alabama Huntsville X-Band Profiling Radar (UAH-XPR) was deployed at SC as part of the UAH Mobile Integrated Profiling System (MIPS). The XPR is a vertically pointing Doppler radar with a transmit frequency of 9.410 GHz and a beamwidth of 1.2° (Phillips 2009). Data were collected with a range resolution of 50 m starting at a range of 50 m. A pulse repetition frequency of 1250 Hz was used, and 1-s average data are used for analysis. We discard data from the lowest three range gates to avoid near-field effects. Thus, the XPR provides data at 200-m range and above. As compared to the MRRs, the XPR provides data with higher vertical and temporal resolution, with higher sensitivity, and at lower levels in the atmosphere.
c. Additional observations
Surface snow measurements were collected throughout OWLeS at SC and NR. Manual measurements of accumulated snow depth and liquid precipitation equivalent (LPE) were taken at 6-h intervals (0000, 0600, 1200, and 1800 UTC) during IOPs. Measurements were made on 0.61 m × 0.61 m snow boards that were wiped clean after each measurement. LPE measurements were taken by collecting snow samples using a Snowmetrics coring tube and weighing them. Measurements at NR were made in a clearing surrounded by dense low brush and deciduous trees located 338 m from the MRR deployment, while measurements at SC were taken in a clearing surrounded by dense forest located about 1.8 km from the MRR deployment (Fig. 1b). Additional surface precipitation measurements were made during selected periods by the MIPS at SC. These include accumulated LPE from a hotplate precipitation gauge (Rasmussen et al. 2011) and measurements of hydrometeor size distribution from a Particle Size Velocity (PARSIVEL) optical disdrometer (Löffler-Mang and Joss 2000).
Upper-air observations from radiosondes launched as part of OWLeS are used to provide thermodynamic context. We utilize data from GRAW DFM-09 soundings launched from NR, SC, and the north shore of Lake Ontario at Darlington, Ontario (43.873°N, 78.783°W).
3. Case study of an intense lake-effect storm: IOP2b
a. Overview
Here we focus on an intense lake-effect storm, OWLeS IOP2b, that was oriented along the MRR transect. The event took place primarily on 11 December 2013. During the days surrounding IOP2b, a highly amplified synoptic flow pattern included persistent deep troughing over the Great Lakes region, leading to strong surges of cold arctic air over Lake Ontario. IOP2b took place during one of these surges that began around 0000 UTC 10 December. Heavy lake-effect snowfall commenced over the MRR transect at about 0000 UTC 11 December, following the passage of an upper-level short-wave trough, and continued over the transect until about 0000 UTC 12 December, when convection shifted to the south.
An environmental sounding taken from the north shore of Lake Ontario at Darlington is shown in Fig. 3. This was launched at 1755 UTC 11 December, near the time of heaviest snowfall at SC and NR. It exhibits a dry-adiabatic layer from the surface to about 800 hPa, with moisture well mixed below 900 hPa. Dry near-surface air is present with a dewpoint depression exceeding 10°C. The dry-adiabatic layer is overlain by a moist layer of modest stability extending to about 650 hPa, where it is capped by an isothermal layer. Winds are westerly throughout the profile, with very little directional shear. Considering a representative saturated surface-based parcel with temperature equal to the average of the observed lake temperatures (4°C) and the temperature of the lowest sounding level, the sounding implies a lake-induced CAPE of 1257 J kg−1 and an equilibrium level (EL) of 512 hPa. The unidirectional along-lake winds, substantial lake-induced CAPE, and high EL are all typical features associated with strong LLAP band formation in this region (e.g., Niziol et al. 1995).
Skew T–logp plot of a pair of soundings launched during IOP2b on 11 Dec 2013. Solid lines show temperature; dashed lines show dewpoint. Winds are plotted such that a full barb equals 2.5 m s−1. Black lines depict a sounding launched from Darlington, Canada (north shore of Lake Ontario), at 1755 UTC. Gray lines depict a sounding launched from NR at 1727 UTC.
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Skew T–logp plot of a pair of soundings launched during IOP2b on 11 Dec 2013. Solid lines show temperature; dashed lines show dewpoint. Winds are plotted such that a full barb equals 2.5 m s−1. Black lines depict a sounding launched from Darlington, Canada (north shore of Lake Ontario), at 1755 UTC. Gray lines depict a sounding launched from NR at 1727 UTC.
