Search Results
Abstract
Four case studies from the Plains Elevated Convection at Night (PECAN) field experiment are used to investigate the impacts of horizontal and vertical resolution, and vertical mixing parameterization, on predictions of bore structure and upscale impacts of bores on their mesoscale environment. The reduction of environmental convective inhibition (CIN) created by the bore is particularly emphasized. Simulations are run with horizontal grid spacings ranging from 250 to 1000 m, as well as 50 m for one case study, different vertical level configurations, and different closure models for the vertical turbulent mixing at 250-m horizontal resolution. The 11 July case study was evaluated in greatest detail because it was the best observed case and has been the focus of a previous study. For this case, it is found that 250-m grid spacing improves upon 1-km grid spacing, LES configuration provides further improvement, and enhanced low-level vertical resolution also provides further improvement in terms of qualitative agreement between simulated and observed bore structure. Reducing LES grid spacing further to 50 m provided very little additional advantage. Only the LES experiments properly resolved the upscale influence of reduced low-level CIN. Expanding on the 11 July case study, three other cases from PECAN with diverse observed bore structures were also evaluated. Similar to the 11 July case, enhancing the horizontal and vertical grid spacings, and using the LES closure model for vertical turbulent mixing, all contributed to improved simulations of both the bores themselves and the larger-scale modification of CIN to varying degrees on different cases.
Abstract
Four case studies from the Plains Elevated Convection at Night (PECAN) field experiment are used to investigate the impacts of horizontal and vertical resolution, and vertical mixing parameterization, on predictions of bore structure and upscale impacts of bores on their mesoscale environment. The reduction of environmental convective inhibition (CIN) created by the bore is particularly emphasized. Simulations are run with horizontal grid spacings ranging from 250 to 1000 m, as well as 50 m for one case study, different vertical level configurations, and different closure models for the vertical turbulent mixing at 250-m horizontal resolution. The 11 July case study was evaluated in greatest detail because it was the best observed case and has been the focus of a previous study. For this case, it is found that 250-m grid spacing improves upon 1-km grid spacing, LES configuration provides further improvement, and enhanced low-level vertical resolution also provides further improvement in terms of qualitative agreement between simulated and observed bore structure. Reducing LES grid spacing further to 50 m provided very little additional advantage. Only the LES experiments properly resolved the upscale influence of reduced low-level CIN. Expanding on the 11 July case study, three other cases from PECAN with diverse observed bore structures were also evaluated. Similar to the 11 July case, enhancing the horizontal and vertical grid spacings, and using the LES closure model for vertical turbulent mixing, all contributed to improved simulations of both the bores themselves and the larger-scale modification of CIN to varying degrees on different cases.
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
This paper presents a case study from an intensive observing period (IOP) during the Plains Elevated Convection at Night (PECAN) field experiment that was focused on a bore generated by nocturnal convection. Observations from PECAN IOP 25 on 11 July 2015 are used to evaluate the performance of high-resolution Weather Research and Forecasting Model forecasts, initialized using the Gridpoint Statistical Interpolation (GSI)-based ensemble Kalman filter. The focus is on understanding model errors and sensitivities in order to guide forecast improvements for bores associated with nocturnal convection. Model simulations of the bore amplitude are compared against eight retrieved vertical cross sections through the bore during the IOP. Sensitivities of forecasts to microphysics and planetary boundary layer (PBL) parameterizations are also investigated. Forecasts initialized before the bore pulls away from the convection show a more realistic bore than forecasts initialized later from analyses of the bore itself, in part due to the smoothing of the existing bore in the ensemble mean. Experiments show that the different microphysics schemes impact the quality of the simulations with unrealistically weak cold pools and bores with the Thompson and Morrison microphysics schemes, cold pools too strong with the WDM6 and more accurate with the WSM6 schemes. Most PBL schemes produced a realistic bore response to the cold pool, with the exception of the Mellor–Yamada–Nakanishi–Niino (MYNN) scheme, which creates too much turbulent mixing atop the bore. A new method of objectively estimating the depth of the near-surface stable layer corresponding to a simple two-layer model is also introduced, and the impacts of turbulent mixing on this estimate are discussed.
