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Scott D. Landolt, Roy M. Rasmussen, Alan J. Hills, Warren Underwood, Charles A. Knight, Albert Jachcik, and Andrew Schwartz

Abstract

The National Center for Atmospheric Research (NCAR) developed an artificial snow-generation system designed to operate in a laboratory cold chamber for testing aircraft anti-icing fluids under controlled conditions. Flakes of ice are produced by shaving an ice cylinder with a rotating carbide bit; the resulting artificial snow is dispersed by turbulent airflows and falls approximately 2.5 m to the bottom of the device. The resulting fine ice shavings mimic snow in size, distribution, fall velocity, density, and liquid water equivalent (LWE) snowfall rate. The LWE snowfall rate can be controlled using either a mass balance or a precipitation gauge, which measures the snowfall accumulation over time, from which the computer derives the LWE rate. LWE snowfall rates are calculated every 6 s, and the rate the ice cylinder is fed into the carbide bit is continually adjusted to ensure that the LWE snowfall rate matches a user-selected value. The system has been used to generate LWE snowfall rates ranging from 0 to 10 mm h−1 at temperatures from −2 to −30°C and densities of approximately 0.1–0.5 g cm−3. Comparisons of the snow-machine fluid tests with the outdoor fluid tests have shown that the snow machine can mimic natural outdoor rates under a broad range of conditions.

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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.

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Andreas F. Prein, Gregory J. Holland, Roy M. Rasmussen, James Done, Kyoko Ikeda, Martyn P. Clark, and Changhai H. Liu

Abstract

Summer and winter daily heavy precipitation events (events above the 97.5th percentile) are analyzed in regional climate simulations with 36-, 12-, and 4-km horizontal grid spacing over the headwaters of the Colorado River. Multiscale evaluations are useful to understand differences across horizontal scales and to evaluate the effects of upscaling finescale processes to coarser-scale features associated with precipitating systems.

Only the 4-km model is able to correctly simulate precipitation totals of heavy summertime events. For winter events, results from the 4- and 12-km grid models are similar and outperform the 36-km simulation. The main advantages of the 4-km simulation are the improved spatial mesoscale patterns of heavy precipitation (below ~100 km). However, the 4-km simulation also slightly improves larger-scale patterns of heavy precipitation.

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Ethan D. Gutmann, Roy M. Rasmussen, Changhai Liu, Kyoko Ikeda, David J. Gochis, Martyn P. Clark, Jimy Dudhia, and Gregory Thompson

Abstract

Statistical downscaling is widely used to improve spatial and/or temporal distributions of meteorological variables from regional and global climate models. This downscaling is important because climate models are spatially coarse (50–200 km) and often misrepresent extremes in important meteorological variables, such as temperature and precipitation. However, these downscaling methods rely on current estimates of the spatial distributions of these variables and largely assume that the small-scale spatial distribution will not change significantly in a modified climate. In this study the authors compare data typically used to derive spatial distributions of precipitation [Parameter-Elevation Regressions on Independent Slopes Model (PRISM)] to a high-resolution (2 km) weather model [Weather Research and Forecasting model (WRF)] under the current climate in the mountains of Colorado. It is shown that there are regions of significant difference in November–May precipitation totals (>300 mm) between the two, and possible causes for these differences are discussed. A simple statistical downscaling is then presented that is based on the 2-km WRF data applied to a series of regional climate models [North American Regional Climate Change Assessment Program (NARCCAP)], and the downscaled precipitation data are validated with observations at 65 snow telemetry (SNOTEL) sites throughout Colorado for the winter seasons from 1988 to 2000. The authors also compare statistically downscaled precipitation from a 36-km model under an imposed warming scenario with dynamically downscaled data from a 2-km model using the same forcing data. Although the statistical downscaling improved the domain-average precipitation relative to the original 36-km model, the changes in the spatial pattern of precipitation did not match the changes in the dynamically downscaled 2-km model. This study illustrates some of the uncertainties in applying statistical downscaling to future climate.

