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Mikell Warms
,
Katja Friedrich
,
Lulin Xue
,
Sarah Tessendorf
, and
Kyoko Ikeda

Abstract

The western United States region, an economic and agricultural powerhouse, is highly dependent on winter snowpack from the mountain west. Coupled with increasing water and renewable electricity demands, the predictability and viability of snowpack resources in a changing climate are becoming increasingly important. In Idaho, specifically, up to 75% of the state’s electricity production comes from hydropower, which is dependent on the timing and volume of spring snowmelt. While we know that 1 April snowpack is declining from SNOTEL observations and is expected to continue to decline as indicated by GCM predictions, our ability to understand the variability of snowfall accumulation and distribution at the regional level is less robust. In this paper, we analyze snowfall events using 0.9-km-resolution WRF simulations to understand the variability of snowfall accumulation and distribution in the mountains of Idaho between 1 October 2016 and 31 April 2017. Various characteristics of snowfall events throughout the season are evaluated, including the spatial coverage, event durations, and snowfall rates, along with the relationship between cloud microphysical variables—particularly liquid and ice water content—on snowfall amounts. Our findings suggest that efficient snowfall conditions—for example, higher levels of elevated supercooled liquid water—can exist throughout the winter season but are more impactful when surface temperatures are near or below freezing. Inefficient snowfall events are common, exceeding 50% of the total snowfall events for the year, with some of those occurring in peak winter. For such events, glaciogenic cloud seeding could make a significant impact on snowpack development and viability in the region.

Significance Statement

The purpose and significance of this study is to better understand the variability of snowfall event accumulation and distribution in the Payette Mountains region of Idaho as it relates to the local topography, the drivers of snowfall events, the cloud microphysical properties, and what constitutes an efficient or inefficient snowfall event (i.e., its ability to convert atmospheric liquid water into snowfall). As part of this process, we identify how many snowfall events in a season are inefficient to determine the number of snowfall events in a season that are candidates for enhancement by glaciogenic cloud seeding.

Restricted access
James W. Wilson
,
Charles A. Knight
,
Sarah A. Tessendorf
, and
Courtney Weeks

Abstract

During the Queensland Cloud Seeding Research Program, the “CP2” polarimetric radar parameter differential radar reflectivity Z dr was used to examine the raindrop size evolution in both maritime and continental clouds. The focus of this paper is to examine the natural variability of the drop size distribution. The primary finding is that there are two basic raindrop size evolutions, one associated with continental air masses characterized by relatively high aerosol concentrations and long air trajectories over land and the other associated with maritime air masses with lower aerosol concentrations. The size evolution difference is during the growth stage of the radar echoes. The differential radar reflectivity in the growing continental clouds is dominated by large raindrops, whereas in the maritime clouds differential reflectivity is dominated by small raindrops and drizzle. The drop size evolution in many of the maritime air masses was very similar to those observed in the maritime air of the Caribbean Sea observed with the NCAR S-band polarimetric radar (S-Pol) during the Rain in Cumulus over the Ocean (RICO) experiment. Because the tops of the Queensland continental clouds ascended almost 2 times as fast as the maritime ones in their growth stage, both dynamical and aerosol factors may be important for the systematic difference in drop size evolution. Recommendations are advanced for future field programs to understand better the causes for the observed variability in drop size evolution. Also, considering the natural variability in drop size evolution, comments are provided on conducting and evaluating cloud seeding experiments.

Full access
Thomas O. Mazzetti
,
Bart Geerts
,
Lulin Xue
,
Sarah Tessendorf
,
Courtney Weeks
, and
Yonggang Wang

Abstract

Glaciogenic cloud seeding has long been practiced as a way to increase water availability in arid regions, such as the interior western United States. Many seeding programs in this region target cold-season orographic clouds with ground-based silver iodide generators. Here, the “seedability” (defined as the fraction of time that conditions are suitable for ground-based seeding) is evaluated in this region from 10 years of hourly output from a regional climate model with a horizontal resolution of 4 km. Seedability criteria are based on temperature, presence of supercooled liquid water, and Froude number, which is computed here as a continuous field relative to the local terrain. The model’s supercooled liquid water compares reasonably well to microwave radiometer observations. Seedability peaks at 20%–30% for many mountain ranges in the cold season, with the best locations just upwind of crests, over the highest terrain in Colorado and Wyoming, as well as over ranges in the northwest interior. Mountains farther south are less frequently seedable, because of warmer conditions, but when they are, cloud supercooled liquid water content tends to be relatively high. This analysis is extended into a future climate, anticipated for later this century, with a mean temperature 2.0 K warmer than the historical climate. Seedability generally will be lower in this future warmer climate, especially in the most seedable areas, but, when seedable, clouds tend to contain slightly more supercooled liquid water.