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Skew T–logp plot of a pair of soundings launched during IOP2b on 11 Dec 2013. Solid lines show temperature; dashed lines show dewpoint. Winds are plotted such that a full barb equals 2.5 m s−1. Black lines depict a sounding launched from Darlington, Canada (north shore of Lake Ontario), at 1755 UTC. Gray lines depict a sounding launched from NR at 1727 UTC.
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
A sounding launched from within the LLAP band at NR near this time (1727 UTC) is also shown in Fig. 3. It exhibits nearly saturated moist-adiabatic conditions (interrupted by a shallow stable layer at 900 hPa) up to about 675 hPa because of convective transport of air warmed and moistened by surface fluxes over the lake. Saturated conditions near the ground may also be due in part to subcloud sublimation of snowfall. The convective cloud layer is capped by an inversion from about 675 to 600 hPa. The winds are similar in speed and direction in both soundings, down to the 950-hPa level. This suggests that there is minimal orographic flow deflection or deceleration (as might be expected, given the modest topography and weak low-level stratification). Thus, over land there is a strong upslope wind component to facilitate orographic uplift over the western slopes of Tug Hill.
The convective morphology varied over the course of IOP2b. Early in the event (0300–1700 UTC) convection was only loosely organized over the lake, with distinct individual convective elements (e.g., Fig. 4a). During this period, echo coverage and meridional extent increased with inland distance over Tug Hill, suggestive of orographic initiation of new precipitation echoes or an inland increase in echo size. Late in the event (1800–2100 UTC) a pronounced LLAP band structure was observed with a narrow linear organization over both the lake and the land (e.g., Fig. 4b). A map of the frequency of echoes exceeding 10 dB
Measurements from the KTYX WSR-88D during IOP2b (0.5° elevation angle): (a) dB
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Measurements from the KTYX WSR-88D during IOP2b (0.5° elevation angle): (a) dB
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Measurements from the KTYX WSR-88D during IOP2b (0.5° elevation angle): (a) dB
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
According to 6-h manual measurements (Fig. 5), from 0000 UTC 11 December to 0000 UTC 12 December, 6.4 cm of LPE accumulated at NR, and 3.4 cm accumulated at SC. This corresponded with 102.5 cm of accumulated snow depth at NR and 47.8 cm at SC. Thus, as compared to SC, storm-total snowfall at NR was enhanced by a factor of 1.9 for water content and 2.1 for depth. This enhancement was a persistent feature throughout the duration of the storm: the ratio of 6-h accumulated LPE (depth) at NR to that at SC (Fig. 5) ranged from 1.5 to 3.2 (1.6–3.5). Manual and photographic observations of hydrometeor habits at both SC and NR (not shown) indicated aggregates of dendrites throughout most of the event. However, from 1730 to 2100 UTC, rimed crystals and graupel were frequently observed in the core of the well-organized LLAP band. No systematic differences in crystal habit or degree of riming were observed between the two sites.
Manual surface observations of (a) snow depth and (b) LPE at NR and SC. Measurements are collected from boards that are wiped clean at 6-h intervals. The numbers printed over the bars give the ratio between the values at the two sites (NR/SC).
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Manual surface observations of (a) snow depth and (b) LPE at NR and SC. Measurements are collected from boards that are wiped clean at 6-h intervals. The numbers printed over the bars give the ratio between the values at the two sites (NR/SC).
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Manual surface observations of (a) snow depth and (b) LPE at NR and SC. Measurements are collected from boards that are wiped clean at 6-h intervals. The numbers printed over the bars give the ratio between the values at the two sites (NR/SC).
