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Abstract The Winter Precipitation Type Research Multiscale Experiment (WINTRE-MIX) was conducted during February–March 2022 to observe multiscale processes impacting the variability and predictability of precipitation type and amount under near-freezing conditions over the Saint Lawrence River valley. Intensive observation period (IOP) 4 of the campaign occurred 17–18 February 2022 in association with an upper-level trough positioned over the north-central United States and a surface cyclone that traversed the study domain along a frontal boundary that extended northeast of the cyclone. The timing of precipitation-type transitions during the event was consistently too slow within operational forecast models at 2–5-day lead times. Consequently, this study aims to understand how forecast model representations of dynamical and thermodynamical processes on the synoptic scale to mesoscale may have influenced the predictability of precipitation type during IOP4. To do so, an ensemble of operational forecasts from the Global Ensemble Forecast System initialized 5 days prior to IOP4 was divided into three clusters according to the strength and position of the frontal zone over the Saint Lawrence River Valley during the event. Ensemble sensitivity analyses and spatial composites suggest that differences in the position of the frontal zone between clusters are dynamically linked to the differences in the structure of the associated upstream upper-level trough at prior forecast lead times. A diagnosis of the divergent circulation prior to the event suggests that feedback mechanisms between the surface cyclone, its attendant frontal boundaries, and the upper-level flow pattern help to further explain differences in the frontal zone between clusters. Significance StatementMixed-phase precipitation events, which can produce rain, freezing rain, ice pellets, and snow, are difficult to accurately forecast. This study investigates the large-scale processes influencing our ability to accurately forecast the precipitation type and amount during one of these events that was observed by a field campaign in February 2022. In forecasts initialized 5 days prior to the event, differences in the forecast upper-level atmospheric conditions led to differences in the forecast interactions between the upper-level flow and a low pressure system at the surface. As a result, there was large uncertainty in the predicted position of a surface front associated with the low pressure system and the precipitation-type distribution during the event. 

Abstract Investigations into the melting layer (ML) of winter storms have revealed small-scale fluctuations in the horizontal wind that could significantly affect the surface precipitation type (p-type) and the evolution of the ML. Despite previous evidence of such fluctuations, essential questions remain concerning their characteristics and the forces driving them. Therefore, this study characterizes small-scale horizontal wind fluctuations (<1 km in length with perturbation magnitudes < 3 m s⁻¹) and their environments within the ML of winter storms. This analysis uses data from a scanning X-band Doppler radar collected during the Winter Precipitation Type Research Multiscale Experiment (WINTRE-MIX), conducted during February and March 2022. We present three case studies where small-scale horizontal wind fluctuations are identified using along-radial and along-azimuthal radial velocity perturbations. These cases cover the range of environmental conditions observed during WINTRE-MIX, including (i) a descending ML with change in surface p-type from snow to rain, (ii) a steady ML with a surface p-type transition from freezing rain to rain due to surface cold air erosion, and (iii) a steady ML with a surface p-type transition from freezing rain to ice pellets due to surface cold air advection. Forcing mechanisms for small-scale wind fluctuations during each case are attributed to static instability, vertically trapped gravity waves, and/or shear instability inferred from rawinsonde data, HRRR analysis, and radar data. Our findings suggest that static instability, gravity waves, and shear instability drive the ML’s small-scale wind fluctuations and may influence surface precipitation-type transitions. Significance StatementThis research aims to enhance our understanding of horizontal motions (<1 km in length) within melting layers (MLs) of winter storms and their underlying causes. This study uses radar data to detect differences in horizontal motion within the ML of three different winter storms. Weather balloon observations and output from computer weather forecasts are then used to distinguish between horizontal motions generated by convection, vertically trapped gravity waves, or shear. Our findings reveal that horizontal motions within the ML are generated by different forcing mechanisms within different storms and that horizontal motions may influence the surface p-type. 

