Simulated polarimetric fields of ice vapor growth using the Adaptive Habit Model. Part II: A case study from the FROST experiment

Simulated polarimetric fields of ice vapor growth using the Adaptive Habit Model. Part II: A case... AbstractA new adaptive habit model (AHM) grows ice crystals through vapor deposition while evolving ice particle properties, including shape and effective density. The AHM provides an opportunity to investigate observed microphysical processes through the computation of polarimetric variables and corroboration with microphysical model output. This study is unique because the polarimetric scattering calculations are computed using predicted microphysical parameters rather than a priori assumptions that are imposed within the scattering calculations in the forward simulator, allowing for a more effective comparison to radar observations. Through the simulation of a case in the front range of the Rocky Mountains in Colorado using the Advanced Research Weather Research and Forecasting model, it is found that the AHM approximates ice mass, shape, cloud vertical structure, and temporal evolution as reflected through polarimetric quantities compared to observations. AHM reflectivity magnitudes are similar to those observed with radar and is an improvement over microphysical schemes that assume ice crystals as spheres.Further analyses are completed to examine the effect of microphysical processes on the evolution of differential reectivity and specific differential phase, both of which are simulated using the AHM. Simulations reveal a polarimetric response to ice crystal mass, number, size, density, and aspect ratio. While results reveal the need for model improvements (e.g., parameterizations for aggregation rate), testing forward-simulated radar fields against observations is a first step in the validation of model microphysical and precipitation processes. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Monthly Weather Review American Meteorological Society

Simulated polarimetric fields of ice vapor growth using the Adaptive Habit Model. Part II: A case study from the FROST experiment

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0493
eISSN
1520-0493
D.O.I.
10.1175/MWR-D-16-0062.1
Publisher site
See Article on Publisher Site

Abstract

AbstractA new adaptive habit model (AHM) grows ice crystals through vapor deposition while evolving ice particle properties, including shape and effective density. The AHM provides an opportunity to investigate observed microphysical processes through the computation of polarimetric variables and corroboration with microphysical model output. This study is unique because the polarimetric scattering calculations are computed using predicted microphysical parameters rather than a priori assumptions that are imposed within the scattering calculations in the forward simulator, allowing for a more effective comparison to radar observations. Through the simulation of a case in the front range of the Rocky Mountains in Colorado using the Advanced Research Weather Research and Forecasting model, it is found that the AHM approximates ice mass, shape, cloud vertical structure, and temporal evolution as reflected through polarimetric quantities compared to observations. AHM reflectivity magnitudes are similar to those observed with radar and is an improvement over microphysical schemes that assume ice crystals as spheres.Further analyses are completed to examine the effect of microphysical processes on the evolution of differential reectivity and specific differential phase, both of which are simulated using the AHM. Simulations reveal a polarimetric response to ice crystal mass, number, size, density, and aspect ratio. While results reveal the need for model improvements (e.g., parameterizations for aggregation rate), testing forward-simulated radar fields against observations is a first step in the validation of model microphysical and precipitation processes.

Journal

Monthly Weather ReviewAmerican Meteorological Society

Published: Mar 20, 2017

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