Browse
Abstract
Localization is the key component to the successful application of ensemble data assimilation (DA) to high-dimensional problems in the geosciences. We study the impact of sampling error and its amelioration through localization using both analytical development and numerical experiments. Specifically, we show how sampling error in covariance estimates accumulates and spreads throughout the entire domain during the computation of the Kalman gain. This results in a bias, which is the dominant issue in unlocalized ensemble DA and, surprisingly, we find that it depends directly on the number of independent observations, but only indirectly on the state dimension. Our derivations and experiments further make it clear that an important aspect of localization is a significant reduction of bias in the Kalman gain, which in turn leads to an increased accuracy of ensemble DA. We illustrate our findings on a variety of simplified linear and nonlinear test problems, including a cycling ensemble Kalman filter applied to the Lorenz-96 model.
Abstract
Localization is the key component to the successful application of ensemble data assimilation (DA) to high-dimensional problems in the geosciences. We study the impact of sampling error and its amelioration through localization using both analytical development and numerical experiments. Specifically, we show how sampling error in covariance estimates accumulates and spreads throughout the entire domain during the computation of the Kalman gain. This results in a bias, which is the dominant issue in unlocalized ensemble DA and, surprisingly, we find that it depends directly on the number of independent observations, but only indirectly on the state dimension. Our derivations and experiments further make it clear that an important aspect of localization is a significant reduction of bias in the Kalman gain, which in turn leads to an increased accuracy of ensemble DA. We illustrate our findings on a variety of simplified linear and nonlinear test problems, including a cycling ensemble Kalman filter applied to the Lorenz-96 model.
Abstract
Numerous questions remain regarding the influence of environmental inhomogeneities on supercell evolution. Motivated by this topic, this study associates cell-merger occurrence with supercell evolution and tornado production during the prolific 27–28 April 2011 outbreak in the U.S. Southeast. This event included 29 discrete supercells that produced 102 tornadoes and featured 300 cell mergers. Cell-merger frequency increased for supercells that initiated farthest east, possibly owing to changes in overall convective coverage over time. There is some signal for stronger mesocyclones to be associated with more mergers in the primary supercell’s forward flank. There is also a slight tendency for supercells that encounter more cell mergers to produce tornadoes more quickly, especially for those that formed away from a significant zonal boundary. However, there is a slight tendency for supercells spawning the longest-lived tornadoes (especially those with durations over 60 min) to be associated with fewer cell mergers during the 15-min window preceding tornadogenesis. Of particular importance, a significant inverse relationship exists between premerger mesocyclone strength and the subsequent change in mesocyclone strength during the merger (i.e., weaker mesocyclones tended to strengthen as a result of the merger, and vice versa). These findings highlight the influence that cell mergers can have on supercell evolution and tornado production—even within an incredibly volatile environment—and motivate future work exploring the physical processes involved and ways to translate these findings into experimental techniques or guidance for operational forecasters.
Significance Statement
The prolific 27–28 April 2011 supercell tornado outbreak in the U.S. Southeast featured 29 supercells that produced 102 tornadoes. This study analyzes mergers between these tornadic supercells and 300 weaker cells to determine if the mergers corresponded with important supercell characteristics. This appeared to be the case during this event; cell-merger events tended to be associated with tornadic periods of the supercells’ life cycles and influenced low- and midlevel mesocyclone strength, supercell evolutionary time scales, and subsequent tornado duration. These results are important for (i) better understanding of different supercell evolutionary paths in similar background environments and (ii) motivating future work in investigating experimental products related to these findings.
