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- Author or Editor: Ronald E. Stewart x
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Abstract
The effects of particle fallspeeds on the downwind spread of initially vertical columns or curtains are examined in environments with wind shear. Sets of equations describing the column width as a function of time and distance below column top are derived by assuming, first, that the particles fall at a constant rate and, second, that particle fallspeed changes with time. These predictions are compared with measurements of a seeding curtain within a non-turbulent stratus cloud with high wind shear (0.017 s−1). The comparison implies that differential fallspeed effects in a non-turbulent sheared environment can account for much of the spread of the curtains.
Abstract
The effects of particle fallspeeds on the downwind spread of initially vertical columns or curtains are examined in environments with wind shear. Sets of equations describing the column width as a function of time and distance below column top are derived by assuming, first, that the particles fall at a constant rate and, second, that particle fallspeed changes with time. These predictions are compared with measurements of a seeding curtain within a non-turbulent stratus cloud with high wind shear (0.017 s−1). The comparison implies that differential fallspeed effects in a non-turbulent sheared environment can account for much of the spread of the curtains.
Abstract
The microphysical consequences of seeding stratiform clouds near the Sierra Nevada Mountains are examined. Airborne seeding was conducted with droppable AgI flares released every 250 m and with dry ice pellets released at a rate of 0.1 g m−1 into the clouds having widespread liquid water contents ∼0.1 g m−3. The Wyoming King Air penetrated the AgI curtains for ∼1 h after seeding. The CO2 ice crystal curtain could not be determined beyond ∼10 min because of natural cloud glaciation. Precipitation sized particles grew mainly by diffusion, and particle size spectra at particular levels below cloud top reached and maintained equilibrium shapes as a consequence of particles falling from higher levels.
Abstract
The microphysical consequences of seeding stratiform clouds near the Sierra Nevada Mountains are examined. Airborne seeding was conducted with droppable AgI flares released every 250 m and with dry ice pellets released at a rate of 0.1 g m−1 into the clouds having widespread liquid water contents ∼0.1 g m−3. The Wyoming King Air penetrated the AgI curtains for ∼1 h after seeding. The CO2 ice crystal curtain could not be determined beyond ∼10 min because of natural cloud glaciation. Precipitation sized particles grew mainly by diffusion, and particle size spectra at particular levels below cloud top reached and maintained equilibrium shapes as a consequence of particles falling from higher levels.
Abstract
Airborne seeding experiments were conducted over the Sierra Nevada Mountains in essentially ice-free convective clouds on two days in March 1979 as part of the Sierra Cooperative Pilot Project. On 18 March towering cumuli which extended above a stratiform layer of clouds were seeded, while on 21 March individual towering cumuli were seeded as they developed and moved over the windward side of the mountains. Each cloud was seeded with a vertical curtain oriented perpendicular to the winds during a single pass through the cloud top. The seeding mode was either a low (∼0.1 g m−1) or high (∼1 g m−1) CO2 rate or AgI flares (one 20-gram flare per 250 m).
The seeded curtains were penetrated a number of times by the University of Wyoming King Air. The high CO2 rate apparently overseeded the cloud in that the liquid water was depleted and the cloud dissipated in ∼35 min. Even though much of the liquid water was depleted in the other seeded clouds, they persisted and precipitated for over an hour because additional liquid water was condensed through the additional release of convective instability from orographic lifting. The clouds seeded with a low CO2 rate and with AgI flares yielded similar microphysical characteristics and both methods appeared to have converted the non-precipitating clouds to continuously precipitating clouds.
Abstract
Airborne seeding experiments were conducted over the Sierra Nevada Mountains in essentially ice-free convective clouds on two days in March 1979 as part of the Sierra Cooperative Pilot Project. On 18 March towering cumuli which extended above a stratiform layer of clouds were seeded, while on 21 March individual towering cumuli were seeded as they developed and moved over the windward side of the mountains. Each cloud was seeded with a vertical curtain oriented perpendicular to the winds during a single pass through the cloud top. The seeding mode was either a low (∼0.1 g m−1) or high (∼1 g m−1) CO2 rate or AgI flares (one 20-gram flare per 250 m).
The seeded curtains were penetrated a number of times by the University of Wyoming King Air. The high CO2 rate apparently overseeded the cloud in that the liquid water was depleted and the cloud dissipated in ∼35 min. Even though much of the liquid water was depleted in the other seeded clouds, they persisted and precipitated for over an hour because additional liquid water was condensed through the additional release of convective instability from orographic lifting. The clouds seeded with a low CO2 rate and with AgI flares yielded similar microphysical characteristics and both methods appeared to have converted the non-precipitating clouds to continuously precipitating clouds.
Abstract
Cloud physics data measured by aircraft during two successive winter field seasons (1978–79 and 1979–80) of the Sierra Cooperative Pilot Project operating over the Sierra Nevada Range have been examined in order to determine the distributions of supercooled liquid water and ice crystals. Results indicate that convective clouds provide the greatest likelihood of significant supercooled water. The Sierra barrier appears to optimize these conditions 40 to 90 km upwind of the crest within pockets of horizontal extent up to 64 km, although these conditions were greatly reduced at temperatures less than −10°C. The dominance of liquid water content over ice crystal concentration was maximized 7–10 h after the 700 mb trough passage. Area-wide and banded clouds, which make up the remaining precipitation events, showed only small amounts of supercooled water and general abundance of ice crystals. The largest liquid water contents were observed at the greatest temperatures, usually 0° to −5°C. Such climatological information suggests that a weather modification program to enhance snowfall should concentrate primarily on the convective clouds.
