Search Results
You are looking at 1 - 10 of 12 items for :
- Author or Editor: E. S. Takle x
- Article x
- Refine by Access: All Content x
Abstract
A two4Mensional second-order turbulence-closure model based on Mellor-Yamada level 3 is used to examine the nocturnal turbulence characteristics over Rattlesnake Mountain in Washington. Simulations of mean horizontal velocities and potential temperatures agree well with data. The equations for the components of the turbulent kinetic energy (TKE) show that anisotropy contributes in ways that are counter to our intuition developed from mean now considerations: shear production under stable conditions forces the suppression of the vertical component proportion of loud TKE, while potential-temperature variance under stable conditions leads to a positive (countergradient) contribution to the heat flux that increases the vertical component proportion of total TKE. This paper provides a qualitative analysis of simulated turbulence fields, which indicates significant variation over the windward and leeward slopes. From the simulation results, turbulence anisotropy is seen to develop in the katabatic flow region where vertical wind shears and atmospheric stability are large. An enhancement of the vertical component proportion of the total TKE takes place over the leeward slope as the downslope distance increases. The countergradient portion of the turbulent heat flux plays an important role in producing regions of anisotropy.
Abstract
A two4Mensional second-order turbulence-closure model based on Mellor-Yamada level 3 is used to examine the nocturnal turbulence characteristics over Rattlesnake Mountain in Washington. Simulations of mean horizontal velocities and potential temperatures agree well with data. The equations for the components of the turbulent kinetic energy (TKE) show that anisotropy contributes in ways that are counter to our intuition developed from mean now considerations: shear production under stable conditions forces the suppression of the vertical component proportion of loud TKE, while potential-temperature variance under stable conditions leads to a positive (countergradient) contribution to the heat flux that increases the vertical component proportion of total TKE. This paper provides a qualitative analysis of simulated turbulence fields, which indicates significant variation over the windward and leeward slopes. From the simulation results, turbulence anisotropy is seen to develop in the katabatic flow region where vertical wind shears and atmospheric stability are large. An enhancement of the vertical component proportion of the total TKE takes place over the leeward slope as the downslope distance increases. The countergradient portion of the turbulent heat flux plays an important role in producing regions of anisotropy.
Abstract
The authors report results of a numerical model used to simulate wind and turbulence fields for porous, living shelterbelts with seven different cross-sectional shapes. The simulations are consistent with results of Woodruff and Zingg whose wind-tunnel study demonstrated that all shelterbelts with very different shapes have nearly identical reduction of wind and turbulence. The simulations also showed that the pressure-loss (resistance) coefficient for smooth-shaped or streamlined shelterbelts is significantly smaller than that for rectangle-shaped or triangle-shaped shelterbelts with a windward vertical side. However, the shelter effects are not proportional to the pressure-loss coefficient (drag). Analysis of the momentum budget demonstrated that in the near lee and in the far lee, both vertical advection and pressure gradient have opposite roles in the recovery of wind speed. This behavior, combined with differences in permeability, is the likely cause of reduced sensitivity of shelter effects to shelterbelt shape.
Abstract
The authors report results of a numerical model used to simulate wind and turbulence fields for porous, living shelterbelts with seven different cross-sectional shapes. The simulations are consistent with results of Woodruff and Zingg whose wind-tunnel study demonstrated that all shelterbelts with very different shapes have nearly identical reduction of wind and turbulence. The simulations also showed that the pressure-loss (resistance) coefficient for smooth-shaped or streamlined shelterbelts is significantly smaller than that for rectangle-shaped or triangle-shaped shelterbelts with a windward vertical side. However, the shelter effects are not proportional to the pressure-loss coefficient (drag). Analysis of the momentum budget demonstrated that in the near lee and in the far lee, both vertical advection and pressure gradient have opposite roles in the recovery of wind speed. This behavior, combined with differences in permeability, is the likely cause of reduced sensitivity of shelter effects to shelterbelt shape.
