Browse
Abstract
Methods to improve the representation of hail in the Thompson–Eidhammer microphysics scheme are explored. A new two-moment and predicted density graupel category is implemented into the Thompson–Eidhammer scheme. Additionally, the one-moment graupel category’s intercept parameter is modified, based on hail observations, to shift the properties of the graupel category to become more hail-like since the category is designed to represent both graupel and hail. Finally, methods to diagnose maximum expected hail size at the surface and aloft are implemented. The original Thompson–Eidhammer version, the newly implemented two-moment and predicted density graupel version, and the modified (to be more hail-like) one-moment version are evaluated using a case that occurred during the Plains Elevated Convection at Night (PECAN) field campaign, during which hail-producing storms merged into a strong mesoscale convective system. The three versions of the scheme are evaluated for their ability to predict hail sizes compared to observed hail sizes from storm reports and estimated from radar, their ability to predict radar reflectivity signatures at various altitudes, and their ability to predict cold-pool features like temperature and wind speed. One key benefit of using the two-moment and predicted density graupel category is that the simulated reflectivity values in the upper levels of discrete storms are clearly improved. This improvement coincides with a significant reduction in the areal extent of graupel aloft, also seen when using the updated one-moment scheme. The two-moment and predicted density graupel scheme is also better able to predict a wide variety of hail sizes at the surface, including large (>2-in. diameter) hail that was observed during this case.
Abstract
Methods to improve the representation of hail in the Thompson–Eidhammer microphysics scheme are explored. A new two-moment and predicted density graupel category is implemented into the Thompson–Eidhammer scheme. Additionally, the one-moment graupel category’s intercept parameter is modified, based on hail observations, to shift the properties of the graupel category to become more hail-like since the category is designed to represent both graupel and hail. Finally, methods to diagnose maximum expected hail size at the surface and aloft are implemented. The original Thompson–Eidhammer version, the newly implemented two-moment and predicted density graupel version, and the modified (to be more hail-like) one-moment version are evaluated using a case that occurred during the Plains Elevated Convection at Night (PECAN) field campaign, during which hail-producing storms merged into a strong mesoscale convective system. The three versions of the scheme are evaluated for their ability to predict hail sizes compared to observed hail sizes from storm reports and estimated from radar, their ability to predict radar reflectivity signatures at various altitudes, and their ability to predict cold-pool features like temperature and wind speed. One key benefit of using the two-moment and predicted density graupel category is that the simulated reflectivity values in the upper levels of discrete storms are clearly improved. This improvement coincides with a significant reduction in the areal extent of graupel aloft, also seen when using the updated one-moment scheme. The two-moment and predicted density graupel scheme is also better able to predict a wide variety of hail sizes at the surface, including large (>2-in. diameter) hail that was observed during this case.
Abstract
The observation error covariance partially controls the weight assigned to an observation during data assimilation (DA). True observation error statistics are rarely known and likely vary depending on the meteorological state. However, operational DA systems often apply static methods that assign constant observation errors across a dataset. Previous studies show that these methods can degrade forecast quality when assimilating ground-based remote sensing datasets. To improve the impact of assimilating such observations, we propose two novel methods for estimating the observation error variance for high-frequency thermodynamic profilers. These methods include an adaptive observation error inflation technique and the Desroziers method that directly estimates the observation error variances using paired innovation and analysis residuals. Each method is compared for a nocturnal mesoscale convective system (MCS) observed during the Plains Elevated Convection at Night (PECAN) experiment. In general, we find that these novel methods better represent the large variability of observation error statistics for high-frequency profiles collected by Atmospheric Emitted Radiance Interferometers (AERIs). When assimilating AERIs by statically inflating retrieval error variances, the trailing stratiform region of the MCS is degraded compared to a baseline simulation with no AERI data assimilated. Assimilating the AERIs using the adaptive inflation or Desroziers method results in better maintenance of the trailing stratiform region and additional suppression of spurious convection. The forecast improvements from these novel methods are primarily linked to increased error variances for some moisture retrievals. These results indicate the importance of accurately estimating observation error statistics for convective-scale DA and suggest that accounting for flow dependence can improve the impacts from assimilating remote sensing datasets.
