skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Simulated Polarimetric Fields of Ice Vapor Growth Using the Adaptive Habit Model. Part I: Large-Eddy Simulations

 [1];  [2]
  1. Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, New York
  2. Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania
Publication Date:
Sponsoring Org.:
OSTI Identifier:
Grant/Contract Number:
Resource Type:
Journal Article: Published Article
Journal Name:
Monthly Weather Review
Additional Journal Information:
Journal Volume: 145; Journal Issue: 6; Related Information: CHORUS Timestamp: 2017-05-22 13:12:07; Journal ID: ISSN 0027-0644
American Meteorological Society
Country of Publication:
United States

Citation Formats

Sulia, Kara J., and Kumjian, Matthew R. Simulated Polarimetric Fields of Ice Vapor Growth Using the Adaptive Habit Model. Part I: Large-Eddy Simulations. United States: N. p., 2017. Web. doi:10.1175/MWR-D-16-0061.1.
Sulia, Kara J., & Kumjian, Matthew R. Simulated Polarimetric Fields of Ice Vapor Growth Using the Adaptive Habit Model. Part I: Large-Eddy Simulations. United States. doi:10.1175/MWR-D-16-0061.1.
Sulia, Kara J., and Kumjian, Matthew R. Thu . "Simulated Polarimetric Fields of Ice Vapor Growth Using the Adaptive Habit Model. Part I: Large-Eddy Simulations". United States. doi:10.1175/MWR-D-16-0061.1.
title = {Simulated Polarimetric Fields of Ice Vapor Growth Using the Adaptive Habit Model. Part I: Large-Eddy Simulations},
author = {Sulia, Kara J. and Kumjian, Matthew R.},
abstractNote = {},
doi = {10.1175/MWR-D-16-0061.1},
journal = {Monthly Weather Review},
number = 6,
volume = 145,
place = {United States},
year = {Thu Jun 01 00:00:00 EDT 2017},
month = {Thu Jun 01 00:00:00 EDT 2017}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on May 22, 2018
Publisher's Version of Record

Citation Metrics:
Cited by: 1work
Citation information provided by
Web of Science

