Dataset Information

Precipitation formation from orographic cloud seeding.

ABSTRACT: Throughout the western United States and other semiarid mountainous regions across the globe, water supplies are fed primarily through the melting of snowpack. Growing populations place higher demands on water, while warmer winters and earlier springs reduce its supply. Water managers are tantalized by the prospect of cloud seeding as a way to increase winter snowfall, thereby shifting the balance between water supply and demand. Little direct scientific evidence exists that confirms even the basic physical hypothesis upon which cloud seeding relies. The intent of glaciogenic seeding of orographic clouds is to introduce aerosol into a cloud to alter the natural development of cloud particles and enhance wintertime precipitation in a targeted region. The hypothesized chain of events begins with the introduction of silver iodide aerosol into cloud regions containing supercooled liquid water, leading to the nucleation of ice crystals, followed by ice particle growth to sizes sufficiently large such that snow falls to the ground. Despite numerous experiments spanning several decades, no direct observations of this process exist. Here, measurements from radars and aircraft-mounted cloud physics probes are presented that together show the initiation, growth, and fallout to the mountain surface of ice crystals resulting from glaciogenic seeding. These data, by themselves, do not address the question of cloud seeding efficacy, but rather form a critical set of observations necessary for such investigations. These observations are unambiguous and provide details of the physical chain of events following the introduction of glaciogenic cloud seeding aerosol into supercooled liquid orographic clouds.

SUBMITTER: French JR

PROVIDER: S-EPMC5819430 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Precipitation formation from orographic cloud seeding.

French Jeffrey R JR Friedrich Katja K Tessendorf Sarah A SA Rauber Robert M RM Geerts Bart B Rasmussen Roy M RM Xue Lulin L Kunkel Melvin L ML Blestrud Derek R DR

Proceedings of the National Academy of Sciences of the United States of America 20180122 6

Throughout the western United States and other semiarid mountainous regions across the globe, water supplies are fed primarily through the melting of snowpack. Growing populations place higher demands on water, while warmer winters and earlier springs reduce its supply. Water managers are tantalized by the prospect of cloud seeding as a way to increase winter snowfall, thereby shifting the balance between water supply and demand. Little direct scientific evidence exists that confirms even the ba ...[more]

PMID: 29358387

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Project description:Abstract In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth's atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models is divided into two parts: (i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and (ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics. The recently developed Lagrangian particle?based method is advocated as a way to address several conceptual and practical challenges of representing particle populations using traditional bulk and bin microphysics parameterization schemes. For addressing critical gaps in cloud physics knowledge, sustained investment for observational advances from laboratory experiments, new probe development, and next?generation instruments in space is needed. Greater emphasis on laboratory work, which has apparently declined over the past several decades relative to other areas of cloud physics research, is argued to be an essential ingredient for improving process?level understanding. More systematic use of natural cloud and precipitation observations to constrain microphysics schemes is also advocated. Because it is generally difficult to quantify individual microphysical process rates from these observations directly, this presents an inverse problem that can be viewed from the standpoint of Bayesian statistics. Following this idea, a probabilistic framework is proposed that combines elements from statistical and physical modeling. Besides providing rigorous constraint of schemes, there is an added benefit of quantifying uncertainty systematically. Finally, a broader hierarchical approach is proposed to accelerate improvements in microphysics schemes, leveraging the advances described in this paper related to process modeling (using Lagrangian particle?based schemes), laboratory experimentation, cloud and precipitation observations, and statistical methods. Key Points Microphysics is an important component of weather and climate models, but its representation in current models is highly uncertain Two critical challenges are identified: representing cloud and precipitation particle populations and knowledge gaps in cloud physics A possible blueprint for addressing these challenges is proposed to accelerate progress in improving microphysics schemes

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Precipitation formation from orographic cloud seeding.

Publications

Precipitation formation from orographic cloud seeding.

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