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Abstract Assimilating radar reflectivity into convective-scale NWP models remains a challenging topic in radar data assimilation. A primary reason is that the reflectivity forward observation operator is highly nonlinear. To address this challenge, a power transformation function is applied to the WRF Model’s hydrometeor and water vapor mixing ratio variables in this study. Three 3D variational data assimilation experiments are performed and compared for five high-impact weather events that occurred in 2019: (i) a control experiment that assimilates reflectivity using the original hydrometeor mixing ratios as control variables, (ii) an experiment that assimilates reflectivity using power-transformed hydrometeor mixing ratios as control variables, and (iii) an experiment that assimilates reflectivity and retrieved pseudo–water vapor observations using power-transformed hydrometeor and water vapor mixing ratios (qυ) as control variables. Both qualitative and quantitative evaluations are performed for 0–3-h forecasts from the five cases. The analysis and forecast performance in the two experiments with power-transformed mixing ratios is better than the control experiment. Notably, the assimilation of pseudo–water vapor with power-transformedqυas an additional control variable is found to improve the performance of the analysis and short-term forecasts for all cases. In addition, the convergence rate of the cost function minimization for the two experiments that use the power transformation is faster than that of the control experiments. Significance StatementThe effective use of radar reflectivity observations in any data assimilation scheme remains an important research topic because reflectivity observations explicitly include information about hydrometeors and also implicitly include information about the distribution of moisture within storms. However, it is difficult to assimilate reflectivity because the reflectivity forward observation operator is highly nonlinear. This study seeks to identify a more effective way to assimilate reflectivity into a convective-scale NWP model to improve the accuracy of predictions of high-impact weather events.
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This content will become publicly available on January 1, 2027
Direct Assimilation of Polarimetric Radar Data with a Variational Approach Using Both Mixing Ratio and Total Number Concentration as Analysis Variables: Observing System Simulation Experiments
Abstract Assimilating polarimetric radar data within a double-moment microphysics scheme requires that both hydrometeor mixing ratios and number concentrations be updated simultaneously to effectively utilize the radar information. This study directly assimilates polarimetric radar data in a fraternal twin observing system simulation experiment (OSSE) using both mixing ratios and number concentrations as analysis variables within a variational approach. A newly developed set of parameterized forward operators for polarimetric radar data, incorporating a new continuous melting model, is employed. To address challenges in minimizing the cost function, a power transformation function is applied to the analysis variables of mixing ratios and number concentrations. This approach alleviates issues arising from the very large dynamic range of number concentrations and the highly nonlinear relationship between the model’s hydrometeors and radar variables. Results from several groups of sensitivity experiments show that updating number concentrations using an appropriate power transformation function together with mixing ratios of hydrometeors reduces the analysis errors of radar variables and improves the analysis of polarimetric radar signatures. Updating number concentrations proves to be quite sensitive when assimilating differential reflectivity, while the additional assimilation of specific differential phase yields smaller analysis errors for reflectivity and mixing ratios of water vapor and rainwater compared to differential reflectivity assimilation alone. Experiments with smaller observation errors provide better analyses of the radar variables but also increase model variable analysis errors. Among the threshold values tested for reflectivity and polarimetric variables, assimilating polarimetric variables at grids where reflectivity exceeds 15 dBZprovides the best qualitative analysis.
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- PAR ID:
- 10658774
- Publisher / Repository:
- AMS Publications
- Date Published:
- Journal Name:
- Monthly Weather Review
- Volume:
- 154
- Issue:
- 1
- ISSN:
- 0027-0644
- Page Range / eLocation ID:
- 79 to 98
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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