Abstract
Numerous questions remain regarding the influence of environmental inhomogeneities on supercell evolution. Motivated by this topic, this study associates cell-merger occurrence with supercell evolution and tornado production during the prolific 27–28 April 2011 outbreak in the U.S. Southeast. This event included 29 discrete supercells that produced 102 tornadoes and featured 300 cell mergers. Cell-merger frequency increased for supercells that initiated farthest east, possibly owing to changes in overall convective coverage over time. There is some signal for stronger mesocyclones to be associated with more mergers in the primary supercell’s forward flank. There is also a slight tendency for supercells that encounter more cell mergers to produce tornadoes more quickly, especially for those that formed away from a significant zonal boundary. However, there is a slight tendency for supercells spawning the longest-lived tornadoes (especially those with durations over 60 min) to be associated with fewer cell mergers during the 15-min window preceding tornadogenesis. Of particular importance, a significant inverse relationship exists between premerger mesocyclone strength and the subsequent change in mesocyclone strength during the merger (i.e., weaker mesocyclones tended to strengthen as a result of the merger, and vice versa). These findings highlight the influence that cell mergers can have on supercell evolution and tornado production—even within an incredibly volatile environment—and motivate future work exploring the physical processes involved and ways to translate these findings into experimental techniques or guidance for operational forecasters.
Significance Statement
The prolific 27–28 April 2011 supercell tornado outbreak in the U.S. Southeast featured 29 supercells that produced 102 tornadoes. This study analyzes mergers between these tornadic supercells and 300 weaker cells to determine if the mergers corresponded with important supercell characteristics. This appeared to be the case during this event; cell-merger events tended to be associated with tornadic periods of the supercells’ life cycles and influenced low- and midlevel mesocyclone strength, supercell evolutionary time scales, and subsequent tornado duration. These results are important for (i) better understanding of different supercell evolutionary paths in similar background environments and (ii) motivating future work in investigating experimental products related to these findings.
Abstract
Unsteadiness and horizontal heterogeneities frequently characterize atmospheric motions, especially within convective storms, which are frequently studied using large-eddy simulations (LES). The models of near-surface turbulence employed by atmospheric LES, however, predominantly assume statistically steady and horizontally homogeneous conditions (known as the equilibrium approach). The primary objective of this work is to investigate the potential consequences of such unrealistic assumptions in simulations of tornadoes. Cloud Model 1 (CM1) LES runs are performed using three approaches to model near-surface turbulence: the “semi-slip” boundary condition (which is the most commonly used equilibrium approach), a recently proposed nonequilibrium approach that accounts for some of the effects of turbulence memory, and a nonequilibrium approach based on thin boundary layer equations (TBLE) originally proposed by the engineering community for smooth-wall boundary layer applications. To be adopted for atmospheric applications, the TBLE approach is modified to account for the surface roughness. The implementation of TBLE into CM1 is evaluated using LES results of an idealized, neutral atmospheric boundary layer. LES runs are then performed for an idealized tornado characterized by rapid evolution, strongly curved air parcel trajectories, and substantial horizontal heterogeneities. The semi-slip boundary condition, by design, always yields a surface shear stress opposite the horizontal wind at the lowest LES grid level. The nonequilibrium approaches of modeling near-surface turbulence allow for a range of surface-shear-stress directions and enhance the resolved turbulence and wind gusts. The TBLE approach even occasionally permits kinetic energy backscatter from unresolved to resolved scales.
Significance Statement
The traditional approach of modeling the near-surface turbulence is not suitable for a tornado characterized by rapid evolution, strongly curved air parcel trajectories, and substantial horizontal heterogeneities. To understand the influence of statistically unsteady and horizontally heterogeneous near-surface conditions on tornadoes, this work adopts a fairly sophisticated approach from the engineering community and implements it into a widely used atmospheric model with necessary modifications. Compared to the traditional approach, the newly implemented approach produces more turbulent near-surface winds, more flexible surface-drag directions, and stronger wind gusts. These findings suggest a simulated tornado is very sensitive to the modeling approach of near-surface turbulence.
