Search Results

You are looking at 1 - 10 of 10 items for

  • Author or Editor: Anders A. Jensen x
  • All content x
Clear All Modify Search
Anders A. Jensen and Jerry Y. Harrington

Abstract

This paper describes and tests a single-particle ice growth model that evolves both ice crystal mass and shape as a result of vapor growth and riming. Columnar collision efficiencies in the model are calculated using a new theoretical method derived from spherical collision efficiencies. The model is able to evolve mass, shape, and fall speed of growing ice across a range of temperatures, and it compares well with wind tunnel data. The onset time of riming and the effects of riming on mass and fall speed between −3° and −16°C are modeled, as compared with wind tunnel data for a liquid water content of 0.4 g m−3. Under these conditions, riming is constrained to the more isometric habits near −10° and −4°C. It is shown that the mass and fall speed of riming dendrites depend on the liquid drop distribution properties, leading to a range of mass–size and fall speed–size relationships. Riming at low liquid water contents is shown to be sensitive to ice crystal habit and liquid drop size. Moreover, very light riming can affect the shape of ice crystals enough to reduce vapor growth and suppress overall mass growth, as compared with those same ice crystals if they were unrimed.

Full access
Christopher J. Nowotarski and Anders A. Jensen

Abstract

The self-organizing map (SOM) statistical technique is applied to vertical profiles of thermodynamic and kinematic parameters from a Rapid Update Cycle-2 (RUC-2) proximity sounding dataset with the goal of better distinguishing and predicting supercell and tornadic environments. An SOM is a topologically ordered mapping of input data onto a two-dimensional array of nodes that can be used to classify large datasets into meaningful clusters. The relative ability of SOMs derived from each parameter to separate soundings in a way that is useful in discriminating between storm type, location, and time of year is discussed. Sensitivity to SOM configuration is also explored. Simple skill scores are computed for each SOM to evaluate the relative potential of each variable for future development as a method of probabilistic forecasting. It is found that variance in SOM nodes is reduced compared to the overall dataset, indicating that this is a viable classification method. SOMs of profiles of wind-derived variables are more effective in discriminating between storm type than thermodynamic variables. The SOM method also identifies meteorological, geographic, and temporal regimes within the dataset. In general, conditional probabilities of storm-type occurrence generated using SOMs have higher skill when wind-derived variables are considered and when forecasting nonsupercell events. Storm-relative wind variables tend to have better skill than ground-relative wind variables when forecasting nonsupercells, whereas ground-relative variables become more important when forecasting tornadoes.

Full access
Anders A. Jensen, Jerry Y. Harrington, and Hugh Morrison

Abstract

A quasi-idealized 3D squall-line case is simulated using a novel bulk microphysics scheme called the Ice-Spheroids Habit Model with Aspect-ratio Evolution (ISHMAEL). In ISHMAEL, the evolution of ice particle properties (e.g., mass, shape, maximum diameter, density, and fall speed) are predicted during vapor growth, sublimation, riming, and melting, allowing ice properties to evolve from various microphysical processes without needing separate unrimed and rimed ice categories. ISHMAEL produces both a transition zone and an enhanced stratiform precipitation region, and ice particle properties are analyzed to determine the characteristics of ice that lead to the development of these squall-line features. Rimed particles advected rearward from the convective region produce the enhanced stratiform precipitation region. The transition zone results from hydrometeor sorting; the evolution of ice particle properties in the convective region leads to fall speeds that favor ice advecting rearward of the transition zone before reaching the melting level, causing a local minimum in precipitation rate and reflectivity there. Sensitivity studies show that the fall speed of ice particles largely determines the location of the enhanced stratiform precipitation region and whether or not a transition zone forms. The representation of microphysical processes, such as rime splintering and aggregation, and ice size distribution shape can impact the mean ice particle fall speeds enough to significantly impact the location of the enhanced stratiform precipitation region and the existence of the transition zone.

Full access
Anders A. Jensen, Jerry Y. Harrington, and Hugh Morrison

Abstract

An IMPROVE-2 orographic precipitation case is simulated using the Ice-Spheroids Habit Model with Aspect-Ratio Evolution (ISHMAEL) microphysics. In ISHMAEL, the evolution of ice particle properties such as mass, shape, size, density, and fall speed are predicted. These ice particle properties along with the ice size distributions from ISHMAEL and model-derived spatial distribution of accumulated precipitation are compared to observations. ISHMAEL predicts planar and columnar particles at spatial locations that agree with observations. Sensitivity simulations are used to explore the impact of predicting ice particle shape evolution on orographic cloud properties and precipitation compared to the traditional approach of representing snow and graupel using separate categories with conversion from snow to graupel during riming. High biases in both IWCs aloft and surface precipitation accumulation occur in the Umpqua River valley using separate snow and graupel categories because snow that does not convert to graupel is advected over the Coast Range and precipitates out in the valley. Improvements in IWCs aloft and surface precipitation using ISHMAEL occur from both predicting various vapor-grown habits and predicting the impact of partial riming on ice particle properties. Compared to traditional microphysics schemes, ISHMAEL also produces less spatial variability in accumulated precipitation.

