Lake-effect Snow Events Research Articles

Climatology of Lake-Effect Snow Days Along the Southern Shore of Lake Michigan: What Is the Sensitivity to Environmental Factors and Snowband Morphology?

The Laurentian Great Lakes have substantial influences on regional climatology, particularly with impactful lake-effect snow events. This study examines the snowfall, cloud-inferred snow band morphology, and environment of lake-effect snow days along the southern shore of Lake Michigan for the 1997–2017 period. Suitable days for study were identified based on the presence of lake-effect clouds assessed in a previous study and extended through 2017, combined with an independent classification of likely lake-effect snow days based on independent snowfall data and weather map assessments. The primary goals are to identify lake-effect snow days and evaluate the snowfall distribution and modes of variability, the sensitivity to thermodynamic and flow characteristics within the upstream sounding at Green Bay, WI, and the influences of snowband morphology. Over 300 lake-effect days are identified during the study period, with peak mean snowfall within the lake belt extending from southwest Michigan to northern Indiana. Although multiple lake-effect morphological types are often observed on the same day, the most common snow band morphology is wind parallel bands. Relative to days with wind parallel bands, the shoreline band morphology is more common with a reduced lower-tropospheric zonal wind component within the upstream sounding at Green Bay, WI, as well as higher sea-level pressure and 500-hPa geopotential height anomalies to the north of the Great Lakes. Snowfall is sensitive to band morphology, with higher snowfall for shoreline band structures than for wind parallel bands, especially due south of Lake Michigan. Snowfall is also sensitive to thermodynamic and flow properties, with a greater sensitivity to temperature in southwest Michigan and to flow properties in northwest Indiana.

Synoptic Climatology of Lake-Effect Snow Events off the Western Great Lakes

As the mesoscale dynamics of lake-effect snow (LES) are becoming better understood, recent and ongoing research is beginning to focus on the large-scale environments conducive to LES. Synoptic-scale composites are constructed for Lake Michigan and Lake Superior LES events by employing an LES case repository for these regions within the U.S. North American Regional Reanalysis (NARR) data for each LES event were used to construct synoptic maps of dominant LES patterns for each lake. These maps were formulated using a previously implemented composite technique that blends principal component analysis with a k-means cluster analysis. A sample case from each resulting cluster was also selected and simulated using the Advanced Weather Research and Forecast model to obtain an example mesoscale depiction of the LES environment. The study revealed four synoptic setups for Lake Michigan and three for Lake Superior whose primary differences were discrepancies in a surface pressure dipole structure previously linked with Great Lakes LES. These subtle synoptic-scale differences suggested that while overall LES impacts were driven more by the mesoscale conditions for these lakes, synoptic-scale conditions still provided important insight into the character of LES forcing mechanisms, primarily the steering flow and air–lake thermodynamics.

Contribution of winter upwelling in the Gulf of Finland to lake-effect snow in Estonia

The aim of the study was to analyse the sequence of winter (“warm”) upwelling and lake-effect snow (LES) events that deposited up to 50 cm of snow along the North Estonian coast in January–February 2021. Based on weather and aerological data, four episodes of LES were documented. Heavy, localized lake-effect enhanced precipitation occurred along a 30–50 km wide coastal strip bordering the Gulf of Finland when a cold air mass from the north advected over the warmer, unfrozen sea surface. A temperature difference of up to 20°C was revealed between the air mass temperatures measured at the 850 hPa level and at the sea surface. The LES events, in turn, were preceded by upwelling in the southern Gulf of Finland, which was generated by persisting easterly winds. Even when occasionally interrupted by a wind change, the upwelled water still kept sea surface temperature (SST) in the southern half of the Gulf higher, as documented by the water temperature records from the coastal stations of Estonia, SST and salinity imagery retrieved from the SatBaltyk system, and sea ice distribution charts. Differently from summer (cold) upwelling, winter upwelling brought up warmer (2–4°C) water from the sub-surface layers replacing the already cooled down (0–1°C) surface water. Thus, winter upwelling enhanced LES in two ways. Firstly, by not letting the Gulf freeze over, and therefore by providing a fetch. And secondly, by increasing the SST (and therefore also the 850 hPa level – surface temperature difference) by up to 4°C.

