Articles | Volume 25, issue 10
https://doi.org/10.5194/nhess-25-3905-2025
https://doi.org/10.5194/nhess-25-3905-2025
Research article
 | 
13 Oct 2025
Research article |  | 13 Oct 2025

Indirect assimilation of radar reflectivity data with an adaptive hydrometer retrieval scheme for severe short-term weather forecasts

Lixin Song, Feifei Shen, Zhixin He, Lu Yang, Dongmei Xu, Aiqing Shu, and Jiajun Chen

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Cited articles

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Borderies, M., Caumont, O., Delanoë, J., Ducrocq, V., Fourrié, N., and Marquet, P.: Impact of airborne cloud radar reflectivity data assimilation on kilometre-scale numerical weather prediction analyses and forecasts of heavy precipitation events, Nat. Hazards Earth Syst. Sci., 19, 907–926, https://doi.org/10.5194/nhess-19-907-2019, 2019. 
Chen, F. and Dudhia, J.: Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity, Mon. Weather Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001. 
Chen, H., Chen, Y., Gao, J., Sun, T., and Carlin, J. T.: A radar reflectivity data assimilation method based on background-dependent hydrometeor retrieval: An observing system simulation experiment, Atmos. Res., 243, 105022, https://doi.org/10.1016/j.atmosres.2020.105022, 2020. 
Chen, H., Gao, J., Wang, Y., Chen, Y., Sun, T., Carlin, J., and Zheng, Y.: Radar reflectivity data assimilation method based on background-dependent hydrometeor retrieval: Comparison with direct assimilation for real cases, Q. J. Roy. Meteor. Soc., 147, 2409–2428, https://doi.org/10.1002/qj.4031, 2021. 
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Short summary
When retrieving hydrometeors from reflectivity, there are two methods to allocate hydrometeor types: temperature-based and background hydrometer-dependent schemes. The temperature-based method divides hydrometeor proportions based on the background temperature, while the other scheme calculates average weights of each hydrometeor in various reflectivity intervals from background fields. The blending scheme adaptively combines these methods and is found to improve precipitation forecast accuracy.
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