Vertical Structures Associated with Orographic Precipitation during Warm Season in the Sichuan Basin and Its Surrounding Areas at Different Altitudes from 8-Year GPM DPR Observations

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Abstract

Global precipitation measurement (GPM) is one of the effective means employed to observe orographic precipitation, and its inversed GPM DPR data can be used to study the microphysical structure of precipitation particles. This study considers statistics on convective precipitation (CP) and stratiform precipitation (SP) events over three types of terrain (plains, mountains, and high mountains) using the DPR onboard the GPM Core Observatory from May to September of 2014–2021 to analyze the vertical structure of heavy CP and SP. In mountain areas and high mountain areas, the updraft rendered by topography or seeder-feeder mechanism is not only conducive to the collision and merger of raindrops into large raindrops, but also increases the concentration of small drops, which is the main reason why the occurrence probability of not only large but also small raindrops increases and the horizontal distribution domain of mass weighted average raindrop diameter (Dm) widens. For heavy SP, the occurrence probability of medium-diameter precipitation particles below the freezing height (FzH) over high mountains is greater than those over plains. The precipitation particles above 10 km altitude of high mountains have characteristics, such as lower droplet number concentration and larger diameter, compared with those over plains. Furthermore, the study also analyzed the correlation between storm top altitude (STA) and Dm, water vapor and STA respectively. This study is helpful to further understand the effect of topography on heavy precipitation through cloud microphysical processes and the vertical structure of precipitation.

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Shen, C., Li, G., & Dong, Y. (2022). Vertical Structures Associated with Orographic Precipitation during Warm Season in the Sichuan Basin and Its Surrounding Areas at Different Altitudes from 8-Year GPM DPR Observations. Remote Sensing, 14(17). https://doi.org/10.3390/rs14174222

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