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- Author or Editor: Yansen Wang x
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Abstract
A k–ε turbulence model for the stable atmosphere is extended for the convective atmosphere. The new model represents the buoyancy-induced increase in the kinetic energy and scale of eddies, and is consistent with the Monin–Obukhov similarity theory for convective atmospheric boundary layers (ABLs). After being incorporated into an ABL model with the Coriolis force, the model is tested by comparing the ABL model results with the Businger–Dyer (BD) relationship. ABL model simulations are carried out to reveal the sensitivity of the vertical wind profile to model parameters (e.g., the Obukhov length, friction velocity, and geostrophic wind). When the friction velocity is consistent with geostrophic wind speed (or the turbulence in the inner regime is in equilibrium with that in the outer regime), the modeled wind profile is close to the BD relationship near the ground surface. Otherwise, the modeled wind profile deviates from the BD relationship, resembling the hockey stick transition model.
Abstract
A k–ε turbulence model for the stable atmosphere is extended for the convective atmosphere. The new model represents the buoyancy-induced increase in the kinetic energy and scale of eddies, and is consistent with the Monin–Obukhov similarity theory for convective atmospheric boundary layers (ABLs). After being incorporated into an ABL model with the Coriolis force, the model is tested by comparing the ABL model results with the Businger–Dyer (BD) relationship. ABL model simulations are carried out to reveal the sensitivity of the vertical wind profile to model parameters (e.g., the Obukhov length, friction velocity, and geostrophic wind). When the friction velocity is consistent with geostrophic wind speed (or the turbulence in the inner regime is in equilibrium with that in the outer regime), the modeled wind profile is close to the BD relationship near the ground surface. Otherwise, the modeled wind profile deviates from the BD relationship, resembling the hockey stick transition model.
Abstract
A two-dimensional cloud-resolving model is linked with a TOGA COARE flux algorithm to examine the impact of the ocean surface fluxes on the development of a tropical squall line and its associated precipitation processes. The model results show that the 12-h total surface rainfall amount in the run excluding the surface fluxes is about 80% of that for the run including surface fluxes (domain-averaged rainfall, 3.4 mm). The model results also indicate that latent heat flux or evaporation from the ocean is the most influential factor among the three fluxes (latent heat, sensible heat, and momentum) for the development of the squall system. The average latent and sensible heat fluxes in the convective (disturbed) region are 60 and 11 W m−2 larger, respectively, than those of the nonconvective (clear) region due to the gust wind speed, a cool pool near the surface, and drier air from downdrafts associated with the convective activity. These results are in good agreement with observations.
In addition, sensitivity tests using a simple bulk aerodynamic approximation as well as a Blackadar-type surface flux formulation have predicted much larger latent and sensible heat fluxes than those obtained using the TOGA COARE flux algorithm. Consequently, much more surface rainfall was simulated using a simple aerodynamic approximation or a Blackadar-type surface flux formulation. The results presented here also suggest that a fine vertical resolution (at least in the lowest model grid point) is needed in order to study the interactive processes between the ocean and convection using a cloud-resolving model.
Abstract
A two-dimensional cloud-resolving model is linked with a TOGA COARE flux algorithm to examine the impact of the ocean surface fluxes on the development of a tropical squall line and its associated precipitation processes. The model results show that the 12-h total surface rainfall amount in the run excluding the surface fluxes is about 80% of that for the run including surface fluxes (domain-averaged rainfall, 3.4 mm). The model results also indicate that latent heat flux or evaporation from the ocean is the most influential factor among the three fluxes (latent heat, sensible heat, and momentum) for the development of the squall system. The average latent and sensible heat fluxes in the convective (disturbed) region are 60 and 11 W m−2 larger, respectively, than those of the nonconvective (clear) region due to the gust wind speed, a cool pool near the surface, and drier air from downdrafts associated with the convective activity. These results are in good agreement with observations.
In addition, sensitivity tests using a simple bulk aerodynamic approximation as well as a Blackadar-type surface flux formulation have predicted much larger latent and sensible heat fluxes than those obtained using the TOGA COARE flux algorithm. Consequently, much more surface rainfall was simulated using a simple aerodynamic approximation or a Blackadar-type surface flux formulation. The results presented here also suggest that a fine vertical resolution (at least in the lowest model grid point) is needed in order to study the interactive processes between the ocean and convection using a cloud-resolving model.
