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Title: A Moment-based Polarimetric Radar Forward Operator for Rain Microphysics

Abstract

There is growing interest in combining microphysical models and polarimetric radar observations to improve our understanding of storms and precipitation. Mapping model-predicted variables into the radar observational space necessitates a forward operator, which requires assumptions that introduce uncertainties into model-observation comparisons. These include uncertainties arising from the microphysics scheme a priori assumptions of a fixed drop size distribution (DSD) functional form, whereas natural DSDs display far greater variability. To address this concern, this study presents a moment-based polarimetric radar forward operator with no fundamental restrictions on the DSD form by linking radar observables to integrated DSD moments. The forward operator is built upon a dataset of >200 million realistic DSDs from one-dimensional bin microphysical rain-shaft simulations, and surface disdrometer measurements from around the world. This allows for a robust statistical assessment of forward operator uncertainty and quantification of the relationship between polarimetric radar observables and DSD moments. Comparison of "truth" and forward-simulated vertical profiles of the polarimetric radar variables are shown for bin simulations using a variety of moment combinations. Higher-order moments (especially those optimized for use with the polarimetric radar variables: the sixth and ninth) perform better than the lower-order moments (zeroth and third) typically predicted by many bulkmore » microphysics schemes.« less

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science - Office of Biological and Environmental Research - Atmospheric System Research; USDOE Office of Science - Office of Biological and Environmental Research; National Science Foundation (NSF)
OSTI Identifier:
1510038
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
Journal of Applied Meteorology and Climatology
Additional Journal Information:
Journal Volume: 58; Journal Issue: 1
Country of Publication:
United States
Language:
English

Citation Formats

Kumjian, Matthew, Martinkus, Charlotte, Prat, Olivier, Collis, Scott, van Lier-Walqui, Marcus, and Morrison, Hugh. A Moment-based Polarimetric Radar Forward Operator for Rain Microphysics. United States: N. p., 2019. Web. doi:10.1175/JAMC-D-18-0121.1.
Kumjian, Matthew, Martinkus, Charlotte, Prat, Olivier, Collis, Scott, van Lier-Walqui, Marcus, & Morrison, Hugh. A Moment-based Polarimetric Radar Forward Operator for Rain Microphysics. United States. doi:10.1175/JAMC-D-18-0121.1.
Kumjian, Matthew, Martinkus, Charlotte, Prat, Olivier, Collis, Scott, van Lier-Walqui, Marcus, and Morrison, Hugh. Tue . "A Moment-based Polarimetric Radar Forward Operator for Rain Microphysics". United States. doi:10.1175/JAMC-D-18-0121.1.
@article{osti_1510038,
title = {A Moment-based Polarimetric Radar Forward Operator for Rain Microphysics},
author = {Kumjian, Matthew and Martinkus, Charlotte and Prat, Olivier and Collis, Scott and van Lier-Walqui, Marcus and Morrison, Hugh},
abstractNote = {There is growing interest in combining microphysical models and polarimetric radar observations to improve our understanding of storms and precipitation. Mapping model-predicted variables into the radar observational space necessitates a forward operator, which requires assumptions that introduce uncertainties into model-observation comparisons. These include uncertainties arising from the microphysics scheme a priori assumptions of a fixed drop size distribution (DSD) functional form, whereas natural DSDs display far greater variability. To address this concern, this study presents a moment-based polarimetric radar forward operator with no fundamental restrictions on the DSD form by linking radar observables to integrated DSD moments. The forward operator is built upon a dataset of >200 million realistic DSDs from one-dimensional bin microphysical rain-shaft simulations, and surface disdrometer measurements from around the world. This allows for a robust statistical assessment of forward operator uncertainty and quantification of the relationship between polarimetric radar observables and DSD moments. Comparison of "truth" and forward-simulated vertical profiles of the polarimetric radar variables are shown for bin simulations using a variety of moment combinations. Higher-order moments (especially those optimized for use with the polarimetric radar variables: the sixth and ninth) perform better than the lower-order moments (zeroth and third) typically predicted by many bulk microphysics schemes.},
doi = {10.1175/JAMC-D-18-0121.1},
journal = {Journal of Applied Meteorology and Climatology},
number = 1,
volume = 58,
place = {United States},
year = {2019},
month = {1}
}