Improvements to Lake-Effect Snow Forecasts Using a One-Way Air–Lake Model Coupling Approach

AbstractLake-effect convective snowstorms frequently produce high-impact, hazardous winter weather conditions downwind of the North American Great Lakes. During lake-effect snow events, the lake surfaces can cool rapidly, and in some cases, notable development of ice cover occurs. Such rapid changes in the lake-surface conditions are not accounted for in existing operational weather forecast models, such as the National Oceanic and Atmospheric Administration’s (NOAA) High-Resolution Rapid Refresh (HRRR) model, resulting in reduced performance of lake-effect snow forecasts. As a milestone to future implementations in the Great Lakes Operational Forecast System (GLOFS) and HRRR, this study examines the one-way linkage between the hydrodynamic-ice model [the Finite-Volume Community Ocean Model coupled with the unstructured grid version of the Los Alamos Sea Ice Model (FVCOM-CICE), the physical core model of GLOFS] and the atmospheric model [the Weather Research and Forecasting (WRF) Model, the physical core model of HRRR]. The realistic representation of lake-surface cooling and ice development or its fractional coverage during three lake-effect snow events was achieved by feeding the FVCOM-CICE simulated lake-surface conditions to WRF (using a regional configuration of HRRR), resulting in the improved simulation of the turbulent heat fluxes over the lakes and resulting snow water equivalent in the downwind areas. This study shows that the one-way coupling is a practical approach that is well suited to the operational environment, as it requires little to no increase in computational resources yet can result in improved forecasts of regional weather and lake conditions.

An Updated Synoptic Climatology of Lake Erie and Lake Ontario Heavy Lake-Effect Snow Events

Lake-effect snow (LES) storms pose numerous hazards, including extreme snowfall and blizzard conditions, and insight into the large-scale precursor conditions associated with LES can aid local forecasters and potentially allow risks to be mitigated. In this study, a synoptic climatology of severe LES events over Lakes Erie and Ontario was created using an updated methodology based on previous studies with similar research objectives. Principal component analysis (PCA) coupled with cluster analysis (CA) was performed on a case set of LES events from a study domain encompassing both lakes, grouping LES events with similar spatial characteristics into the primary composite structures for LES. Synoptic scale composites were constructed for each cluster using the North American Regional Reanalysis (NARR). Additionally, one case from each cluster was simulated using the Weather Research and Forecast (WRF) model to analyze mesoscale conditions associated with each of the clusters. Three synoptic setups were identified that consisted of discrepancies, mostly in the surface fields, from a common pattern previously identified as being conducive to LES, which features a dipole and upper-level low pressure anomaly located near the Hudson Bay. Mesoscale conditions associated with each composite support differing LES impacts constrained to individual lakes or a combination of both.

Impact of Lake Surface Temperature Variations on Lake Effect Snow Over the Great Lakes Region

AbstractA high‐resolution three‐dimensional Weather Research and Forecasting (WRF) model is used to investigate the coupled impact of lake surface temperature (LST) and surface wind on the lake effect snow (LES) over the Great Lakes region. A set of twin WRF simulations, with and without resolving LST spatial variations in the model's surface boundary condition, is performed to quantify the impact of LST variation on LES. Both observations and model results reveal a positive correlation between the downwind LST gradient and surface wind convergence over the Great Lakes region. Furthermore, model simulations show that resolving the spatial variation of LST increases the surface wind convergence, correspondingly enhances local vertical motions in the atmospheric boundary layer, and creates favorable conditions for the LES formation on the lee sides of the Great Lakes. The contribution of LST spatial variations to the increase in precipitation on the lee sides of the lakes varies between 5% and 30% in individual LES events. The increase in the winter‐mean snow water equivalent due to LST spatial variations is between 3% and 15%. The most significant impact of LST variation on the winter‐mean snow water equivalent is on the lee side of Lake Huron.