Abstract
The observation error covariance partially controls the weight assigned to an observation during data assimilation (DA). True observation error statistics are rarely known and likely vary depending on the meteorological state. However, operational DA systems often apply static methods that assign constant observation errors across a dataset. Previous studies show that these methods can degrade forecast quality when assimilating ground-based remote sensing datasets. To improve the impact of assimilating such observations, we propose two novel methods for estimating the observation error variance for high-frequency thermodynamic profilers. These methods include an adaptive observation error inflation technique and the Desroziers method that directly estimates the observation error variances using paired innovation and analysis residuals. Each method is compared for a nocturnal mesoscale convective system (MCS) observed during the Plains Elevated Convection at Night (PECAN) experiment. In general, we find that these novel methods better represent the large variability of observation error statistics for high-frequency profiles collected by Atmospheric Emitted Radiance Interferometers (AERIs). When assimilating AERIs by statically inflating retrieval error variances, the trailing stratiform region of the MCS is degraded compared to a baseline simulation with no AERI data assimilated. Assimilating the AERIs using the adaptive inflation or Desroziers method results in better maintenance of the trailing stratiform region and additional suppression of spurious convection. The forecast improvements from these novel methods are primarily linked to increased error variances for some moisture retrievals. These results indicate the importance of accurately estimating observation error statistics for convective-scale DA and suggest that accounting for flow dependence can improve the impacts from assimilating remote sensing datasets.
Abstract
There is a growing interest in the use of ground-based remote sensors for numerical weather prediction, which is sparked by their potential to address the currently existing observation gap within the planetary boundary layer. Nevertheless, open questions still exist regarding the relative importance of and synergy among various instruments. To shed light on these important questions, the present study examines the forecast benefits associated with several different ground-based profiling networks using 10 diverse cases from the Plains Elevated Convection at Night (PECAN) field campaign. Aggregated verification statistics reveal that a combination of in situ and remote sensing profilers leads to the largest increase in forecast skill, in terms of both the parent mesoscale convective system and the explicitly resolved bore. These statistics also indicate that it is often advantageous to collocate thermodynamic and kinematic remote sensors. By contrast, the impacts of networks consisting of single profilers appear to be flow-dependent, with thermodynamic (kinematic) remote sensors being most useful in cases with relatively low (high) convective predictability. Deficiencies in the data assimilation method as well as inherent complexities in the governing moisture dynamics are two factors that can further limit the forecast value extracted from such networks.
Abstract
There is a growing interest in the use of ground-based remote sensors for numerical weather prediction, which is sparked by their potential to address the currently existing observation gap within the planetary boundary layer. Nevertheless, open questions still exist regarding the relative importance of and synergy among various instruments. To shed light on these important questions, the present study examines the forecast benefits associated with several different ground-based profiling networks using 10 diverse cases from the Plains Elevated Convection at Night (PECAN) field campaign. Aggregated verification statistics reveal that a combination of in situ and remote sensing profilers leads to the largest increase in forecast skill, in terms of both the parent mesoscale convective system and the explicitly resolved bore. These statistics also indicate that it is often advantageous to collocate thermodynamic and kinematic remote sensors. By contrast, the impacts of networks consisting of single profilers appear to be flow-dependent, with thermodynamic (kinematic) remote sensors being most useful in cases with relatively low (high) convective predictability. Deficiencies in the data assimilation method as well as inherent complexities in the governing moisture dynamics are two factors that can further limit the forecast value extracted from such networks.
Abstract
Data from scanning radars, radiosondes, and vertical profilers deployed during three field campaigns are analyzed to study interactions between cloud-scale updrafts associated with initiating deep moist convection and the surrounding environment. Three cases are analyzed in which the radar networks permitted dual-Doppler wind retrievals in clear air preceding and during the onset of surface precipitation. These observations capture the evolution of (i) the mesoscale and boundary layer flow, and (ii) low-level updrafts associated with deep moist convection initiation (CI) events yielding sustained or short-lived precipitating storms. The elimination of convective inhibition did not distinguish between sustained and unsustained CI events, though the vertical distribution of convective available potential energy may have played a role. The clearest signal differentiating the initiation of sustained versus unsustained precipitating deep convection was the depth of the low-level horizontal wind convergence associated with the mesoscale flow feature triggering CI, a sharp surface wind shift boundary, or orographic upslope flow. The depth of the boundary layer relative to the height of the LFC failed to be a consistent indicator of CI potential. Widths of the earliest detectable low-level updrafts associated with sustained precipitating deep convection were ~3–5 km, larger than updrafts associated with surrounding boundary layer turbulence (~1–3 km wide). It is hypothesized that updrafts of this larger size are important for initiating cells to survive the destructive effects of buoyancy dilution via entrainment.