Abstract
We have developed a two-dimensional finite-element model for simulating atmospheric flow in the planetary boundary layer (PBL) of the earth. The finite-element method provides a useful alternative to the conventional finite-difference method in studying Bow phenomena that involve graded meshes and (or) irregular computational domains. It also provides a more natural way of incorporating Dirichlet-type boundary conditions. These properties make the finite-element method especially suitable for studying PBL flows. With the Deardorff-O'Brien turbulence scheme, the model was able to generate reasonable results in the simulations of a neutral PBL wind profile and a sea-breeze circulation.
Abstract
We have developed a two-dimensional finite-element model for simulating atmospheric flow in the planetary boundary layer (PBL) of the earth. The finite-element method provides a useful alternative to the conventional finite-difference method in studying Bow phenomena that involve graded meshes and (or) irregular computational domains. It also provides a more natural way of incorporating Dirichlet-type boundary conditions. These properties make the finite-element method especially suitable for studying PBL flows. With the Deardorff-O'Brien turbulence scheme, the model was able to generate reasonable results in the simulations of a neutral PBL wind profile and a sea-breeze circulation.
Abstract
Temperature and wind speed measurements over a 6-year period from a 32 m tower located in a primarily rural area are used to assess the pollutant-dispersive characteristics of a rural site. A monthly comparison of a crude pollution-trapping index shows July through September the most favorable, and December through February the least favorable, months for the trapping of contaminants emitted from ground-based sources in rural areas.
Abstract
Temperature and wind speed measurements over a 6-year period from a 32 m tower located in a primarily rural area are used to assess the pollutant-dispersive characteristics of a rural site. A monthly comparison of a crude pollution-trapping index shows July through September the most favorable, and December through February the least favorable, months for the trapping of contaminants emitted from ground-based sources in rural areas.
Abstract
In the U.S. state of Iowa, the increase in wind power production has motivated interest into the impacts of low-level jets on turbine performance. In this study, two commercial lidar systems were used to sample wind profiles in August 2013. Jets were systematically detected and assigned an intensity rating from 0 (weak) to 3 (strong). Many similarities were found between observed jets and the well-studied Great Plains low-level jet in summer, including average jet heights between 300 and 500 m above ground level, a preference for southerly wind directions, and a nighttime bias for stronger jets. Strong vertical wind shear and veer were observed, as well as veering over time associated with the LLJs. Speed, shear, and veer increases extended into the turbine-rotor layer during intense jets. Ramp events, in which winds rapidly increase or decrease in the rotor layer, were also commonly observed during jet formation periods. The lidar data were also used to evaluate various configurations of the Weather Research and Forecasting Model. Jet occurrence exhibited a stronger dependence on the choice of initial and boundary condition data, while reproduction of the strongest jets was influenced more strongly by the choice of planetary boundary layer scheme. A decomposition of mean model winds suggested that the main forcing mechanism for observed jets was the inertial oscillation. These results have implications for wind energy forecasting and site assessment in the Midwest.
Abstract
In the U.S. state of Iowa, the increase in wind power production has motivated interest into the impacts of low-level jets on turbine performance. In this study, two commercial lidar systems were used to sample wind profiles in August 2013. Jets were systematically detected and assigned an intensity rating from 0 (weak) to 3 (strong). Many similarities were found between observed jets and the well-studied Great Plains low-level jet in summer, including average jet heights between 300 and 500 m above ground level, a preference for southerly wind directions, and a nighttime bias for stronger jets. Strong vertical wind shear and veer were observed, as well as veering over time associated with the LLJs. Speed, shear, and veer increases extended into the turbine-rotor layer during intense jets. Ramp events, in which winds rapidly increase or decrease in the rotor layer, were also commonly observed during jet formation periods. The lidar data were also used to evaluate various configurations of the Weather Research and Forecasting Model. Jet occurrence exhibited a stronger dependence on the choice of initial and boundary condition data, while reproduction of the strongest jets was influenced more strongly by the choice of planetary boundary layer scheme. A decomposition of mean model winds suggested that the main forcing mechanism for observed jets was the inertial oscillation. These results have implications for wind energy forecasting and site assessment in the Midwest.