Abstract
Unsteadiness and horizontal heterogeneities frequently characterize atmospheric motions, especially within convective storms, which are frequently studied using large-eddy simulations (LES). The models of near-surface turbulence employed by atmospheric LES, however, predominantly assume statistically steady and horizontally homogeneous conditions (known as the equilibrium approach). The primary objective of this work is to investigate the potential consequences of such unrealistic assumptions in simulations of tornadoes. Cloud Model 1 (CM1) LES runs are performed using three approaches to model near-surface turbulence: the “semi-slip” boundary condition (which is the most commonly used equilibrium approach), a recently proposed nonequilibrium approach that accounts for some of the effects of turbulence memory, and a nonequilibrium approach based on thin boundary layer equations (TBLE) originally proposed by the engineering community for smooth-wall boundary layer applications. To be adopted for atmospheric applications, the TBLE approach is modified to account for the surface roughness. The implementation of TBLE into CM1 is evaluated using LES results of an idealized, neutral atmospheric boundary layer. LES runs are then performed for an idealized tornado characterized by rapid evolution, strongly curved air parcel trajectories, and substantial horizontal heterogeneities. The semi-slip boundary condition, by design, always yields a surface shear stress opposite the horizontal wind at the lowest LES grid level. The nonequilibrium approaches of modeling near-surface turbulence allow for a range of surface-shear-stress directions and enhance the resolved turbulence and wind gusts. The TBLE approach even occasionally permits kinetic energy backscatter from unresolved to resolved scales.
Significance Statement
The traditional approach of modeling the near-surface turbulence is not suitable for a tornado characterized by rapid evolution, strongly curved air parcel trajectories, and substantial horizontal heterogeneities. To understand the influence of statistically unsteady and horizontally heterogeneous near-surface conditions on tornadoes, this work adopts a fairly sophisticated approach from the engineering community and implements it into a widely used atmospheric model with necessary modifications. Compared to the traditional approach, the newly implemented approach produces more turbulent near-surface winds, more flexible surface-drag directions, and stronger wind gusts. These findings suggest a simulated tornado is very sensitive to the modeling approach of near-surface turbulence.
Abstract
Convectively generated cold pools play a crucial role in the convection initiation and development, but observations of their vertical structure are insufficient. In this study, quantitative vertical evolutions of cold pools during a high-impact heavy rainfall event near the south coast of China were examined using observations from a 356-m-high Shenzhen Met-Tower, and their potential impacts on heavy rainfall were further discussed through high-resolution surface station network and radar observations. On 11 April 2019, heavy precipitation occurred near metropolitan Shenzhen, lasting for 50 min at its southern downtown and resulting in 11 deaths. During this event, a shallow cold pool was first observed by the tower and yielded a long-lasting cooling of 2.6 K. Approximately 1 h later, another deeper cold pool accompanied by a squall line was added from the west. This addition resulted in a more abrupt and intense surface temperature deficit (5.1 K) and stronger gusty winds (23 m s−1). When the two cold pools collided near Shenzhen, the low-level winds converged at their intersection, dynamically enhancing the heavy-rain-producing squall line. Moreover, the collision of the two cold pools reduced the temperature gradient at the northern edge of the merged cold pool, which could inhibit development of the squall line. The area south of the squall line became a relatively favorable environment for convection initiation, given the warm and moist oceanic environment. Consequently, the squall line turned northeast–southwest, forming a training line mode that was nearly parallel to the eastward movement. This training line mode prolonged the precipitation duration in the southern downtown area.
