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Abstract
The sensitivity of high-resolution mesoscale simulations to boundary layer turbulence parameterizations is investigated using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) and observations from two field campaigns. Three widely used turbulence parameterizations were selected for evaluation, two of which [Blackadar (BK) and Medium Range Forecast (MRF) schemes] are simple first-order nonlocal schemes and one [Gayno–Seaman (GS) scheme] of which is a more complex 1.5-order local scheme that solves a prognostic equation for turbulence kinetic energy (TKE). The two datasets are the summer 1996 Boundary Layer Experiment (BLX96) in the southern Great Plains and the autumn 2000 Vertical Transport and Mixing (VTMX) field campaign in the Salt Lake Valley in Utah. Comparisons are made between observed and simulated mean variables and turbulence statistics. Despite the differences in their complexity, all three schemes show similar skill predicting near-surface and boundary layer mean temperature, humidity, and winds at both locations. The BK and MRF schemes produced daytime boundary layers that are more mixed than those produced by the GS scheme. The mixed-layer depths are generally overestimated by the MRF scheme, underestimated by the GS scheme, and well estimated by the BK scheme. All of the schemes predicted surface latent heat fluxes that agreed reasonably well with the observed values, but they substantially overestimated surface sensible heat fluxes because of a significant overprediction of net radiation. In addition, each parameterization overestimated the sensible and latent heat flux aloft. The results suggest that there is little gain in the overall accuracy of forecasts with increasing complexity of turbulence parameterizations.
Abstract
The sensitivity of high-resolution mesoscale simulations to boundary layer turbulence parameterizations is investigated using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) and observations from two field campaigns. Three widely used turbulence parameterizations were selected for evaluation, two of which [Blackadar (BK) and Medium Range Forecast (MRF) schemes] are simple first-order nonlocal schemes and one [Gayno–Seaman (GS) scheme] of which is a more complex 1.5-order local scheme that solves a prognostic equation for turbulence kinetic energy (TKE). The two datasets are the summer 1996 Boundary Layer Experiment (BLX96) in the southern Great Plains and the autumn 2000 Vertical Transport and Mixing (VTMX) field campaign in the Salt Lake Valley in Utah. Comparisons are made between observed and simulated mean variables and turbulence statistics. Despite the differences in their complexity, all three schemes show similar skill predicting near-surface and boundary layer mean temperature, humidity, and winds at both locations. The BK and MRF schemes produced daytime boundary layers that are more mixed than those produced by the GS scheme. The mixed-layer depths are generally overestimated by the MRF scheme, underestimated by the GS scheme, and well estimated by the BK scheme. All of the schemes predicted surface latent heat fluxes that agreed reasonably well with the observed values, but they substantially overestimated surface sensible heat fluxes because of a significant overprediction of net radiation. In addition, each parameterization overestimated the sensible and latent heat flux aloft. The results suggest that there is little gain in the overall accuracy of forecasts with increasing complexity of turbulence parameterizations.
Abstract
Joint frequency distributions (JFDs) of potential temperature (θ) versus water vapor mixing ratio (r) within the convective boundary layer were measured during a new field experiment named Boundary Layer Experiment 1996 (BLX96). These JFDs were found to be tilted, with the tilt a function of both height and boundary layer dynamics. These distributions are also skewed and more peaked than a joint Gaussian distribution.
Three different methods are used to generate joint probability density functions (JPDFs) that approximate observed JFDs. Two classical methods, one based on a Gaussian fit and another based on surface-layer processes, are reviewed. A new method is devised, which treats the observed JFD as a mixing diagram. In the absence of advection, the only source regions for air in the mixing diagram are the surface and the entrainment zone. Thus, the tilt of the JFD can be explained by various mixtures from these two source regions. Methods that can be used to parameterize the mixing JPDF are presented. The primary advantage of this method is that the tilt is determined explicitly from properties of the surface, mixed layer, and entrainment zone.
Similarity methods are used to parameterize all variables needed by the Gaussian model. The Bowen ratio and the total energy input are used to parameterize the tilt of the surface energy budget JPDF, while similarity methods are used to define the spread of the JPDF along the two axes. Relationships between the surface and mixed layer, and the mixed layer and free atmosphere are used to tilt the mixing diagram JPDF, while similarity methods are used to estimate the spread of the JPDF. The parameterizations are developed using a “calibration” subset of data acquired during BLX96. A “verification” subset of data, also acquired during BLX96, is used to show that the parameterized mixing diagram method is superior to the other two methods, because it has either a smaller error or is less sensitive to the value of the correlation between θ and r.
