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- Author or Editor: Paul Bieringer x
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Abstract
The installation of the network of NEXRAD (Next Generation Weather Radar) WSR-88D (Weather Surveillance Radar—1988 Doppler) radars has been an ongoing process for more than three years. An assessment is made on how these radars and related changes at National Weather Service Offices have impacted the warning of tornadoes. Tornado warning statistics were employed to evaluate the improvements in warning lead times and detection after the installation of the WSR-88D. In an effort to remove a bias from the warning dataset, the statistics based on the first tornado event of each day were also considered. This early evaluation of the warning capability of these radars indicates an improvement at selected sites over the previous five years.
Abstract
The installation of the network of NEXRAD (Next Generation Weather Radar) WSR-88D (Weather Surveillance Radar—1988 Doppler) radars has been an ongoing process for more than three years. An assessment is made on how these radars and related changes at National Weather Service Offices have impacted the warning of tornadoes. Tornado warning statistics were employed to evaluate the improvements in warning lead times and detection after the installation of the WSR-88D. In an effort to remove a bias from the warning dataset, the statistics based on the first tornado event of each day were also considered. This early evaluation of the warning capability of these radars indicates an improvement at selected sites over the previous five years.
Abstract
A study by Bieringer et al., which is Part I of this two-part study, demonstrated analytically using the shallow-water equations and numerically in controlled experiments that the presence of terrain can result in an enhancement of sensitivities to initial condition adjustments. The increased impact of adjustments to initial conditions corresponds with gradients in the flow field induced by the presence of the terrain obstacle. In cross-barrier flow situations the impact of the initial condition adjustments on the final forecast increases linearly as the height of the terrain obstacle increases. While this property associated with initial condition perturbations may be present in an analytic and controlled numerical environment, it is often difficult to realize these benefits in a more operationally realistic setting. This study extends the prior work to a situation with actual terrain, Doppler radar wind observations over the terrain, and observations from a surface mesonet for model verification. The results indicate that the downstream surface wind forecast was improved more when the initial conditions adjusted through the assimilation of Doppler radar data originated from areas with terrain gradients than from regions where the terrain was relatively flat. This result is consistent with the findings presented in Part I and suggests that when varying terrain elevation is present upstream of a target forecast area, a greater benefit (in terms of forecast accuracy) can be made by targeting additional observations in the regions containing variable terrain than regions where the terrain is relatively flat.
Abstract
A study by Bieringer et al., which is Part I of this two-part study, demonstrated analytically using the shallow-water equations and numerically in controlled experiments that the presence of terrain can result in an enhancement of sensitivities to initial condition adjustments. The increased impact of adjustments to initial conditions corresponds with gradients in the flow field induced by the presence of the terrain obstacle. In cross-barrier flow situations the impact of the initial condition adjustments on the final forecast increases linearly as the height of the terrain obstacle increases. While this property associated with initial condition perturbations may be present in an analytic and controlled numerical environment, it is often difficult to realize these benefits in a more operationally realistic setting. This study extends the prior work to a situation with actual terrain, Doppler radar wind observations over the terrain, and observations from a surface mesonet for model verification. The results indicate that the downstream surface wind forecast was improved more when the initial conditions adjusted through the assimilation of Doppler radar data originated from areas with terrain gradients than from regions where the terrain was relatively flat. This result is consistent with the findings presented in Part I and suggests that when varying terrain elevation is present upstream of a target forecast area, a greater benefit (in terms of forecast accuracy) can be made by targeting additional observations in the regions containing variable terrain than regions where the terrain is relatively flat.
Abstract
Tornadoes are a rare event, with the period of reliable record-keeping for many locations small compared to the likelihood of occurrence. The total number of recorded tornadoes varies a great deal from year to year. However, the number of recorded tornadoes has steadily increased. The cause of the variability includes climate, remoteness of an event from populations centers and dectection equipment such as radars, lack of reports, and incorrect reports. It is found that there is a positive correlation with observed occurrence and population density and radar locations. This paper seeks to remove the temporal variability and the biases in space due to the underreporting of tornado occurrence in a midwestern region. The resulting probability density function suggests an approximately 60% increase in the total likely number of tornado occurrence for a given year, as reflected in the increase in the average spatial probability density function.
Abstract
Tornadoes are a rare event, with the period of reliable record-keeping for many locations small compared to the likelihood of occurrence. The total number of recorded tornadoes varies a great deal from year to year. However, the number of recorded tornadoes has steadily increased. The cause of the variability includes climate, remoteness of an event from populations centers and dectection equipment such as radars, lack of reports, and incorrect reports. It is found that there is a positive correlation with observed occurrence and population density and radar locations. This paper seeks to remove the temporal variability and the biases in space due to the underreporting of tornado occurrence in a midwestern region. The resulting probability density function suggests an approximately 60% increase in the total likely number of tornado occurrence for a given year, as reflected in the increase in the average spatial probability density function.
