Statistical properties of the normalized ice particle size distribution
Statistical properties of the normalized ice particle size distribution
复制标题
归一化冰粒径分布的统计特性
DOI：
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发表时间：
2005
期刊：
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通讯作者：
R. Forbes
中科院分区：
文献类型：
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作者：
J. Delanoë;A. Protat;J. Testud;D. Bouniol;A. Heymsfield;A. Bansemer;P. Brown;R. Forbes
[i]Testud等人（2001年）最近开发了一种称为归一化粒度分布（PSD）的形式，该形式包括以归一化PSD与含水量和平均体积加权直径无关的方式缩放直径和浓度轴。在本文中，我们调查的统计特性的归一化PSD的特殊情况下的冰云，这是已知的地球的辐射平衡中发挥了至关重要的作用。为此，建立了一个广泛的空中原位微物理测量数据库。一个显着的稳定性，在形状的归一化PSD。通过对仪器（雷达反射率和衰减）和云（冰水含量，有效半径，冰晶的终端下降速度，可见光消光）属性的误差分析，估计使用一个单一的分析形状来表示数据库中的所有PSD的影响。这导致了仪器和云参数的大致无偏估计，小的标准偏差范围从5%到12%。发现该误差大致与温度范围无关。这种形状的稳定性及其单一的解析近似意味着两个参数现在足以描述冰云中的任何归一化PSD：截距参数N* 0和平均体积加权直径D m。N* 0和DM之间的统计关系（参数化），然后进行评估，以再次减少未知数的数量。结果表明，不能设想通过温度对N* 0和Dm进行参数化来检索云参数。尽管如此，已经推导出D m -T和平均最大尺寸直径-T参数化，并与目前用于描述气候模式中颗粒大小的Kristjansson等人（2000年）的参数化进行了比较。新的参数化通常在任何温度下产生比Kristjansson等人（2000年）参数化更大的颗粒尺寸。这些新的参数化被认为是更好地代表颗粒尺寸在全球范围内，由于一个更好的代表性的原位微物理数据库用于推导it. We然后评估了直接的N* 0 -D m关系的潜力。虽然由温度参数化的模型在云参数上产生很大的误差，但由雷达反射率参数化的N* 0 -Dm模型产生准确的云参数（小于3%的偏差和16%的标准偏差）。这一结果意味着，云参数可以估计从只有一个参数的归一化PSD（N* 0或Dm）和雷达反射率测量的估计。
[i] Testud et al. (2001) have recently developed a formalism, known as the normalized particle size distribution (PSD), which consists in scaling the diameter and concentration axes in such a way that the normalized PSDs are independent of water content and mean volume-weighted diameter. In this paper we investigate the statistical properties of the normalized PSD for the particular case of ice clouds, which are known to play a crucial role in the Earth's radiation balance. To do so, an extensive database of airborne in situ microphysical measurements has been constructed. A remarkable stability in shape of the normalized PSD is obtained. The impact of using a single analytical shape to represent all PSDs in the database is estimated through an error analysis on the instrumental (radar reflectivity and attenuation) and cloud (ice water content, effective radius, terminal fall velocity of ice crystals, visible extinction) properties. This resulted in a roughly unbiased estimate of the instrumental and cloud parameters, with small standard deviations ranging from 5 to 12%. This error is found to be roughly independent of the temperature range. This stability in shape and its single analytical approximation implies that two parameters are now sufficient to describe any normalized PSD in ice clouds: the intercept parameter N* 0 and the mean volume-weighted diameter D m . Statistical relationships (parameterizations) between N* 0 and D m have then been evaluated in order to reduce again the number of unknowns. It has been shown that a parameterization of N* 0 and D m by temperature could not be envisaged to retrieve the cloud parameters. Nevertheless, D m -T and mean maximum dimension diameter -T parameterizations have been derived and compared to the parameterization of Kristjansson et al. (2000) currently used to characterize particle size in climate models. The new parameterization generally produces larger particle sizes at any temperature than the Kristjansson et al. (2000) parameterization. These new parameterizations are believed to better represent particle size at global scale, owing to a better representativity of the in situ microphysical database used to derive it. We then evaluated the potential of a direct N* 0 -D m relationship. While the model parameterized by temperature produces strong errors on the cloud parameters, the N* 0 -D m model parameterized by radar reflectivity produces accurate cloud parameters (less than 3% bias and 16% standard deviation). This result implies that the cloud parameters can be estimated from the estimate of only one parameter of the normalized PSD (N* 0 or D m ) and a radar reflectivity measurement.