Abstract
Data from scanning radars, radiosondes, and vertical profilers deployed during three field campaigns are analyzed to study interactions between cloud-scale updrafts associated with initiating deep moist convection and the surrounding environment. Three cases are analyzed in which the radar networks permitted dual-Doppler wind retrievals in clear air preceding and during the onset of surface precipitation. These observations capture the evolution of (i) the mesoscale and boundary layer flow, and (ii) low-level updrafts associated with deep moist convection initiation (CI) events yielding sustained or short-lived precipitating storms. The elimination of convective inhibition did not distinguish between sustained and unsustained CI events, though the vertical distribution of convective available potential energy may have played a role. The clearest signal differentiating the initiation of sustained versus unsustained precipitating deep convection was the depth of the low-level horizontal wind convergence associated with the mesoscale flow feature triggering CI, a sharp surface wind shift boundary, or orographic upslope flow. The depth of the boundary layer relative to the height of the LFC failed to be a consistent indicator of CI potential. Widths of the earliest detectable low-level updrafts associated with sustained precipitating deep convection were ~3–5 km, larger than updrafts associated with surrounding boundary layer turbulence (~1–3 km wide). It is hypothesized that updrafts of this larger size are important for initiating cells to survive the destructive effects of buoyancy dilution via entrainment.
Abstract
This observational study documents the consequences of a collision between two converging shallow atmospheric boundaries over the central Great Plains on the evening of 7 June 2015. This study uses data from a profiling airborne Raman lidar [the compact Raman lidar (CRL)] and other airborne and ground-based data collected during the Plains Elevated Convection at Night (PECAN) field campaign to investigate the collision between a weak cold front and the outflow from an MCS. The collision between these boundaries led to the lofting of high-CAPE, low-CIN air, resulting in deep convection, as well as an undular bore. Both boundaries behaved as density currents prior to collision. Because the MCS outflow boundary was denser and less deep than the cold-frontal air mass, the bore propagated over the latter. This bore was tracked by the CRL for about 3 h as it traveled north over the shallow cold-frontal surface and evolved into a soliton. This case study is unique by using the high temporal and spatial resolution of airborne Raman lidar measurements to describe the thermodynamic structure of interacting boundaries and a resulting bore.
Abstract
This observational study documents the consequences of a collision between two converging shallow atmospheric boundaries over the central Great Plains on the evening of 7 June 2015. This study uses data from a profiling airborne Raman lidar [the compact Raman lidar (CRL)] and other airborne and ground-based data collected during the Plains Elevated Convection at Night (PECAN) field campaign to investigate the collision between a weak cold front and the outflow from an MCS. The collision between these boundaries led to the lofting of high-CAPE, low-CIN air, resulting in deep convection, as well as an undular bore. Both boundaries behaved as density currents prior to collision. Because the MCS outflow boundary was denser and less deep than the cold-frontal air mass, the bore propagated over the latter. This bore was tracked by the CRL for about 3 h as it traveled north over the shallow cold-frontal surface and evolved into a soliton. This case study is unique by using the high temporal and spatial resolution of airborne Raman lidar measurements to describe the thermodynamic structure of interacting boundaries and a resulting bore.
Abstract
The 2015 Plains Elevated Convection at Night (PECAN) field campaign provided a wealth of intensive observations for improving understanding of interplay between the Great Plains low-level jet (LLJ), mesoscale convective systems (MCSs), and other phenomena in the nocturnal boundary layer. This case study utilizes PECAN ground-based Doppler and water vapor lidar and airborne water vapor lidar observations for a detailed examination of water vapor transport in the Great Plains. The chosen case, 11 July 2015, featured a strong LLJ that helped sustain an MCS overnight. The lidars resolved boundary layer moisture being transported northward, leading to a large increase in water vapor in the lowest several hundred meters above the surface in northern Kansas. A branch of nocturnal convection initiated coincident with the observed maximum water vapor flux. Radiosondes confirmed an increase in convective potential within the LLJ layer. Moist static energy (MSE) growth was generated by increasing moisture in spite of a temperature decrease in the LLJ layer. This unique dataset is also used to evaluate the Rapid Refresh (RAP) analysis model performance, comparing model output against the continuous lidar profiles of water vapor and wind. While the RAP analysis captured the large-scale trends, errors in water vapor mixing ratio were found ranging from 0 to 2 g kg−1 at the ground-based lidar sites. Comparison with the airborne lidar throughout the PECAN domain yielded an RMSE of 1.14 g kg−1 in the planetary boundary layer. These errors mostly manifested as contiguous dry or wet regions spanning spatial scales on the order of ten to hundreds of kilometers.