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Event-total accumulated LPE is estimated from the KTYX WSR-88D, using the database on the 
b. MRR observations
The transect of MRR observations characterizes the inland evolution of lake-effect convection. Figure 6 shows time–height plots of 
Time–height plot of MRR equivalent radar reflectivity factor 
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Time–height plot of MRR equivalent radar reflectivity factor
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Time–height plot of MRR equivalent radar reflectivity factor
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Overlain on the time–height plots from SC and NR are profiles of equivalent potential temperature 
There is no systematic increase in echo-top height with increasing inland distance. For example, the event-averaged echo top (defined using a threshold of 4.5 dB
The vertical and temporal structures of 
CFADs of MRR 
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
CFADs of MRR
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
CFADs of MRR
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
At all four sites the CFAD values generally shift toward higher 
Figure 8 further emphasizes the differences between the coastal and inland CFADs by directly comparing SIB and NR. The median and IQR at the two sites (Fig. 8a) suggests that snowfall intensity is similar at the two sites near the surface (~1 km MSL).3 However, 
Comparison of vertical distribution of MRR 
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Comparison of vertical distribution of MRR
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Comparison of vertical distribution of MRR
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Comparing the time–height structure of Doppler radial (vertical) velocities 
As in Fig. 6, but for MRR Doppler radial velocity 
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
As in Fig. 6, but for MRR Doppler radial velocity
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
As in Fig. 6, but for MRR Doppler radial velocity
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Frequency of MRR-observed upward hydrometeor motions (
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Frequency of MRR-observed upward hydrometeor motions (
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Frequency of MRR-observed upward hydrometeor motions (
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
The MRR retrievals of Doppler spectral width 
As in Fig. 6, but for MRR Doppler spectral width 
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
As in Fig. 6, but for MRR Doppler spectral width
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
As in Fig. 6, but for MRR Doppler spectral width
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
c. XPR observations
The MIPS-XPR collected data at SC throughout the duration of IOP2b. These data provide a critical check on the vertical structures inferred from the MRR dataset, since the XPR is a more sensitive radar and operates at a different wavelength. Furthermore, the XPR provides additional details regarding the structure of precipitation because of its much higher vertical and temporal resolution and its ability to record measurements closer to the ground. The time–height structures observed by the XPR (Fig. 12) are broadly similar to the MRR results. At times, the echo tops in the XPR data are more than 500 m higher (Fig. 12a) because of the XPR’s superior sensitivity that allows it to sense small ice crystals. The two radars agree well in terms of the depth and timing of the strongest echoes. The radars also generally agree in terms of the magnitude and timing of the strongest 
Time–height plot of XPR observations at SC during IOP2b: (a) 
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Time–height plot of XPR observations at SC during IOP2b: (a)
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Time–height plot of XPR observations at SC during IOP2b: (a)
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
The XPR’s measurements of 
Figure 13a shows the 
CFAD of XPR 
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
CFAD of XPR
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
CFAD of XPR
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
The median and IQR of the XPR and MRR CFADs are compared directly in Fig. 13b. Some important differences are apparent. The XPR 
4. Multistorm statistics
a. MRR observations
To what extent are the observed structures from IOP2b characteristic of other storms? To address this question, we repeat the CFAD analysis for the full selection of lake-effect events observed by the MRRs located at SIB and NR. We focus on these two sites because they were instrumented for the longest duration of the four sites and sample both near-lake and upland conditions. Following Veals and Steenburgh (2015), KTYX data are used to manually identify times with lake-effect snowfall affecting the MRR transect. Some additional times with low echo tops are added based on MRR data. We select events for analysis that have at least 4 h of lake-effect echoes measured by an MRR with gaps of less than 2 h.
The 29 events meeting these criteria and their durations are listed in Table 3. Most of the events correspond to OWLeS IOPs, but several correspond to events that occurred before or after the main OWLeS observing period. A total of 446 h of lake-effect snowfall is included. Of this, 189 h exhibited LLAP band morphology, and 335 h exhibited less-organized broad coverage morphology (many periods exhibited both, as in IOP2b). Event durations ranged from 5 to 39 h (compared with 24 h for IOP2b).
Lake-effect snow events measured by the MRRs deployed at SIB and NR and used in the bulk CFAD analysis. Start time, end time, and duration is noted for each event.
A map of the frequency of echoes >10 dB
Frequency of KTYX-measured echoes >10 dB
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Frequency of KTYX-measured echoes >10 dB
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Frequency of KTYX-measured echoes >10 dB
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Figure 15 shows profiles of median and IQR of 
Median and IQR profiles from SIB and NR (as in Fig. 8a) for select events listed in Table 3.
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Median and IQR profiles from SIB and NR (as in Fig. 8a) for select events listed in Table 3.
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Median and IQR profiles from SIB and NR (as in Fig. 8a) for select events listed in Table 3.