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Sarah A. Tessendorf
,
Kyoko Ikeda
,
Courtney Weeks
,
Roy Rasmussen
,
Jamie Wolff
, and
Lulin Xue

Abstract

This paper presents an evaluation of the precipitation patterns and seedability of orographic clouds in Wyoming using SNOTEL precipitation data and a high-resolution multiyear model simulation over an 8-yr period. A key part of assessing the potential for cloud seeding is to understand the natural precipitation patterns and how often atmospheric conditions and clouds meet cloud-seeding criteria. The analysis shows that high-resolution model simulations are useful tools for studying patterns of orographic precipitation and establishing the seedability of clouds by providing information that is either missed by or not available from current observational networks. This study indicates that the ground-based seeding potential in some mountain ranges in Wyoming is limited by flow blocking and/or prevailing winds that were not normal to the barrier to produce upslope flow. Airborne seeding generally had the most potential for all of the mountain ranges that were studied.

Free access
Lulin Xue
,
Akihiro Hashimoto
,
Masataka Murakami
,
Roy Rasmussen
,
Sarah A. Tessendorf
,
Daniel Breed
,
Shaun Parkinson
,
Pat Holbrook
, and
Derek Blestrud

Abstract

A silver iodide (AgI) cloud-seeding parameterization has been implemented into the Thompson microphysics scheme of the Weather Research and Forecasting model to investigate glaciogenic cloud-seeding effects. The sensitivity of the parameterization to meteorological conditions, cloud properties, and seeding rates was examined by simulating two-dimensional idealized moist flow over a bell-shaped mountain. The results verified that this parameterization can reasonably simulate the physical processes of cloud seeding with the limitations of the constant cloud droplet concentration assumed in the scheme and the two-dimensional model setup. The results showed the following: 1) Deposition was the dominant nucleation mode of AgI from simulated aircraft seeding, whereas immersion freezing was the most active mode for ground-based seeding. Deposition and condensation freezing were also important for ground-based seeding. Contact freezing was the weakest nucleation mode for both ground-based and airborne seeding. 2) Diffusion and riming on AgI-nucleated ice crystals depleted vapor and liquid water, resulting in more ice-phase precipitation on the ground for all of the seeding cases relative to the control cases. Most of the enhancement came from vapor depletion. The relative enhancement by seeding ranged from 0.3% to 429% under various conditions. 3) The maximum local AgI activation ratio was 60% under optimum conditions. Under most seeding conditions, however, this ratio was between 0.02% and 2% in orographic clouds. 4) The seeding effect was inversely related to the natural precipitation efficiency but was positively related to seeding rates. 5) Ground-based seeding enhanced precipitation on the lee side of the mountain, whereas airborne seeding from lower flight tracks enhanced precipitation on the windward side of the mountain.

Full access
Lulin Xue
,
Sarah A. Tessendorf
,
Eric Nelson
,
Roy Rasmussen
,
Daniel Breed
,
Shaun Parkinson
,
Pat Holbrook
, and
Derek Blestrud

Abstract

Four cloud-seeding cases over southern Idaho during the 2010/11 winter season have been simulated by the Weather Research and Forecasting (WRF) model using the coupled silver iodide (AgI) cloud-seeding scheme that was described in Part I. The seeding effects of both ground-based and airborne seeding as well as the impacts of model physics, seeding rates, location, timing, and cloud properties on seeding effects have been investigated. The results were compared with those from Part I and showed the following: 1) For the four cases tested in this study, control simulations driven by the Real-Time Four Dimensional Data Assimilation (RTFDDA) WRF forecast data generated more realistic atmospheric conditions and precipitation patterns than those driven by the North America Regional Reanalysis data. Sensitivity experiments therefore used the RTFDDA data. 2) Glaciogenic cloud seeding increased orographic precipitation by less than 1% over the simulation domain, including the Snake River basin, and by up to 5% over the target areas. The local values of the relative precipitation enhancement by seeding were ~20%. Most of the enhancement came from vapor depletion. 3) The seeding effect was inversely related to the natural precipitation efficiency but was positively related to seeding rates. 4) Airborne seeding is generally more efficient than ground-based seeding in terms of targeting, but its efficiency depends on local meteorological conditions. 5) The normalized seeding effects ranged from 0.4 to 1.6 under various conditions for a certain seeding event.

