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 bulk microphysics schemes.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 113-130 |
| Number of pages | 18 |
| Journal | Journal of Applied Meteorology and Climatology |
| Volume | 58 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 1 2019 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Atmospheric Science
Cite this
}
A moment-based polarimetric radar forward operator for rain microphysics. / Kumjian, Matthew Robert; Martinkus, Charlotte P.; Prat, Olivier P.; Collis, Scott; Van Lier-Walqui, Marcus; Morrison, Hugh C.
In: Journal of Applied Meteorology and Climatology, Vol. 58, No. 1, 01.01.2019, p. 113-130.Research output: Contribution to journal › Article
TY - JOUR
T1 - A moment-based polarimetric radar forward operator for rain microphysics
AU - Kumjian, Matthew Robert
AU - Martinkus, Charlotte P.
AU - Prat, Olivier P.
AU - Collis, Scott
AU - Van Lier-Walqui, Marcus
AU - Morrison, Hugh C.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85060913484&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060913484&partnerID=8YFLogxK
U2 - 10.1175/JAMC-D-18-0121.1
DO - 10.1175/JAMC-D-18-0121.1
M3 - Article
AN - SCOPUS:85060913484
VL - 58
SP - 113
EP - 130
JO - Journal of Applied Meteorology and Climatology
JF - Journal of Applied Meteorology and Climatology
SN - 1558-8424
IS - 1
ER -