Abstract
Joint frequency distributions (JFDs) of potential temperature (θ) versus water vapor mixing ratio (r) within the convective boundary layer were measured during a new field experiment named Boundary Layer Experiment 1996 (BLX96). These JFDs were found to be tilted, with the tilt a function of both height and boundary layer dynamics. These distributions are also skewed and more peaked than a joint Gaussian distribution.
Three different methods are used to generate joint probability density functions (JPDFs) that approximate observed JFDs. Two classical methods, one based on a Gaussian fit and another based on surface-layer processes, are reviewed. A new method is devised, which treats the observed JFD as a mixing diagram. In the absence of advection, the only source regions for air in the mixing diagram are the surface and the entrainment zone. Thus, the tilt of the JFD can be explained by various mixtures from these two source regions. Methods that can be used to parameterize the mixing JPDF are presented. The primary advantage of this method is that the tilt is determined explicitly from properties of the surface, mixed layer, and entrainment zone.
Similarity methods are used to parameterize all variables needed by the Gaussian model. The Bowen ratio and the total energy input are used to parameterize the tilt of the surface energy budget JPDF, while similarity methods are used to define the spread of the JPDF along the two axes. Relationships between the surface and mixed layer, and the mixed layer and free atmosphere are used to tilt the mixing diagram JPDF, while similarity methods are used to estimate the spread of the JPDF. The parameterizations are developed using a “calibration” subset of data acquired during BLX96. A “verification” subset of data, also acquired during BLX96, is used to show that the parameterized mixing diagram method is superior to the other two methods, because it has either a smaller error or is less sensitive to the value of the correlation between θ and r.
Abstract
Many authors have used upward-looking instruments, such as a laser ceilometer, to estimate the cover of fair-weather cumuli, but little has been mentioned as to the accuracy of these measurements. Results are presented, using a simulated cloud field and a virtual aircraft, that show that sampling errors can be very large for averaging times commonly used with surface instruments. A set of empirical equations is found to provide an estimate of the errors associated with averaging time and earth cover. These relationships can be used to design observation strategies (averaging time or flight-leg length) that provide earth-cover estimates within desired error bounds. These results are used to guide a comparison between earth cover measured by an airborne upward-looking pyranometer and earth cover observed by airborne scientists in a research aircraft. In general, the agreement between these two methods is good.
Abstract
Many authors have used upward-looking instruments, such as a laser ceilometer, to estimate the cover of fair-weather cumuli, but little has been mentioned as to the accuracy of these measurements. Results are presented, using a simulated cloud field and a virtual aircraft, that show that sampling errors can be very large for averaging times commonly used with surface instruments. A set of empirical equations is found to provide an estimate of the errors associated with averaging time and earth cover. These relationships can be used to design observation strategies (averaging time or flight-leg length) that provide earth-cover estimates within desired error bounds. These results are used to guide a comparison between earth cover measured by an airborne upward-looking pyranometer and earth cover observed by airborne scientists in a research aircraft. In general, the agreement between these two methods is good.
Abstract
A new parameterization for boundary layer cumulus clouds, called the cumulus potential (CuP) scheme, is introduced. This scheme uses joint probability density functions (JPDFs) of virtual potential temperature (θυ ) and water-vapor mixing ratio (r), as well as the mean vertical profiles of θυ , to predict the amount and size distribution of boundary layer cloud cover. This model considers the diversity of air parcels over a heterogeneous surface, and recognizes that some parcels rise above their lifting condensation level to become cumulus, while other parcels might rise as noncloud updrafts. This model has several unique features: 1) cloud cover is determined from the boundary layer JPDF of θυ versus r, 2) clear and cloudy thermals are allowed to coexist at the same altitude, and 3) a range of cloud-base heights, cloud-top heights, and cloud thicknesses are predicted within any one cloud field, as observed.
Using data from Boundary Layer Experiment 1996 and a model intercomparsion study using large eddy simulation (LES) based on Barbados Oceanographic and Meteorological Experiment (BOMEX), it is shown that the CuP model does a good job predicting cloud-base height and cloud-top height. The model also shows promise in predicting cloud cover, and is found to give better cloud-cover estimates than three other cumulus parameterizations: one based on relative humidity, a statistical scheme based on the saturation deficit, and a slab model.