Abstract
Convectively generated cold pools play a crucial role in the convection initiation and development, but observations of their vertical structure are insufficient. In this study, quantitative vertical evolutions of cold pools during a high-impact heavy rainfall event near the south coast of China were examined using observations from a 356-m-high Shenzhen Met-Tower, and their potential impacts on heavy rainfall were further discussed through high-resolution surface station network and radar observations. On 11 April 2019, heavy precipitation occurred near metropolitan Shenzhen, lasting for 50 min at its southern downtown and resulting in 11 deaths. During this event, a shallow cold pool was first observed by the tower and yielded a long-lasting cooling of 2.6 K. Approximately 1 h later, another deeper cold pool accompanied by a squall line was added from the west. This addition resulted in a more abrupt and intense surface temperature deficit (5.1 K) and stronger gusty winds (23 m s−1). When the two cold pools collided near Shenzhen, the low-level winds converged at their intersection, dynamically enhancing the heavy-rain-producing squall line. Moreover, the collision of the two cold pools reduced the temperature gradient at the northern edge of the merged cold pool, which could inhibit development of the squall line. The area south of the squall line became a relatively favorable environment for convection initiation, given the warm and moist oceanic environment. Consequently, the squall line turned northeast–southwest, forming a training line mode that was nearly parallel to the eastward movement. This training line mode prolonged the precipitation duration in the southern downtown area.
Abstract
This article describes proposed revised methods for the statistical postprocessing of precipitation amount intended for the NOAA’s National Blend of Models using the Global Ensemble Forecast System version 12 data (GEFSv12). The procedure updates the previously established procedure of quantile mapping, weighting of sorted members, and dressing of the ensemble. The revised method leverages the long reforecast training dataset that has become available to improve quantile mapping of GEFSv12 data by eliminating the use of supplemental locations, that is, training data from other grid points. It establishes improved definitions of cumulative distributions through a spline-fitting approach. It provides updated algorithms for the weighting of sorted members based on closest-member histogram statistics, and it establishes an objective method for the dressing of the quantile-mapped, weighted ensemble. Verification statistics and case studies are provided in the accompanying article (Part II).
Abstract
This article describes proposed revised methods for the statistical postprocessing of precipitation amount intended for the NOAA’s National Blend of Models using the Global Ensemble Forecast System version 12 data (GEFSv12). The procedure updates the previously established procedure of quantile mapping, weighting of sorted members, and dressing of the ensemble. The revised method leverages the long reforecast training dataset that has become available to improve quantile mapping of GEFSv12 data by eliminating the use of supplemental locations, that is, training data from other grid points. It establishes improved definitions of cumulative distributions through a spline-fitting approach. It provides updated algorithms for the weighting of sorted members based on closest-member histogram statistics, and it establishes an objective method for the dressing of the quantile-mapped, weighted ensemble. Verification statistics and case studies are provided in the accompanying article (Part II).
Abstract
This second part of the series presents results from verifying a precipitation forecast calibration method discussed in the first part, based on quantile mapping (QM), weighting of sorted members, and dressing of the ensemble. NOAA’s Global Ensemble Forecast System, version 12 (GEFSv12), reforecasts were used in this study. The method was validated with preoperational GEFSv12 forecasts from December 2017 to November 2019. The method is proposed as an enhancement for GEFSv12 precipitation postprocessing in NOAA’s National Blend of Models. The first part described adaptations to the methodology to leverage the ∼20-yr GEFSv12 reforecast data. As shown here in this part, when compared with probabilistic quantitative precipitation forecasts from the raw ensemble, the adapted method produced downscaled, high-resolution forecasts that were significantly more reliable and skillful than raw ensemble-derived probabilities, especially at shorter lead times (i.e., <5 days) and for forecasts of events from light precipitation to >10 mm (6 h)−1. Cool-season events in the western United States were especially improved when the QM algorithm was applied, providing a statistical downscaling with realistic smaller-scale detail related to terrain features. The method provided less value added for forecasts of longer lead times and for the heaviest precipitation.
