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Abstract
A system which measures vertical velocity of the air from an aircraft is discussed and evaluated. Basically, the vertical air velocity system (VAVS) utilizes an incidence vane, vertical accelerometer, and computer-directed high-accuracy vertical gyro to measure and display vertical air velocity in real time. This technique is found to have several advantages over computational techniques which use aircraft response to estimate vertical air velocity.
The VAVS is compared in a formation flight with the vertical air velocity output from a system employing an inertial navigation system (INS) mounted on an NCAR Queen Air. Spectral density plots for the VAVS and INS agreed well with each other for wavelengths from 2 km to 150 m. Also shown is a representative VAVS data output from penetrations of a cumulus cloud during the 1976 HIPLEX program.
Abstract
A system which measures vertical velocity of the air from an aircraft is discussed and evaluated. Basically, the vertical air velocity system (VAVS) utilizes an incidence vane, vertical accelerometer, and computer-directed high-accuracy vertical gyro to measure and display vertical air velocity in real time. This technique is found to have several advantages over computational techniques which use aircraft response to estimate vertical air velocity.
The VAVS is compared in a formation flight with the vertical air velocity output from a system employing an inertial navigation system (INS) mounted on an NCAR Queen Air. Spectral density plots for the VAVS and INS agreed well with each other for wavelengths from 2 km to 150 m. Also shown is a representative VAVS data output from penetrations of a cumulus cloud during the 1976 HIPLEX program.
Abstract
The basic design requirements and dynamic performance evaluation techniques are discussed for a vertical air velocity system (VAVS) installed on a Learjet. An empirical technique is presented which compensates the measured angle of attack for the effects of upwash. Flight tests of the VAVS indicated dynamic errors on the order of 0.6 m s−1 plus 3–12% of the aircraft induced vertical velocity during maneuvers where horizontal accelerations were <0.2 m s−2. Substantially larger dynamic errors were seen in the VAVS during maneuvers where the horizontal acceleration exceeded about 0.5 m s−2.
Abstract
The basic design requirements and dynamic performance evaluation techniques are discussed for a vertical air velocity system (VAVS) installed on a Learjet. An empirical technique is presented which compensates the measured angle of attack for the effects of upwash. Flight tests of the VAVS indicated dynamic errors on the order of 0.6 m s−1 plus 3–12% of the aircraft induced vertical velocity during maneuvers where horizontal accelerations were <0.2 m s−2. Substantially larger dynamic errors were seen in the VAVS during maneuvers where the horizontal acceleration exceeded about 0.5 m s−2.
Abstract
Thunderstorms are difficult to predict because of their small length scale and fast predictability destruction. A cell’s predictability is constrained by properties of the flow in which it is embedded (e.g., vertical wind shear), and associated instabilities (e.g., convective available potential energy). To assess how predictability of thunderstorms changes with environment, two groups of 780 idealized simulations (each using a different microphysics scheme) were performed over a range of buoyancy and shear profiles. Results were not sensitive to the scheme chosen. The gradient in diagnostics (updraft speed, storm speed, etc.) across shear–buoyancy phase space represents sensitivity to small changes in initial conditions: a proxy for inherent predictability. Storm evolution is split into two groups, separated by a U-shaped bifurcation in phase space, comprising 1) cells that continue strengthening after 1 h versus 2) those that weaken. Ensemble forecasts in regimes near this bifurcation are hence expected to have larger uncertainty, and adequate dispersion and reliability is essential. Predictability loss takes two forms: (i) chaotic error growth from the largest and most powerful storms, and (ii) tipping points at the U-shaped perimeter of the stronger storms. The former is associated with traditional forecast error between corresponding grid points, and is here counterintuitive; the latter is associated with object-based error, and matches the mental filtering performed by human forecasters for the convective scale.
Abstract
Thunderstorms are difficult to predict because of their small length scale and fast predictability destruction. A cell’s predictability is constrained by properties of the flow in which it is embedded (e.g., vertical wind shear), and associated instabilities (e.g., convective available potential energy). To assess how predictability of thunderstorms changes with environment, two groups of 780 idealized simulations (each using a different microphysics scheme) were performed over a range of buoyancy and shear profiles. Results were not sensitive to the scheme chosen. The gradient in diagnostics (updraft speed, storm speed, etc.) across shear–buoyancy phase space represents sensitivity to small changes in initial conditions: a proxy for inherent predictability. Storm evolution is split into two groups, separated by a U-shaped bifurcation in phase space, comprising 1) cells that continue strengthening after 1 h versus 2) those that weaken. Ensemble forecasts in regimes near this bifurcation are hence expected to have larger uncertainty, and adequate dispersion and reliability is essential. Predictability loss takes two forms: (i) chaotic error growth from the largest and most powerful storms, and (ii) tipping points at the U-shaped perimeter of the stronger storms. The former is associated with traditional forecast error between corresponding grid points, and is here counterintuitive; the latter is associated with object-based error, and matches the mental filtering performed by human forecasters for the convective scale.