Go With the Flow, on Jupiter and Snow. Coherence from Model-Free Video Data Without Trajectories

Viewing a data set such as the clouds of Jupiter, coherence is readily apparent to human observers, especially the Great Red Spot, but also other great storms and persistent structures. There are now many different definitions and perspectives mathematically describing coherent structures, but we will take an image processing perspective here. We describe an image processing perspective inference of coherent sets from a fluidic system directly from image data, without attempting to first model underlying flow fields, related to a concept in image processing called motion tracking. In contrast to standard spectral methods for image processing which are generally related to a symmetric affinity matrix, leading to standard spectral graph theory, we need a not symmetric affinity which arises naturally from the underlying arrow of time. We develop an anisotropic, directed diffusion operator corresponding to flow on a directed graph, from a directed affinity matrix developed with coherence in mind, and corresponding spectral graph theory from the graph Laplacian. Our methodology is not offered as more accurate than other traditional methods of finding coherent sets, but rather our approach works with alternative kinds of data sets, in the absence of vector field. Our examples will include partitioning the weather and cloud structures of Jupiter, and a local to Potsdam, NY, lake effect snow event on Earth, as well as the benchmark test double-gyre system.

Turbulent Heat Fluxes during an Extreme Lake-Effect Snow Event

Abstract Proper modeling of the turbulent heat fluxes over lakes is critical for accurate predictions of lake-effect snowfall (LES). However, model evaluation of such a process has not been possible because of the lack of direct flux measurements over lakes. The authors conducted the first-ever comparison of the turbulent latent and sensible heat fluxes between state-of-the-art numerical models and direct flux measurements over Lake Erie, focusing on a record LES event in southwest New York in November 2014. The model suite consisted of numerical models that were operationally and experimentally used to provide nowcasts and forecasts of weather and lake conditions. The models captured the rise of the observed turbulent heat fluxes, while the peak values varied significantly. This variation resulted in an increased spread of simulated lake temperature and cumulative evaporation as the representation of the model uncertainty. The water budget analysis of the atmospheric model results showed that the majority of the moisture during this event came from lake evaporation rather than a larger synoptic system. The unstructured-grid Finite-Volume Community Ocean Model (FVCOM) simulations, especially those using the Coupled Ocean–Atmosphere Response Experiment (COARE)-Met Flux algorithm, presented better agreement with the observed fluxes likely due to the model’s capability in representing the detailed spatial patterns of the turbulent heat fluxes and the COARE algorithm’s more realistic treatment of the surface boundary layer than those in the other models.

Airborne Radar Observations of Lake-Effect Snowbands over the New York Finger Lakes

AbstractThe vast majority of lake-effect snow research throughout the years has focused on the North American Great Lakes since they are often associated with strong lake-effect events that produce heavy downstream snowfall. This study investigates a lake-effect snow event that instead occurred over two smaller lakes, the New York Finger Lakes, which are just O(5) km wide and O(50) km long. A pair of well-defined snowbands that formed over Seneca and Cayuga Lakes, the two largest of the Finger Lakes, were sampled from above by a vertically pointing Doppler radar and lidar on board the University of Wyoming King Air (UWKA). With typical widths matching the widths of the lakes, and depths of less than 1000 m, the long-lake-axis-parallel bands were actually quite intense for their size. For example, updrafts of 2–3 m s−1 or greater within the band cores were common, and reflectivity occasionally exceeded 5 dBZ. Airborne dual-Doppler data show that both bands were sometimes accompanied by a well-defined thermally driven secondary circulation. Lidar data reveal that the Cayuga Lake band contained significantly more liquid water than the band over Seneca Lake, which was composed mainly of ice. Dissipating lake-effect ice clouds, carried downstream from Lake Ontario toward Seneca Lake, likely seeded the emerging convection over Seneca Lake, resulting in an accelerated depletion of liquid in the Seneca Lake band via more efficient snow growth.