Abstract
This second part of the series presents results from verifying a precipitation forecast calibration method discussed in the first part, based on quantile mapping (QM), weighting of sorted members, and dressing of the ensemble. NOAA’s Global Ensemble Forecast System, version 12 (GEFSv12), reforecasts were used in this study. The method was validated with preoperational GEFSv12 forecasts from December 2017 to November 2019. The method is proposed as an enhancement for GEFSv12 precipitation postprocessing in NOAA’s National Blend of Models. The first part described adaptations to the methodology to leverage the ∼20-yr GEFSv12 reforecast data. As shown here in this part, when compared with probabilistic quantitative precipitation forecasts from the raw ensemble, the adapted method produced downscaled, high-resolution forecasts that were significantly more reliable and skillful than raw ensemble-derived probabilities, especially at shorter lead times (i.e., <5 days) and for forecasts of events from light precipitation to >10 mm (6 h)−1. Cool-season events in the western United States were especially improved when the QM algorithm was applied, providing a statistical downscaling with realistic smaller-scale detail related to terrain features. The method provided less value added for forecasts of longer lead times and for the heaviest precipitation.
Abstract
In large-eddy simulations (LES), it is crucial to ensure that discretization errors do not contaminate the subgrid effect of the turbulence model in a wavelength range larger than the effective resolution. Recently, we showed that a seventh- or eighth-order accuracy is required for advection terms in planetary boundary layer simulations when using conventional gridpoint methods. However, a significant amount of communication between parallel computers is necessary to achieve high-order accuracy in gridpoint methods, and this can degrade computational efficiency. The discontinuous Galerkin method (DGM) is a promising approach for overcoming these limitations. Therefore, this study focuses on the numerical criteria of the DGM at LES from the viewpoint of numerical diffusion and dispersion. We extend our earlier study to the DGM framework and clarify the necessary order of the polynomial (p). We find that p = 4 is required based on the numerical criteria at the grid spacing of O(10) m with sufficiently scale-selective modal filters. The examination of temporal accuracy suggests that the fourth-order is sufficient when a fully explicit temporal scheme is used. In addition, we investigate the effect of hyperupwinding that is usually met when the Rusanov flux is employed in the low Mach number flows. It suggests that the choice of numerical flux has little effect on simulation results when the high-order DGM is used. Furthermore, we perform a series of LES in the planetary boundary layer and confirm that the indication obtained from the criteria holds for an actual LES.
Abstract
In large-eddy simulations (LES), it is crucial to ensure that discretization errors do not contaminate the subgrid effect of the turbulence model in a wavelength range larger than the effective resolution. Recently, we showed that a seventh- or eighth-order accuracy is required for advection terms in planetary boundary layer simulations when using conventional gridpoint methods. However, a significant amount of communication between parallel computers is necessary to achieve high-order accuracy in gridpoint methods, and this can degrade computational efficiency. The discontinuous Galerkin method (DGM) is a promising approach for overcoming these limitations. Therefore, this study focuses on the numerical criteria of the DGM at LES from the viewpoint of numerical diffusion and dispersion. We extend our earlier study to the DGM framework and clarify the necessary order of the polynomial (p). We find that p = 4 is required based on the numerical criteria at the grid spacing of O(10) m with sufficiently scale-selective modal filters. The examination of temporal accuracy suggests that the fourth-order is sufficient when a fully explicit temporal scheme is used. In addition, we investigate the effect of hyperupwinding that is usually met when the Rusanov flux is employed in the low Mach number flows. It suggests that the choice of numerical flux has little effect on simulation results when the high-order DGM is used. Furthermore, we perform a series of LES in the planetary boundary layer and confirm that the indication obtained from the criteria holds for an actual LES.
Abstract
Upper-ocean temperatures from 72 airborne expendable bathythermographs (AXBTs) collected during U.S. Air Force Hurricane Hunter flights into Hurricane Dorian (2019) over a 72-h period are examined. Three transects collected behind the storm reveal increased cross-track sea surface temperature gradient magnitudes as Dorian intensified to a category-5 hurricane and slowed while approaching the Bahamas. The cold wake, evident in vertical and horizontal cross sections from in situ and satellite sensors, appears as an expected response to tropical cyclone passage. Atypical, however, is the 2°C surface cooling observed over 36 h in a pair of transects ahead of hurricane force winds in Dorian, likely due to changes in the tropical cyclone’s translation speed and direction and/or proximity to the Gulf Stream and continental shelf. Collocated AXBT pairs document a dynamical regime shift from mixing to upwelling as Dorian slows and turns. Relationships between time-integrated wind stress and sea surface temperature indicate track-relative differences varying with storm translation speed and heading changes, paralleling the shift in cooling dynamics.
