Abstract Passive microwave (PMW) remote sensing has proven to be a useful approach to characterize the volume of seasonal snowpack in remote northern regions at the synoptic scale. Modeling emitted microwave brightness temperatures (TB) is made possible using a physical radiative transfer model that takes into account microstructural and stratigraphic structure of the snowpack. However, prescribing the microstructure remains a difficult task. This paper aims to find proper microstructure parametrization and the snow emission model formulation that best optimize TB simulations for Arctic and Subarctic snowpacks. Surfaced-based radiometric measurements in conjunction with in-situ snowpack characterization were used for testing different configurations based on the Snow Microwave Radiative Transfer model (SMRT), with two electromagnetic models (Dense Media Radiative Transfer Quasi Crystalline Approximation, DMRT, and Improved Born Approximation, IBA) and two microstructure description theories (Sticky Hard Sphere, SHS, and Exponential, Exp). We compare the performance of three configurations (DMRT-SHS, IBA-SHS and IBA-Exp) with a unique large dataset (119 snowpits with concomitant microwave ground-based radiometer observations) covering a wide range of Arctic and Subarctic snow types in Northern and Eastern Canada. Results show that the input measured microstructure parameters must be scaled up in order to better match simulated and observed TB at 11, 19, 37 and 89 GHz. We show that the IBA-Exp gives the best results, with a Root-Mean-Square Error (RMSE) lower by up to 30% for Subarctic snow and 24% for Arctic snow compare to the other model configurations we used. In addition, we undertake a complementary experiment on isolated homogeneous snow slabs to investigate the sensitivity of the scaling factor to snow microstructure. The retrieved microwave correlation length appears significantly different than the in-situ Debye correlation length. At high frequencies, the observed variability of these scaling factors with frequency and snowpack types means that density, SSA and estimated correlation length seem insufficient to appropriately fully characterize snow microstructure for microwave modeling purposes.

中文翻译：

---

用于微波亮温模拟的北极和亚北极雪微结构分析

摘要 被动微波 (PMW) 遥感已被证明是一种在天气尺度上表征偏远北部地区季节性积雪量的有用方法。使用物理辐射传递模型可以模拟发射的微波亮温 (TB)，该模型考虑了积雪的微观结构和地层结构。然而，规定微观结构仍然是一项艰巨的任务。本文旨在找到合适的微观结构参数化和雪排放模型公式，以最好地优化北极和亚北极雪堆的 TB 模拟。基于地表的辐射测量与原位积雪表征结合用于测试基于雪微波辐射传输模型 (SMRT) 的不同配置，具有两个电磁模型（密集介质辐射传递准晶体近似，DMRT 和改进的玻恩近似，IBA）和两个微观结构描述理论（粘性硬球，SHS 和指数，Exp）。我们将三种配置（DMRT-SHS、IBA-SHS 和 IBA-Exp）的性能与一个独特的大型数据集（119 个雪坑，伴随着微波地基辐射计观测）进行了比较，该数据集涵盖了北极和亚北极地区的各种雪类型。加拿大东部。结果表明，输入测量的微观结构参数必须按比例放大，以便更好地匹配 11、19、37 和 89 GHz 下模拟和观察到的 TB。我们表明 IBA-Exp 给出了最好的结果，与我们使用的其他模型配置相比，亚北极雪的均方根误差 (RMSE) 降低了 30%，北极雪降低了 24%。此外，我们对孤立的均质雪板进行了补充实验，以研究比例因子对雪微结构的敏感性。检索到的微波相关长度与原位德拜相关长度明显不同。在高频下，观察到的这些比例因子随频率和积雪类型的变化意味着密度、SSA 和估计的相关长度似乎不足以充分表征用于微波建模的雪微结构。我们对孤立的均匀雪板进行了补充实验，以研究比例因子对雪微结构的敏感性。检索到的微波相关长度与原位德拜相关长度明显不同。在高频下，观察到的这些比例因子随频率和积雪类型的变化意味着密度、SSA 和估计的相关长度似乎不足以充分表征用于微波建模的雪微结构。我们对孤立的均匀雪板进行了补充实验，以研究比例因子对雪微结构的敏感性。检索到的微波相关长度与原位德拜相关长度明显不同。在高频下，观察到的这些比例因子随频率和积雪类型的变化意味着密度、SSA 和估计的相关长度似乎不足以充分表征用于微波建模的雪微结构。