ABSTRACT
A three-dimensional, prognostic Atmospheric Boundary Layer Environment–Lattice Boltzmann Model (ABLE-LBM) using the multiple-relaxation-time lattice Boltzmann method was developed for large-eddy simulation of urban boundary layer atmospheric flows. In this article we describe the details of the ABLE-LBM for urban flow, its implementation of complex boundaries, and the subgrid turbulence parameterizations. As a first validation of this newly developed model, the simulation results were evaluated with two wind-tunnel datasets that were collected using particle image velocimetry and Irwin probes, respectively. The ABLE-LBM simulations use the same building layout and Reynolds numbers used in the laboratory wind tunnels. The ABLE-LBM simulations compare favorably to both laboratory studies in terms of the mean wind fields. The turbulent fluxes simulated by the model in the observational planes also agreed reasonably well with the laboratory results. The model produced urban canyon flows and vortices on the lee side and over the building tops that are similar to those of the laboratory studies in strength and location. This validation study using laboratory data indicates that our new ABLE-LBM is a viable approach for modeling atmospheric turbulent flows in urban environments. A numerical implementation using a graphics processing unit shows that real-time simulations are achieved for these two validation cases.
ABSTRACT
A three-dimensional, prognostic Atmospheric Boundary Layer Environment–Lattice Boltzmann Model (ABLE-LBM) using the multiple-relaxation-time lattice Boltzmann method was developed for large-eddy simulation of urban boundary layer atmospheric flows. In this article we describe the details of the ABLE-LBM for urban flow, its implementation of complex boundaries, and the subgrid turbulence parameterizations. As a first validation of this newly developed model, the simulation results were evaluated with two wind-tunnel datasets that were collected using particle image velocimetry and Irwin probes, respectively. The ABLE-LBM simulations use the same building layout and Reynolds numbers used in the laboratory wind tunnels. The ABLE-LBM simulations compare favorably to both laboratory studies in terms of the mean wind fields. The turbulent fluxes simulated by the model in the observational planes also agreed reasonably well with the laboratory results. The model produced urban canyon flows and vortices on the lee side and over the building tops that are similar to those of the laboratory studies in strength and location. This validation study using laboratory data indicates that our new ABLE-LBM is a viable approach for modeling atmospheric turbulent flows in urban environments. A numerical implementation using a graphics processing unit shows that real-time simulations are achieved for these two validation cases.
Abstract
A realizable k–ε turbulence model of incompressible fluid is extended for the stable atmosphere after taking account of the buoyancy damping of gravity waves. The new model is consistent with the Monin–Obukhov similarity theory on the stable atmospheric boundary layer (ABL) over a horizontally uniform surface. The model is incorporated into an ABL model to simulate mean flow against observations. Its ABL-model output is compared with the Leipzig dataset, showing the turbulence model works well for a stable ABL. Specifically, the ABL model properly replicates 1) the mixing length, turbulent viscosity, and mean wind; 2) a significant decrease of the mixing length with height in the upper ABL and thus a reasonable altitude of the ABL top; and 3) a sensitivity of the mixing length and turbulent viscosity to atmospheric stability.
Abstract
A realizable k–ε turbulence model of incompressible fluid is extended for the stable atmosphere after taking account of the buoyancy damping of gravity waves. The new model is consistent with the Monin–Obukhov similarity theory on the stable atmospheric boundary layer (ABL) over a horizontally uniform surface. The model is incorporated into an ABL model to simulate mean flow against observations. Its ABL-model output is compared with the Leipzig dataset, showing the turbulence model works well for a stable ABL. Specifically, the ABL model properly replicates 1) the mixing length, turbulent viscosity, and mean wind; 2) a significant decrease of the mixing length with height in the upper ABL and thus a reasonable altitude of the ABL top; and 3) a sensitivity of the mixing length and turbulent viscosity to atmospheric stability.