Significance Statement
We studied in situ and satellite ocean temperature observations beneath Hurricane Dorian (2019) as the storm moved slowly, turned north, and weakened near Grand Bahama Island. We found a distinct change in the spatial distribution of cool upper-ocean temperatures beneath the storm, which indicated a shift in the primary cooling mechanism from ocean mixing to upwelling. This mechanism shift is important because hurricanes depend on warm ocean temperatures for energy, and upwelling roughly doubles the area of cooling beneath the storm. Our results highlight the effects of large heading changes on the upper-ocean response beneath tropical cyclones, especially in tandem with slow translation speeds.
Abstract
Upper-ocean temperatures from 72 airborne expendable bathythermographs (AXBTs) collected during U.S. Air Force Hurricane Hunter flights into Hurricane Dorian (2019) over a 72-h period are examined. Three transects collected behind the storm reveal increased cross-track sea surface temperature gradient magnitudes as Dorian intensified to a category-5 hurricane and slowed while approaching the Bahamas. The cold wake, evident in vertical and horizontal cross sections from in situ and satellite sensors, appears as an expected response to tropical cyclone passage. Atypical, however, is the 2°C surface cooling observed over 36 h in a pair of transects ahead of hurricane force winds in Dorian, likely due to changes in the tropical cyclone’s translation speed and direction and/or proximity to the Gulf Stream and continental shelf. Collocated AXBT pairs document a dynamical regime shift from mixing to upwelling as Dorian slows and turns. Relationships between time-integrated wind stress and sea surface temperature indicate track-relative differences varying with storm translation speed and heading changes, paralleling the shift in cooling dynamics.
Significance Statement
We studied in situ and satellite ocean temperature observations beneath Hurricane Dorian (2019) as the storm moved slowly, turned north, and weakened near Grand Bahama Island. We found a distinct change in the spatial distribution of cool upper-ocean temperatures beneath the storm, which indicated a shift in the primary cooling mechanism from ocean mixing to upwelling. This mechanism shift is important because hurricanes depend on warm ocean temperatures for energy, and upwelling roughly doubles the area of cooling beneath the storm. Our results highlight the effects of large heading changes on the upper-ocean response beneath tropical cyclones, especially in tandem with slow translation speeds.
Abstract
Many studies have aimed to identify novel storm characteristics that are indicative of current or future severe weather potential using a combination of ground-based radar observations and severe reports. However, this is often done on a small scale using limited case studies on the order of tens to hundreds of storms due to how time-intensive this process is. Herein, we introduce the GridRad-Severe dataset, a database including ∼100 severe weather days per year and upwards of 1.3 million objectively tracked storms from 2010-2019. Composite radar volumes spanning objectively determined, report-centered domains are created for each selected day using the GridRad compositing technique, with dates objectively determined using report thresholds defined to capture the highest-end severe weather days from each year, evenly distributed across all severe report types (tornadoes, severe hail, and severe wind). Spatiotemporal domain bounds for each event are objectively determined to encompass both the majority of reports as well as the time of convection initiation. Severe weather reports are matched to storms that are objectively tracked using the radar data, so the evolution of the storm cells and their severe weather production can be evaluated. Herein, we apply storm mode (single cell, multicell, or mesoscale convective system) and right-moving supercell classification techniques to the dataset, and revisit various questions about severe storms and their bulk characteristics posed and evaluated in past work. Additional applications of this dataset are reviewed for possible future studies.