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
As for IOP2b, there is no evidence for an inland increase in the depth of the convection in the multistorm statistics. This holds true for each individual event and the dataset as a whole. For both, the difference in average echo top (threshold of 4.5 dB
To isolate structures in the vertical profiles that are common across most events, we create bulk (multievent) CFADs, wherein the MRR observations from all 29 events described in Table 3 are combined and compared between SC and NR (Figs. 16, 17). The bulk CFADs share several important similarities with the CFADs from IOP2b. At NR and SIB, there is a general reduction in echo frequency with height above 1.5 km MSL (Fig. 16). The median 
CFADs of MRR 
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
CFADs of MRR
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
CFADs of MRR
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Comparison of vertical distribution of MRR 
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Comparison of vertical distribution of MRR
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Comparison of vertical distribution of MRR
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Frequency of MRR-observed upward hydrometeor motions (
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Frequency of MRR-observed upward hydrometeor motions (
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Frequency of MRR-observed upward hydrometeor motions (
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
b. XPR observations
The XPR was collocated with the MRR at SC for portions of three other LLAP events in addition to IOP2b. Here, we compare MRR and XPR profiles during these periods to explore the generality of the comparison made for IOP2b. Table 4 lists the time periods considered. These periods include portions of IOPs 3, 5, and 7. We focus on times when both radars were operating. For the 7 January event, we separately analyze two periods: a period of light snow from shallow clouds on the southern margin of the LLAP band and a period of intense snow associated with the core of the band. To provide context, for each of these periods two estimates of accumulated LPE are given in Table 4: 1) measurements from the MIPS’s hotplate precipitation gauge and 2) manual 6-h measurements from the SC snow-study station.
Range of times corresponding to the MRR–XPR comparisons shown in Fig. 19. LPE at SC is shown for each time period, as measured by a hotplate gauge and by manual snow-board measurements. Note: the 0600–1200 UTC 7 Jan 2014 manual LPE were affected by strong winds and are thus only a very rough estimate.
As in IOP2b, comparing the MRR and XPR profiles for these periods reveals general agreement between the two radars in the overlapping portions of their range (Fig. 19). Both radars show a layer of near-constant 
Comparison of median and IQR of profiles from XPR and MRR at SC (as in Fig. 13b). (a)–(d) Different time periods, as defined in Table 4.
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Comparison of median and IQR of profiles from XPR and MRR at SC (as in Fig. 13b). (a)–(d) Different time periods, as defined in Table 4.
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
Comparison of median and IQR of profiles from XPR and MRR at SC (as in Fig. 13b). (a)–(d) Different time periods, as defined in Table 4.
Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0117.1
5. Discussion
Comparing the above-described case study and multievent statistics with Table 1 helps to narrow the range of potential mechanisms for the Tug Hill snowfall maximum. Most notably, profiling radar observations are inconsistent with the hypothesis of downwind snowfall enhancement because of convective invigoration by orographic lifting (Fig. 2a). With increasing inland distance, our data show (i) no systematic increase in echo depth, (ii) a shift in the reflectivity distribution toward weaker echoes above 1 km MSL, (iii) a decrease in the occurrence of hydrometeor lofting, and (iv) a weakening of turbulent fluctuations. These all point toward an inland decrease in the vigor of convection. The lack of convective invigoration might relate to the modest topography of Tug Hill that only forces weak ascent with minimal impacts on cloud buoyancy or inversion height. Alternatively, vigorous upwind convective cells may effectively remove most of the moist instability form the air before it reaches Tug Hill, limiting the ability of orographic lifting to invigorate convection. In either case, the lack of convective invigoration over Tug Hill may not be representative of lake-effect snowfall enhancement processes over taller mountain ranges (e.g., the Wasatch Mountains) where orographically forced ascent is stronger.
The implications of our observations for other potential mechanisms of snowfall enhancement are more ambiguous. The inland transition of cloud properties may be analogous to the convective-to-stratiform transition that many deep convective clouds undergo (Yuter and Houze 1995b; Houze 1997). In certain settings, stratiform clouds are similarly or more efficient at producing precipitation as compared to convective clouds (Tao and Simpson 1989; Kirshbaum and Smith 2008; Cannon et al. 2012). It is possible that there is a transient phase in the evolution of lake-effect convection as it moves inland and transitions toward stratiform structure when it is more effective at producing heavy snowfall (Fig. 2e). This may be because of increases in echo number/size that enhance snowfall frequency or decreases in hydrometeor lofting that allow snow to fall to the ground within the cloud core, avoiding exposure to the sublimating effects of dry environmental air.