Abstract
This paper presents a case study from an intensive observing period (IOP) during the Plains Elevated Convection at Night (PECAN) field experiment that was focused on a bore generated by nocturnal convection. Observations from PECAN IOP 25 on 11 July 2015 are used to evaluate the performance of high-resolution Weather Research and Forecasting Model forecasts, initialized using the Gridpoint Statistical Interpolation (GSI)-based ensemble Kalman filter. The focus is on understanding model errors and sensitivities in order to guide forecast improvements for bores associated with nocturnal convection. Model simulations of the bore amplitude are compared against eight retrieved vertical cross sections through the bore during the IOP. Sensitivities of forecasts to microphysics and planetary boundary layer (PBL) parameterizations are also investigated. Forecasts initialized before the bore pulls away from the convection show a more realistic bore than forecasts initialized later from analyses of the bore itself, in part due to the smoothing of the existing bore in the ensemble mean. Experiments show that the different microphysics schemes impact the quality of the simulations with unrealistically weak cold pools and bores with the Thompson and Morrison microphysics schemes, cold pools too strong with the WDM6 and more accurate with the WSM6 schemes. Most PBL schemes produced a realistic bore response to the cold pool, with the exception of the Mellor–Yamada–Nakanishi–Niino (MYNN) scheme, which creates too much turbulent mixing atop the bore. A new method of objectively estimating the depth of the near-surface stable layer corresponding to a simple two-layer model is also introduced, and the impacts of turbulent mixing on this estimate are discussed.
Abstract
The initiation of new convection at night in the Great Plains contributes to a nocturnal maximum in precipitation and produces localized heavy rainfall and severe weather hazards in the region. Although previous work has evaluated numerical model forecasts and data assimilation (DA) impacts for convection initiation (CI), most previous studies focused only on convection that initiates during the afternoon and not explicitly on nocturnal thunderstorms. In this study, we investigate the impact of assimilating in situ and radar observations for a nocturnal CI event on 25 June 2013 using an ensemble-based DA and forecast system. Results in this study show that a successful CI forecast resulted only when assimilating conventional in situ observations on the inner, convection-allowing domain. Assimilating in situ observations strengthened preexisting convection in southwestern Kansas by enhancing buoyancy and locally strengthening low-level convergence. The enhanced convection produced a cold pool that, together with increased convergence along the northwestern low-level jet (LLJ) terminus near the region of CI, was an important mechanism for lifting parcels to their level of free convection. Gravity waves were also produced atop the cold pool that provided further elevated ascent. Assimilating radar observations further improved the forecast by suppressing spurious convection and reducing the number of ensemble members that produced CI along a spurious outflow boundary. The fact that the successful CI forecasts resulted only when the in situ observations were assimilated suggests that accurately capturing the preconvective environment and specific mesoscale features is especially important for nocturnal CI forecasts.
Abstract
The initiation of new convection at night in the Great Plains contributes to a nocturnal maximum in precipitation and produces localized heavy rainfall and severe weather hazards in the region. Although previous work has evaluated numerical model forecasts and data assimilation (DA) impacts for convection initiation (CI), most previous studies focused only on convection that initiates during the afternoon and not explicitly on nocturnal thunderstorms. In this study, we investigate the impact of assimilating in situ and radar observations for a nocturnal CI event on 25 June 2013 using an ensemble-based DA and forecast system. Results in this study show that a successful CI forecast resulted only when assimilating conventional in situ observations on the inner, convection-allowing domain. Assimilating in situ observations strengthened preexisting convection in southwestern Kansas by enhancing buoyancy and locally strengthening low-level convergence. The enhanced convection produced a cold pool that, together with increased convergence along the northwestern low-level jet (LLJ) terminus near the region of CI, was an important mechanism for lifting parcels to their level of free convection. Gravity waves were also produced atop the cold pool that provided further elevated ascent. Assimilating radar observations further improved the forecast by suppressing spurious convection and reducing the number of ensemble members that produced CI along a spurious outflow boundary. The fact that the successful CI forecasts resulted only when the in situ observations were assimilated suggests that accurately capturing the preconvective environment and specific mesoscale features is especially important for nocturnal CI forecasts.