Abstract
Many studies have aimed to identify novel storm characteristics that are indicative of current or future severe weather potential using a combination of ground-based radar observations and severe reports. However, this is often done on a small scale using limited case studies on the order of tens to hundreds of storms due to how time-intensive this process is. Herein, we introduce the GridRad-Severe dataset, a database including ∼100 severe weather days per year and upwards of 1.3 million objectively tracked storms from 2010-2019. Composite radar volumes spanning objectively determined, report-centered domains are created for each selected day using the GridRad compositing technique, with dates objectively determined using report thresholds defined to capture the highest-end severe weather days from each year, evenly distributed across all severe report types (tornadoes, severe hail, and severe wind). Spatiotemporal domain bounds for each event are objectively determined to encompass both the majority of reports as well as the time of convection initiation. Severe weather reports are matched to storms that are objectively tracked using the radar data, so the evolution of the storm cells and their severe weather production can be evaluated. Herein, we apply storm mode (single cell, multicell, or mesoscale convective system) and right-moving supercell classification techniques to the dataset, and revisit various questions about severe storms and their bulk characteristics posed and evaluated in past work. Additional applications of this dataset are reviewed for possible future studies.
Abstract
On 7 February 2020 a relatively deep cyclone (~980 hPa) with mid-level frontogenesis produced heavy snow (20-30 mm liquid equivalent) over western and central New York State. Despite these characteristics, the precipitation was not organized into a narrow band of intensive snowfall. This event occurred during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. Using coordinated flight legs across New York State, a remote sensing aircraft (ER-2) sampled above the cloud while a P-3 aircraft collected in-cloud data. These data are used to validate several Weather Research and Forecasting (WRF) model simulations at 2-km and 0.67-km grid spacing using different initial and boundary conditions (RAP, GFS, and ERA5 analyses) and microphysics schemes (Thompson and P3). The differences between the WRF runs are used to explore sensitivity to initial conditions and microphysics schemes. All 18–24 h runs realistically produced a broad sloping region of frontogenesis at mid-levels typically; however, there were relatively large (20–30%) uncertainties in the magnitude of this forcing using different analyses and initialization times. The differences in surface precipitation distribution are small (< 10%) among the microphysics schemes, likely because there was little riming in the region of heaviest precipitation. Those runs with frontogenesis closest to the RAP analysis and a surface precipitation underprediction of 20–30% have too little ice aloft and at low-levels, suggesting deficiencies in ice generation and snow growth aloft in those runs. The 0.67-km grid produced more realistic convective cells aloft, but only 5–10% more precipitation than the 2-km grid.
Abstract
On 7 February 2020 a relatively deep cyclone (~980 hPa) with mid-level frontogenesis produced heavy snow (20-30 mm liquid equivalent) over western and central New York State. Despite these characteristics, the precipitation was not organized into a narrow band of intensive snowfall. This event occurred during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. Using coordinated flight legs across New York State, a remote sensing aircraft (ER-2) sampled above the cloud while a P-3 aircraft collected in-cloud data. These data are used to validate several Weather Research and Forecasting (WRF) model simulations at 2-km and 0.67-km grid spacing using different initial and boundary conditions (RAP, GFS, and ERA5 analyses) and microphysics schemes (Thompson and P3). The differences between the WRF runs are used to explore sensitivity to initial conditions and microphysics schemes. All 18–24 h runs realistically produced a broad sloping region of frontogenesis at mid-levels typically; however, there were relatively large (20–30%) uncertainties in the magnitude of this forcing using different analyses and initialization times. The differences in surface precipitation distribution are small (< 10%) among the microphysics schemes, likely because there was little riming in the region of heaviest precipitation. Those runs with frontogenesis closest to the RAP analysis and a surface precipitation underprediction of 20–30% have too little ice aloft and at low-levels, suggesting deficiencies in ice generation and snow growth aloft in those runs. The 0.67-km grid produced more realistic convective cells aloft, but only 5–10% more precipitation than the 2-km grid.
