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- Author or Editor: R. Paul Lawson x
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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
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
Corrections are made to the results, and interpretation thereof, presented in earlier work by Baker and Lawson. The main results regarding the improvement obtained using additional image parameters are unchanged. Secondary results regarding the applicability of subgroup parameterizations are corrected. Whereas it was found in the earlier work that very few subgroup parameterizations could be applied, it is now found that more subgroup parameterizations could be applied in situations in which crystal habits are sufficiently identifiable.
Abstract
Corrections are made to the results, and interpretation thereof, presented in earlier work by Baker and Lawson. The main results regarding the improvement obtained using additional image parameters are unchanged. Secondary results regarding the applicability of subgroup parameterizations are corrected. Whereas it was found in the earlier work that very few subgroup parameterizations could be applied, it is now found that more subgroup parameterizations could be applied in situations in which crystal habits are sufficiently identifiable.
Abstract
A brief review of errors associated with aircraft measurements of temperature in cumulus clouds is presented. This analysis forms the basis for the introduction of a compilation of in-cloud temperature measurements that the authors deem reliable. The measurements are mostly from radiometric thermometers, along with some carefully selected measurements taken with immersion thermometers. The data were collected in cumuli and cumulonimbi in Russia, the United States, and the central Pacific. An estimate of the in-cloud temperature measurement uncertainty is on the order of 0.5°C. The results suggest that the average temperature excess in cumulus clouds, when averaged over the cloud lifetime, is about 0.2°–0.3°C; this value may be biased to an unknown extent, however, by latencies inherent in identification and aircraft sampling of candidate clouds. The maximum temperature excess in growing cumulus congestus is about 2.5°–4°C. In the weak-echo regions of large thunderstorms, the temperature excess is at least 6°–8°C. The average and maximum temperature excesses in cumulus congestus over land are about 0.5°–1°C greater than over the ocean. Measurements of the spatial and vertical distributions of in-cloud temperature excess are presented. Some measurements that pertain to the structure of in-cloud temperature are also discussed.
Abstract
A brief review of errors associated with aircraft measurements of temperature in cumulus clouds is presented. This analysis forms the basis for the introduction of a compilation of in-cloud temperature measurements that the authors deem reliable. The measurements are mostly from radiometric thermometers, along with some carefully selected measurements taken with immersion thermometers. The data were collected in cumuli and cumulonimbi in Russia, the United States, and the central Pacific. An estimate of the in-cloud temperature measurement uncertainty is on the order of 0.5°C. The results suggest that the average temperature excess in cumulus clouds, when averaged over the cloud lifetime, is about 0.2°–0.3°C; this value may be biased to an unknown extent, however, by latencies inherent in identification and aircraft sampling of candidate clouds. The maximum temperature excess in growing cumulus congestus is about 2.5°–4°C. In the weak-echo regions of large thunderstorms, the temperature excess is at least 6°–8°C. The average and maximum temperature excesses in cumulus congestus over land are about 0.5°–1°C greater than over the ocean. Measurements of the spatial and vertical distributions of in-cloud temperature excess are presented. Some measurements that pertain to the structure of in-cloud temperature are also discussed.
Abstract
Relative dispersion of ice crystals was measured in 30 seeded cumulus clouds. A quasi-instantaneous, vertical area source of ice was generated by releasing dry-ice pellets from an airplane. The ice concentration distribution and relative dispersion were measured normal to the source and were complemented by cloud turbulence measurements, namely, velocity variances and the energy dissipation rate ε. The clouds were selected based on an objective set of criteria and were treated as members of the same ensemble.
The observed mean relative dispersion σ rx agreed well with predictions from a Lagrangian stochastic two-particle model, which reproduces Batchelor's theoretical results for σ rx . For short times t after the seeding time ts , the predictions and observations suggested a growth like σ rx ∝ t − ts rather than Batchelor's “intermediate” time prediction, σ rx ∝ ε1/2 (t − ts )3/2. This difference was attributed to the rather large initial dispersion σ0 of ice crystals, 27–53 m, inferred from the measurements; Batchelor's result is only valid for σ0 ≪ σ va 3/ε, where σ va 2 is the average velocity variance. At long times, the predictions and observations approached the same asymptotic limit, σ rx ∝ (t − ts )1/2.
In addition to the mean dispersion, probability density functions (pdfs) of the individual dispersion observations were constructed and showed an evolution from a highly skewed pdf at small times to a more symmetrical one at large times. This is one of the first reports of the σrx pdf, which is important for determining the variance and pdf of the randomly varying concentration in a small ice cloud or plume of material.
Abstract
Relative dispersion of ice crystals was measured in 30 seeded cumulus clouds. A quasi-instantaneous, vertical area source of ice was generated by releasing dry-ice pellets from an airplane. The ice concentration distribution and relative dispersion were measured normal to the source and were complemented by cloud turbulence measurements, namely, velocity variances and the energy dissipation rate ε. The clouds were selected based on an objective set of criteria and were treated as members of the same ensemble.
The observed mean relative dispersion σ rx agreed well with predictions from a Lagrangian stochastic two-particle model, which reproduces Batchelor's theoretical results for σ rx . For short times t after the seeding time ts , the predictions and observations suggested a growth like σ rx ∝ t − ts rather than Batchelor's “intermediate” time prediction, σ rx ∝ ε1/2 (t − ts )3/2. This difference was attributed to the rather large initial dispersion σ0 of ice crystals, 27–53 m, inferred from the measurements; Batchelor's result is only valid for σ0 ≪ σ va 3/ε, where σ va 2 is the average velocity variance. At long times, the predictions and observations approached the same asymptotic limit, σ rx ∝ (t − ts )1/2.