Abstract
Daily precipitation and maximum and minimum temperature time series from a regional climate model (RegCM2) configured using the continental United States as a domain and run on a 52-km (approximately) spatial resolution were used as input to a distributed hydrologic model for one rainfall-dominated basin (Alapaha River at Statenville, Georgia) and three snowmelt-dominated basins (Animas River at Durango, Colorado; east fork of the Carson River near Gardnerville, Nevada; and Cle Elum River near Roslyn, Washington). For comparison purposes, spatially averaged daily datasets of precipitation and maximum and minimum temperature were developed from measured data for each basin. These datasets included precipitation and temperature data for all stations (hereafter, All-Sta) located within the area of the RegCM2 output used for each basin, but excluded station data used to calibrate the hydrologic model.
Both the RegCM2 output and All-Sta data capture the gross aspects of the seasonal cycles of precipitation and temperature. However, in all four basins, the RegCM2- and All-Sta-based simulations of runoff show little skill on a daily basis [Nash–Sutcliffe (NS) values range from 0.05 to 0.37 for RegCM2 and −0.08 to 0.65 for All-Sta]. When the precipitation and temperature biases are corrected in the RegCM2 output and All-Sta data (Bias-RegCM2 and Bias-All, respectively) the accuracy of the daily runoff simulations improve dramatically for the snowmelt-dominated basins (NS values range from 0.41 to 0.66 for RegCM2 and 0.60 to 0.76 for All-Sta). In the rainfall-dominated basin, runoff simulations based on the Bias-RegCM2 output show no skill (NS value of 0.09) whereas Bias-All simulated runoff improves (NS value improved from −0.08 to 0.72).
These results indicate that measured data at the coarse resolution of the RegCM2 output can be made appropriate for basin-scale modeling through bias correction (essentially a magnitude correction). However, RegCM2 output, even when bias corrected, does not contain the day-to-day variability present in the All-Sta dataset that is necessary for basin-scale modeling. Future work is warranted to identify the causes for systematic biases in RegCM2 simulations, develop methods to remove the biases, and improve RegCM2 simulations of daily variability in local climate.
Abstract
Daily precipitation and maximum and minimum temperature time series from a regional climate model (RegCM2) configured using the continental United States as a domain and run on a 52-km (approximately) spatial resolution were used as input to a distributed hydrologic model for one rainfall-dominated basin (Alapaha River at Statenville, Georgia) and three snowmelt-dominated basins (Animas River at Durango, Colorado; east fork of the Carson River near Gardnerville, Nevada; and Cle Elum River near Roslyn, Washington). For comparison purposes, spatially averaged daily datasets of precipitation and maximum and minimum temperature were developed from measured data for each basin. These datasets included precipitation and temperature data for all stations (hereafter, All-Sta) located within the area of the RegCM2 output used for each basin, but excluded station data used to calibrate the hydrologic model.
Both the RegCM2 output and All-Sta data capture the gross aspects of the seasonal cycles of precipitation and temperature. However, in all four basins, the RegCM2- and All-Sta-based simulations of runoff show little skill on a daily basis [Nash–Sutcliffe (NS) values range from 0.05 to 0.37 for RegCM2 and −0.08 to 0.65 for All-Sta]. When the precipitation and temperature biases are corrected in the RegCM2 output and All-Sta data (Bias-RegCM2 and Bias-All, respectively) the accuracy of the daily runoff simulations improve dramatically for the snowmelt-dominated basins (NS values range from 0.41 to 0.66 for RegCM2 and 0.60 to 0.76 for All-Sta). In the rainfall-dominated basin, runoff simulations based on the Bias-RegCM2 output show no skill (NS value of 0.09) whereas Bias-All simulated runoff improves (NS value improved from −0.08 to 0.72).
These results indicate that measured data at the coarse resolution of the RegCM2 output can be made appropriate for basin-scale modeling through bias correction (essentially a magnitude correction). However, RegCM2 output, even when bias corrected, does not contain the day-to-day variability present in the All-Sta dataset that is necessary for basin-scale modeling. Future work is warranted to identify the causes for systematic biases in RegCM2 simulations, develop methods to remove the biases, and improve RegCM2 simulations of daily variability in local climate.