The results do show a modest inland increase in the total frequency of low-level (below 2 km MSL) echoes stronger than 5 dB
Observations at lowland sites indicate a near-surface (below 600 m AGL) suppression of echo frequency and 
Timescales associated with precipitation fallout and the dynamical response to changes in surface fluxes may allow advection to shift the snowfall maximum downwind of the location of maximum surface forcing of convection. This may help explain why snowfall maximizes inland over Tug Hill even though convective forcing is greatest near the shoreline (Fig. 2f). The elevated maximum in 
6. Summary and conclusions
A pronounced snowfall maximum exists to the east of Lake Ontario, inland over the 600-m-high Tug Hill Plateau, a region frequently impacted by intense lake-effect snow storms. As lake-effect convection moves inland, it is affected by the transition from water to land and orographic uplift. This study utilized profiling radar observations from the OWLeS field campaign, including an east–west-oriented transect of four K-band Micro Rain Radars, to investigate the inland evolution of lake-effect convection over Tug Hill.
During OWLeS IOP2b, lake-effect convection produced 6.4-cm liquid equivalent snowfall at north Redfield (NR) on the western margin of Tug Hill in a 24-h period. This contrasted with only 3.4 cm just 20 km east at Sandy Creek (SC), closer to the Ontario shoreline. During this event, the KTYX WSR-88D indicated an increase in echo frequency over Tug Hill relative to the shoreline region. MRR observations indicated echoes were confined by a capping inversion near 3.5 km MSL. The equivalent reflectivity factor 
MRR profiles indicate that the inland enhancement of snowfall totals was not due to orographic invigoration of convection; there was no inland increase in the depth or intensity of lake-effect convection observed. Instead, with increasing inland distance, echoes become less intense, more uniform, more frequent, less turbulent, and less effective at lofting snow. This evolution is similar to the convective-to-stratiform transition that often occurs with deep convection.
The relative roles of other mechanisms in producing the Tug Hill snowfall maximum remain uncertain. An inland increase in echo frequency appears to contribute to the Tug Hill snowfall enhancement and may be related to orographic initiation of convective or stratiform precipitation. Low-level suppression of 
Multistorm statistics from 29 lake-effect events observed by MRRs at a coastal site (SIB) and at NR demonstrate that the inland weakening of convection, convective-to-stratiform transition, and increase in echo frequency observed during IOP2b are robust features, common to most storms. Four events that include XPR data at SC all indicate a suppression of 
The data presented here are inconsistent with one hypothetical mechanism that attempts to explain the inland evolution of lake-effect snowfall (orographic invigoration of convection) and provide constraints on others. However, these results do not allow for a complete diagnosis of the relevant mechanisms. Further hypothesis testing will require a more detailed accounting of cloud dynamics and microphysical processes utilizing other datasets collected during OWLeS and numerical modeling experiments.
Acknowledgments
This material is based upon work supported by National Science Foundation Grant 1262090 to the University of Utah and the University at Albany Faculty Research Award Program. We acknowledge the other PIs and participants that contributed to the planning and execution of the OWLeS field campaign. NCAR-EOL maintains the OWLeS data repository. Soundings from Ontario were collected by David Kristovich (University of Illinois), and data from the MIPs was provided by Kevin Knupp (University of Alabama in Huntsville). One of the MRRs was provided by Ronald Smith (Yale). The comparison of MRR and XPR profiles benefited from comments by Bart Geerts and Kevin Knupp. Adam Massmann provided assistance with analysis of sounding and PARSIVEL data. The work also benefited from the helpful comments of three anonymous reviewers and editor Daniel Kirshbaum. We are very grateful to homeowners in the Tug Hill region, the Sandy Creek Central School, and Sandy Island Beach State Park for allowing instrumentation to be deployed on their property. The University of Utah Center for High Performance Computing provided computer support and services. KTYX data was attained from the National Climatic Data Center’s archives.
REFERENCES
Alcott, T. I., and W. J. Steenburgh, 2013: Orographic influences on a Great Salt Lake–effect snowstorm. Mon. Wea. Rev., 141, 2432–2450, doi:10.1175/MWR-D-12-00328.1.
Bergeron, T., 1965: On the low-level redistribution of atmospheric water caused by orography. Proc. Int. Cloud Physics, Tokyo, Japan, Amer. Meteor. Soc., 96–100.
