In this study, a polarimetric radar forward model operator was developed for the Weather Research and Forecasting (WRF) model that was based on a scattering algorithm using the T-matrix methodology. Three microphysics schemes—Thompson, Morrison 2-moment, and Milbrandt-Yau 2-moment—were supported in the operator. This radar forward operator used the microphysics, thermodynamic, and wind fields from WRF model forecasts to compute horizontal reflectivity, radial velocity, and polarimetric variables including differential reflectivity (ZDR) and specific differential phase (KDP) for S-band radar. A case study with severe convective storms was used to examine the accuracy of the radar operator. Output from the radar operator was compared to real radar observations from the Weather Surveillance Radar–1988 Doppler (WSR-88D) radar. The results showed that the radar forward operator generated realistic polarimetric signatures. The distribution of polarimetric variables agreed well with the hydrometer properties produced by different microphysics schemes. Similar to the observed polarimetric signatures, radar operator output showed ZDR and KDP columns from low-to-mid troposphere, reflecting the large amount of rain within strong updrafts. The Thompson scheme produced a better simulation for the hail storm with a ZDR hole to indicate the existence of graupel in the low troposphere.
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This content will become publicly available on May 16, 2025
A New Melting Model and Its Implementation in Parameterized Forward Operators for Polarimetric Radar Data Simulation With Double Moment Microphysics Schemes
To improve short‐term severe weather forecasts through assimilation of polarimetric radar data (PRD), the use of accurate and efficient forward operators for polarimetric radar variables is required. In this study, a new melting model is proposed to estimate the mixing ratio and number concentration of melting hydrometeor species and incorporated in a set of parameterized polarimetric radar forward operators. The new melting model depends only on the mixing ratio and number concentration of rain and ice species and is characterized by its independence from ambient temperature and its simplicity and ease of linearization. To assess the impact of this newly proposed melting model on the simulated polarimetric radar variables, a real mesoscale convective system is simulated using three double‐moment microphysics schemes. Compared with the output of the original implementation of the parameterized forward operators (PFO_Old) that rely on an “old” melting model which only estimates the mixing ratio of the melting species, the updated implementation with the new melting model (PFO_New) that estimates both the mixing ratio and number concentration of melting species eliminates the very large mass/volume‐weighted mean diameter (
- Award ID(s):
- 2136161
- PAR ID:
- 10552808
- Publisher / Repository:
- AGU
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Atmospheres
- Volume:
- 129
- Issue:
- 9
- ISSN:
- 2169-897X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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In this study, a polarimetric radar forward model operator was developed for the Weather Research and Forecasting (WRF) model that was based on a scattering algorithm using the T-matrix methodology. Three microphysics schemes—Thompson, Morrison 2-moment, and Milbrandt-Yau 2-moment—were supported in the operator. This radar forward operator used the microphysics, thermodynamic, and wind fields from WRF model forecasts to compute horizontal reflectivity, radial velocity, and polarimetric variables including differential reflectivity (ZDR) and specific differential phase (KDP) for S-band radar. A case study with severe convective storms was used to examine the accuracy of the radar operator. Output from the radar operator was compared to real radar observations from the Weather Surveillance Radar–1988 Doppler (WSR-88D) radar. The results showed that the radar forward operator generated realistic polarimetric signatures. The distribution of polarimetric variables agreed well with the hydrometer properties produced by different microphysics schemes. Similar to the observed polarimetric signatures, radar operator output showed ZDR and KDP columns from low-to-mid troposphere, reflecting the large amount of rain within strong updrafts. The Thompson scheme produced a better simulation for the hail storm with a ZDR hole to indicate the existence of graupel in the low troposphere.more » « less
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Abstract The assimilation of radar reflectivity requires an accurate and efficient forward operator that links the model state variables to radar observations. In this study, newly developed parameterized forward operators (PFO) for radar reflectivity with a new continuous melting model are implemented to assimilate observed radar data. To assess the impact of the novel parameterized reflectivity forward operators on convective storm analysis and forecasting, two distinct sets of cycled assimilation and forecast experiments are conducted. One set of experiments (ExpRFO) uses a conventional Rayleigh‐scattering‐approximation‐based forward operator (RFO) with hydrometeor classification, while the other uses the PFO (ExpPFO_New) for radar reflectivity with a new continuous melting model. Eight high‐impact severe convective weather events from the Hazardous Weather Testbed (HWT) 2019 Spring Experiments are selected for this study. The analysis and forecast results are first examined in detail for a classic tornadic supercell case on 24 May 2019, with the potential benefits provided by the PFO then evaluated for all eight cases. It is demonstrated that ExpPFO_New provides more robust results in terms of improving the short‐term severe weather forecasts. Compared to ExpRFO, ExpPFO_New better reproduces all observed supercells in the analysis field, yields a more continuous and reasonable reflectivity distribution near the melting layer, and improves the strength of the cold pool compared to observations. Overall, ExpPFO_New, initialized from the more accurate analysis fields, produces better forecasts of reflectivity and hourly precipitation with smaller biases, especially at heavy precipitation thresholds.
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Abstract The discovery of a polarimetric radar signature indicative of hydrometeor refreezing has shown promise in its utility to identify periods of ice pellet production. Uniquely characterized well below the melting layer by locally enhanced values of differential reflectivity ( Z DR ) within a layer of decreasing radar reflectivity factor at horizontal polarization ( Z H ), the signature has been documented in cases where hydrometeors were completely melted prior to refreezing. However, polarimetric radar features associated with the refreezing of partially melted hydrometeors have not been examined as rigorously in either an observational or microphysical modeling framework. Here, polarimetric radar data—including vertically pointing Doppler spectral data from the Ka-band Scanning Polarimetric Radar (KASPR)—are analyzed for an ice pellets and rain mixture event where the ice pellets formed via the refreezing of partially melted hydrometeors. Observations show that no such distinct localized Z DR enhancement is present, and that values instead decrease directly beneath enhanced values associated with melting. A simplified, explicit bin microphysical model is then developed to simulate the refreezing of partially melted hydrometeors, and coupled to a polarimetric radar forward operator to examine the impacts of such refreezing on simulated radar variables. Simulated vertical profiles of polarimetric radar variables and Doppler spectra have similar features to observations, and confirm that a Z DR enhancement is not produced. This suggests the possibility of two distinct polarimetric features of hydrometeor refreezing: ones associated with refreezing of completely melted hydrometeors, and those associated with refreezing of partially melted hydrometeors. Significance Statement There exist two pathways for the formation of ice pellets: refreezing of fully melted hydrometeors, and refreezing of partially melted hydrometeors. A polarimetric radar signature indicative of fully melted hydrometeor refreezing has been extensively documented in the past, yet no study has documented the refreezing of partially melted hydrometeors. Here, observations and idealized modeling simulations are presented to show different polarimetric radar features associated with partially melted hydrometeor refreezing. The distinction in polarimetric features may be beneficial to identifying layers of supercooled liquid drops within transitional winter storms.more » « less
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