Abstract
A novel object-based algorithm capable of identifying and tracking convective outflow boundaries in convection-allowing numerical models is presented in this study. The most distinct feature of the proposed algorithm is its ability to seamlessly analyze numerically simulated density currents and bores, both of which play an important role in the dynamics of nocturnal convective systems. The unified identification and classification of these morphologically different phenomena is achieved through a multivariate approach combined with appropriate image processing techniques. The tracking component of the algorithm utilizes two dynamical constraints, which improve the object association results in comparison to methods based on statistical assumptions alone. Special attention is placed on some of the outstanding challenges regarding the formulation of the algorithm and possible ways to address those in future research. Apart from describing the technical details behind the algorithm, this study also introduces specific algorithm applications relevant to the analysis and prediction of bores. These applications are illustrated for a retrospective case study simulated with a convection-allowing ensemble prediction system. The paper highlights how the newly developed algorithm tools naturally form a foundation for understanding the initiation, structure, and evolution of bores and convective systems in the nocturnal environment.
Abstract
A novel object-based algorithm capable of identifying and tracking convective outflow boundaries in convection-allowing numerical models is presented in this study. The most distinct feature of the proposed algorithm is its ability to seamlessly analyze numerically simulated density currents and bores, both of which play an important role in the dynamics of nocturnal convective systems. The unified identification and classification of these morphologically different phenomena is achieved through a multivariate approach combined with appropriate image processing techniques. The tracking component of the algorithm utilizes two dynamical constraints, which improve the object association results in comparison to methods based on statistical assumptions alone. Special attention is placed on some of the outstanding challenges regarding the formulation of the algorithm and possible ways to address those in future research. Apart from describing the technical details behind the algorithm, this study also introduces specific algorithm applications relevant to the analysis and prediction of bores. These applications are illustrated for a retrospective case study simulated with a convection-allowing ensemble prediction system. The paper highlights how the newly developed algorithm tools naturally form a foundation for understanding the initiation, structure, and evolution of bores and convective systems in the nocturnal environment.
Abstract
Numerical weather prediction models often fail to correctly forecast convection initiation (CI) at night. To improve our understanding of such events, researchers collected a unique dataset of thermodynamic and kinematic remote sensing profilers as part of the Plains Elevated Convection at Night (PECAN) experiment. This study evaluates the impacts made to a nocturnal CI forecast on 26 June 2015 by assimilating a network of atmospheric emitted radiance interferometers (AERIs), Doppler lidars, radio wind profilers, high-frequency rawinsondes, and mobile surface observations using an advanced, ensemble-based data assimilation system. Relative to operational forecasts, assimilating the PECAN dataset improves the timing, location, and orientation of the CI event. Specifically, radio wind profilers and rawinsondes are shown to be the most impactful instrument by enhancing the moisture advection into the region of CI in the forecast. Assimilating thermodynamic profiles collected by the AERIs increases midlevel moisture and improves the ensemble probability of CI in the forecast. The impacts of assimilating the radio wind profilers, AERI retrievals, and rawinsondes remain large throughout forecasting the growth of the CI event into a mesoscale convective system. Assimilating Doppler lidar and surface data only slightly improves the CI forecast by enhancing the convergence along an outflow boundary that partially forces the nocturnal CI event. Our findings suggest that a mesoscale network of profiling and surface instruments has the potential to greatly improve short-term forecasts of nocturnal convection.
Abstract
Numerical weather prediction models often fail to correctly forecast convection initiation (CI) at night. To improve our understanding of such events, researchers collected a unique dataset of thermodynamic and kinematic remote sensing profilers as part of the Plains Elevated Convection at Night (PECAN) experiment. This study evaluates the impacts made to a nocturnal CI forecast on 26 June 2015 by assimilating a network of atmospheric emitted radiance interferometers (AERIs), Doppler lidars, radio wind profilers, high-frequency rawinsondes, and mobile surface observations using an advanced, ensemble-based data assimilation system. Relative to operational forecasts, assimilating the PECAN dataset improves the timing, location, and orientation of the CI event. Specifically, radio wind profilers and rawinsondes are shown to be the most impactful instrument by enhancing the moisture advection into the region of CI in the forecast. Assimilating thermodynamic profiles collected by the AERIs increases midlevel moisture and improves the ensemble probability of CI in the forecast. The impacts of assimilating the radio wind profilers, AERI retrievals, and rawinsondes remain large throughout forecasting the growth of the CI event into a mesoscale convective system. Assimilating Doppler lidar and surface data only slightly improves the CI forecast by enhancing the convergence along an outflow boundary that partially forces the nocturnal CI event. Our findings suggest that a mesoscale network of profiling and surface instruments has the potential to greatly improve short-term forecasts of nocturnal convection.
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.
