Browse
Abstract
Lake-effect snowstorms are often observed to manifest as dominant bands, commonly produce heavy localized snowfall, and may extend large distances inland, resulting in hazards and high societal impact. Some studies of dominant bands have documented concomitant environmental baroclinity (i.e., baroclinity occurring at a scale larger than the width of the parent lake), but the interaction of this baroclinity with the inland structure of dominant bands has been largely unexplored. In this study, the thermodynamic environment and thermodynamic and kinematic structure of simulated dominant bands are examined using WRF reanalyses at 3-km horizontal resolution and an innovative technique for selecting the most representative member from the WRF ensemble. Three reanalysis periods are selected from the Ontario Winter Lake-effect Systems (OWLeS) field campaign, encompassing 185 simulation hours, including 155 h in which dominant bands are identified. Environmental baroclinity is commonly observed during dominant-band periods and occurs in both the north–south and east–west directions. Sources of this baroclinity are identified and discussed. In addition, case studies are conducted for simulation hours featuring weak and strong along-band environmental baroclinity, resulting in weak and strong inland extent, respectively. These contrasting cases offer insight into one mechanism by which along-band environmental baroclinity can influence the inland structure and intensity of dominant bands: in the case with strong environmental baroclinity, inland portions of this band formed under weak instability and therefore exhibit slow overturning, enabling advection far inland under strong winds, whereas the nearshore portion forms under strong instability, and the enhanced overturning eventually leads to the demise of the inland portion of the band.
Abstract
Lake-effect snowstorms are often observed to manifest as dominant bands, commonly produce heavy localized snowfall, and may extend large distances inland, resulting in hazards and high societal impact. Some studies of dominant bands have documented concomitant environmental baroclinity (i.e., baroclinity occurring at a scale larger than the width of the parent lake), but the interaction of this baroclinity with the inland structure of dominant bands has been largely unexplored. In this study, the thermodynamic environment and thermodynamic and kinematic structure of simulated dominant bands are examined using WRF reanalyses at 3-km horizontal resolution and an innovative technique for selecting the most representative member from the WRF ensemble. Three reanalysis periods are selected from the Ontario Winter Lake-effect Systems (OWLeS) field campaign, encompassing 185 simulation hours, including 155 h in which dominant bands are identified. Environmental baroclinity is commonly observed during dominant-band periods and occurs in both the north–south and east–west directions. Sources of this baroclinity are identified and discussed. In addition, case studies are conducted for simulation hours featuring weak and strong along-band environmental baroclinity, resulting in weak and strong inland extent, respectively. These contrasting cases offer insight into one mechanism by which along-band environmental baroclinity can influence the inland structure and intensity of dominant bands: in the case with strong environmental baroclinity, inland portions of this band formed under weak instability and therefore exhibit slow overturning, enabling advection far inland under strong winds, whereas the nearshore portion forms under strong instability, and the enhanced overturning eventually leads to the demise of the inland portion of the band.
Abstract
Predicting the inland penetration of lake-effect long-lake-axis-parallel (LLAP) snowbands is crucial to public safety because LLAP bands can produce hazardous weather well downwind of the parent lake. Accordingly, hypotheses for the variation in inland penetration of LLAP-band radar echoes (InPen) are formulated and tested. The hypothesis testing includes an examination of statistical relationships between environmental variables and InPen for 34 snapshots of LLAP bands observed during the Ontario Winter Lake-effect Systems (OWLeS) field campaign. Several previously proposed predictors of LLAP-band formation or InPen demonstrate weak correlations with InPen during OWLeS. A notable exception is convective boundary layer (CBL) depth, which is strongly correlated with InPen. In addition to CBL depth, InPen is strongly correlated with cold-air advection in the upper portion of the CBL, suggesting that boundary layer destabilization produced by vertically differential cold-air advection may be an important inland power source for preexisting LLAP bands. This power production is quantified through atmospheric energetics and the resulting variable, differential thermal advection power (DTAP), yields reasonably skillful predictions of InPen. Nevertheless, an InPen model developed using DTAP is outperformed by an empirical model combining CBL depth and potential temperature advection in the upper portion of the CBL. This two-variable model explains 76% of the observed InPen variance when tested on independent data. Finally, implications for operational forecasting of InPen are discussed.