Abstract
The 2015 Plains Elevated Convection at Night (PECAN) field campaign provided a wealth of intensive observations for improving understanding of interplay between the Great Plains low-level jet (LLJ), mesoscale convective systems (MCSs), and other phenomena in the nocturnal boundary layer. This case study utilizes PECAN ground-based Doppler and water vapor lidar and airborne water vapor lidar observations for a detailed examination of water vapor transport in the Great Plains. The chosen case, 11 July 2015, featured a strong LLJ that helped sustain an MCS overnight. The lidars resolved boundary layer moisture being transported northward, leading to a large increase in water vapor in the lowest several hundred meters above the surface in northern Kansas. A branch of nocturnal convection initiated coincident with the observed maximum water vapor flux. Radiosondes confirmed an increase in convective potential within the LLJ layer. Moist static energy (MSE) growth was generated by increasing moisture in spite of a temperature decrease in the LLJ layer. This unique dataset is also used to evaluate the Rapid Refresh (RAP) analysis model performance, comparing model output against the continuous lidar profiles of water vapor and wind. While the RAP analysis captured the large-scale trends, errors in water vapor mixing ratio were found ranging from 0 to 2 g kg−1 at the ground-based lidar sites. Comparison with the airborne lidar throughout the PECAN domain yielded an RMSE of 1.14 g kg−1 in the planetary boundary layer. These errors mostly manifested as contiguous dry or wet regions spanning spatial scales on the order of ten to hundreds of kilometers.
Abstract
In a mesoscale convective system (MCS), convection that redevelops over (i.e., back-builds), and/or repeatedly passes over (i.e., trains) a region for an extended period of time can contribute to extreme rainfall and flash flooding. Past studies have indicated that both mesoscale ascent and lifting of the inflow layer by a cold pool or bore are important when this back-building/training convection is displaced from the leading line [sometimes called rearward off-boundary development (ROD)]. However, Plains Elevated Convection At Night (PECAN) field campaign observations suggest that the stability of the nocturnal boundary layer is highly variable and some MCSs with ROD have only a weak surface cold pool. Numerical simulations presented in this study suggest that in an environment with strong boundary layer stability, ROD can be supported by mechanisms other than those mentioned above. Simulations were initialized using a sounding from ahead of a PECAN MCS with a strong stable layer and ROD, and the three-dimensional simulation produced an MCS similar to that observed despite the homogeneous initial conditions. Some of the findings presented herein challenge existing understanding of nocturnal MCSs, and especially how downdrafts interact with a stable boundary layer. Notably, downdrafts can reach the surface, and different regions of the MCS may have different propagation mechanisms and different relevant inflow layers. Unlike previous studies of ROD, parcel lifting may be supported by an intrusion (an elevated layer of downdraft air) modified by the three-dimensional vertical wind shear.
Abstract
In a mesoscale convective system (MCS), convection that redevelops over (i.e., back-builds), and/or repeatedly passes over (i.e., trains) a region for an extended period of time can contribute to extreme rainfall and flash flooding. Past studies have indicated that both mesoscale ascent and lifting of the inflow layer by a cold pool or bore are important when this back-building/training convection is displaced from the leading line [sometimes called rearward off-boundary development (ROD)]. However, Plains Elevated Convection At Night (PECAN) field campaign observations suggest that the stability of the nocturnal boundary layer is highly variable and some MCSs with ROD have only a weak surface cold pool. Numerical simulations presented in this study suggest that in an environment with strong boundary layer stability, ROD can be supported by mechanisms other than those mentioned above. Simulations were initialized using a sounding from ahead of a PECAN MCS with a strong stable layer and ROD, and the three-dimensional simulation produced an MCS similar to that observed despite the homogeneous initial conditions. Some of the findings presented herein challenge existing understanding of nocturnal MCSs, and especially how downdrafts interact with a stable boundary layer. Notably, downdrafts can reach the surface, and different regions of the MCS may have different propagation mechanisms and different relevant inflow layers. Unlike previous studies of ROD, parcel lifting may be supported by an intrusion (an elevated layer of downdraft air) modified by the three-dimensional vertical wind shear.