Abstract
A new parameterization for boundary layer cumulus clouds, called the cumulus potential (CuP) scheme, is introduced. This scheme uses joint probability density functions (JPDFs) of virtual potential temperature (θυ ) and water-vapor mixing ratio (r), as well as the mean vertical profiles of θυ , to predict the amount and size distribution of boundary layer cloud cover. This model considers the diversity of air parcels over a heterogeneous surface, and recognizes that some parcels rise above their lifting condensation level to become cumulus, while other parcels might rise as noncloud updrafts. This model has several unique features: 1) cloud cover is determined from the boundary layer JPDF of θυ versus r, 2) clear and cloudy thermals are allowed to coexist at the same altitude, and 3) a range of cloud-base heights, cloud-top heights, and cloud thicknesses are predicted within any one cloud field, as observed.
Using data from Boundary Layer Experiment 1996 and a model intercomparsion study using large eddy simulation (LES) based on Barbados Oceanographic and Meteorological Experiment (BOMEX), it is shown that the CuP model does a good job predicting cloud-base height and cloud-top height. The model also shows promise in predicting cloud cover, and is found to give better cloud-cover estimates than three other cumulus parameterizations: one based on relative humidity, a statistical scheme based on the saturation deficit, and a slab model.
Abstract
Continental fair-weather cumuli exhibit significant diurnal, day-to-day, and year-to-year variability. This study describes the climatology of cloud macroscale properties, over the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) Southern Great Plains (SGP) site. The diurnal cycle of cloud fraction, cloud-base height, cloud-top height, and cloud thickness were well defined. The cloud fraction reached its maximum value near 1400 central standard time. The average cloud-base height increased throughout the day, while the average cloud thickness decreased with time. In contrast to the other cloud properties, the average cloud-chord length remained nearly constant throughout the day. The sensitivity of the cloud properties to the year-to-year variability of precipitation and day-to-day changes in the height of the lifting condensation level (z LCL) and surface fluxes were compared. The cloud-base height was found to be sensitive to both the year, z LCL, and the surface fluxes of heat and moisture; the cloud thickness was found to be more sensitive to the year than to z LCL; the cloud fraction was sensitive to both the low-level moisture and the surface sensible heat flux; and cloud-chord length was sensitive to z LCL. Distributions of the cloud-chord length over the ACRF SGP site were computed and were well fit by an exponential distribution. The contribution to the total cloud fraction by clouds of each cloud-chord length was computed, and it was found that the clouds with a chord length of about 1 km contributed most to the observed cloud fraction. This result is similar to observations made with other remote sensing instruments or in modeling studies, but it is different from aircraft observations of the contribution to the total cloud fraction by clouds of different sizes.
Abstract
Continental fair-weather cumuli exhibit significant diurnal, day-to-day, and year-to-year variability. This study describes the climatology of cloud macroscale properties, over the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) Southern Great Plains (SGP) site. The diurnal cycle of cloud fraction, cloud-base height, cloud-top height, and cloud thickness were well defined. The cloud fraction reached its maximum value near 1400 central standard time. The average cloud-base height increased throughout the day, while the average cloud thickness decreased with time. In contrast to the other cloud properties, the average cloud-chord length remained nearly constant throughout the day. The sensitivity of the cloud properties to the year-to-year variability of precipitation and day-to-day changes in the height of the lifting condensation level (z LCL) and surface fluxes were compared. The cloud-base height was found to be sensitive to both the year, z LCL, and the surface fluxes of heat and moisture; the cloud thickness was found to be more sensitive to the year than to z LCL; the cloud fraction was sensitive to both the low-level moisture and the surface sensible heat flux; and cloud-chord length was sensitive to z LCL. Distributions of the cloud-chord length over the ACRF SGP site were computed and were well fit by an exponential distribution. The contribution to the total cloud fraction by clouds of each cloud-chord length was computed, and it was found that the clouds with a chord length of about 1 km contributed most to the observed cloud fraction. This result is similar to observations made with other remote sensing instruments or in modeling studies, but it is different from aircraft observations of the contribution to the total cloud fraction by clouds of different sizes.
Abstract
One year of coherent Doppler lidar data collected at the U.S. Department of Energy’s Atmospheric Radiation Measurement site in Oklahoma was analyzed to provide profiles of vertical velocity variance, skewness, and kurtosis for cases of cloud-free convective boundary layers. The variance was normalized by the Deardorff convective velocity scale, which was successful when the boundary layer depth was stationary but failed in situations in which the layer was changing rapidly. In this study, the data are sorted according to time of day, season, wind direction, surface shear stress, degree of instability, and wind shear across the boundary layer top. The normalized variance was found to have its peak value near a normalized height of 0.25. The magnitude of the variance changes with season, shear stress, degree of instability, and wind shear across the boundary layer top. The skewness was largest in the top half of the boundary layer (with the exception of wintertime conditions). The skewness was also found to be a function of the season, shear stress, and wind shear across the boundary layer top. Like skewness, the vertical profile of kurtosis followed a consistent pattern, with peak values near the boundary layer top. The normalized altitude of the peak values of kurtosis was found to be higher when there was a large amount of wind shear at the boundary layer top.
