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• W4311624653 abstract "Abstract The raindrop size distribution observed from ground-based or airborne disdrometers has been widely used to understand the characteristics of clouds and precipitation. However, its variability needs to be studied further and properly considered for improving precipitation prediction. In this study, using disdrometer data, the diagnostic relations for the intercept parameter of the exponential raindrop size distribution N 0 are derived for different rain types and the impacts of the diagnostic relations on precipitation prediction are examined. The disdrometer data observed at four sites in South Korea show spatiotemporal variations of N 0 . Three different derivation methods proposed by previous studies are used to derive the diagnostic relations, and the diagnostic relation that best reproduces the observed N 0 is selected. The diagnostic relation is implemented into the WRF single-moment 6-class microphysics (WSM6) scheme, and its impacts are investigated through the simulations of summertime precipitation events in South Korea. Compared to the simulation using the original WSM6 scheme (WSM6-O) where a constant N 0 is used, the simulation where N 0 is diagnosed by the diagnostic relation using the rainwater content at the lowest level (WSM6-L) yields better precipitation prediction. The WSM6-L simulation represents the variability of N 0 . Also, the WSM6-L simulation predicts N 0 that is on average smaller than the prescribed value in the WSM6-O simulation, agreeing with the observation to some extent. The smaller N 0 in the WSM6-L simulation decreases the rainwater production by the accretion of cloud water and the melting of ice hydrometeors, decreasing the rainwater mixing ratio." @default.
• W4311624653 created "2022-12-27" @default.
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• W4311624653 date "2022-12-05" @default.
• W4311624653 modified "2023-10-05" @default.
• W4311624653 title "Diagnostic Relations for the Intercept Parameter of Exponential Raindrop Size Distribution According to Rain Types Derived from Disdrometer Data and Their Impacts on Precipitation Prediction" @default.
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• W4311624653 doi "https://doi.org/10.1007/s13143-022-00306-0" @default.
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