Abstract
A multigrid numerical method has been applied to a three-dimensional, high-resolution diagnostic model for flow over complex terrain using a mass-consistent approach. The theoretical background for the model is based on a variational analysis using mass conservation as a constraint. The model was designed for diagnostic wind simulation at the microscale in complex terrain and in urban areas. The numerical implementation takes advantage of a multigrid method that greatly improves the computation speed. Three preliminary test cases for the model’s numerical efficiency and its accuracy are given. The model results are compared with an analytical solution for flow over a hemisphere. Flow over a bell-shaped hill is computed to demonstrate that the numerical method is applicable in the case of parameterized lee vortices. A simulation of the mean wind field in an urban domain has also been carried out and compared with observational data. The comparison indicated that the multigrid method takes only 3%–5% of the time that is required by the traditional Gauss–Seidel method.
Abstract
A multigrid numerical method has been applied to a three-dimensional, high-resolution diagnostic model for flow over complex terrain using a mass-consistent approach. The theoretical background for the model is based on a variational analysis using mass conservation as a constraint. The model was designed for diagnostic wind simulation at the microscale in complex terrain and in urban areas. The numerical implementation takes advantage of a multigrid method that greatly improves the computation speed. Three preliminary test cases for the model’s numerical efficiency and its accuracy are given. The model results are compared with an analytical solution for flow over a hemisphere. Flow over a bell-shaped hill is computed to demonstrate that the numerical method is applicable in the case of parameterized lee vortices. A simulation of the mean wind field in an urban domain has also been carried out and compared with observational data. The comparison indicated that the multigrid method takes only 3%–5% of the time that is required by the traditional Gauss–Seidel method.
Abstract
The stability of ice crystal orientation is studied by modeling the airflow around ice crystals at moderate Reynolds number, where an ice crystal is approximated by a cylinder with three parameters: diameter D, length L, and zenith angle of the axis θ. In this paper, the torque acting on ice crystals is simulated at different θ first, and then a special θ with zero horizontal torque, denoted as θe , is sought as an equilibrium of ice crystal orientation. The equilibrium is classified into two kinds: stable and unstable. Ice crystals rotate to θe of stable equilibriums while deviating from θe of unstable ones once they are released into quiet air. Multiple equilibriums of ice crystal orientation are found via numerical simulations. A cylinder with D/L close to one has three equilibriums, two of which are stable (i.e., θe = 0° and 90°). A cylinder with D/L away from one has only two equilibriums, one of which is stable (i.e., either θe = 0° or 90°). In addition, an asymmetric cylinder has two, three, or five equilibriums, and their θe is sensitive to the distance between its geometrical center and its center of gravity. The sensitivity of θe to crystal asymmetry suggests large symmetric ice crystals tend to become asymmetric (or irregular) and subsequently oriented randomly.
Significance Statement
Ice crystal orientation impacts high-cloud reflectance and satellite-based observations of high clouds significantly. However, its laboratory and field observations look dissimilar: the percentage of horizontally oriented ice crystals (HOICs) observed in the laboratory is quite high, while in the field it is often low and varies greatly in space and time. The motivation for this study is to elucidate what causes the difference between the laboratory and field observations. The torque acting on ice crystals are computed by modeling the airflow around ice crystals, revealing the conditions for nonhorizontal orientations of ice crystals. In quiet air, an ice crystal is oriented either horizontally or vertically when its shape is close to sphere. When its shape is elongated in one direction, its orientation depends on its asymmetry in density and shape. The sensitivity of ice crystal orientation to ice crystal asymmetry explains the low percentage of HOICs in the field, because asymmetric ice crystals are common in clouds. As an application, this sensitivity together with the observed percentage of HOICs can be used to infer the processes of ice crystal growth in clouds, providing clues to better representation of ice crystals in weather and climate models.