Abstract
The concept of improving the accuracy of numerical weather forecasts by targeting additional meteorological observations in areas where the initial condition error is suspected to grow rapidly has been the topic of numerous studies and field programs. The challenge faced by this approach is that it typically requires a costly observation system that can be quickly adapted to place instrumentation where needed. The present study examines whether the underlying terrain in a mesoscale model influences model initial condition sensitivity and if knowledge of the terrain and corresponding predominant flow patterns for a region can be used to direct the placement of instrumentation. This follows the same concept on which earlier targeted observation approaches were based, but eliminates the need for an observation system that needs to be continually reconfigured. Simulations from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and its adjoint are used to characterize the locations, variables, and magnitudes of initial condition perturbations that have the most significant impact on the surface wind forecast. This study examines a relatively simple case where an idealized mountain surrounded by a flat plain is located upwind of the forecast verification region. The results suggest that, when elevated terrain is present upstream of the target forecast area, the largest forecast impact (defined as the difference between the simulation with perturbed initial conditions and a control simulation where the initial condition was not perturbed) occurs when the initial analysis perturbations are made in regions with complex terrain.
Abstract
The concept of improving the accuracy of numerical weather forecasts by targeting additional meteorological observations in areas where the initial condition error is suspected to grow rapidly has been the topic of numerous studies and field programs. The challenge faced by this approach is that it typically requires a costly observation system that can be quickly adapted to place instrumentation where needed. The present study examines whether the underlying terrain in a mesoscale model influences model initial condition sensitivity and if knowledge of the terrain and corresponding predominant flow patterns for a region can be used to direct the placement of instrumentation. This follows the same concept on which earlier targeted observation approaches were based, but eliminates the need for an observation system that needs to be continually reconfigured. Simulations from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and its adjoint are used to characterize the locations, variables, and magnitudes of initial condition perturbations that have the most significant impact on the surface wind forecast. This study examines a relatively simple case where an idealized mountain surrounded by a flat plain is located upwind of the forecast verification region. The results suggest that, when elevated terrain is present upstream of the target forecast area, the largest forecast impact (defined as the difference between the simulation with perturbed initial conditions and a control simulation where the initial condition was not perturbed) occurs when the initial analysis perturbations are made in regions with complex terrain.
Abstract
Chemical and biological (CB) defense systems require significant testing and evaluation before they are deployed for real-time use. Because it is not feasible to evaluate these systems with open-air testing alone, researchers rely on numerical models to supplement the defense-system analysis process. These numerical models traditionally describe the statistical properties of CB-agent atmospheric transport and dispersion (AT&D). While the statistical representation of AT&D is appropriate to use in some CB defense analyses, it is not appropriate to use this class of dispersion model for all such analyses. Many of these defense-system analyses require AT&D models that are capable of simulating dispersion properties with very short time-averaging periods that more closely emulate a “single realization” of a contaminant or CB agent dispersing in a turbulent atmosphere. The latter class of AT&D models is superior to the former for performing CB-system analyses when one or more of the following factors are important in the analysis: high-frequency sampling of the contaminant, spatial and temporal correlations within the contaminant concentration field, and nonlinear operations performed on the contaminant concentration. This paper describes and contrasts these AT&D modeling tools and provides specific examples in which utilizing ensembles of single realizations of CB-agent AT&D is advantageous over using the statistical, “ensemble-average” representation of the agent AT&D. These examples demonstrate the importance of using an AT&D modeling tool that is appropriate for the analysis.
Abstract
Chemical and biological (CB) defense systems require significant testing and evaluation before they are deployed for real-time use. Because it is not feasible to evaluate these systems with open-air testing alone, researchers rely on numerical models to supplement the defense-system analysis process. These numerical models traditionally describe the statistical properties of CB-agent atmospheric transport and dispersion (AT&D). While the statistical representation of AT&D is appropriate to use in some CB defense analyses, it is not appropriate to use this class of dispersion model for all such analyses. Many of these defense-system analyses require AT&D models that are capable of simulating dispersion properties with very short time-averaging periods that more closely emulate a “single realization” of a contaminant or CB agent dispersing in a turbulent atmosphere. The latter class of AT&D models is superior to the former for performing CB-system analyses when one or more of the following factors are important in the analysis: high-frequency sampling of the contaminant, spatial and temporal correlations within the contaminant concentration field, and nonlinear operations performed on the contaminant concentration. This paper describes and contrasts these AT&D modeling tools and provides specific examples in which utilizing ensembles of single realizations of CB-agent AT&D is advantageous over using the statistical, “ensemble-average” representation of the agent AT&D. These examples demonstrate the importance of using an AT&D modeling tool that is appropriate for the analysis.