Abstract
A new airborne thermometer has been designed using results from numerical simulators of airflow and particle (drop) trajectories. Initial flight tests with the NCAR King Air show that the new thermometer, which uses a fine-wire thermocouple for the sensor and lacks a probe housing, has a response time that is significantly faster than thermometers currently in use. An example of heat-flux calculations in a convective boundary layer shows that, compared to measurements using the Rosemount thermometer and NCAR K probes, the turbulent heat flux is greater by about 20% when using measurements from the new thermometer. Theoretical calculations of time response support the claim that the improved response is due to the absence of a probe housing.
The new thermometer was designed to inertially separate cloud drops from the airflow, and flights in warm clouds suggest that the thermocouple sensor stays dry except in clouds that contain high concentrations of drizzle-size drops. In small cumulus clouds with approximately 1 g m−3 of liquid water that contained low concentrations (∼10 l−1) of drizzle drops, the new thermocouple probe consistently measured warmer temperatures than the reverse-flow and Rosemount thermometers, suggesting that in these clouds the thermocouple probe may not have been affected by errors from sensor wetting. Thus, static temperature measured by the new thermometer in clouds with continental drop spectra should be reliable. An example of data collected in a mixed region of a small cumulus cloud shows that there may be more temperature structure at scales of 2–50 m than previously observed.
Abstract
A new airborne thermometer has been designed using results from numerical simulators of airflow and particle (drop) trajectories. Initial flight tests with the NCAR King Air show that the new thermometer, which uses a fine-wire thermocouple for the sensor and lacks a probe housing, has a response time that is significantly faster than thermometers currently in use. An example of heat-flux calculations in a convective boundary layer shows that, compared to measurements using the Rosemount thermometer and NCAR K probes, the turbulent heat flux is greater by about 20% when using measurements from the new thermometer. Theoretical calculations of time response support the claim that the improved response is due to the absence of a probe housing.
The new thermometer was designed to inertially separate cloud drops from the airflow, and flights in warm clouds suggest that the thermocouple sensor stays dry except in clouds that contain high concentrations of drizzle-size drops. In small cumulus clouds with approximately 1 g m−3 of liquid water that contained low concentrations (∼10 l−1) of drizzle drops, the new thermocouple probe consistently measured warmer temperatures than the reverse-flow and Rosemount thermometers, suggesting that in these clouds the thermocouple probe may not have been affected by errors from sensor wetting. Thus, static temperature measured by the new thermometer in clouds with continental drop spectra should be reliable. An example of data collected in a mixed region of a small cumulus cloud shows that there may be more temperature structure at scales of 2–50 m than previously observed.
Abstract
The general characteristics of the clouds that were included in the HIPLEX-1 experiment are reviewed, and the results for the response variables are interpreted in light of other measurements from the instrumented aircraft. In most seeded clouds, the HIPLEX-1 experimental hypothesis corresponded with the observed precipitation development for only the first ∼8 min after seeding. The failure to obtain a stronger statistical result is attributed to the inherent inefficiency of the small cumulus congestus selected as experimental units. This inefficiency was only partly due to low ice concentrations; a more significant cause of the low precipitation efficiency was the limited lifetime and low liquid water content of these clouds. Some calculations which indicate that these clouds could not support a rapid enough accretional growth process to lead to precipitation after seeding are discussed. Other reasons for the successes and failures of the experiment are discussed.
Abstract
The general characteristics of the clouds that were included in the HIPLEX-1 experiment are reviewed, and the results for the response variables are interpreted in light of other measurements from the instrumented aircraft. In most seeded clouds, the HIPLEX-1 experimental hypothesis corresponded with the observed precipitation development for only the first ∼8 min after seeding. The failure to obtain a stronger statistical result is attributed to the inherent inefficiency of the small cumulus congestus selected as experimental units. This inefficiency was only partly due to low ice concentrations; a more significant cause of the low precipitation efficiency was the limited lifetime and low liquid water content of these clouds. Some calculations which indicate that these clouds could not support a rapid enough accretional growth process to lead to precipitation after seeding are discussed. Other reasons for the successes and failures of the experiment are discussed.
