黄河三角洲自然保护区不同季节和不同类型降水云的雨滴谱特征

Characteristics of Raindrop Size Distributions for Different Seasons and Precipitation Clouds in Yellow River Delta Nature Reserve

摘要:

为深入了解黄河三角洲自然保护区雨滴谱和降水微物理的特征，更深认识降水对植被水文、生态系统等的调控作用，利用距离其南北区域最近的2个国家基本气象站2021—2024年降水现象仪和自动气象站等资料，分析其不同季节和不同类型降水云雨滴谱特征及Gamma函数参数之间的关系，结果表明：①北部区域春季和夏季、层状云和对流云（秋季和冬季、混合云）的降水雨滴谱为双峰型（单峰型）结构，而南部区域四季和三类云降水均为单峰型结构。②南北两区域四季降水的标准化截距参数lgN_w（质量加权平均直径D_m）均值皆为秋季＞夏季＞冬季＞春季（夏季＞春季＞秋季＞冬季），而三类云降水皆为对流云＞层状云＞混合云，且lgN_w、D_m均随雨强R增大而增大。③北部（南部）区域四季降水的形状因子μ和斜率参数λ均值皆为秋季＞冬季＞春季＞夏季（冬季＞秋季＞夏季＞春季），而三类云降水皆为混合云＞层状云＞对流云，且μ、λ均随R增大而减小。④北部（南部）区域雷达估测降水春季应用Z=331.4R^1.56（Z=413.5R^1.54）、夏秋冬三季应用Z=276.3R^1.48（Z=339.8R^1.45）更接近实测值，且都比经典关系Z=300R^1.4误差更小。

Abstract:

To gain an in-depth understanding of the raindrop size distribution (DSD) and microphysical characteristics of precipitation in the Yellow River Delta Nature Reserve, and to better understand the regulatory effects of precipitation on vegetation, hydrology, and ecosystems, we analyse data from 2021 to 2024 collected by precipitation disdrometers and automatic weather stations at the two national basic meteorological stations closest to the northern and southern parts of the reserve. We examine the characteristics of DSDs across different seasons and cloud types (stratiform, convective, and mixed), as well as the relationships between the parameters of the Gamma distribution. The results show that: (1) DSDs exhibit notable spatial and typological differences. In the northern region, precipitation in spring and summer, as well as from stratiform and convective clouds, shows a bimodal distribution, while autumn, winter, and mixed clouds exhibit a unimodal distribution. In contrast, the southern region displays unimodal DSDs across all seasons and cloud types. In both regions, raindrop concentration is dominated by small and medium-sized drops. However, rainfall is primarily contributed by medium and large drops in summer and from convective clouds, whereas small and medium drops play a major role in winter and in stratiform and mixed clouds. (2) The normalised intercept parameter (lgN_w) shows a consistent seasonal order of autumn > summer > winter > spring in both regions, while the mass-weighted mean diameter (D_m) follows summer > spring > autumn > winter. For different cloud types, both parameters decrease in the order: convective > stratiform > mixed. Both lgN_w and D_m increase with rainfall intensity (R). The increase in R is mainly attributed to an increase in D_m (i.e., a broadening of the drop size spectrum) and, to a lesser extent, to an increase in lgN_w (i.e., a higher drop concentration). Convective precipitation in both regions exhibits transitional continental-maritime characteristics. (3) Seasonal variations in the shape (μ) and slope (λ) parameters differ between the two regions: in the north, the order is autumn > winter > spring > summer, while in the south, it is winter > autumn > summer > spring. For cloud types, the order is mixed > stratiform > convective clouds in both regions. Both μ and λ decrease with increasing R. (4) The classic Z-R relationship (Z=300R^1.4) performs well for autumn and winter precipitation in the northern region but systematically overestimates rainfall in northern spring and summer, as well as in all seasons in the southern region. To improve quantitative precipitation estimation (QPE) accuracy, we derive localised Z-R relationships: for the northern region, Z=331.4R^1.56 is recommended for spring, and Z=276.3R^1.48 for the spring-summer-autumn period; for the southern region, Z=413.5R^1.54 for spring and Z=339.8R^1.45 for the spring-summer-autumn period. These localised relationships significantly reduce estimation errors.