Biggerstaff, M., and R. A. Houze Jr., 1991: Kinematic and precipitation structure of the 10–11 June 1985 squall line. Mon. Wea. Rev., 119, 3034–3065, doi:10.1175/1520-0493(1991)119<3034:KAPSOT>2.0.CO;2.
Brown, R. A., T. A. Niziol, N. R. Donaldson, P. I. Joe, and V. T. Wood, 2007: Improved detection using negative elevation angles for mountaintop WSR-88Ds. Part III: Simulations of shallow convective activity over and around Lake Ontario. Wea. Forecasting, 22, 839–852, doi:10.1175/WAF1019.1.
Browning, K. A., F. F. Hill, and C. W. Pardoe, 1974: Structure and mechanism of precipitation and effect of orography in a wintertime warm sector. Quart. J. Roy. Meteor. Soc., 100, 309–330, doi:10.1002/qj.49710042505.
Burt, C. C., 2007: Extreme Weather: A Guide and Record Book. W. W. Norton & Company, 320 pp.
Cannon, D. J., D. J. Kirshbaum, and S. L. Gray, 2012: Under what conditions does embedded convection enhance orographic precipitation? Quart. J. Roy. Meteor. Soc., 138, 391–406, doi:10.1002/qj.926.
Colle, B. A., Y. Lin, S. Medina, and B. F. Smull, 2008: Orographic modification of convection and flow kinematics by the Oregon Coast Range and Cascades during IMPROVE-2. Mon. Wea. Rev., 136, 3894–3916, doi:10.1175/2008MWR2369.1.
Eichenlaub, V. L., and T. W. Hodler, 1979: Weather and Climate of the Great Lakes Region. University of Notre Dame Press, 335 pp.
Hartnett, J. J., J. M. Collins, M. A. Baxter, and D. P. Chambers, 2014: Spatiotemporal snowfall trends in central New York. J. Appl. Meteor. Climatol., 53, 2685–2697, doi:10.1175/JAMC-D-14-0084.1.
Hjelmfelt, M. R., 1990: Numerical study of the influence of environmental conditions on lake-effect snowstorms over Lake Michigan. Mon. Wea. Rev., 118, 138–150, doi:10.1175/1520-0493(1990)118<0138:NSOTIO>2.0.CO;2.
Hjelmfelt, M. R., 1992: Orographic effects in simulated lake-effect snowstorms over Lake Michigan. Mon. Wea. Rev., 120, 373–377, doi:10.1175/1520-0493(1992)120<0373:OEISLE>2.0.CO;2.
Hjelmfelt, M. R., and R. R. Braham Jr., 1983: Numerical simulation of the airflow over Lake Michigan for a major lake-effect snow event. Mon. Wea. Rev., 111, 205–219, doi:10.1175/1520-0493(1983)111<0205:NSOTAO>2.0.CO;2.
Holroyd, E. W. I., 1971: Lake-effect cloud bands as seen from weather satellites. J. Atmos. Sci., 28, 1165–1170, doi:10.1175/1520-0469(1971)028<1165:LECBAS>2.0.CO;2.
Houze, R. A., Jr., 1997: Stratiform precipitation in regions of convection: A meteorological paradox? Bull. Amer. Meteor. Soc., 78, 2179–2196, doi:10.1175/1520-0477(1997)078<2179:SPIROC>2.0.CO;2.
Houze, R. A., Jr., 2014: Cloud Dynamics. 2nd ed., International Geophysics Series, Vol. 53, Academic Press, 573 pp.
Kirshbaum, D. J., and R. B. Smith, 2008: Temperature and moist-stability effects on midlatitude orographic precipitation. Quart. J. Roy. Meteor. Soc., 134, 1183–1199, doi:10.1002/qj.274.
Kirshbaum, D. J., and R. B. Smith, 2009: Orographic precipitation in the tropics: Large-eddy simulations and theory. J. Atmos. Sci., 66, 2559–2578, doi:10.1175/2009JAS2990.1.
Kirshbaum, D. J., and A. Grant, 2012: Invigoration of cumulus cloud fields by mesoscale ascent. Quart. J. Roy. Meteor. Soc., 138, 2136–2150, doi:10.1002/qj.1954.
Klugmann, D., K. Heinsohn, and H.-J. Kirtzel, 1996: A low cost 24 GHz FM-CW Doppler radar rain profiler. Contrib. Atmos. Phys., 69, 247–253.
