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Creators/Authors contains: "Kunkel, Melvin L."

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  1. Abstract The spatial distribution and magnitude of snowfall resulting from cloud seeding with silver iodide (AgI) is closely linked to atmospheric conditions, seeding operations, and dynamical, thermodynamical, and microphysical processes. Here, microphysical processes leading to ice and snow production are analyzed in orographic clouds for three cloud-seeding events, each with light or no natural precipitation and well-defined, traceable seeding lines. Airborne and ground-based radar observations are linked to in situ cloud and precipitation measurements to determine the spatiotemporal evolution of ice initiation, particle growth, and snow fallout in seeded clouds. These processes and surface snow amounts are explored as particle plumes evolve from varying amounts of AgI released, and within changing environmental conditions, including changes in liquid water content (LWC) along and downwind of the seeding track, wind speed, and shear. More AgI did not necessarily produce more liquid equivalent snowfall (LESnow). The greatest amount of LESnow, largest area covered by snowfall, and highest peak snowfall produced through seeding occurred on the day with the largest and most widespread occurrence of supercooled drizzle, highest wind shear, and greater LWC along and downwind of the seeding track. The day with the least supercooled drizzle and the lowest LWC downwind of themore »seeding track produced the smallest amount of LESnow through seeding. The stronger the wind was, the farther away the snowfall occurred from the seeding track.« less
  2. Climate change and population growth have increased demand for water in arid regions. For over half a century, cloud seeding has been evaluated as a technology to increase water supply; statistical approaches have compared seeded to nonseeded events through precipitation gauge analyses. Here, a physically based approach to quantify snowfall from cloud seeding in mountain cloud systems is presented. Areas of precipitation unambiguously attributed to cloud seeding are isolated from natural precipitation (<1 mm h−1). Spatial and temporal evolution of precipitation generated by cloud seeding is then quantified using radar observations and snow gauge measurements. This study uses the approach of combining radar technology and precipitation gauge measurements to quantify the spatial and temporal evolution of snowfall generated from glaciogenic cloud seeding of winter mountain cloud systems and its spatial and temporal evolution. The results represent a critical step toward quantifying cloud seeding impact. For the cases presented, precipitation gauges measured increases between 0.05 and 0.3 mm as precipitation generated by cloud seeding passed over the instruments. The total amount of water generated by cloud seeding ranged from 1.2 × 105m3(100 ac ft) for 20 min of cloud seeding, 2.4 × 105m3(196 ac ft) for 86 min of seeding tomore »3.4 x 105m3(275 ac ft) for 24 min of cloud seeding.

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  3. Abstract This paper reviews research conducted over the last six decades to understand and quantify the efficacy of wintertime orographic cloud seeding to increase winter snowpack and water supplies within a mountain basin. The fundamental hypothesis underlying cloud seeding as a method to enhance precipitation from wintertime orographic cloud systems is that a cloud’s natural precipitation efficiency can be enhanced by converting supercooled water to ice upstream and over a mountain range in such a manner that newly created ice particles can grow and fall to the ground as additional snow on a specified target area. The review summarizes the results of physical, statistical, and modeling studies aimed at evaluating this underlying hypothesis, with a focus on results from more recent experiments that take advantage of modern instrumentation and advanced computation capabilities. Recent advances in assessment and operations are also reviewed, and recommendations for future experiments, based on the successes and failures of experiments of the past, are given.