Abstract
Changes in daily precipitation versus intensity under a global warming scenario in two regional climate simulations of the United States show a well-recognized feature of more intense precipitation. More important, by resolving the precipitation intensity spectrum, the changes show a relatively simple pattern for nearly all regions and seasons examined whereby nearly all high-intensity daily precipitation contributes a larger fraction of the total precipitation, and nearly all low-intensity precipitation contributes a reduced fraction. The percentile separating relative decrease from relative increase occurs around the 70th percentile of cumulative precipitation, irrespective of the governing precipitation processes or which model produced the simulation. Changes in normalized distributions display these features much more consistently than distribution changes without normalization.
Further analysis suggests that this consistent response in precipitation intensity may be a consequence of the intensity spectrum’s adherence to a gamma distribution. Under the gamma distribution, when the total precipitation or number of precipitation days changes, there is a single transition between precipitation rates that contribute relatively more to the total and rates that contribute relatively less. The behavior is roughly the same as the results of the numerical models and is insensitive to characteristics of the baseline climate, such as average precipitation, frequency of rain days, and the shape parameter of the precipitation’s gamma distribution. Changes in the normalized precipitation distribution give a more consistent constraint on how precipitation intensity may change when climate changes than do changes in the nonnormalized distribution. The analysis does not apply to extreme precipitation for which the theory of statistical extremes more likely provides the appropriate description.
Abstract
Changes in daily precipitation versus intensity under a global warming scenario in two regional climate simulations of the United States show a well-recognized feature of more intense precipitation. More important, by resolving the precipitation intensity spectrum, the changes show a relatively simple pattern for nearly all regions and seasons examined whereby nearly all high-intensity daily precipitation contributes a larger fraction of the total precipitation, and nearly all low-intensity precipitation contributes a reduced fraction. The percentile separating relative decrease from relative increase occurs around the 70th percentile of cumulative precipitation, irrespective of the governing precipitation processes or which model produced the simulation. Changes in normalized distributions display these features much more consistently than distribution changes without normalization.
Further analysis suggests that this consistent response in precipitation intensity may be a consequence of the intensity spectrum’s adherence to a gamma distribution. Under the gamma distribution, when the total precipitation or number of precipitation days changes, there is a single transition between precipitation rates that contribute relatively more to the total and rates that contribute relatively less. The behavior is roughly the same as the results of the numerical models and is insensitive to characteristics of the baseline climate, such as average precipitation, frequency of rain days, and the shape parameter of the precipitation’s gamma distribution. Changes in the normalized precipitation distribution give a more consistent constraint on how precipitation intensity may change when climate changes than do changes in the nonnormalized distribution. The analysis does not apply to extreme precipitation for which the theory of statistical extremes more likely provides the appropriate description.
Abstract
A regional climate model simulation of the period of 1979–88 over the contiguous United States, driven by lateral boundary conditions from the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis, was analyzed to assess the ability of the model to simulate heavy precipitation events and seasonal precipitation anomalies. Heavy events were defined by precipitation totals that exceed the threshold value for a specified return period and duration. The model magnitudes of the thresholds for 1-day heavy precipitation events were in good agreement with observed thresholds for much of the central United States. Model thresholds were greater than observed for the eastern and intermountain western portions of the region and were smaller than observed for the lower Mississippi River basin. For 7-day events, model thresholds were in good agreement with observed thresholds for the eastern United States and Great Plains, were less than observed for the most of the Mississippi River valley, and were greater than observed for the intermountain western region. The interannual variability in frequency of heavy events in the model simulation exhibited similar behavior to that of the observed variability in the South, Southwest, West, and North-Central study regions. The agreement was poorer for the Midwest and Northeast, although the magnitude of variability was similar for both model and observations. There was good agreement between the model and observational data in the seasonal distribution of extreme events for the West and North-Central study regions; in the Southwest, Midwest, and Northeast, there were general similarities but some differences in the details of the distributions. The most notable differences occurred for the southern Gulf Coast region, for which the model produced a summer peak that is not present in the observational data. There was not a very high correlation in the timing of individual heavy events between the model and observations, reflecting differences between model and observations in the speed and path of many of the synoptic-scale events triggering the precipitation.