Kristovich, D. A. R., 2014: Tighten your snow belt—New efforts to understand lake-effect storms in the eastern Great Lakes. 16th Conf. on Mountain Meteorology, Amer. Meteor. Soc., 8.1. [Availible online at: https://ams.confex.com/ams/16MountMet/webprogram/Paper252116.html.]
Kristovich, D. A. R., and R. A. Steve, 1995: A satellite study of cloud-band frequencies over the Great Lakes. J. Appl. Meteor., 34, 2083–2090, doi:10.1175/1520-0450(1995)034<2083:ASSOCB>2.0.CO;2.
Lackmann, G., 2011: Midlatitude Synoptic Meteorology: Dynamics, Analysis, and Forecasting. Amer. Meteor. Soc., 360 pp.
Laird, N. F., and D. A. Kristovich, 2004: Comparison of observations with idealized model results for a method to resolve winter lake-effect mesoscale morphology. Mon. Wea. Rev., 132, 1093–1103, doi:10.1175/1520-0493(2004)132<1093:COOWIM>2.0.CO;2.
Laird, N. F., D. A. Kristovich, and J. E. Walsh, 2003a: Idealized model simulations examining the mesoscale structure of winter lake-effect circulations. Mon. Wea. Rev., 131, 206–221, doi:10.1175/1520-0493(2003)131<0206:IMSETM>2.0.CO;2.
Laird, N. F., J. E. Walsh, and D. A. Kristovich, 2003b: Model simulations examining the relationship of lake-effect morphology to lake shape, wind direction, and wind speed. Mon. Wea. Rev., 131, 2102–2111, doi:10.1175/1520-0493(2003)131<2102:MSETRO>2.0.CO;2.
Lavoie, R. L., 1972: A mesoscale numerical model of lake-effect storms. J. Atmos. Sci., 29, 1025–1040, doi:10.1175/1520-0469(1972)029<1025:AMNMOL>2.0.CO;2.
Löffler-Mang, M., and J. Joss, 2000: An optical disdrometer for measuring size and velocity of hydrometeors. J. Atmos. Oceanic Technol., 17, 130–139, doi:10.1175/1520-0426(2000)017<0130:AODFMS>2.0.CO;2.
Löffler-Mang, M., M. Kunz, and W. Schmid, 1999: On the performance of a low-cost K-band Doppler radar for quantitative rain measurements. J. Atmos. Oceanic Technol., 16, 379–387, doi:10.1175/1520-0426(1999)016<0379:OTPOAL>2.0.CO;2.
Maahn, M., and P. Kollias, 2012: Improved Micro Rain Radar snow measurements using Doppler spectra post-processing. Atmos. Meas. Tech. Discuss., 5, 4771–4808, doi:10.5194/amtd-5-4771-2012.
Matrosov, S., 1992: Radar reflectivity in snowfall. IEEE Trans. Geosci. Remote Sens., 30, 454–461, doi:10.1109/36.142923.
Minder, J. R., R. B. Smith, and A. D. Nugent, 2013: The dynamics of ascent-forced orographic convection in the tropics: Results from Dominica. J. Atmos. Sci., 70, 4067–4088, doi:10.1175/JAS-D-13-016.1.
Murakami, M., T. Clark, and W. Hall, 1994: Numerical simulations of convective snow clouds over the Sea of Japan: 2-dimensional simulations of mixed-layer development and convective snow cloud formation. J. Meteor. Soc. Japan, 72, 43–62.
Niziol, T. A., W. R. Snyder, and J. S. Waldstreicher, 1995: Winter weather forecasting throughout the eastern United States. Part IV: Lake effect snow. Wea. Forecasting, 10, 61–77, doi:10.1175/1520-0434(1995)010<0061:WWFTTE>2.0.CO;2.
Onton, D. J., and W. J. Steenburgh, 2001: Diagnostic and sensitivity studies of the 7 December 1998 Great Salt Lake–effect snowstorm. Mon. Wea. Rev., 129, 1318–1338, doi:10.1175/1520-0493(2001)129<1318:DASSOT>2.0.CO;2.
Passarelli, R. E., Jr., and R. R. Braham Jr., 1981: The role of the winter land breeze in the formation of Great Lake snow storms. Bull. Amer. Meteor. Soc., 62, 482–492, doi:10.1175/1520-0477(1981)062<0482:TROTWL>2.0.CO;2.