Abstract The St. Lawrence River Valley experiences a variety of precipitation types (p-types) during the cold season, such as rain, freezing rain, ice pellets, and snow. These varied precipitation types exert considerable impacts on aviation, road transportation, power generation and distribution, and winter recreation and are shaped by diverse multiscale processes that interact with the region’s complex topography. This study utilizes ERA5 reanalysis data, surface cyclone climatology, and hourly station observations from Montréal, Québec, and Burlington, Vermont, during October–April 2000–18 to investigate the spectrum of synoptic-scale weather regimes that induce cold-season precipitation across the St. Lawrence River Valley. In particular,k-means clustering and self-organizing maps (SOMs) are used to classify cyclone tracks passing near the St. Lawrence River Valley, and their accompanying thermodynamic profiles, into a set of event types that include a U.S. East Coast track, a central U.S. track, and two Canadian clipper tracks. Composite analyses are subsequently performed to reveal the synoptic-scale environments and the characteristic p-types that most frequently accompany each event type. Global Ensemble Forecast System version 12 (GEFSv12) reforecasts are then used to examine the relative predictability of cyclone characteristics and the local thermodynamic profile associated with each event type at 0–5-day forecast lead times. The analysis suggests that forecasted cyclones near the St. Lawrence River Valley develop too quickly and are located left-of-track relative to the reanalysis on average, which has implications for forecasts of the local thermodynamic profile and p-type across the region when the temperature is near 0°C. Significance StatementDiverse precipitation types are observed when near-surface temperatures approach 0°C during the cold season, especially across the St. Lawrence River Valley in southern Québec. This study classifies cold-season precipitation events impacting the St. Lawrence River Valley based on the track of storm systems across the region and quantifies the average meteorological characteristics and predictability of each track. Our analysis reveals that forecasted low pressure systems develop too quickly and are left of their observed track 0–5 days prior to an event on average, which has implications for forecasted temperatures and the type of precipitation observed across the region. Our results can inform future operational forecasts of cold-season precipitation events by providing a storm-focused perspective on forecast errors during these impactful events. 

The dataset consists of vertical profiles of horizontal winds derived from radar Plan Position Indicator (PPI) scans conducted at a 18° elevation angle. The wind profiles were obtained using the Vertical Azimuth Display (VAD) technique, which involves measuring the radial velocity of targets at various azimuth angles and applying a least square fitting procedure to extract the horizontal wind speed and direction as a function of altitude. This method assumes that the wind field is horizontally homogeneous over the radar sampling volume. For more details on the VAD technique, refer to Browning and Wexler (1968). The data within the tar.gz files are in readable ASCII format. 

The dataset consists of vertical profiles of horizontal winds derived from radar Plan Position Indicator (PPI) scans conducted at a 18° elevation angle. The wind profiles were obtained using the Vertical Azimuth Display (VAD) technique, which involves measuring the radial velocity of targets at various azimuth angles and applying a least square fitting procedure to extract the horizontal wind speed and direction as a function of altitude. This method assumes that the wind field is horizontally homogeneous over the radar sampling volume. For more details on the VAD technique, refer to Browning and Wexler (1968). The data format is readable ASCII. 

The dataset consists of vertical profiles of horizontal winds derived from radar Plan Position Indicator (PPI) scans conducted at a 18° elevation angle. The wind profiles were obtained using the Vertical Azimuth Display (VAD) technique, which involves measuring the radial velocity of targets at various azimuth angles and applying a least square fitting procedure to extract the horizontal wind speed and direction as a function of altitude. This method assumes that the wind field is horizontally homogeneous over the radar sampling volume. For more details on the VAD technique, refer to Browning and Wexler (1968). The data are readable ASCII. 

Doppler spectra are derived from vertical radar scans (89° elevation) by analyzing the in-phase (I) and quadrature (Q) components of the returned signal. The I/Q time-series data are divided into range gates, and a Fast Fourier Transform (FFT) is applied to convert the data from the time domain to the frequency domain. This reveals Doppler frequency shifts caused by moving scatterers, producing spectra that show the distribution of power across velocities. We corrected the data for the influence of the horizontal wind since the scans were not perfectly vertical and removed returns from ground-clutter. The data format is netCDF. 

Doppler spectra are derived from vertical radar scans (89° elevation) by analyzing the in-phase (I) and quadrature (Q) components of the returned signal. The I/Q time-series data are divided into range gates, and a Fast Fourier Transform (FFT) is applied to convert the data from the time domain to the frequency domain. This reveals Doppler frequency shifts caused by moving scatterers, producing spectra that show the distribution of power across velocities. We corrected the data for the influence of the horizontal wind since the scans were not perfectly vertical and removed returns from ground-clutter.