Full access
Hugh Morrison, Anders A. Jensen, Jerry Y. Harrington, and Jason A. Milbrandt

Abstract

This paper discusses the advection of coupled hydrometeor quantities by air motion in atmospheric models. It is shown that any bulk property derived from a set of advected microphysical variables must meet certain conditions in order to be preserved during transport using linear or semilinear advection schemes when the property is initially uniform, with implications for physical consistency of the property. A new, efficient flux-based method for calculating hydrometeor advection, similar to vector transport applied previously in aerosol modeling, is also presented. In this method, called scaled flux vector transport (SFVT), lead scalars (the mass mixing ratios) are advected using the host model’s unmodified advection scheme and secondary scalars (e.g., number mixing ratios) are advected by appropriately scaling the lead scalar fluxes. By design, SFVT retains linear relationships between the advected scalars. Analytic tests reveal that mean errors using SFVT are similar to those incurred using the traditional approach of separately advecting each variable. SFVT is applied to the multimoment predicted particle properties bulk microphysics scheme in idealized two-dimensional squall-line simulations using the Weather Research and Forecasting Model. The computational cost in total wall clock run time is reduced by 10%–15% while producing solutions similar to the traditional approach. Thus, SFVT can reduce the overall cost of using multimoment bulk microphysics schemes, making them competitive with simpler schemes having fewer prognostic variables.

Full access
Anders A. Jensen, Jerry Y. Harrington, Hugh Morrison, and Jason A. Milbrandt

Abstract

A novel bulk microphysics scheme that predicts the evolution of ice properties, including aspect ratio (shape), mass, number, size, and density is described, tested, and demonstrated. The scheme is named the Ice-Spheroids Habit Model with Aspect-Ratio Evolution (ISHMAEL). Ice is modeled as spheroids and is nucleated as one of two species depending on nucleation temperature. Microphysical process rates determine how shape and other ice properties evolve. A third aggregate species is also employed, diversifying ice properties in the model. Tests of ice shape evolution during vapor growth and riming are verified against wind tunnel data, revealing that the model captures habit-dependent riming and its effect on fall speed. Lagrangian parcel studies demonstrate that the bulk model captures ice property evolution during riming and melting compared with a bin model. Finally, the capabilities of ISHMAEL are shown in a 2D kinematic framework with a simple updraft. A direct result of predicting ice shape evolution is that various states of ice from unrimed to lightly rimed to densely rimed can be modeled without converting ice mass between predefined ice categories (e.g., snow and graupel). This leads to a different spatial precipitation distribution compared with the traditional method of separating snow and graupel and converting between the two categories, because ice in ISHMAEL sorts in physical space based on the amount of rime, which controls the thickness and therefore fall speed. Predicting these various states of rimed ice leads to a reduction in vapor growth rate and an increase in riming rate in a simple updraft compared with the traditional approach.

Full access
Anders A. Jensen, Philip T. Bergmaier, Bart Geerts, Hugh Morrison, and Leah S. Campbell

Abstract

The OWLeS IOP2b lake-effect case is simulated using the Weather Research and Forecasting (WRF) Model with a horizontal grid spacing of 148 m (WRF-LES mode). The dynamics and microphysics of the simulated high-resolution snowband and a coarser-resolution band from the parent nest (1.33-km horizontal grid spacing) are compared to radar and aircraft observations. The Ice Spheroids Habit Model with Aspect-ratio Evolution (ISHMAEL) microphysics is used, which predicts the evolution of ice particle properties including shape, maximum diameter, density, and fall speed. The microphysical changes within the band that occur when going from 1.33-km to 148-m grid spacing are explored. Improved representation of the dynamics at higher resolution leads to a better representation of the microphysics of the snowband compared to radar and aircraft observations. Stronger updrafts in the high-resolution grid produce higher ice number concentrations and produce ice particles that are more heavily rimed and thus more spherical, smaller (in terms of mean maximum diameter), and faster falling. These changes to the ice particle properties in the high-resolution grid limit the production of aggregates and improve reflectivity compared to observations. Graupel, observed in the band at the surface, is simulated in the strongest convective updrafts, but only at the higher resolution. Ultimately, the duration of heavy precipitation just onshore from the collapse of convection is better predicted in the high-resolution domain compared to surface and radar observations.

