Thursday, 31 August 2023
Boundary Waters (Hyatt Regency Minneapolis)
Radar transmits waves through the atmosphere and observes high-resolution information of hydrometeors in the atmosphere. Single polarized radar variables of reflectivity and doppler velocity can provide information on the amount and speed of raindrops, while dual-polarization (dual-pol) radar variables, which include differential reflectivity (ZDR), specific differential phase (KDP), and co-polar correlation coefficient (ρhv), can provide additional information on the phase, size, and liquid water content of hydrometeors. When assimilating radar variables into a Numerical Weather Prediction (NWP) model, it can enhance the accuracy of predicting both large-scale and rapidly developing meso-β scale precipitation. Therefore, it is necessary to develop and apply high-precision observation operator for data assimilation that considers the microphysical information of a NWP model with dual-pol radar data.
In this study, a dual-pol radar operator based on microphysical variables, such as mixing ratio and total number concentration of hydrometeors, have been developed for the purpose of data assimilation and model verification. The newly modified operator is based on that of Jung et al. (2007), but categorizes snow and graupel into dry-snow, wet-snow, dry-graupel, and wet-graupel using the water fraction and considers the density of wet-snow and graupel as variables. The improved method can simulate the characteristics of the dual-pol radar variables in the melting layer more accurately. This allows a better estimate of the increments of ice phase hydrometeors from direct assimilation of dual-pol radar data and can further improve the prediction of mesoscale precipitation effects. The study will showcase recent development efforts and numerical simulation results from the improved dual-pol radar operator.
Key words: Dual-polarization radar operator, Radar data assimilation, Observation operator.
Acknowledgments: This work was supported by the National Research Foundation (NRF) grant funded by the Korea government (MSIT)(No. 2021R1A4A1032646, 2022R1A2C1012361 and 2022R1A6A3A13073165).
In this study, a dual-pol radar operator based on microphysical variables, such as mixing ratio and total number concentration of hydrometeors, have been developed for the purpose of data assimilation and model verification. The newly modified operator is based on that of Jung et al. (2007), but categorizes snow and graupel into dry-snow, wet-snow, dry-graupel, and wet-graupel using the water fraction and considers the density of wet-snow and graupel as variables. The improved method can simulate the characteristics of the dual-pol radar variables in the melting layer more accurately. This allows a better estimate of the increments of ice phase hydrometeors from direct assimilation of dual-pol radar data and can further improve the prediction of mesoscale precipitation effects. The study will showcase recent development efforts and numerical simulation results from the improved dual-pol radar operator.
Key words: Dual-polarization radar operator, Radar data assimilation, Observation operator.
Acknowledgments: This work was supported by the National Research Foundation (NRF) grant funded by the Korea government (MSIT)(No. 2021R1A4A1032646, 2022R1A2C1012361 and 2022R1A6A3A13073165).
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