Abstract
The stability of ice crystal orientation is studied by modeling the airflow around ice crystals at moderate Reynolds number, where an ice crystal is approximated by a cylinder with three parameters: diameter D, length L, and zenith angle of the axis θ. In this paper, the torque acting on ice crystals is simulated at different θ first, and then a special θ with zero horizontal torque, denoted as θe , is sought as an equilibrium of ice crystal orientation. The equilibrium is classified into two kinds: stable and unstable. Ice crystals rotate to θe of stable equilibriums while deviating from θe of unstable ones once they are released into quiet air. Multiple equilibriums of ice crystal orientation are found via numerical simulations. A cylinder with D/L close to one has three equilibriums, two of which are stable (i.e., θe = 0° and 90°). A cylinder with D/L away from one has only two equilibriums, one of which is stable (i.e., either θe = 0° or 90°). In addition, an asymmetric cylinder has two, three, or five equilibriums, and their θe is sensitive to the distance between its geometrical center and its center of gravity. The sensitivity of θe to crystal asymmetry suggests large symmetric ice crystals tend to become asymmetric (or irregular) and subsequently oriented randomly.
Significance Statement
Ice crystal orientation impacts high-cloud reflectance and satellite-based observations of high clouds significantly. However, its laboratory and field observations look dissimilar: the percentage of horizontally oriented ice crystals (HOICs) observed in the laboratory is quite high, while in the field it is often low and varies greatly in space and time. The motivation for this study is to elucidate what causes the difference between the laboratory and field observations. The torque acting on ice crystals are computed by modeling the airflow around ice crystals, revealing the conditions for nonhorizontal orientations of ice crystals. In quiet air, an ice crystal is oriented either horizontally or vertically when its shape is close to sphere. When its shape is elongated in one direction, its orientation depends on its asymmetry in density and shape. The sensitivity of ice crystal orientation to ice crystal asymmetry explains the low percentage of HOICs in the field, because asymmetric ice crystals are common in clouds. As an application, this sensitivity together with the observed percentage of HOICs can be used to infer the processes of ice crystal growth in clouds, providing clues to better representation of ice crystals in weather and climate models.
Abstract
Data are provided from 17 single-swath aerial spray trials that were conducted over a fully leafed, 16-m tall, mixed oak forest. The distribution of cross-swath spray deposits was sampled at the top of the canopy and below the canopy. Micrometeorological conditions were measured above and within the canopy during the spray trials. The USDA Forest Service FSCBG (Forest Service-Cramer-Barry-Grim) model was run to predict the target sampler catch for each trial using forest stand, airplane-application-equipment configuration, and micrometeorological conditions as inputs. Observations showed an average cross-swath deposition of 100 IU cm−2 with large run-to-run variability in deposition patterns, magnitudes, and drift. Eleven percent of the spray material that reached the top of the canopy penetrated through the tree canopy to the forest floor.
The FSCBG predictions of the ensemble-averaged deposition were within 17% of the measured deposition at the canopy top and within 8% on the ground beneath the canopy. Run-to-run deposit predictions by FSCBG were considerably less variable than the measured deposits. Individual run predictions were much less accurate than the ensemble-averaged predictions as demonstrated by an average root-mean-square-error (rmse) of 27.9 IU CM−2 at the top of the canopy. Comparisons of the differences between predicted and observed deposits indicated that the model accuracy was sensitive to atmospheric stability conditions. In neutral and stable conditions, a regular pattern of error was indicated by overprediction of the canopy-top deposit at distances from 0 to 20 m downwind from the flight line and underprediction of the deposit both farther downwind than 20 m and upwind of the flight line. In unstable conditions the model generally underpredicted the deposit downwind from the flight line, but showed no regular pattern of error.
Abstract
Data are provided from 17 single-swath aerial spray trials that were conducted over a fully leafed, 16-m tall, mixed oak forest. The distribution of cross-swath spray deposits was sampled at the top of the canopy and below the canopy. Micrometeorological conditions were measured above and within the canopy during the spray trials. The USDA Forest Service FSCBG (Forest Service-Cramer-Barry-Grim) model was run to predict the target sampler catch for each trial using forest stand, airplane-application-equipment configuration, and micrometeorological conditions as inputs. Observations showed an average cross-swath deposition of 100 IU cm−2 with large run-to-run variability in deposition patterns, magnitudes, and drift. Eleven percent of the spray material that reached the top of the canopy penetrated through the tree canopy to the forest floor.
