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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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Robert J. Trapp
,
Sarah A. Tessendorf
,
Elaine Savageau Godfrey
, and
Harold E. Brooks

Abstract

The primary objective of this study was to estimate the percentage of U.S. tornadoes that are spawned annually by squall lines and bow echoes, or quasi-linear convective systems (QLCSs). This was achieved by examining radar reflectivity images for every tornado event recorded during 1998–2000 in the contiguous United States. Based on these images, the type of storm associated with each tornado was classified as cell, QLCS, or other.

Of the 3828 tornadoes in the database, 79% were produced by cells, 18% were produced by QLCSs, and the remaining 3% were produced by other storm types, primarily rainbands of landfallen tropical cyclones. Geographically, these percentages as well as those based on tornado days exhibited wide variations. For example, 50% of the tornado days in Indiana were associated with QLCSs.

In an examination of other tornado attributes, statistically more weak (F1) and fewer strong (F2–F3) tornadoes were associated with QLCSs than with cells. QLCS tornadoes were more probable during the winter months than were cells. And finally, QLCS tornadoes displayed a comparatively higher and statistically significant tendency to occur during the late night/early morning hours. Further analysis revealed a disproportional decrease in F0–F1 events during this time of day, which led the authors to propose that many (perhaps as many as 12% of the total) weak QLCSs tornadoes were not reported.

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David J. Serke
,
Scott M. Ellis
,
Sarah A. Tessendorf
,
David E. Albo
,
John C. Hubbert
, and
Julie A. Haggerty

Abstract

Detection of in-flight icing hazard is a priority of the aviation safety community. The “Radar Icing Algorithm” (RadIA) has been developed to indicate the presence, phase, and relative size of supercooled drops. This paper provides an evaluation of RadIA via comparison to in situ microphysical measurements collected with a research aircraft during the 2017 “Seeded and Natural Orographic Wintertime clouds: the Idaho Experiment” (SNOWIE) field campaign. RadIA uses level-2 dual-polarization radar moments from operational National Weather Service WSR-88D and a numerical weather prediction model temperature profile as inputs. Moment membership functions are defined based on the results of previous studies, and fuzzy logic is used to combine the output of these functions to create a 0 to 1 interest for detecting small-drop, large-drop, and mixed-phase icing. Data from the two-dimensional stereo (2D-S) particle probe on board the University of Wyoming King Air aircraft were categorized as either liquid or solid phase water with a shape classification algorithm and binned by size. RadIA interest values from 17 cases were matched to statistical measures of the solid/liquid particle size distributions (such as maximum particle diameter) and values of LWC from research aircraft flights. Receiver operating characteristic area under the curve (AUC) values for RadIA algorithms were 0.75 for large-drop, 0.73 for small-drop, and 0.83 for mixed-phase cases. RadIA is proven to be a valuable new capability for detecting the presence of in-flight icing hazards from ground-based precipitation radar.

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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.

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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.

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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.

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Sarah A. Tessendorf
,
Allyson Rugg
,
Alexei Korolev
,
Ivan Heckman
,
Courtney Weeks
,
Gregory Thompson
,
Darcy Jacobson
,
Dan Adriaansen
, and
Julie Haggerty

Abstract

Supercooled large drop (SLD) icing poses a unique hazard for aircraft and has resulted in new regulations regarding aircraft certification to fly in regions of known or forecast SLD icing conditions. The new regulations define two SLD icing categories based upon the maximum supercooled liquid water drop diameter (Dmax): freezing drizzle (100–500 μm) and freezing rain (>500 μm). Recent upgrades to U.S. operational numerical weather prediction models lay a foundation to provide more relevant aircraft icing guidance including the potential to predict explicit drop size. The primary focus of this paper is to evaluate a proposed method for estimating the maximum drop size from model forecast data to differentiate freezing drizzle from freezing rain conditions. Using in situ cloud microphysical measurements collected in icing conditions during two field campaigns between January and March 2017, this study shows that the High-Resolution Rapid Refresh model is capable of distinguishing SLD icing categories of freezing drizzle and freezing rain using a Dmax extracted from the rain category of the microphysics output. It is shown that the extracted Dmax from the model correctly predicted the observed SLD icing category as much as 99% of the time when the HRRR accurately forecast SLD conditions; however, performance varied by the method to define Dmax and by the field campaign dataset used for verification.

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Vaughan T. J. Phillips
,
Jun-Ichi Yano
,
Marco Formenton
,
Eyal Ilotoviz
,
Vijay Kanawade
,
Innocent Kudzotsa
,
Jiming Sun
,
Aaron Bansemer
,
Andrew G. Detwiler
,
Alexander Khain
, and
Sarah A. Tessendorf

Abstract

In Part I of this two-part paper, a formulation was developed to treat fragmentation in ice–ice collisions. In the present Part II, the formulation is implemented in two microphysically advanced cloud models simulating a convective line observed over the U.S. high plains. One model is 2D with a spectral bin microphysics scheme. The other has a hybrid bin–two-moment bulk microphysics scheme in 3D. The case consists of cumulonimbus cells with cold cloud bases (near 0°C) in a dry troposphere.

Only with breakup included in the simulation are aircraft observations of particles with maximum dimensions >0.2 mm in the storm adequately predicted by both models. In fact, breakup in ice–ice collisions is by far the most prolific process of ice initiation in the simulated clouds (95%–98% of all nonhomogeneous ice), apart from homogeneous freezing of droplets. Inclusion of breakup in the cloud-resolving model (CRM) simulations increased, by between about one and two orders of magnitude, the average concentration of ice between about 0° and −30°C. Most of the breakup is due to collisions of snow with graupel/hail. It is broadly consistent with the theoretical result in Part I about an explosive tendency for ice multiplication.

Breakup in collisions of snow (crystals >~1 mm and aggregates) with denser graupel/hail was the main pathway for collisional breakup and initiated about 60%–90% of all ice particles not from homogeneous freezing, in the simulations by both models. Breakup is predicted to reduce accumulated surface precipitation in the simulated storm by about 20%–40%.

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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.

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