In addition to the mean dispersion, probability density functions (pdfs) of the individual dispersion observations were constructed and showed an evolution from a highly skewed pdf at small times to a more symmetrical one at large times. This is one of the first reports of the σrx pdf, which is important for determining the variance and pdf of the randomly varying concentration in a small ice cloud or plume of material.
Abstract
In this study several ice cloud retrieval products that utilize active and passive A-Train measurements are evaluated using in situ data collected during the Small Particles in Cirrus (SPARTICUS) field campaign. The retrieval datasets include ice water content (IWC), effective radius re , and visible extinction σ from CloudSat level-2C ice cloud property product (2C-ICE), CloudSat level-2B radar-visible optical depth cloud water content product (2B-CWC-RVOD), radar–lidar (DARDAR), and σ from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). When the discrepancies between the radar reflectivity Ze derived from 2D stereo probe (2D-S) in situ measurements and Ze measured by the CloudSat radar are less than 10 dBZe , the flight mean ratios of the retrieved IWC to the IWC estimated from in situ data are 1.12, 1.59, and 1.02, respectively for 2C-ICE, DARDAR, and 2B-CWC-RVOD. For re , the flight mean ratios are 1.05, 1.18, and 1.61, respectively. For σ, the flight mean ratios for 2C-ICE, DARDAR, and CALIPSO are 1.03, 1.42, and 0.97, respectively. The CloudSat 2C-ICE and DARDAR retrieval products are typically in close agreement. However, the use of parameterized radar signals in ice cloud volumes that are below the detection threshold of the CloudSat radar in the 2C-ICE algorithm provides an extra constraint that leads to slightly better agreement with in situ data. The differences in assumed mass–size and area–size relations between CloudSat 2C-ICE and DARDAR also contribute to some subtle difference between the datasets: re from the 2B-CWC-RVOD dataset is biased more than the other retrieval products and in situ measurements by about 40%. A slight low (negative) bias in CALIPSO σ may be due to 5-km averaging in situations in which the cirrus layers have significant horizontal gradients in σ.
Abstract
In this study several ice cloud retrieval products that utilize active and passive A-Train measurements are evaluated using in situ data collected during the Small Particles in Cirrus (SPARTICUS) field campaign. The retrieval datasets include ice water content (IWC), effective radius re , and visible extinction σ from CloudSat level-2C ice cloud property product (2C-ICE), CloudSat level-2B radar-visible optical depth cloud water content product (2B-CWC-RVOD), radar–lidar (DARDAR), and σ from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). When the discrepancies between the radar reflectivity Ze derived from 2D stereo probe (2D-S) in situ measurements and Ze measured by the CloudSat radar are less than 10 dBZe , the flight mean ratios of the retrieved IWC to the IWC estimated from in situ data are 1.12, 1.59, and 1.02, respectively for 2C-ICE, DARDAR, and 2B-CWC-RVOD. For re , the flight mean ratios are 1.05, 1.18, and 1.61, respectively. For σ, the flight mean ratios for 2C-ICE, DARDAR, and CALIPSO are 1.03, 1.42, and 0.97, respectively. The CloudSat 2C-ICE and DARDAR retrieval products are typically in close agreement. However, the use of parameterized radar signals in ice cloud volumes that are below the detection threshold of the CloudSat radar in the 2C-ICE algorithm provides an extra constraint that leads to slightly better agreement with in situ data. The differences in assumed mass–size and area–size relations between CloudSat 2C-ICE and DARDAR also contribute to some subtle difference between the datasets: re from the 2B-CWC-RVOD dataset is biased more than the other retrieval products and in situ measurements by about 40%. A slight low (negative) bias in CALIPSO σ may be due to 5-km averaging in situations in which the cirrus layers have significant horizontal gradients in σ.
Abstract
Data from the new two-dimensional stereo (2D-S) probe are used to evaluate drop size distributions in rain shafts observed during the Rain in Shallow Cumulus over the Ocean (RICO) experiment. The 2D-S takes images of both precipitation drops and cloud droplets with 10-μm resolution. These are the first reported measurements of rain to include sizes smaller than 100 μm. The primary result is that there are almost no hydrometeors smaller than about 100 μm in these rain shafts. The measured low concentration of small hydrometeors implies that their rate of production is slow relative to their removal rate. Algorithms for removing the spurious effects of splashing precipitation and noisy photodiodes on 2D probes are also described.
Abstract
Data from the new two-dimensional stereo (2D-S) probe are used to evaluate drop size distributions in rain shafts observed during the Rain in Shallow Cumulus over the Ocean (RICO) experiment. The 2D-S takes images of both precipitation drops and cloud droplets with 10-μm resolution. These are the first reported measurements of rain to include sizes smaller than 100 μm. The primary result is that there are almost no hydrometeors smaller than about 100 μm in these rain shafts. The measured low concentration of small hydrometeors implies that their rate of production is slow relative to their removal rate. Algorithms for removing the spurious effects of splashing precipitation and noisy photodiodes on 2D probes are also described.