Abstract
This case study analyzes a nocturnal mesoscale convective system (MCS) that was observed on 25–26 June 2015 in northeastern Kansas during the Plains Elevated Convection At Night (PECAN) project. Over the course of the observational period, a broken line of elevated nocturnal convective cells initiated around 0230 UTC on the cool side of a stationary front and subsequently merged to form a quasi-linear MCS that later developed strong, surface-based outflow and a trailing stratiform region. This study combines radar observations with mobile and fixed mesonet and sounding data taken during PECAN to analyze the kinematics and thermodynamics of the MCS from 0300 to 0630 UTC. This study is unique in that 38 consecutive multi-Doppler wind analyses are examined over the 3.5 h observation period, facilitating a long-duration analysis of the kinematic evolution of the nocturnal MCS. Radar analyses reveal that the initial convective cells and linear MCS are elevated and sustained by an elevated residual layer formed via weak ascent over the stationary front. During upscale growth, individual convective cells develop storm-scale cold pools due to pockets of descending rear-to-front flow that are measured by mobile mesonets. By 0500 UTC, kinematic analysis and mesonet observations show that the MCS has a surface-based cold pool and that convective line updrafts are ingesting parcels from below the stable layer. In this environment, the elevated system has become surface based since the cold pool lifting is sufficient for surface-based parcels to overcome the CIN associated with the frontal stable layer.
Abstract
This case study analyzes a nocturnal mesoscale convective system (MCS) that was observed on 25–26 June 2015 in northeastern Kansas during the Plains Elevated Convection At Night (PECAN) project. Over the course of the observational period, a broken line of elevated nocturnal convective cells initiated around 0230 UTC on the cool side of a stationary front and subsequently merged to form a quasi-linear MCS that later developed strong, surface-based outflow and a trailing stratiform region. This study combines radar observations with mobile and fixed mesonet and sounding data taken during PECAN to analyze the kinematics and thermodynamics of the MCS from 0300 to 0630 UTC. This study is unique in that 38 consecutive multi-Doppler wind analyses are examined over the 3.5 h observation period, facilitating a long-duration analysis of the kinematic evolution of the nocturnal MCS. Radar analyses reveal that the initial convective cells and linear MCS are elevated and sustained by an elevated residual layer formed via weak ascent over the stationary front. During upscale growth, individual convective cells develop storm-scale cold pools due to pockets of descending rear-to-front flow that are measured by mobile mesonets. By 0500 UTC, kinematic analysis and mesonet observations show that the MCS has a surface-based cold pool and that convective line updrafts are ingesting parcels from below the stable layer. In this environment, the elevated system has become surface based since the cold pool lifting is sufficient for surface-based parcels to overcome the CIN associated with the frontal stable layer.
Abstract
The 25–26 June 2015 nocturnal mesoscale convective system (MCS) from the Plains Elevated Convection at Night (PECAN) field project produced severe winds within an environment that might customarily be associated with elevated convection. This work incorporates both a full-physics real-world simulation and an idealized single-sounding simulation to explore the MCS’s evolution. Initially, the simulated convective systems were elevated, being maintained by wavelike disturbances and lacking surface cold pools. As the systems matured, surface outflows began to appear, particularly where heavy precipitation was occurring, with air in the surface cold pools originating from up to 4–5 km AGL. Via this progression, the MCSs exhibited a degree of self-organization (i.e., structures that are dependent upon an MCS’s particular history). The cold pools eventually became 1.5–3.5 km deep, by which point passive tracers revealed that the convection was at least partly surface based. Soon after becoming surface based, both simulations produced severe surface winds, the strongest of which were associated with embedded low-level mesovortices and their attendant outflow surges and bowing segments. The origin of the simulated mesovortices was likely the downward tilting of system-generated horizontal vorticity (from baroclinity, but also possibly friction) within the simulated MCSs’ outflow, as has been argued in a number of previous studies. Taken altogether, it appears that severe nocturnal MCSs may often resemble their cold pool-driven, surface-based afternoon counterparts.