Abstract
Cloud physics data measured by aircraft during two successive winter field seasons (1978–79 and 1979–80) of the Sierra Cooperative Pilot Project operating over the Sierra Nevada Range have been examined in order to determine the distributions of supercooled liquid water and ice crystals. Results indicate that convective clouds provide the greatest likelihood of significant supercooled water. The Sierra barrier appears to optimize these conditions 40 to 90 km upwind of the crest within pockets of horizontal extent up to 64 km, although these conditions were greatly reduced at temperatures less than −10°C. The dominance of liquid water content over ice crystal concentration was maximized 7–10 h after the 700 mb trough passage. Area-wide and banded clouds, which make up the remaining precipitation events, showed only small amounts of supercooled water and general abundance of ice crystals. The largest liquid water contents were observed at the greatest temperatures, usually 0° to −5°C. Such climatological information suggests that a weather modification program to enhance snowfall should concentrate primarily on the convective clouds.
Abstract
The phase of precipitation formed within the atmosphere is highly dependent on the vertical temperature profile through which it falls. In particular, several precipitation types can form in an environment with a melting layer aloft and a refreezing layer below. These precipitation types include freezing rain, ice pellets, wet snow, and slush. To examine the formation of such precipitation, a bulk microphysics scheme was used to compare the characteristics of the hydrometeors produced by the model and observed by a research aircraft flight during the 1998 ice storm near Montreal, Canada. The model reproduced several of the observed key precipitation characteristics. Sensitivity tests on the precipitation types formed during the ice storm were also performed. These tests utilized temperature profiles produced by the North American Regional Reanalysis. The results show that small variations (±0.5°C) in the temperature profiles as well as in the precipitation rate can have major impacts on the types of precipitation formed at the surface. These results impose strong requirements on the accuracy needed by prediction models.
Abstract
The phase of precipitation formed within the atmosphere is highly dependent on the vertical temperature profile through which it falls. In particular, several precipitation types can form in an environment with a melting layer aloft and a refreezing layer below. These precipitation types include freezing rain, ice pellets, wet snow, and slush. To examine the formation of such precipitation, a bulk microphysics scheme was used to compare the characteristics of the hydrometeors produced by the model and observed by a research aircraft flight during the 1998 ice storm near Montreal, Canada. The model reproduced several of the observed key precipitation characteristics. Sensitivity tests on the precipitation types formed during the ice storm were also performed. These tests utilized temperature profiles produced by the North American Regional Reanalysis. The results show that small variations (±0.5°C) in the temperature profiles as well as in the precipitation rate can have major impacts on the types of precipitation formed at the surface. These results impose strong requirements on the accuracy needed by prediction models.
Abstract
The investigation reported here involves the automatic classification of binary (black and white) images of hydrometeors (ice particles and raindrops) taken from cloud samples. The goal is to classify such images (both complete and fractional) into the seven most common classes of hydrometeors by statistical pattern recognition techniques. Detailed investigation about the data acquisition system and preprocessing is made. Four moment invariants which yield good class separation were used as features for the classification process. A Bayes decision function which minimizes the probability of misclassification is used for classification.
Bayes theorem is employed to update mean vectors and covariance matrices involved in the decision function. A discrete Kalman filtering algorithm is developed for the on-line estimation of the probability of occurrence of each class. For such estimation a discrete adaptive Kalman filtering algorithm is also developed which adjusts the filter gain matrix such as to whiten the innovations sequence. These techniques were shown to work well but the adaptive algorithm was found to converge to the correct probability more rapidly.
The classification algorithm was modified to classify incomplete or fractional images and two metrics were successfully developed to detect the unclassifiable images. The adaptive Kalman filter with the Bayes decision function was employed to classify about 2000 images per minute of CPU time with about 10% error.
Abstract
The investigation reported here involves the automatic classification of binary (black and white) images of hydrometeors (ice particles and raindrops) taken from cloud samples. The goal is to classify such images (both complete and fractional) into the seven most common classes of hydrometeors by statistical pattern recognition techniques. Detailed investigation about the data acquisition system and preprocessing is made. Four moment invariants which yield good class separation were used as features for the classification process. A Bayes decision function which minimizes the probability of misclassification is used for classification.
Bayes theorem is employed to update mean vectors and covariance matrices involved in the decision function. A discrete Kalman filtering algorithm is developed for the on-line estimation of the probability of occurrence of each class. For such estimation a discrete adaptive Kalman filtering algorithm is also developed which adjusts the filter gain matrix such as to whiten the innovations sequence. These techniques were shown to work well but the adaptive algorithm was found to converge to the correct probability more rapidly.
The classification algorithm was modified to classify incomplete or fractional images and two metrics were successfully developed to detect the unclassifiable images. The adaptive Kalman filter with the Bayes decision function was employed to classify about 2000 images per minute of CPU time with about 10% error.
