Snow Covers impact on Antarctic Sea Ice - Remote Sensing (SCASI-RS)

南极海冰通常被雪覆盖，因此雪的性质决定了冰的表面特征，并影响大气和海洋之间的相互作用。南极的冰雪形成比北极更为常见，并对海冰质量平衡做出了重要贡献。此外，还需要关于积雪深度和密度的资料，以便从高度计的测量数据中得出冰厚。然而，到目前为止，南极海冰上的雪的知识主要来自个人的现场测量和船舶观测。在更大的尺度上，也可以从19和37 GHz频率的被动微波卫星测量中反演雪深，但方法的验证和误差来源的调查仍在进行中。 在积雪对南极海冰的影响（SCASI）项目中，我们的目标是量化海冰上积雪的数量和分布，以及积雪的物理特性及其时间演变。总体目标是为南极海冰开发一个新的、一致的雪数据产品，代表不同的长度尺度和不同的季节。为了实现这一目标，并桥接从点测量到卫星足迹的尺度，我们综合实地观测，卫星遥感和数值模拟。一维SNOWPACK模型是一种广泛应用的积雪模型，已成功地应用于高山地区的积雪模拟。在SCASI项目中，我们将瑞士和德国的合作伙伴聚集在一起，进一步开发SNOWPACK的海冰版本，并将其与现场和浮标测量以及被动微波卫星观测相结合。SCASI-RS项目涉及SCASI的卫星遥感部分。在SCASI-RS中，我们将SNOWPACK模拟与现场测量进行比较，以确定合适的情况下验证卫星检索。通过将SNOWPACK与辐射模式相结合，我们可以模拟微波辐射，并研究雪的属性对反演方法的影响。这将不仅适用于迄今为止用于雪深反演的19和37 GHz微波频率，而且适用于1.4 GHz的较低频率。自2009年以来，1.4GHz的全球卫星观测数据已经可用，我们调查南极的积雪深度是否也可以从这个非常低的微波频率中得出，就像最近在北极提出的那样。然而，由于冰雪条件的明显差异，结果可能无法直接转移到南极地区，所产生的产品将有助于海冰和辐射模型、基于高度测量的冰厚反演和其他依赖海冰上积雪信息的研究，例如生物生产或地球化学循环。

Antarctic sea ice is generally covered with snow, and thus the snow properties determine the surface characteristics of the ice and influence the interaction between atmosphere and ocean.Snow-ice formation is much more common in the Antarctic than in the Arctic and contributes significantly to the sea ice mass balance. Additionally, information on snow depth and density are required to derive ice thickness from altimeter measurements. However, so far knowledge on Antarctic snow on sea ice mainly originates from individual field measurements and ship observations. On larger scales, snow depth can also be retrieved from passive microwave satellites measuring at 19 and 37GHz frequency, but validation of the method and investigation of error sources are ongoing work. In the Snow Covers impact on Antarctic Sea Ice (SCASI) project, we aim for quantifying the amount and distribution of snow on sea ice, as well as the physical properties of snow and their temporal evolution. The overall goal is to develop a new and consistent snow data product for Antarctic sea ice that represents various length scales and different seasons. In order to achieve this and to bridge the scales from point measurements to satellite footprints, we synthesize field observations, satellite remote sensing and numerical modeling. A widely-used snow cover model that has been successfully applied for modeling snow in alpine regions is the one-dimensional SNOWPACK model. In the SCASI project, we bring together Swiss and German partners to further develop a sea ice version of SNOWPACK and to combine it with in-situ and buoy measurements as well as passive microwave satellite observations. The SCASI-Remote Sensing (SCASI-RS) project deals with the satellite remote sensing part of SCASI. In SCASI-RS, we compare SNOWPACK simulations with in-situ measurements to identify suitable cases for validation of satellite retrievals. By combining SNOWPACK with emission models we can simulate microwave radiation and investigate the impact of snow properties on the retrieval methods. This will not only be done for the microwave frequencies at 19 and 37GHz, used for snow depth retrievals so far, but also for a lower frequency of 1.4 GHz. Global satellite observations at 1.4GHz have been available since 2009, and we investigate whether snow depth in the Antarctic can also be derived from this very low microwave frequency, as it has been suggested for the Arctic recently. Due to the distinct differences of the ice and snow conditions, however, the results may not be transferred directly to Antarctic regions.The resulting product will be useful for sea ice and radiation models, altimetry-based ice thickness retrievals and other research that depends on information on snow on sea ice, for example regarding biological production or geo-chemical cycles.