Save / Share:
  • Measurement of ice number concentration in clouds is important but still challenging. Stratiform mixed-phase clouds (SMCs) provide a simple scenario for retrieving ice number concentration from remote sensing measurements. The simple ice generation and growth pattern in SMCs offers opportunities to use cloud radar reflectivity (Ze) measurements and other cloud properties to infer ice number concentration quantitatively. To understand the strong temperature dependency of ice habit and growth rate quantitatively, we develop a 1-D ice growth model to calculate the ice diffusional growth along its falling trajectory in SMCs. The radar reflectivity and fall velocity profiles of ice crystals calculatedmore » from the 1-D ice growth model are evaluated with the Atmospheric Radiation Measurements (ARM) Climate Research Facility (ACRF) ground-based high vertical resolution radar measurements. Combining Ze measurements and 1-D ice growth model simulations, we develop a method to retrieve the ice number concentrations in SMCs at given cloud top temperature (CTT) and liquid water path (LWP). The retrieved ice concentrations in SMCs are evaluated with in situ measurements and with a three-dimensional cloud-resolving model simulation with a bin microphysical scheme. These comparisons show that the retrieved ice number concentrations are within an uncertainty of a factor of 2, statistically.« less
  • Microphysical processes of formation and dissipation of water and ice clouds have been incorporated in a general circulation model to yield a more physically based representation of atmospheric moisture budget components, link distribution and optical properties of modeled clouds to predicted cloud water and ice amounts, and produce more realistic simulations of cloudiness and radiation budget. The bulk cloud microphysics scheme encompasses variables for water vapor mass, cloud water, cloud ice, rain, and snow. Cloud water and cloud ice are predicted to form through large-scale condensation and deposition and through detrainment at the tops of cumulus towers. Two annual-cycle simulationsmore » assess the impact of cloud microphysics on the hydrological cycle. In the EAULIQ run, large-scale moist processes and cloud optical properties are driven by bulk cloud microphysics parameterization. In the CONTROL run, condensed water is immediately removed from the atmosphere as rain, which may evaporate as it falls through subsaturated layers. Results are presented for January and July monthly averages. Emphasis is placed on spatial distributions of cloud water, cloud ice, rain, and snow. In EAULIQ, cloud water and cloud ice are more abundant in middle latitudes than in the tropics, suggesting that large-scale condensation contributes a major part to condensed water production. Comparisons between simulated vertically integrated cloud water and columnar cloud water retrievals from satellite measurements indicate reasonable agreement. Interactions between cloud microphysics and cumulus convection parameterizations lead to smaller, more realistic precipitation rates. In particular, the cumulus precipitation rate is strongly reduced when compared to CONTROL. 95 refs., 18 figs., 4 tabs.« less
  • In this study, wind turbine impacts on the atmospheric flow are investigated using data from the Crop Wind Energy Experiment (CWEX-11) and large-eddy simulations (LESs) utilizing a generalized actuator disk (GAD) wind turbine model. CWEX-11 employed velocity-azimuth display (VAD) data from two Doppler lidar systems to sample vertical profiles of flow parameters across the rotor depth both upstream and in the wake of an operating 1.5 MW wind turbine. Lidar and surface observations obtained during four days of July 2011 are analyzed to characterize the turbine impacts on wind speed and flow variability, and to examine the sensitivity of thesemore » changes to atmospheric stability. Significant velocity deficits (VD) are observed at the downstream location during both convective and stable portions of four diurnal cycles, with large, sustained deficits occurring during stable conditions. Variances of the streamwise velocity component, σ u, likewise show large increases downstream during both stable and unstable conditions, with stable conditions supporting sustained small increases of σ u , while convective conditions featured both larger magnitudes and increased variability, due to the large coherent structures in the background flow. Two representative case studies, one stable and one convective, are simulated using LES with a GAD model at 6 m resolution to evaluate the compatibility of the simulation framework with validation using vertically profiling lidar data in the near wake region. Virtual lidars were employed to sample the simulated flow field in a manner consistent with the VAD technique. Simulations reasonably reproduced aggregated wake VD characteristics, albeit with smaller magnitudes than observed, while σu values in the wake are more significantly underestimated. The results illuminate the limitations of using a GAD in combination with coarse model resolution in the simulation of near wake physics, and validation thereof using VAD data.« less
  • A model nesting approach has been used to simulate the regional climate over the Pacific Northwest. The present-day global climatology is first simulated using the NCAR Community Climate Model (CCM3) driven by observed sea surface temperature and sea ice distribution at T42 (2.8{sup o}) resolution. This large-scale simulation is used to provide lateral boundary conditions for driving the Pacific Northwest National Laboratory Regional Climate Model (RCM). One notable feature of the RCM is the use of subgrid parameterizations of orographic precipitation and vegetation cover, in which subgrid variations of surface elevation and vegetation are aggregated to a limited number ofmore » elevation-vegetation classes. An airflow model and a thermodynamic model are used to parameterize the orographic uplift/descent as air parcels cross over mountain barriers or valleys. The 7-yr climatologies as simulated by CCM3 and RCM are evaluated and compared in terms of large-scale spatial patterns and regional means. Biases are found in the simulation of large-scale circulations, which also affect the regional model simulation. Therefore, the regional simulation is not very different from the CCM3 simulation in terms of large-scale features. However, the regional model greatly improves the simulation of precipitation, surface temperature, and snow cover at the local scales. This is shown by improvements in the spatial correlation between the observations and simulations. The RCM simulation is further evaluated using station observations of surface temperature and precipitation to compare the simulated and observed relationships between surface temperature-precipitation and altitude. The model is found to correctly capture the surface temperature-precipitation variations as functions of surface topography over different mountain ranges, and under different climate regimes.« less