Abstract
The 25–26 June 2015 nocturnal mesoscale convective system (MCS) from the Plains Elevated Convection at Night (PECAN) field project produced severe winds within an environment that might customarily be associated with elevated convection. This work incorporates both a full-physics real-world simulation and an idealized single-sounding simulation to explore the MCS’s evolution. Initially, the simulated convective systems were elevated, being maintained by wavelike disturbances and lacking surface cold pools. As the systems matured, surface outflows began to appear, particularly where heavy precipitation was occurring, with air in the surface cold pools originating from up to 4–5 km AGL. Via this progression, the MCSs exhibited a degree of self-organization (i.e., structures that are dependent upon an MCS’s particular history). The cold pools eventually became 1.5–3.5 km deep, by which point passive tracers revealed that the convection was at least partly surface based. Soon after becoming surface based, both simulations produced severe surface winds, the strongest of which were associated with embedded low-level mesovortices and their attendant outflow surges and bowing segments. The origin of the simulated mesovortices was likely the downward tilting of system-generated horizontal vorticity (from baroclinity, but also possibly friction) within the simulated MCSs’ outflow, as has been argued in a number of previous studies. Taken altogether, it appears that severe nocturnal MCSs may often resemble their cold pool-driven, surface-based afternoon counterparts.
Abstract
The overarching goal of the Plains Elevated Convection At Night (PECAN) field campaign was to improve understanding of the processes contributing to the nocturnal precipitation maximum in the U.S. Great Plains. This study presents the precipitation pattern surrounding PECAN and addresses the origin, timing, duration, and potential causes contributing to that pattern. It is shown that the precipitation occurs most frequently at night, as expected. The maximum in the precipitation pattern occurred in the northeastern portion of the PECAN radar domain. The source of the rainfall was attributed to mountain-initiated precipitation, plains-initiated precipitation, precipitation advecting over the border of the radar domain, and episodes in which different initiation categories merged together. Through the combination of mountain-initiated, border, and merged episodes, 70% of the Great Plains precipitation was caused by episodes that formed outside of the PECAN domain and propagated into the region. The remaining 30% of the precipitation was attributed to plains-initiated storms. The mountain-initiated storms formed primarily in the afternoon and typically dissipated near the mountains. For those that survived, they propagated eastward, grew upscale, and contributed 27% of the precipitation in the plains. The plains-initiated precipitation fell mostly during the afternoon but also contributed to overnight rainfall and those locally triggered systems tended to be relatively smaller and shorter lived. For the top 10% rain-producing events, composite reanalysis fields showed that synoptic-scale features influenced the precipitation pattern and timing: an approaching trough established southwesterly moist flow throughout the region and a nocturnal low-level jet transported moisture to its terminus in the northeast corner of the PECAN domain.
Abstract
The overarching goal of the Plains Elevated Convection At Night (PECAN) field campaign was to improve understanding of the processes contributing to the nocturnal precipitation maximum in the U.S. Great Plains. This study presents the precipitation pattern surrounding PECAN and addresses the origin, timing, duration, and potential causes contributing to that pattern. It is shown that the precipitation occurs most frequently at night, as expected. The maximum in the precipitation pattern occurred in the northeastern portion of the PECAN radar domain. The source of the rainfall was attributed to mountain-initiated precipitation, plains-initiated precipitation, precipitation advecting over the border of the radar domain, and episodes in which different initiation categories merged together. Through the combination of mountain-initiated, border, and merged episodes, 70% of the Great Plains precipitation was caused by episodes that formed outside of the PECAN domain and propagated into the region. The remaining 30% of the precipitation was attributed to plains-initiated storms. The mountain-initiated storms formed primarily in the afternoon and typically dissipated near the mountains. For those that survived, they propagated eastward, grew upscale, and contributed 27% of the precipitation in the plains. The plains-initiated precipitation fell mostly during the afternoon but also contributed to overnight rainfall and those locally triggered systems tended to be relatively smaller and shorter lived. For the top 10% rain-producing events, composite reanalysis fields showed that synoptic-scale features influenced the precipitation pattern and timing: an approaching trough established southwesterly moist flow throughout the region and a nocturnal low-level jet transported moisture to its terminus in the northeast corner of the PECAN domain.