Peace, R., Jr., and R. Sykes Jr., 1966: Mesoscale study of a lake effect snow storm. Mon. Wea. Rev., 94, 495–507, doi:10.1175/1520-0493(1966)094<0495:MSOALE>2.3.CO;2.
Peters, G., B. Fischer, and T. Andersson, 2002: Rain observations with a vertically looking Micro Rain Radar (MRR). Boreal Environ. Res., 7, 353–362.
Phillips, D. W., 2009: UAHuntsville X-Band Profiling Radar (XPR). 34th Conf. on Radar Meteorology, Williamsburg, VA, Amer. Meteor. Soc., 11.7. [Available online at https://ams.confex.com/ams/34Radar/techprogram/paper_155613.htm.]
Rasmussen, R. M., J. Hallett, R. Purcell, S. D. Landolt, and J. Cole, 2011: The hotplate precipitation gauge. J. Atmos. Oceanic Technol., 28, 148–164, doi:10.1175/2010JTECHA1375.1.
Reinking, R. F., and Coauthors, 1993: The Lake Ontario Winter Storms (LOWS) project. Bull. Amer. Meteor. Soc., 74, 1828–1828, doi:10.1175/1520-0477-74-10-1828.
Saito, K., M. Murakami, T. Matsuo, and H. Mizuno, 1996: Sensitivity experiments on the orographic snowfall over the mountainous region of northern Japan. J. Meteor. Soc. Japan, 74, 797–813.
Schultz, D. M., and R. J. Trapp, 2003: Nonclassical cold-frontal structure caused by dry subcloud air in northern Utah during the Intermountain Precipitation Experiment (IPEX). Mon. Wea. Rev., 131, 2222–2246, doi:10.1175/1520-0493(2003)131<2222:NCSCBD>2.0.CO;2.
Smith, R. B., P. Schafer, D. J. Kirshbaum, and E. Regina, 2009: Orographic precipitation in the tropics: Experiments in Dominica. J. Atmos. Sci., 66, 1698–1716, doi:10.1175/2008JAS2920.1.
Steiger, S. M., and Coauthors, 2013: Circulations, bounded weak echo regions, and horizontal vortices observed within long-lake-axis-parallel–lake-effect storms by the Doppler on Wheels. Mon. Wea. Rev., 141, 2821–2840, doi:10.1175/MWR-D-12-00226.1.
Tao, W.-K., and J. Simpson, 1989: Modeling study of a tropical squall-type convective line. J. Atmos. Sci., 46, 177–202, doi:10.1175/1520-0469(1989)046<0177:MSOATS>2.0.CO;2.
Vasiloff, S., 2002: Investigation of a WSR-88D Z-R relation for snowfall in northern Utah. Extended Abstracts, 16th Conf. on Hydrology, Orlando, FL, Amer. Meteor. Soc., 3.18. [Available online at https://ams.confex.com/ams/annual2002/webprogram/Paper26411.html.]
Veals, P. G., and W. J. Steenburgh, 2015: Climatological characteristics and orographic enhancement of lake-effect precipitation east of Lake Ontario and over the Tug Hill Plateau. Mon. Wea. Rev., 143, 3591–3609, doi:10.1175/MWR-D-15-0009.1.
Yuter, S. E., and R. A. Houze Jr., 1995a: Three-dimensional kinematic and microphysical evolution of Florida cumulonimbus. Part II: Frequency distributions of vertical velocity, reflectivity, and differential reflectivity. Mon. Wea. Rev., 123, 1941–1963, doi:10.1175/1520-0493(1995)123<1941:TDKAME>2.0.CO;2.
Yuter, S. E., and R. A. Houze, Jr., 1995b: Three-dimensional kinematic and microphysical evolution of Florida cumulonimbus. Part III: Vertical mass transport, mass divergence, and synthesis. Mon. Wea. Rev., 123, 1964–1983, doi:10.1175/1520-0493(1995)123<1964:TDKAME>2.0.CO;2.
LLAP bands are also sometimes referred to as midlake or shoreline bands, depending on the location of the convection relative to the lake shore.
At times, both morphologies were observed simultaneously.
Alternatively, snowfall rates could differ between the sites if particle size, shape, or density varied in such a way to give similar 