Abstract
Ice water content in natural clouds is an important but difficult quantity to measure. The goal of a number of past studies was to find average relationships between the masses and lengths of ice particles to determine ice water content from in situ data, such as those routinely recorded with two-dimensional imaging probes. The general approach in these past studies was to measure maximum length L and mass M of a dataset of ice crystals collected at a ground site. Linear regression analysis was performed on the logarithms of the data to estimate an average mass-to-length relationship of the form M = αLβ . Relationships were determined for subsets of the dataset based on crystal habit (shape) as well as for the full dataset. In this study, alternative relationships for determining mass using the additional parameters of width W, area A, and perimeter P are explored. A 50% reduction in rms error in the determination of mass relative to using L alone is achieved using a single parameter that is a combination of L, W, A, and P. The new parameter is designed to take into account the shape of the ice particle without the need to classify the crystals first. An interesting result is that, when applied to the test dataset, the same reduction in rms error is also shown to be achievable using A alone. Using A alone facilitates the reanalysis and improvement of the determination of ice water content from large existing datasets of two-dimensional images, because A is simply the number of occulted pixels in the digital images. Possible sources of error in this study are investigated, as is the usefulness of first segregating the particles into crystal habits.
Abstract
Ice water content in natural clouds is an important but difficult quantity to measure. The goal of a number of past studies was to find average relationships between the masses and lengths of ice particles to determine ice water content from in situ data, such as those routinely recorded with two-dimensional imaging probes. The general approach in these past studies was to measure maximum length L and mass M of a dataset of ice crystals collected at a ground site. Linear regression analysis was performed on the logarithms of the data to estimate an average mass-to-length relationship of the form M = αLβ . Relationships were determined for subsets of the dataset based on crystal habit (shape) as well as for the full dataset. In this study, alternative relationships for determining mass using the additional parameters of width W, area A, and perimeter P are explored. A 50% reduction in rms error in the determination of mass relative to using L alone is achieved using a single parameter that is a combination of L, W, A, and P. The new parameter is designed to take into account the shape of the ice particle without the need to classify the crystals first. An interesting result is that, when applied to the test dataset, the same reduction in rms error is also shown to be achievable using A alone. Using A alone facilitates the reanalysis and improvement of the determination of ice water content from large existing datasets of two-dimensional images, because A is simply the number of occulted pixels in the digital images. Possible sources of error in this study are investigated, as is the usefulness of first segregating the particles into crystal habits.
Abstract
In Part I of this two-part series, a new relationship for ice particle mass M was derived based on an expanded dataset of photographed ice particles and melted drops. The new relationship resulted in a reduction of nearly 50% in the rms error in M. In this paper, new relationships for computing particle mass and ice water content from 2D particle imagery are compared with other relationships previously used in the literature. Comparison of the old and new relationships, when applied to data collected in natural clouds, shows that results using the old relationships differ from the new relationships by up to a factor of 3, depending on particle size and shape. One of the new relationships can be applied to existing (archived) datasets of two-dimensional images, provided that the number of occulted pixels in each image (i.e., projected area) is available.
Abstract
In Part I of this two-part series, a new relationship for ice particle mass M was derived based on an expanded dataset of photographed ice particles and melted drops. The new relationship resulted in a reduction of nearly 50% in the rms error in M. In this paper, new relationships for computing particle mass and ice water content from 2D particle imagery are compared with other relationships previously used in the literature. Comparison of the old and new relationships, when applied to data collected in natural clouds, shows that results using the old relationships differ from the new relationships by up to a factor of 3, depending on particle size and shape. One of the new relationships can be applied to existing (archived) datasets of two-dimensional images, provided that the number of occulted pixels in each image (i.e., projected area) is available.
Abstract
The ability of airborne instruments to measure temperature in cloud is studied using theoretical analyses and experimental data. Theoretical predictions of the effects of sensor wetting are reviewed and modified, and are then compared to measurements. Two airborne immersion thermometers, the NCAR “reverse-flow” thermometer and the Rosemount 102 thermometer, are compared to each other and to a new radiometric thermometer. The comparisons show that out of cloud all three thermometers agree well with each other. However, there is clear evidence that the immersion thermometers become wet in some clouds and measure erroneously low temperatures as a result. The evidence, particularly from measurements in unmixed parcels, supports the validity of the measurements from the radiometric thermometer both inside and outside clouds. Supporting evidence that the immersion sensors are susceptible to wetting is provided from tests in a wind tunnel and from measurements using a conductivity sensor placed at the location of the immersion sensors. The scientific consequences of these measurement errors, particularly in studies of entrainment and of cloud buoyancy, are discussed.
Abstract
The ability of airborne instruments to measure temperature in cloud is studied using theoretical analyses and experimental data. Theoretical predictions of the effects of sensor wetting are reviewed and modified, and are then compared to measurements. Two airborne immersion thermometers, the NCAR “reverse-flow” thermometer and the Rosemount 102 thermometer, are compared to each other and to a new radiometric thermometer. The comparisons show that out of cloud all three thermometers agree well with each other. However, there is clear evidence that the immersion thermometers become wet in some clouds and measure erroneously low temperatures as a result. The evidence, particularly from measurements in unmixed parcels, supports the validity of the measurements from the radiometric thermometer both inside and outside clouds. Supporting evidence that the immersion sensors are susceptible to wetting is provided from tests in a wind tunnel and from measurements using a conductivity sensor placed at the location of the immersion sensors. The scientific consequences of these measurement errors, particularly in studies of entrainment and of cloud buoyancy, are discussed.