A GIS-based assessment of lake-effect snow warnings in Upstate New York

Lake-effect snow (LES) occurs downwind of the Great Lakes every late autumn and winter. This phenomenon often generates significant localized snowfall that can cripple one section of a community while the remainder is spared. Detrimental impacts on travel from snowfall rates of 2.5 cm h –1 (1 in h –1 ) are not uncommon. Storm-total snowfall gradients of 12.5 cm km –1 (8 in mi –1 ) also have been documented. Twenty-four-hour snowfall measurements taken by National Weather Service (NWS) observers during Lake Ontario LES events were collected. Utilizing a geographic information system, snowfall was interpolated from spotter reports with the kriging method. Then, the area of warning-criteria snowfall was compared to the area of the corresponding county-based LES warning for calculation of false alarm percentage. This warning performance evaluator identified the proportion of a county-based LES warning that received sub-warning-criteria snowfall. Percent of false alarm was calculated using temporally comparable snowfall reports and all available reports from each event. A total of 13 events from the 2009–2010 through 2011–2012 LES seasons were analyzed. The average percent of false alarm was approximately 90%. This suggests the NWS should consider smaller warning polygons. Limited-size warnings would enable meteorologists to provide greater specificity regarding the location of anticipated significant snowfall, while simultaneously reducing the number of people who are unnecessarily warned.

Multivariate analysis of lake-effect snowstorms in western New York

The Great Lakes region of the United States is subjected to a wintertime convective phenomenon known as lake-effect snow (LES). These events are capable of producing significant quantities of snow over localized areas by developing elongated bands that tap into heat and moisture exchanges between the warm lake surface and the overlying continental polar air. Several factors are believed to affect the snowfall intensity associated with LES events, including the ice coverage over the lake where the snowbands originate. Improvements in the quality of snowfall forecasts associated with these LES events require an approach that uses a larger number of events than used in previously investigated case studies. The intensity of 91 LES events was assessed using the total volume of snow (snow depth multiplied by snow coverage area) per day in western New York as reported by the National Weather Service in Buffalo, New York, from 1998–2011. These events were cross-referenced against the fetch, capping inversion height, thermodynamic instability, wind speed, and ice coverage over Lake Erie during each event. A multivariate regression revealed that only fetch, inversion height, and ice coverage were statistically significant in determining the intensity of the LES event, with ice coverage having the greatest impact. These parameters accounted for approximately 30% of the overall snowfall volume variability in our LES events. An examination of two events demonstrated that other environmental controls—such as orography and low-level moisture—may have affected the quality of the snowfall predicted by our regression.

Observations of the Cross-Lake Cloud and Snow Evolution in a Lake-Effect Snow Event

AbstractWhile the total snowfall produced in lake-effect storms can be considerable, little is known about how clouds and snow evolve within lake-effect boundary layers. Data collected over Lake Michigan on 10 January 1998 during the Lake-Induced Convection Experiment (Lake-ICE) are analyzed to better understand and quantify the evolution of clouds and snow. On this date, relatively cold air flowed from west to east across Lake Michigan, creating a quasi-steady-state boundary layer that increased from ≈675 to ≈910 m in depth over a distance of 80 km. Once a cloud deck formed 14–18 km from the upwind shoreline, maximum cloud particle concentrations and liquid water content increased from west to east across the lake. Correspondingly, maximum ice water contents, snowfall rates, and maximum snow particle diameters also increased across the lake. Maximum particle concentrations were found below the mean top of the boundary layer and above the cloud base for both cloud and snow particles.Surprisingly, snow particles were observed 3–7 km upwind of the upwind edge of the lake-effect cloud deck. These snow particles were observed to be rather spatially uniform throughout the boundary layer. Based on available observations, it is hypothesized that of the mechanisms that could produce this snow, the majority of it originated from transient clouds located near the upwind shore. In addition, maximum snow particle concentrations peaked near the middle of the lake before decreasing toward the downwind shore, indicating the location after which aggregation became an important snow growth mechanism. These results show that the evolution of clouds and snow within lake-effect boundary layers may not occur in the uniform manner often depicted in conceptual models.

The Influence of Ice Cover on Two Lake-Effect Snow Events over Lake Erie

Abstract It is generally understood that extensive regions of significant lake ice cover impact lake-effect (LE) snow storms by decreasing the upward heat and moisture fluxes from the lake surface; however, it is only recently that studies have been conducted to more thoroughly examine this relationship. This study provides the first examination of Great Lakes LE snow storms that developed in association with an extensively ice-covered lake. The LE snow events that occurred downwind of Lake Erie on 12–14 February 2003 and 28–31 January 2004 produced maximum snowfall totals of 43 and 64 cm in western New York state, respectively. The presence of widespread ice cover led these snows to be less anticipated than snowfalls from Lake Ontario, which had limited ice cover. For both events, a variety of ice-cover conditions and meso- and synoptic-scale factors (i) helped support LE snow storm development, (ii) lead to the transitions in LE convective type, and (iii) resulted in noteworthy snowfalls near Lake Erie. Thinner ice cover along with favorable fetch directions during the 2004 event likely aided the development of more significant snowband time periods and the resulting greater snowfall. Although Lake Erie had regions with lower ice concentration during the 2003 event, thicker ice cover was present across a greater area of the lake, fetch directions during lake-effect time periods were positioned over higher ice concentration regions, and snowbands had a shorter duration and impacted the same region to a lesser degree than the 2004 case.