Full access
Roy M. Rasmussen
,
Sarah A. Tessendorf
,
Lulin Xue
,
Courtney Weeks
,
Kyoko Ikeda
,
Scott Landolt
,
Dan Breed
,
Terry Deshler
, and
Barry Lawrence

Abstract

The Wyoming Weather Modification Pilot Project randomized cloud seeding experiment was a crossover statistical experiment conducted over two mountain ranges in eastern Wyoming and lasted for 6 years (2008–13). The goal of the experiment was to determine if cloud seeding of orographic barriers could increase snowfall and snowpack. The experimental design included triply redundant snow gauges deployed in a target–control configuration, covariate snow gauges to account for precipitation variability, and ground-based seeding with silver iodide (AgI). The outcomes of this experiment are evaluated with the statistical–physical experiment design and with ensemble modeling. The root regression ratio (RRR) applied to 118 experimental units provided insufficient statistical evidence (p value of 0.28) to reject the null hypothesis that there was no effect from ground-based cloud seeding. Ensemble modeling estimates of the impact of ground-based seeding provide an alternate evaluation of the 6-yr experiment. The results of the model ensemble approach with and without seeding estimated a mean enhancement of precipitation of 5%, with an inner-quartile range of 3%–7%. Estimating the impact on annual precipitation over these mountain ranges requires results from another study that indicated that approximately 30% of the annual precipitation results from clouds identified as seedable within the seeding experiment. Thus the seeding impact is on the order of 1.5% of the annual precipitation, compared to 1% for the statistical–physical experiment, which was not sufficient to reject the null hypothesis. These results provide an estimate of the impact of ground-based cloud seeding in the Sierra Madre and Medicine Bow Mountains in Wyoming that accounts for uncertainties in both initial conditions and model physics.

Open access
Sisi Chen
,
Lulin Xue
,
Sarah Tessendorf
,
Thomas Chubb
,
Andrew Peace
,
Luis Ackermann
,
Artur Gevorgyan
,
Yi Huang
,
Steven Siems
,
Roy Rasmussen
,
Suzanne Kenyon
, and
Johanna Speirs

Abstract

This study presents the first numerical simulations of seeded clouds over the Snowy Mountains of Australia. WRF-WxMod, a novel glaciogenic cloud-seeding model, was utilized to simulate the cloud response to winter orographic seeding under various meteorological conditions. Three cases during the 2018 seeding periods were selected for model evaluation, coinciding with an intensive ground-based measurement campaign. The campaign data were used for model validation and evaluation. Comparisons between simulations and observations demonstrate that the model realistically represents cloud structures, liquid water path, and precipitation. Sensitivity tests were performed to pinpoint key uncertainties in simulating natural and seeded clouds and precipitation processes. They also shed light on the complex interplay between various physical parameters/processes and their interaction with large-scale meteorology. Our study found that in unseeded scenarios, the warm and cold biases in different initialization datasets can heavily influence the intensity and phase of natural precipitation. Secondary ice production via Hallett–Mossop processes exerts a secondary influence. On the other hand, the seeding impacts are primarily sensitive to aerosol conditions and the natural ice nucleation process. Both factors alter the supercooled liquid water availability and the precipitation phase, consequently impacting the silver iodide (AgI) nucleation rate. Furthermore, model sensitivities were inconsistent across cases, indicating that no single model configuration optimally represents all three cases. This highlights the necessity of employing an ensemble approach for a more comprehensive and accurate assessment of the seeding impact.

Significance Statement

Winter orographic cloud seeding has been conducted for decades over the Snowy Mountains of Australia for securing water resources. However, this study is the first to perform cloud-seeding simulation for a robust, event-based seeding impact evaluation. A state-of-the-art cloud-seeding model (WRF-WxMod) was used to simulate the cloud seeding and quantified its impact on the region. The Southern Hemisphere, due to low aerosol emissions and highly pristine cloud conditions, has distinctly different cloud microphysical characteristics than the Northern Hemisphere, where WRF-WxMod has been successfully applied in a few regions over the United States. The results showed that WRF-WxMod could accurately capture the clouds and precipitation in both the natural and seeded conditions.

Restricted access
Katja Friedrich
,
Jeffrey R. French
,
Sarah A. Tessendorf
,
Melinda Hatt
,
Courtney Weeks
,
Robert M. Rauber
,
Bart Geerts
,
Lulin Xue
,
Roy M. Rasmussen
,
Derek R. Blestrud
,
Melvin L. Kunkel
,
Nicholas Dawson
, and
Shaun Parkinson

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 the 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.

Free access
Robert M. Rauber
,
Bart Geerts
,
Lulin Xue
,
Jeffrey French
,
Katja Friedrich
,
Roy M. Rasmussen
,
Sarah A. Tessendorf
,
Derek R. Blestrud
,
Melvin L. Kunkel
, and
Shaun Parkinson

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.

Full access