Abstract
One year of coherent Doppler lidar data collected at the U.S. Department of Energy’s Atmospheric Radiation Measurement site in Oklahoma was analyzed to provide profiles of vertical velocity variance, skewness, and kurtosis for cases of cloud-free convective boundary layers. The variance was normalized by the Deardorff convective velocity scale, which was successful when the boundary layer depth was stationary but failed in situations in which the layer was changing rapidly. In this study, the data are sorted according to time of day, season, wind direction, surface shear stress, degree of instability, and wind shear across the boundary layer top. The normalized variance was found to have its peak value near a normalized height of 0.25. The magnitude of the variance changes with season, shear stress, degree of instability, and wind shear across the boundary layer top. The skewness was largest in the top half of the boundary layer (with the exception of wintertime conditions). The skewness was also found to be a function of the season, shear stress, and wind shear across the boundary layer top. Like skewness, the vertical profile of kurtosis followed a consistent pattern, with peak values near the boundary layer top. The normalized altitude of the peak values of kurtosis was found to be higher when there was a large amount of wind shear at the boundary layer top.
Abstract
In this study, the authors incorporate an operational-like irrigation scheme into the Noah land surface model as part of the Weather Research and Forecasting Model (WRF). A series of simulations, with and without irrigation, is conducted over the Southern Great Plains (SGP) for an extremely dry (2006) and wet (2007) year. The results show that including irrigation reduces model bias in soil moisture and surface latent heat (LH) and sensible heat (SH) fluxes, especially during a dry year. Irrigation adds additional water to the surface, leading to changes in the planetary boundary layer. The increase in soil moisture leads to increases in the surface evapotranspiration and near-surface specific humidity but decreases in the SH and surface temperature. Those changes are local and occur during daytime. There is an irrigation-induced decrease in both the lifting condensation level (Z LCL) and mixed-layer depth. The decrease in Z LCL is larger than the decrease in mixed-layer depth, suggesting an increasing probability of shallow clouds. The simulated changes in precipitation induced by irrigation are highly variable in space, and the average precipitation over the SGP region only slightly increases. A high correlation is found among soil moisture, SH, and Z LCL. Larger values of soil moisture in the irrigated simulation due to irrigation in late spring and summer persist into the early fall, suggesting that irrigation-induced soil memory could last a few weeks to months. The results demonstrate the importance of irrigation parameterization for climate studies and improve the process-level understanding on the role of human activity in modulating land–air–cloud interactions.
Abstract
In this study, the authors incorporate an operational-like irrigation scheme into the Noah land surface model as part of the Weather Research and Forecasting Model (WRF). A series of simulations, with and without irrigation, is conducted over the Southern Great Plains (SGP) for an extremely dry (2006) and wet (2007) year. The results show that including irrigation reduces model bias in soil moisture and surface latent heat (LH) and sensible heat (SH) fluxes, especially during a dry year. Irrigation adds additional water to the surface, leading to changes in the planetary boundary layer. The increase in soil moisture leads to increases in the surface evapotranspiration and near-surface specific humidity but decreases in the SH and surface temperature. Those changes are local and occur during daytime. There is an irrigation-induced decrease in both the lifting condensation level (Z LCL) and mixed-layer depth. The decrease in Z LCL is larger than the decrease in mixed-layer depth, suggesting an increasing probability of shallow clouds. The simulated changes in precipitation induced by irrigation are highly variable in space, and the average precipitation over the SGP region only slightly increases. A high correlation is found among soil moisture, SH, and Z LCL. Larger values of soil moisture in the irrigated simulation due to irrigation in late spring and summer persist into the early fall, suggesting that irrigation-induced soil memory could last a few weeks to months. The results demonstrate the importance of irrigation parameterization for climate studies and improve the process-level understanding on the role of human activity in modulating land–air–cloud interactions.
Abstract
We investigate the impact of three land surface models (LSMs) on simulating hub-height wind speed under three different soil regimes (dry, wet, and frozen) to improve understanding of the physics of wind energy forecasts using the Weather Research and Forecasting (WRF) Model. A 6-day representative period is selected for each soil condition. The simulated wind speed, surface energy budget, and soil properties are compared with the observations collected from the second Wind Forecast Improvement Project (WFIP2). For the selected cases, our simulation results suggest that the impact of LSMs on hub-height wind speed are sensitive to the soil states but not so much to the choice of LSM. The simulated hub-height wind speed is in much better agreement with the observations for the dry soil case than the wet and frozen soil cases. Over the dry soil, there is a strong physical connection between the land surface and hub-height wind speed through near-surface turbulent mixing. Over the wet soil, the simulated hub-height wind speed is less impacted by the land surface due to weaker surface fluxes and large-scale synoptic disturbances. Over the frozen soil, the LSM seems to have limited impact on hub-height wind speed variability due to the decoupling of the land surface with the overlying atmosphere. Two main sources of modeling uncertainties are proposed. The first is the insufficient model physics representing the surface energy budget, especially the ground heat flux, and the second is the inaccurate initial soil states such as soil temperature and soil moisture.