Abstract
Updated analyses of in situ microphysical properties of three Arctic cloud systems sampled by aircraft in July 1998 during the Surface Heat Budget of the Arctic Ocean (SHEBA)/First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment–Arctic Clouds Experiment (FIRE–ACE) are examined in detail and compared with surface-based millimeter Doppler radar. A fourth case is given a cursory examination. The clouds were at 78°N over a melting ice surface, in distinctly different yet typical synoptic conditions. The cases comprise a midlevel all-ice cloud on 8 July; a deep, weakly forced, layered, mixed-phase stratus cloud system with pockets of drizzle, large dendrites, rimed ice and aggregates on 18 July; and a deep, mixed-phase cloud system with embedded convection on 28 July followed by an all-water boundary layer cloud on 29 July. The new observations include measured ice water content exceeding 2 g m−3 on 18 and 28 July and 3-cm snowflakes and 5-mm graupel particles on 28 July, unexpected in clouds close to the North Pole. Radar–aircraft agreement in reflectivity and derived microphysical parameters was reasonably good for the all-water and all-ice cases. In contrast, agreement in radar–aircraft reflectivity and derived parameters was generally inconsistent and sometimes poor for the two mixed-phase cases. The inconsistent agreement in radar–aircraft retrievals may be a result of large uncertainties in both instrument platforms and the algorithms used to retrieve derived parameters. The data also suggest that (single-wavelength) radar alone may not be capable of accurately retrieving the microphysical effects of cloud drops and drizzle in mixed-phase clouds, especially radiative properties such as extinction, albedo, and optical depth. However, more research is required before this generalization can be considered conclusive.
Abstract
Updated analyses of in situ microphysical properties of three Arctic cloud systems sampled by aircraft in July 1998 during the Surface Heat Budget of the Arctic Ocean (SHEBA)/First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment–Arctic Clouds Experiment (FIRE–ACE) are examined in detail and compared with surface-based millimeter Doppler radar. A fourth case is given a cursory examination. The clouds were at 78°N over a melting ice surface, in distinctly different yet typical synoptic conditions. The cases comprise a midlevel all-ice cloud on 8 July; a deep, weakly forced, layered, mixed-phase stratus cloud system with pockets of drizzle, large dendrites, rimed ice and aggregates on 18 July; and a deep, mixed-phase cloud system with embedded convection on 28 July followed by an all-water boundary layer cloud on 29 July. The new observations include measured ice water content exceeding 2 g m−3 on 18 and 28 July and 3-cm snowflakes and 5-mm graupel particles on 28 July, unexpected in clouds close to the North Pole. Radar–aircraft agreement in reflectivity and derived microphysical parameters was reasonably good for the all-water and all-ice cases. In contrast, agreement in radar–aircraft reflectivity and derived parameters was generally inconsistent and sometimes poor for the two mixed-phase cases. The inconsistent agreement in radar–aircraft retrievals may be a result of large uncertainties in both instrument platforms and the algorithms used to retrieve derived parameters. The data also suggest that (single-wavelength) radar alone may not be capable of accurately retrieving the microphysical effects of cloud drops and drizzle in mixed-phase clouds, especially radiative properties such as extinction, albedo, and optical depth. However, more research is required before this generalization can be considered conclusive.
Abstract
The spacing of cloud droplets observed along an approximately horizontal line through a cloud may be analyzed using a variety of techniques to reveal structure on small scales, sometimes called clustering, if such structure exists. A number of techniques have been applied and others have been suggested but not yet rigorously defined and applied. In this paper techniques are studied and evaluated using synthetic droplet spacing data. For the type of small-scale structure (clustering) modeled in this study, the most promising analysis approach is to use a combination of the power spectrum and the fishing statistic. Standard deviations and confidence intervals are determined for the power spectrum, the pair correlation function, and a modified fishing statistic. The clustering index and the volume-averaged pair correlation are shown to be less usefully normalized forms of the fishing statistic.
Abstract
The spacing of cloud droplets observed along an approximately horizontal line through a cloud may be analyzed using a variety of techniques to reveal structure on small scales, sometimes called clustering, if such structure exists. A number of techniques have been applied and others have been suggested but not yet rigorously defined and applied. In this paper techniques are studied and evaluated using synthetic droplet spacing data. For the type of small-scale structure (clustering) modeled in this study, the most promising analysis approach is to use a combination of the power spectrum and the fishing statistic. Standard deviations and confidence intervals are determined for the power spectrum, the pair correlation function, and a modified fishing statistic. The clustering index and the volume-averaged pair correlation are shown to be less usefully normalized forms of the fishing statistic.