Abstract
Results are presented from a tracer-release modeling study designed to examine atmospheric transport and dispersion (“T&D”) behavior surrounding the complex coastal–urban region of New York City, New York, where air–sea interaction and urban influences are prominent. The puff-based Hazard Prediction Assessment Capability (HPAC, version 5) model is run for idealized conditions, and it is also linked with the urbanized COAMPS (1 km) meteorological model and the NAM (12 km) meteorological model. Results are compared with “control” plumes utilizing surface meteorological input from 22 weather stations. In all configurations, nighttime conditions result in plume predictions that are more sensitive to small changes in wind direction. Plume overlap is reduced by up to 70% when plumes are transported during the night. An analysis of vertical plume cross sections and the nature of the underlying transport and the dispersion equations both suggest that heat flux gradients and boundary layer height gradients determine vertical transport of pollutants across land–sea boundaries in the T&D model. As a consequence, in both idealized and realistic meteorological configurations, waterfront releases generate greater plume discrepancies relative to plumes transported over land/urban surfaces. For transport over water (northwest winds), the higher-fidelity meteorological model (COAMPS) generated plumes with overlap reduced by about one-half when compared with that of the coarser-resolution NAM model (13% vs 24% during the daytime and 11% vs 18% during the nighttime). This study highlights the need for more sophisticated treatment of land–sea transition zones in T&D calculations covering waterside releases.
Abstract
Results are presented from a tracer-release modeling study designed to examine atmospheric transport and dispersion (“T&D”) behavior surrounding the complex coastal–urban region of New York City, New York, where air–sea interaction and urban influences are prominent. The puff-based Hazard Prediction Assessment Capability (HPAC, version 5) model is run for idealized conditions, and it is also linked with the urbanized COAMPS (1 km) meteorological model and the NAM (12 km) meteorological model. Results are compared with “control” plumes utilizing surface meteorological input from 22 weather stations. In all configurations, nighttime conditions result in plume predictions that are more sensitive to small changes in wind direction. Plume overlap is reduced by up to 70% when plumes are transported during the night. An analysis of vertical plume cross sections and the nature of the underlying transport and the dispersion equations both suggest that heat flux gradients and boundary layer height gradients determine vertical transport of pollutants across land–sea boundaries in the T&D model. As a consequence, in both idealized and realistic meteorological configurations, waterfront releases generate greater plume discrepancies relative to plumes transported over land/urban surfaces. For transport over water (northwest winds), the higher-fidelity meteorological model (COAMPS) generated plumes with overlap reduced by about one-half when compared with that of the coarser-resolution NAM model (13% vs 24% during the daytime and 11% vs 18% during the nighttime). This study highlights the need for more sophisticated treatment of land–sea transition zones in T&D calculations covering waterside releases.
Abstract
The mosquito virus vector Aedes (Ae.) aegypti exploits a wide range of containers as sites for egg laying and development of the immature life stages, yet the approaches for modeling meteorologically sensitive container water dynamics have been limited. This study introduces the Water Height and Temperature in Container Habitats Energy Model (WHATCH’EM), a state-of-the-science, physically based energy balance model of water height and temperature in containers that may serve as development sites for mosquitoes. The authors employ WHATCH’EM to model container water dynamics in three cities along a climatic gradient in México ranging from sea level, where Ae. aegypti is highly abundant, to ~2100 m, where Ae. aegypti is rarely found. When compared with measurements from a 1-month field experiment in two of these cities during summer 2013, WHATCH’EM realistically simulates the daily mean and range of water temperature for a variety of containers. To examine container dynamics for an entire season, WHATCH’EM is also driven with field-derived meteorological data from May to September 2011 and evaluated for three commonly encountered container types. WHATCH’EM simulates the highly nonlinear manner in which air temperature, humidity, rainfall, clouds, and container characteristics (shape, size, and color) determine water temperature and height. Sunlight exposure, modulated by clouds and shading from nearby objects, plays a first-order role. In general, simulated water temperatures are higher for containers that are larger, darker, and receive more sunlight. WHATCH’EM simulations will be helpful in understanding the limiting meteorological and container-related factors for proliferation of Ae. aegypti and may be useful for informing weather-driven early warning systems for viruses transmitted by Ae. aegypti.
Abstract
The mosquito virus vector Aedes (Ae.) aegypti exploits a wide range of containers as sites for egg laying and development of the immature life stages, yet the approaches for modeling meteorologically sensitive container water dynamics have been limited. This study introduces the Water Height and Temperature in Container Habitats Energy Model (WHATCH’EM), a state-of-the-science, physically based energy balance model of water height and temperature in containers that may serve as development sites for mosquitoes. The authors employ WHATCH’EM to model container water dynamics in three cities along a climatic gradient in México ranging from sea level, where Ae. aegypti is highly abundant, to ~2100 m, where Ae. aegypti is rarely found. When compared with measurements from a 1-month field experiment in two of these cities during summer 2013, WHATCH’EM realistically simulates the daily mean and range of water temperature for a variety of containers. To examine container dynamics for an entire season, WHATCH’EM is also driven with field-derived meteorological data from May to September 2011 and evaluated for three commonly encountered container types. WHATCH’EM simulates the highly nonlinear manner in which air temperature, humidity, rainfall, clouds, and container characteristics (shape, size, and color) determine water temperature and height. Sunlight exposure, modulated by clouds and shading from nearby objects, plays a first-order role. In general, simulated water temperatures are higher for containers that are larger, darker, and receive more sunlight. WHATCH’EM simulations will be helpful in understanding the limiting meteorological and container-related factors for proliferation of Ae. aegypti and may be useful for informing weather-driven early warning systems for viruses transmitted by Ae. aegypti.