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Andrew J. Monaghan, Martyn P. Clark, Michael P. Barlage, Andrew J. Newman, Lulin Xue, Jeffrey R. Arnold, and Roy M. Rasmussen

Abstract

Weather and climate variability strongly influence the people, infrastructure, and economy of Alaska. However, the sparse observational network in Alaska limits our understanding of meteorological variability, particularly of precipitation processes that influence the hydrologic cycle. Here, a new 14-yr (September 2002–August 2016) dataset for Alaska with 4-km grid spacing is described and evaluated. The dataset, generated with the Weather Research and Forecasting (WRF) Model, is useful for gaining insight into meteorological and hydrologic processes, and provides a baseline against which to measure future environmental change. The WRF fields are evaluated at annual, seasonal, and daily time scales against observation-based gridded and station records of 2-m air temperature, precipitation, and snowfall. Pattern correlations between annual mean WRF and observation-based gridded fields are r = 0.89 for 2-m temperature, r = 0.75 for precipitation, r = 0.82 for snow-day fraction, r = 0.55 for first snow day of the season, and r = 0.71 for last snow day of the season. A shortcoming of the WRF dataset is that spring snowmelt occurs too early over a majority of the state, due partly to positive 2-m temperature biases in winter and spring. Strengths include an improved representation of the interannual variability of 2-m temperature and precipitation and accurately simulated (relative to regional station observations) winter and summer precipitation maxima. This initial evaluation suggests that the 4-km WRF climate dataset robustly simulates meteorological processes and recent climatic variability in Alaska. The dataset may be particularly useful for applications that require high-temporal-frequency weather fields, such as driving hydrologic or glacier models. Future studies will provide further insight on its ability to represent other aspects of Alaska’s climate.

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Jason M. Keeler, Robert M. Rauber, Brian F. Jewett, Greg M. McFarquhar, Roy M. Rasmussen, Lulin Xue, Changhai Liu, and Gregory Thompson

Abstract

Cloud-top generating cells (GCs) are a common feature atop stratiform clouds within the comma head of winter cyclones. The dynamics of cloud-top GCs are investigated using very high-resolution idealized WRF Model simulations to examine the role of shear in modulating the structure and intensity of GCs. Simulations were run for the same combinations of radiative forcing and instability as in of this series, but with six different shear profiles ranging from 0 to 10 m s−1 km−1 within the layer encompassing the GCs.

The primary role of shear was to modulate the organization of GCs, which organized as closed convective cells in simulations with radiative forcing and no shear. In simulations with shear and radiative forcing, GCs organized in linear streets parallel to the wind. No GCs developed in the initially stable simulations with no radiative forcing. In the initially unstable and neutral simulations with no radiative forcing or shear, GCs were exceptionally weak, with no clear organization. In moderate-shear (Δuz = 2, 4 m s−1 km−1) simulations with no radiative forcing, linear organization of the weak cells was apparent, but this organization was less coherent in simulations with high shear (Δuz = 6, 8, 10 m s−1 km−1). The intensity of the updrafts was primarily related to the mode of radiative forcing but was modulated by shear. The more intense GCs in nighttime simulations were either associated with no shear (closed convective cells) or strong shear (linear streets). Updrafts within GCs under conditions with radiative forcing were typically ~1–2 m s−1 with maximum values < 4 m s−1.

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Jason M. Keeler, Brian F. Jewett, Robert M. Rauber, Greg M. McFarquhar, Roy M. Rasmussen, Lulin Xue, Changhai Liu, and Gregory Thompson

