This study first compares two different passive microwave snow water equivalent (SWE) retrievals, namely the retrieval from the Suomi National Polar-orbiting Partnership (S-NPP) Advanced Technology Microwave Sounder (ATMS) and that from the Global Change Observation Mission – Water (GCOM-W1) Advanced Microwave Scanning Radiometer 2 (AMSR2); it further creates an optimal blending mechanism that merges the two retrievals with in situ observations from the Snow Telemetry (SNOTEL) and Cooperative Observer Program (COOP) networks. The assessments of the two products are done over conterminous United States (CONUS) for the snow seasons (November–June) of the water years 2017–2019 using in situ data and the SNOw Data Assimilation System (SNODAS) SWE analysis. Both satellite products tend to underestimate SWE. Between the two, AMSR2 retrieval outperforms in terms of correlation with observations and depth of saturation, but it exhibits a distinctive, seasonally varying bias that is not seen in ATMS retrieval. The negative bias over the early snow season, as further analysis indicates, most likely stems from AMSR2 retrieval's use of a high frequency channel (i.e., 89 GHz) for shallow snow detection, while the impact of differing assumptions of snow density is marginal. The blending scheme, developed on the basis of the validation experiment, features a histogram-based bias correction as a supplement to optimal interpolation. Cross-validation suggests that interpolated station product without the satellite background broadly underperforms the blended in situ-satellite product, confirming the utility of the satellite retrievals. Furthermore, the a priori bias correction mechanism is shown to be effective in mitigating large fluctuations in bias. Finally, the bias-corrected, blended in situ-satellite product performs comparably or even favorably against SNODAS over many parts of the CONUS, with important implications for joint use of satellite and in situ observations for hydrological monitoring and forecasting.

这项研究首先比较了两种不同的被动微波雪水当量（SWE）检索，即从索米国家极地轨道伙伴关系（S-NPP）先进技术微波探测仪（ATMS）和全球变化观测团–水（ GCOM-W1）先进的微波扫描辐射计2（AMSR2）；它进一步创建了一种最佳的混合机制，该机制将两次检索与来自Snow Telemetry（SNOTEL）和合作观察员计划（COOP）网络的原位观测合并。使用原位数据和SNOw数据同化系统（SNODAS）SWE分析，在2017-2019年水域的下雪季节（11月至6月）的美国本土（CONUS）进行了两种产品的评估。两种卫星产品都倾向于低估SWE。两者之间，在与观测值和饱和深度的相关性方面，AMSR2检索的性能要好，但是它表现出独特的，随季节变化的偏差，这在ATMS检索中是看不到的。进一步分析表明，早期降雪季节的负偏差很可能源于AMSR2检索使用高频信道（即89 GHz）进行浅雪检测，而不同的雪密度假设影响很小。在验证实验的基础上开发的混合方案具有基于直方图的偏差校正功能，可作为最佳插值的补充。交叉验证表明，没有卫星背景的内插站产品在性能上远远低于混合的原位卫星产品，这证实了卫星检索的实用性。此外，先验偏差校正机制被证明可有效减轻偏差的大幅度波动。最后，在CONUS的许多地区，经过偏斜校正的混合原位卫星产品的性能与SNODAS相当甚至更好，这对于联合使用卫星和现场观测进行水文监测和预报具有重要意义。