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首页> 外文期刊>ournal of the Meteorological Society of Japan >Assessment of Ku- and Ka-band Dual-frequency Radar for Snow Retrieval
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Assessment of Ku- and Ka-band Dual-frequency Radar for Snow Retrieval

机译:评估KU和KA波段双频雷达用于雪检索

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Dual-frequency Ku/Ka-band radar retrievals of snow parameters such as liquid-equivalent snowfall rate ( R ) and mass-weighted diameter ( D m ) have two principal errors, namely, the differences between the assumed particle size distribution (PSD) model from the actual PSD and inadequacies in characterizing the single-scattering properties of snowflakes. Regarding the first issue, this study, based on radar simulations from a large amount of observed PSD data, shows that there exist relatively high correlations between the estimated snow parameters and their true values derived directly from the measured PSD. For PSD data with R greater than 0.1 mm h ?1 , a gamma PSD model with a fixed shape factor ( μ ) equal to 0 (or exponential distribution) provides the best estimates of R and D m . This is despite negative biases of up to ?15 % in R and underestimates and overestimates in D m for small and large D m , respectively. The μ = 0 assumption, however, produces relatively poor estimates of normalized intercepts of a gamma PSD ( N w ), whereas the best estimates are obtained when μ is considered either 3 or 6. However, the use of an inappropriate scattering table increases the errors in snow retrieval. Simple evaluations are made for cases where the scattering databases used for the algorithm input differ from that used for retrieval. The mismatched scattering databases alone could cause at least 30–50 % changes in the estimates of snow water content ( SWC ) and R and could affect the retrievals of D m and N w and their dependence on μ .
机译:双频KU / KA波段雷达检索雪参数,如液体等效的降雪率(R)和大量加权直径(D M)具有两个主要误差,即假定的粒度分布(PSD)之间的差异从实际PSD和表征雪花单散射性能的模型和不足的模型。关于第一个问题,本研究基于来自大量观察到的PSD数据的雷达模拟,表明估计的雪参数与直接从测量的PSD导出的真实值之间存在相对高的相关性。对于具有大于0.1mm H的PSD数据,具有等于0(或指数分布)的固定形状因子(μ)的伽马PSD模型提供了R和D M的最佳估计。尽管抗偏差高达15%,但分别为小而大的D m分别低估了和高估和高估。然而,μ= 0假设产生伽马PSD(N W)的标准化截距的相对较差的估计,而当μ被认为是3或6时,获得最佳估计值。然而,使用不适当的散射表的使用增加了雪中​​的错误。对于用于算法输入的散射数据库与用于检索的散射数据库不同的情况进行了简单的评估。单独的不匹配的散射数据库可能导致降雪含量(SWC)和R的估计变化至少30-50%,并且可能影响D M和N W的检索及其对μ的依赖性。

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