Convective Evolution across Lake Michigan during a Widespread Lake-Effect Snow Event

Abstract Lake-effect snowstorms generally develop within convective boundary layers, which are induced when cold air flows over relatively warm lakes in fall and winter. Mesoscale circulations within the boundary layers largely control which communities near the downwind shores of the lakes receive the most intense snow. The lack of quantitative observations over the lakes during lake-effect storms limits the ability to fully understand and predict these mesoscale circulations. This study provides the first observations of the concurrent spatial and temporal evolution of the thermodynamic and microphysical boundary layer structure and mesoscale convective patterns across Lake Michigan during an intense lake-effect event. Observations analyzed in this study were taken during the Lake-Induced Convection Experiment (Lake-ICE). Aircraft and sounding observations indicate that the lake-effect snows of 13 January 1998 developed within a convective boundary layer that grew rapidly across Lake Michigan. Boundary ...

Assessment of Potential Effects of Climate Change on Heavy Lake-Effect Snowstorms Near Lake Erie

The potential effects of future climate change on the frequency of heavy lake-effect snowstorms in the Lake Erie snowbelt were assessed using recent transient simulations from two General Circulation Models (GCMs): the second-generation Hadley Centre (HadCM2) and the first generation Canadian Climate Centre (CGCM1) coupled ocean-atmosphere models. An analysis of historical heavy lake-effect snowstorms identified six weather conditions to be closely related to heavy lake-effect snowstorm occurrence: surface wind speed > 6 m/s, surface wind direction of south southwest to west northwest, surface air temperature in the range of −10°C to 0°C, lake surface to air temperature difference > 7°C, lower tropospheric stability ( T lake − 850 >15°C), and a highly amplified middle tropospheric wave train. These criteria were applied to daily grid point data from the GCMs for two periods, the late 20 th Century and the late 21 st Century, to determine the relative frequency with which heavy lake-effect conditions were predicted. Surface conditions favorable for heavy lake-effect snow decreased in frequency by 50% and 90% for the HadCM2 and CGCM1, respectively, by the late 21 st Century. This reduction was due almost entirely to a decrease in the number of occurrences of surface air temperature in the range of −10 to 0°C, which in turn was the result of an increase in average winter air temperatures. Other surface conditions favorable for lake-effect snow occurred at about the same frequency in the late 21 st Century as in the late 20 th Century, suggesting that lake-effect rain events may replace lake-effect snow events. Changes in the middle tropospheric wave train were also noted in both models. However, there were sizable biases in the simulation of the present-day climate, raising questions about the validity of the future projections.

Moisture Analysis of a Type I Cloud-Topped Boundary Layer from Doppler Radar and Rawinsonde Observations

Abstract Moisture data from radar and rawinsonde observations during three lake-effect snow events are analyzed to determine entrainment rates. Type I convective boundary layers, which are those driven largely by surface heating, typically accompany these storms. Gathered during the winter of 1990, the data are a subset from the Lake Ontario Winter Storms (LOWS) Project, which deployed a mesoscale network of sensors. Doppler wind profiler signal-to-noise ratio (SNR) data are used to derive humidity structure function parameter (C2q) time–height series analysis, which are then compared to rawinsonde specific humidity (q) plots. Visual comparison of log(C2q) and q analysis indicated a strongly positive correlation. Radar-derived humidity analysis is used to estimate the depth of the Type I (driven by surface heating), cloud-topped boundary layer (CTBL), which corresponded well with results from LOWS rawinsonde data. Calculations of the contribution of (C2q) to the refractive index structure parameter (C2n) ...

