We proposed new snow grain model and snow impurity mixture models for the purpose of accurate retrievals of snow grain size and concentration of light‐absorbing particles (LAP) in snow from the optical remote sensing data. Two kinds of ice crystal models, irregularly shaped Voronoi columns, and Voronoi aggregates were employed. LAP can be captured by the snow through two processes: dry and wet deposition. Two different snow impurity mixture models were proposed. One is an external mixture model. We employed a coated sphere model in which soot were hydrophilic particles to represent a hygroscopic property of aerosol and assumed the hydrophilic soot particles to be externally mixed with snow particles. The other is an internal mixture model. We employed a dynamic effective medium approximation method in which soot particles were randomly located within snow particle with any size distribution and number concentration. Validation of these models is conducted using a ground‐based spectral radiometer system with in situ measurement data. For snow grain size retrievals, a Voronoi mixture model seamlessly representing a geometrical shape and an optical properties of various snow types can provide accurate retrievals. For the retrieval of LAP concentration in snow, employing different mixing state models depending on season and measurement site gives accurate results. We also discussed the uncertainty of retrieved snow parameters on the surface slope involved in the retrieval accuracy. These models are expected to be useful for advanced airborne/satellite remote sensing and climate studies via radiative transfer modeling in the atmosphere‐snow system.

中文翻译：

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雪粒形状和雪杂质混合状态对地基光学仪器雪物理参数反演的影响

我们提出了新的雪粒模型和雪杂质混合物模型，目的是从光学遥感数据中准确检索雪粒大小和雪中光吸收颗粒（LAP）的浓度。两种冰晶模型，不规则形状的Voronoi柱和Voronoi聚集体被使用。LAP可以通过两个过程被雪捕获：干沉降和湿沉降。提出了两种不同的雪杂质混合模型。一种是外部混合模型。我们采用了一个涂层球体模型，其中烟灰是亲水性颗粒，代表了气溶胶的吸湿性，并假设亲水性烟灰颗粒在外部与雪颗粒混合。另一个是内部混合模型。我们采用了一种动态有效的介质近似方法，在该方法中，烟尘颗粒以任意大小分布和数量浓度随机分布在雪颗粒中。这些模型的验证是使用具有现场测量数据的地面光谱辐射计系统进行的。对于雪粒大小的检索，Voronoi混合模型可无缝表示几何形状和各种雪类型的光学特性，可以提供准确的检索。为了获取雪中LAP的浓度，根据季节和测量地点采用不同的混合状态模型可得出准确的结果。我们还讨论了与反演精度有关的地表斜坡上反演雪参数的不确定性。