Restricted access
Robert S. Schrom, Marcus van Lier-Walqui, Matthew R. Kumjian, Jerry Y. Harrington, Anders A. Jensen, and Yao-Sheng Chen

Abstract

The potential for polarimetric Doppler radar measurements to improve predictions of ice microphysical processes within an idealized model–observational framework is examined. In an effort to more rigorously constrain ice growth processes (e.g., vapor deposition) with observations of natural clouds, a novel framework is developed to compare simulated and observed radar measurements, coupling a bulk adaptive-habit model of vapor growth to a polarimetric radar forward model. Bayesian inference on key microphysical model parameters is then used, via a Markov chain Monte Carlo sampler, to estimate the probability distribution of the model parameters. The statistical formalism of this method allows for robust estimates of the optimal parameter values, along with (non-Gaussian) estimates of their uncertainty. To demonstrate this framework, observations from Department of Energy radars in the Arctic during a case of pristine ice precipitation are used to constrain vapor deposition parameters in the adaptive habit model. The resulting parameter probability distributions provide physically plausible changes in ice particle density and aspect ratio during growth. A lack of direct constraint on the number concentration produces a range of possible mean particle sizes, with the mean size inversely correlated to number concentration. Consistency is found between the estimated inherent growth ratio and independent laboratory measurements, increasing confidence in the parameter PDFs and demonstrating the effectiveness of the radar measurements in constraining the parameters. The combined Doppler and polarimetric observations produce the highest-confidence estimates of the parameter PDFs, with the Doppler measurements providing a stronger constraint for this case.

Restricted access
Anders A. Jensen, James O. Pinto, Sean C. C. Bailey, Ryan A. Sobash, Gijs de Boer, Adam L. Houston, Phillip B. Chilson, Tyler Bell, Glen Romine, Suzanne W. Smith, Dale A. Lawrence, Cory Dixon, Julie K. Lundquist, Jamey D. Jacob, Jack Elston, Sean Waugh, and Matthias Steiner

Abstract

Uncrewed aircraft system (UAS) observations collected during the 2018 LAPSE-RATE field campaign were assimilated into a high-resolution configuration of the Weather Research and Forecasting model using an ensemble Kalman filter. The benefit of UAS observations was assessed for a terrain-driven (drainage and upvalley) flow event that occurred within Colorado’s San Luis Valley (SLV) using independent observations. The analysis and prediction of the strength, depth and horizontal extent of drainage flow from the Saguache Canyon and the subsequent transition to upvalley and up-canyon flow was improved compared to that obtained both without DA (benchmark) and when only surface observations were assimilated. Assimilation of UAS observations greatly improved the analyses of vertical variations in temperature, relative humidity, and winds at multiple locations in the northern portion of the SLV with reductions in both bias and the root mean square error of roughly 40% for each variable compared to the benchmark run. Despite these noted improvements, some biases remain that were tied to measurement error and/or the impact of the boundary layer parameterization on vertically spreading the observations, both of which require further exploration. The results presented here highlight how observations obtained with a fleet of profiling UAS improve limited-area, high-resolution analyses and short-term forecasts in complex terrain.

Restricted access
Gijs de Boer, Constantin Diehl, Jamey Jacob, Adam Houston, Suzanne W. Smith, Phillip Chilson, David G. Schmale III, Janet Intrieri, James Pinto, Jack Elston, David Brus, Osku Kemppinen, Alex Clark, Dale Lawrence, Sean C. C. Bailey, Michael P. Sama, Amy Frazier, Christopher Crick, Victoria Natalie, Elizabeth Pillar-Little, Petra Klein, Sean Waugh, Julie K. Lundquist, Lindsay Barbieri, Stephan T. Kral, Anders A. Jensen, Cory Dixon, Steven Borenstein, Daniel Hesselius, Kathleen Human, Philip Hall, Brian Argrow, Troy Thornberry, Randy Wright, and Jason T. Kelly

ABSTRACT

Because unmanned aircraft systems (UAS) offer new perspectives on the atmosphere, their use in atmospheric science is expanding rapidly. In support of this growth, the International Society for Atmospheric Research Using Remotely-Piloted Aircraft (ISARRA) has been developed and has convened annual meetings and “flight weeks.” The 2018 flight week, dubbed the Lower Atmospheric Profiling Studies at Elevation–A Remotely-Piloted Aircraft Team Experiment (LAPSE-RATE), involved a 1-week deployment to Colorado’s San Luis Valley. Between 14 and 20 July 2018 over 100 students, scientists, engineers, pilots, and outreach coordinators conducted an intensive field operation using unmanned aircraft and ground-based assets to develop datasets, community, and capabilities. In addition to a coordinated “Community Day” which offered a chance for groups to share their aircraft and science with the San Luis Valley community, LAPSE-RATE participants conducted nearly 1,300 research flights totaling over 250 flight hours. The measurements collected have been used to advance capabilities (instrumentation, platforms, sampling techniques, and modeling tools), conduct a detailed system intercomparison study, develop new collaborations, and foster community support for the use of UAS in atmospheric science.

Full access