The FSCBG predictions of the ensemble-averaged deposition were within 17% of the measured deposition at the canopy top and within 8% on the ground beneath the canopy. Run-to-run deposit predictions by FSCBG were considerably less variable than the measured deposits. Individual run predictions were much less accurate than the ensemble-averaged predictions as demonstrated by an average root-mean-square-error (rmse) of 27.9 IU CM−2 at the top of the canopy. Comparisons of the differences between predicted and observed deposits indicated that the model accuracy was sensitive to atmospheric stability conditions. In neutral and stable conditions, a regular pattern of error was indicated by overprediction of the canopy-top deposit at distances from 0 to 20 m downwind from the flight line and underprediction of the deposit both farther downwind than 20 m and upwind of the flight line. In unstable conditions the model generally underpredicted the deposit downwind from the flight line, but showed no regular pattern of error.
Abstract
Boundary layer wind data observed by a Doppler lidar and sonic anemometers during the mornings of three intensive observational periods (IOP2, IOP3, and IOP7) of the Joint Urban 2003 (JU2003) field experiment are analyzed to extract the mean and turbulent characteristics of airflow over Oklahoma City, Oklahoma. A strong nocturnal low-level jet (LLJ) dominated the flow in the boundary layer over the measurement domain from midnight to the morning hours. Lidar scans through the LLJ taken after sunrise indicate that the LLJ elevation shows a gradual increase of 25–100 m over the urban area relative to that over the upstream suburban area. The mean wind speed beneath the jet over the urban area is about 10%–15% slower than that over the suburban area. Sonic anemometer observations combined with Doppler lidar observations in the urban and suburban areas are also analyzed to investigate the boundary layer turbulence production in the LLJ-dominated atmospheric boundary layer. The turbulence kinetic energy was higher over the urban domain mainly because of the shear production of building surfaces and building wakes. Direct transport of turbulent momentum flux from the LLJ to the urban street level was very small because of the relatively high elevation of the jet. However, since the LLJ dominated the mean wind in the boundary layer, the turbulence kinetic energy in the urban domain is correlated directly with the LLJ maximum speed and inversely with its height. The results indicate that the jet Richardson number is a reasonably good indicator for turbulent kinetic energy over the urban domain in the LLJ-dominated atmospheric boundary layer.
Abstract
Boundary layer wind data observed by a Doppler lidar and sonic anemometers during the mornings of three intensive observational periods (IOP2, IOP3, and IOP7) of the Joint Urban 2003 (JU2003) field experiment are analyzed to extract the mean and turbulent characteristics of airflow over Oklahoma City, Oklahoma. A strong nocturnal low-level jet (LLJ) dominated the flow in the boundary layer over the measurement domain from midnight to the morning hours. Lidar scans through the LLJ taken after sunrise indicate that the LLJ elevation shows a gradual increase of 25–100 m over the urban area relative to that over the upstream suburban area. The mean wind speed beneath the jet over the urban area is about 10%–15% slower than that over the suburban area. Sonic anemometer observations combined with Doppler lidar observations in the urban and suburban areas are also analyzed to investigate the boundary layer turbulence production in the LLJ-dominated atmospheric boundary layer. The turbulence kinetic energy was higher over the urban domain mainly because of the shear production of building surfaces and building wakes. Direct transport of turbulent momentum flux from the LLJ to the urban street level was very small because of the relatively high elevation of the jet. However, since the LLJ dominated the mean wind in the boundary layer, the turbulence kinetic energy in the urban domain is correlated directly with the LLJ maximum speed and inversely with its height. The results indicate that the jet Richardson number is a reasonably good indicator for turbulent kinetic energy over the urban domain in the LLJ-dominated atmospheric boundary layer.
Abstract
A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5°-resolution range from approximately 50% at 1 mm h−1 to 20% at 14 mm h−1. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%–80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5° resolution is relatively small (less than 6% at 5 mm day−1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%–35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%–15% at 5 mm day−1, with proportionate reductions in latent heating sampling errors.
Abstract
A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5°-resolution range from approximately 50% at 1 mm h−1 to 20% at 14 mm h−1. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%–80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5° resolution is relatively small (less than 6% at 5 mm day−1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%–35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%–15% at 5 mm day−1, with proportionate reductions in latent heating sampling errors.
