Abstract Knowledge of snow water equivalent (SWE) magnitude and spatial distribution are keys to improving snowmelt flood predictions. Since the 1980s, the operational National Oceanic and Atmospheric Administration's (NOAA) airborne gamma radiation soil moisture (SM) and SWE survey has provided over 20,000 SWE observations to regional National Weather Service (NWS) River Forecast Centers (RFCs). Because the gamma SWE algorithm is based on the difference in natural gamma emission measurements from the soil between bare (fall) and snow-covered (winter) conditions, it requires a baseline fall SM for each flight line. The operational approach assumes the fall SM remains constant throughout that winter's SWE survey. However, early-winter snowmelt and rainfall events after the fall SM surveys have the potential to introduce large biases into airborne gamma SWE estimates. In this study, operational airborne gamma radiation SWE measurements were improved by updating the baseline fall SM with Soil Moisture Active Passive (SMAP) enhanced SM measurements immediately prior to winter onset over the north-central and eastern United States and southern Canada from September 2015 to April 2018. The operational airborne gamma SM had strong agreement with the SMAP SM (Pearson's correlation coefficient, R = 0.69, unbiased root mean square difference, ubRMSD = 0.057 m3/m3), compared to the Advanced Microwave Scanning Radiometer 2 (AMSR2) SM (R = 0.45, ubRMSD = 0.072 m3/m3) and the North American Land Data Assimilation System Phase 2 (NLDAS-2) Mosaic SM products (R = 0.53, ubRMSD = 0.069 m3/m3) in non-forested regions. The SMAP-enhanced gamma SWE was evaluated with satellite-based SWE (R = 0.57, ubRMSD = 34 mm) from the Special Sensor Microwave Imager Sounder (SSMIS) and in-situ SWE (R = 0.71–0.96) from the Soil Climate Analysis Network and United States Army Corps of Engineer (USACE) St. Paul District, which had better agreement than the operational gamma SWE (R = 0.48, ubRMSD = 36 mm for SSMIS and R = 0.65–0.75 for in-situ SWE). The results contribute to improving snowmelt flood predictions as well as the accuracy of the NOAA SNOw Data Assimilation System.

中文翻译：

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使用 SMAP 土壤水分改进操作空气伽马辐射雪水当量估计

摘要 了解雪水当量 (SWE) 量级和空间分布是改进融雪洪水预测的关键。自 1980 年代以来，国家海洋和大气管理局 (NOAA) 的空中伽马辐射土壤水分 (SM) 和 SWE 调查已向区域国家气象局 (NWS) 河流预报中心 (RFC) 提供了 20,000 多次 SWE 观测。因为伽马 SWE 算法基于裸露（秋季）和积雪覆盖（冬季）条件之间土壤自然伽马发射测量值的差异，所以它需要每个飞行路线的基线坠落 SM。操作方法假设秋季 SM 在整个冬天的 SWE 调查中保持不变。然而，秋季 SM 调查之后的初冬融雪和降雨事件有可能给空气中的伽马 SWE 估计引入很大的偏差。在这项研究中，2015 年 9 月至 2015 年 9 月至 2015 年 9 月至 2015 年 9 月至 2015 年 9 月至 2015 年 9 月至 2015 年 9 月至 2015 年 9 月至 2015 年至2018 年 4 月。与高级微波扫描辐射计 2 (AMSR2) SM 相比，操作机载伽马 SM 与 SMAP SM 具有很强的一致性（Pearson 相关系数，R = 0.69，无偏均方根差，ubRMSD = 0.057 m3/m3） （R = 0.45，ubRMSD = 0.072 m3/m3）和北美土地数据同化系统第 2 阶段 (NLDAS-2) Mosaic SM 产品（R = 0.53，ubRMSD = 0.069 m3/m3) 在非森林地区。使用来自特殊传感器微波成像仪 (SSMIS) 的基于卫星的 SWE (R = 0.57, ubRMSD = 34 mm) 和来自土壤气候分析的原位 SWE (R = 0.71–0.96) 评估 SMAP 增强的伽马 SWE网络和美国陆军工程兵团 (USACE) 圣保罗区，其比操作伽马 SWE 具有更好的一致性（对于 SSMIS，R = 0.48，ubRMSD = 36 mm，对于原位 SWE，R = 0.65–0.75）。结果有助于改进融雪洪水预测以及 NOAA 雪地数据同化系统的准确性。96) 来自土壤气候分析网络和美国陆军工程兵团 (USACE) 圣保罗区，这比操作伽马 SWE 具有更好的一致性（对于 SSMIS，R = 0.48，ubRMSD = 36 mm，对于 SSMIS，R = 0.65–0.75原位 SWE）。结果有助于改进融雪洪水预测以及 NOAA 雪地数据同化系统的准确性。96) 来自土壤气候分析网络和美国陆军工程兵团 (USACE) 圣保罗区，这比操作伽马 SWE 具有更好的一致性（对于 SSMIS，R = 0.48，ubRMSD = 36 mm，对于 SSMIS，R = 0.65–0.75原位 SWE）。结果有助于改进融雪洪水预测以及 NOAA 雪地数据同化系统的准确性。