Abstract
We investigate the impact of three land surface models (LSMs) on simulating hub-height wind speed under three different soil regimes (dry, wet, and frozen) to improve understanding of the physics of wind energy forecasts using the Weather Research and Forecasting (WRF) Model. A 6-day representative period is selected for each soil condition. The simulated wind speed, surface energy budget, and soil properties are compared with the observations collected from the second Wind Forecast Improvement Project (WFIP2). For the selected cases, our simulation results suggest that the impact of LSMs on hub-height wind speed are sensitive to the soil states but not so much to the choice of LSM. The simulated hub-height wind speed is in much better agreement with the observations for the dry soil case than the wet and frozen soil cases. Over the dry soil, there is a strong physical connection between the land surface and hub-height wind speed through near-surface turbulent mixing. Over the wet soil, the simulated hub-height wind speed is less impacted by the land surface due to weaker surface fluxes and large-scale synoptic disturbances. Over the frozen soil, the LSM seems to have limited impact on hub-height wind speed variability due to the decoupling of the land surface with the overlying atmosphere. Two main sources of modeling uncertainties are proposed. The first is the insufficient model physics representing the surface energy budget, especially the ground heat flux, and the second is the inaccurate initial soil states such as soil temperature and soil moisture.
Abstract
While fractional entrainment rates for cumulus clouds have typically been derived from airborne observations, this limits the size and scope of available datasets. To increase the number of continental cumulus entrainment rate observations available for study, an algorithm for retrieving them from ground-based remote sensing observations has been developed. This algorithm, called the Entrainment Rate In Cumulus Algorithm (ERICA), uses the suite of instruments at the Southern Great Plains (SGP) site of the U.S. Department of Energy's Atmospheric Radiation Measurement Program (ARM) Climate Research Facility as inputs into a Gauss–Newton optimal estimation scheme, in which an assumed guess of the entrainment rate is iteratively adjusted through intercomparison of modeled cloud attributes to their observed counterparts. The forward model in this algorithm is the explicit mixing parcel model (EMPM), a cloud parcel model that treats entrainment as a series of discrete entrainment events. A quantified value for the uncertainty in the retrieved entrainment rate is also returned as part of the retrieval. Sensitivity testing and information content analysis demonstrate the robust nature of this method for retrieving accurate observations of the entrainment rate without the drawbacks of airborne sampling. Results from a test of ERICA on 3 months of shallow cumulus cloud events show significant variability of the entrainment rate of clouds in a single day and from one day to the next. The mean value of 1.06 km−1 for the entrainment rate in this dataset corresponds well with prior observations and simulations of the entrainment rate in cumulus clouds.
Abstract
While fractional entrainment rates for cumulus clouds have typically been derived from airborne observations, this limits the size and scope of available datasets. To increase the number of continental cumulus entrainment rate observations available for study, an algorithm for retrieving them from ground-based remote sensing observations has been developed. This algorithm, called the Entrainment Rate In Cumulus Algorithm (ERICA), uses the suite of instruments at the Southern Great Plains (SGP) site of the U.S. Department of Energy's Atmospheric Radiation Measurement Program (ARM) Climate Research Facility as inputs into a Gauss–Newton optimal estimation scheme, in which an assumed guess of the entrainment rate is iteratively adjusted through intercomparison of modeled cloud attributes to their observed counterparts. The forward model in this algorithm is the explicit mixing parcel model (EMPM), a cloud parcel model that treats entrainment as a series of discrete entrainment events. A quantified value for the uncertainty in the retrieved entrainment rate is also returned as part of the retrieval. Sensitivity testing and information content analysis demonstrate the robust nature of this method for retrieving accurate observations of the entrainment rate without the drawbacks of airborne sampling. Results from a test of ERICA on 3 months of shallow cumulus cloud events show significant variability of the entrainment rate of clouds in a single day and from one day to the next. The mean value of 1.06 km−1 for the entrainment rate in this dataset corresponds well with prior observations and simulations of the entrainment rate in cumulus clouds.