Abstract
A regional climate model simulation of the period of 1979–88 over the contiguous United States, driven by lateral boundary conditions from the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis, was analyzed to assess the ability of the model to simulate heavy precipitation events and seasonal precipitation anomalies. Heavy events were defined by precipitation totals that exceed the threshold value for a specified return period and duration. The model magnitudes of the thresholds for 1-day heavy precipitation events were in good agreement with observed thresholds for much of the central United States. Model thresholds were greater than observed for the eastern and intermountain western portions of the region and were smaller than observed for the lower Mississippi River basin. For 7-day events, model thresholds were in good agreement with observed thresholds for the eastern United States and Great Plains, were less than observed for the most of the Mississippi River valley, and were greater than observed for the intermountain western region. The interannual variability in frequency of heavy events in the model simulation exhibited similar behavior to that of the observed variability in the South, Southwest, West, and North-Central study regions. The agreement was poorer for the Midwest and Northeast, although the magnitude of variability was similar for both model and observations. There was good agreement between the model and observational data in the seasonal distribution of extreme events for the West and North-Central study regions; in the Southwest, Midwest, and Northeast, there were general similarities but some differences in the details of the distributions. The most notable differences occurred for the southern Gulf Coast region, for which the model produced a summer peak that is not present in the observational data. There was not a very high correlation in the timing of individual heavy events between the model and observations, reflecting differences between model and observations in the speed and path of many of the synoptic-scale events triggering the precipitation.
A new approach, called transferability intercomparisons, is described for advancing both understanding and modeling of the global water cycle and energy budget. Under this approach, individual regional climate models perform simulations with all modeling parameters and parameterizations held constant over a specific period on several prescribed domains representing different climatic regions. The transferability framework goes beyond previous regional climate model intercomparisons to provide a global method for testing and improving model parameterizations by constraining the simulations within analyzed boundaries for several domains. Transferability intercomparisons expose the limits of our current regional modeling capacity by examining model accuracy on a wide range of climate conditions and realizations. Intercomparison of these individual model experiments provides a means for evaluating strengths and weaknesses of models outside their “home domains” (domain of development and testing). Reference sites that are conducting coordinated measurements under the continental-scale experiments under the Global Energy and Water Cycle Experiment (GEWEX) Hydrometeorology Panel provide data for evaluation of model abilities to simulate specific features of the water and energy cycles. A systematic intercomparison across models and domains more clearly exposes collective biases in the modeling process. By isolating particular regions and processes, regional model transferability intercomparisons can more effectively explore the spatial and temporal heterogeneity of predictability. A general improvement of model ability to simulate diverse climates will provide more confidence that models used for future climate scenarios might be able to simulate conditions on a particular domain that are beyond the range of previously observed climates.
A new approach, called transferability intercomparisons, is described for advancing both understanding and modeling of the global water cycle and energy budget. Under this approach, individual regional climate models perform simulations with all modeling parameters and parameterizations held constant over a specific period on several prescribed domains representing different climatic regions. The transferability framework goes beyond previous regional climate model intercomparisons to provide a global method for testing and improving model parameterizations by constraining the simulations within analyzed boundaries for several domains. Transferability intercomparisons expose the limits of our current regional modeling capacity by examining model accuracy on a wide range of climate conditions and realizations. Intercomparison of these individual model experiments provides a means for evaluating strengths and weaknesses of models outside their “home domains” (domain of development and testing). Reference sites that are conducting coordinated measurements under the continental-scale experiments under the Global Energy and Water Cycle Experiment (GEWEX) Hydrometeorology Panel provide data for evaluation of model abilities to simulate specific features of the water and energy cycles. A systematic intercomparison across models and domains more clearly exposes collective biases in the modeling process. By isolating particular regions and processes, regional model transferability intercomparisons can more effectively explore the spatial and temporal heterogeneity of predictability. A general improvement of model ability to simulate diverse climates will provide more confidence that models used for future climate scenarios might be able to simulate conditions on a particular domain that are beyond the range of previously observed climates.