Abstract
Predicting the inland penetration of lake-effect long-lake-axis-parallel (LLAP) snowbands is crucial to public safety because LLAP bands can produce hazardous weather well downwind of the parent lake. Accordingly, hypotheses for the variation in inland penetration of LLAP-band radar echoes (InPen) are formulated and tested. The hypothesis testing includes an examination of statistical relationships between environmental variables and InPen for 34 snapshots of LLAP bands observed during the Ontario Winter Lake-effect Systems (OWLeS) field campaign. Several previously proposed predictors of LLAP-band formation or InPen demonstrate weak correlations with InPen during OWLeS. A notable exception is convective boundary layer (CBL) depth, which is strongly correlated with InPen. In addition to CBL depth, InPen is strongly correlated with cold-air advection in the upper portion of the CBL, suggesting that boundary layer destabilization produced by vertically differential cold-air advection may be an important inland power source for preexisting LLAP bands. This power production is quantified through atmospheric energetics and the resulting variable, differential thermal advection power (DTAP), yields reasonably skillful predictions of InPen. Nevertheless, an InPen model developed using DTAP is outperformed by an empirical model combining CBL depth and potential temperature advection in the upper portion of the CBL. This two-variable model explains 76% of the observed InPen variance when tested on independent data. Finally, implications for operational forecasting of InPen are discussed.
Abstract
Lake-effect snow (LES) is a cold-season mesoscale convective phenomenon that can lead to significant snowfall rates and accumulations in the Great Lakes region of the United States. While limited-area numerical weather prediction models have shown skill in prediction of warm-season convective storms, forecasting the sharp nature of LES precipitation timing, intensity, and location is difficult because of model error and initial and boundary condition uncertainties. Ensemble forecasting can incorporate and quantify some sources of forecast error, but ensemble design must be considered. This study examines the relative contributions of forecast uncertainties to LES forecast error using a regional convection-allowing data assimilation and ensemble prediction system. Ensembles are developed using various methods of perturbations to simulate a long-lived and high-precipitation LES event in December 2013, and forecast performance is evaluated using observations including those from the Ontario Winter Lake-Effect Systems (OWLeS) campaign. Model lateral boundary conditions corresponding to weather conditions beyond the Great Lakes region play an influential role in LES precipitation forecasts and their uncertainty, as evidenced by ensemble spread, particularly at lead times beyond one day. A strong forecast dependence on regional initial conditions was shown using data assimilation. This sensitivity impacts the timing and intensity of predicted precipitation, as well as band location and orientation assessed with an object-based verification approach, giving insight into the time scales of practical predictability of LES. Overall, an assimilation-cycling convection-allowing ensemble prediction system could improve future lake-effect snow precipitation forecasts and analyses and can help quantify and understand sources of forecast uncertainty.
Abstract
Lake-effect snow (LES) is a cold-season mesoscale convective phenomenon that can lead to significant snowfall rates and accumulations in the Great Lakes region of the United States. While limited-area numerical weather prediction models have shown skill in prediction of warm-season convective storms, forecasting the sharp nature of LES precipitation timing, intensity, and location is difficult because of model error and initial and boundary condition uncertainties. Ensemble forecasting can incorporate and quantify some sources of forecast error, but ensemble design must be considered. This study examines the relative contributions of forecast uncertainties to LES forecast error using a regional convection-allowing data assimilation and ensemble prediction system. Ensembles are developed using various methods of perturbations to simulate a long-lived and high-precipitation LES event in December 2013, and forecast performance is evaluated using observations including those from the Ontario Winter Lake-Effect Systems (OWLeS) campaign. Model lateral boundary conditions corresponding to weather conditions beyond the Great Lakes region play an influential role in LES precipitation forecasts and their uncertainty, as evidenced by ensemble spread, particularly at lead times beyond one day. A strong forecast dependence on regional initial conditions was shown using data assimilation. This sensitivity impacts the timing and intensity of predicted precipitation, as well as band location and orientation assessed with an object-based verification approach, giving insight into the time scales of practical predictability of LES. Overall, an assimilation-cycling convection-allowing ensemble prediction system could improve future lake-effect snow precipitation forecasts and analyses and can help quantify and understand sources of forecast uncertainty.