The Utility of Additional Soundings for Forecasting Lake-Effect Snow in the Great Lakes Region

Abstract The impact of initializing a mesoscale model with additional sounding data over the Great Lakes region is investigated. As part of the Lake-Induced Convection Experiment (Lake-ICE) field study during the winter of 1997/98, six supplementary Cross-chain Loran Atmospheric Sounding System (CLASS) units and three Integrated Sounding System (ISS) units were used in addition to those from the standard synoptic upper-air network. The three ISS units were in the vicinity of Lake Michigan, and the six CLASS units were in the data-sparse region of central and northeastern Ontario and western Quebec. The Pennsylvania State University–National Center for Atmospheric Research fifth-generation Mesoscale Model running on a doubly nested grid is used to simulate the lake-effect snow event of 4–5 December 1997. This model output from a 30-km horizontal resolution grid shows that the six CLASS soundings capture a warm layer below 850 hPa that appears to be the result of diabatic heating from the Great Lakes. This ...

Numerical Simulation of Transitions in Boundary Layer Convective Structures in a Lake-Effect Snow Event

Abstract Numerical simulations are used to study transitions between boundary layer rolls and more cellular convective structures observed during a lake-effect snow event over Lake Michigan on 17 December 1983. Weak lake-effect nonroll convection was observed near the eastern (downwind) shore preceding passage of a secondary cold front. After frontal passage horizontal wind speeds in the convective boundary layer increased, with subsequent development of linear convective patterns. Thereafter the convective pattern became more three-dimensional as low-level wind speeds decreased. Little directional shear was observed in any of the wind profiles. Numerical simulations with the Advanced Regional Prediction System model were initialized with an upwind sounding and radar-derived wind profiles corresponding to each of the three convective structure regimes. Model-derived reflectivity fields were in good agreement with the observed regimes. These simulations differed primarily in the initial wind speed profiles...

Transitions in Boundary Layer Meso-γConvective Structures:An Observational Case Study

Boundary layer rolls over Lake Michigan have been observed in wintertime conditions predicted by many past studies to favor nonroll convective structures (such as disorganized convection or cellular convection). This study examines mechanisms that gave rise to transitions between boundary layer rolls and more cellular convective structures observed during a lake-effect snow event over Lake Michigan on 17 December 1983. The purposes of this study are to better understand roll formation in marine boundary layers strongly heated from below and examine the evolution of snowfall rate and mass overturning rate within the boundary layer during periods of convective transition. A method of quantifying the uniformity of convection along the roll axes, based on dual-Doppler radar-derived vertical motions, was developed to quantify changes in boundary layer convective structure. Roll formation was found to occur after (within 1 h) increases in low-level wind speeds and speed shear primarily below about 0.3zi, with little change in directional shear within the convective boundary layer. Roll convective patterns appeared to initiate upstream of the sample region, rather than form locally near the downwind shore of Lake Michigan. These findings suggest that either rolls developed over the upwind half of Lake Michigan or that the convection had a delayed response to changes in the atmospheric surface and wind forcing. Mass overturning rates at midlevels in the boundary layer peaked when rolls were dominant and gradually decreased when cellular convection became more prevalent. Radar-estimated aerial-mean snowfall rates showed little relationship with changes in convective structure. However, when rolls were dominant, the heaviest snow was more concentrated in updraft regions than during more cellular time periods.

A Record Ohio Snowfall during 9–14 November 1996

A 6-day lake effect snow event produced a reported 68.9 in. (175.0 cm) of snowfall in Hambden Township, Geauga County, Ohio, during 9–14 November 1996. This exceeded the previous Ohio single-storm snowfall record of 42 in. The purpose of this research is to describe the meteorological situation that caused the record snowfall, document the site of the record snow and the methods of the measurement, describe the societal impacts of the storm, and assess the validity of the record. A persistent deep trough in the midtroposphere provided cold advection across Lake Erie into northeast Ohio. This combined with a very unstable lower atmosphere to allow deep, moist convection and a prolonged period of heavy snowfall. The observer and site were selected in 1994 for a federal study of lake effect snowfalls and the observer was a “snow spotter” for the National Weather Service office in Cleveland in November 1996. A review of snowfall data from the event indicates the reported snowfall is consistent with respect to...