Abstract

Recent field observations suggest that cloud-top precipitation generating cells (GCs) are ubiquitous in the warm-frontal and comma-head regions of midlatitude winter cyclones. The presence of fallstreaks emanating from the GCs and their persistence either to the surface or until merging into precipitation bands suggests that GCs are a critical component of the precipitation process in these cyclones. This paper is the second part of a three-part series that investigates the dynamics of GCs through very-high-resolution idealized Weather Research and Forecasting (WRF) Model simulations. This paper assesses the role of cloud-top instability paired with nighttime, daytime, or no radiative forcing on the development and maintenance (or lack) of GCs. Under initially unstable conditions at cloud top, GCs develop regardless of radiative forcing but only persist clearly with radiative forcing. Cloud-top destabilization due to longwave cooling leads to development of GCs even under initially neutral and stable conditions, providing a physical explanation for the observed ubiquity of GCs atop winter cyclones. GCs do not develop in initially stable simulations with no radiation. Decreased range in vertical velocity spectra under daytime radiative forcing is consistent with offset of the destabilizing influence of longwave cooling by shortwave heating.

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Jason M. Keeler, Brian F. Jewett, Robert M. Rauber, Greg M. McFarquhar, Roy M. Rasmussen, Lulin Xue, Changhai Liu, and Gregory Thompson

Abstract

This paper assesses the influence of radiative forcing and latent heating on the development and maintenance of cloud-top generating cells (GCs) in high-resolution idealized Weather Research and Forecasting Model simulations with initial conditions representative of the vertical structure of a cyclone observed during the Profiling of Winter Storms campaign. Simulated GC kinematics, structure, and ice mass are shown to compare well quantitatively with Wyoming Cloud Radar, cloud probe, and other observations. Sensitivity to radiative forcing was assessed in simulations with longwave-only (nighttime), longwave-and-shortwave (daytime), and no-radiation parameterizations. The domain-averaged longwave cooling rate exceeded 0.50 K h−1 near cloud top, with maxima greater than 2.00 K h−1 atop GCs. Shortwave warming was weaker by comparison, with domain-averaged values of 0.10–0.20 K h−1 and maxima of 0.50 K h−1 atop GCs. The stabilizing influence of cloud-top shortwave warming was evident in the daytime simulation’s vertical velocity spectrum, with 1% of the updrafts in the 6.0–8.0-km layer exceeding 1.20 m s−1, compared to 1.80 m s−1 for the nighttime simulation. GCs regenerate in simulations with radiative forcing after the initial instability is released but do not persist when radiation is not parameterized, demonstrating that radiative forcing is critical to GC maintenance under the thermodynamic and vertical wind shear conditions in this cyclone. GCs are characterized by high ice supersaturation (RHice > 150%) and latent heating rates frequently in excess of 2.00 K h−1 collocated with vertical velocity maxima. Ice precipitation mixing ratio maxima of greater than 0.15 g kg−1 were common within GCs in the daytime and nighttime simulations.

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Julie M. Thériault, Roy Rasmussen, Trevor Smith, Ruping Mo, Jason A. Milbrandt, Melinda M. Brugman, Paul Joe, George A. Isaac, Jocelyn Mailhot, and Bertrand Denis

Abstract

Accurate forecasting of precipitation phase and intensity was critical information for many of the Olympic venue managers during the Vancouver 2010 Olympic and Paralympic Winter Games. Precipitation forecasting was complicated because of the complex terrain and warm coastal weather conditions in the Whistler area of British Columbia, Canada. The goal of this study is to analyze the processes impacting precipitation phase and intensity during a winter weather storm associated with rain and snow over complex terrain. The storm occurred during the second day of the Olympics when the downhill ski event was scheduled. At 0000 UTC 14 February, 2 h after the onset of precipitation, a rapid cooling was observed at the surface instrumentation sites. Precipitation was reported for 8 h, which coincided with the creation of a nearly 0°C isothermal layer, as well as a shift of the valley flow from up valley to down valley. Widespread snow was reported on Whistler Mountain with periods of rain at the mountain base despite the expectation derived from synoptic-scale models (15-km grid spacing) that the strong warm advection would maintain temperatures above freezing. Various model predictions are compared with observations, and the processes influencing the temperature, wind, and precipitation types are discussed. Overall, this case study provided a well-observed scenario of winter storms associated with rain and snow over complex terrain.

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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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