气候变化领域集成服务门户(CCPortal): Evaluation and blending of ATMS and AMSR2 snow water equivalent retrievals over the conterminous United States

CCPortal

DOI	10.1016/j.rse.2020.112280
	Evaluation and blending of ATMS and AMSR2 snow water equivalent retrievals over the conterminous United States
	Gan Y.; Zhang Y.; Kongoli C.; Grassotti C.; Liu Y.; Lee Y.-K.; Seo D.-J.
发表日期	2021
ISSN	00344257
卷号	254
英文摘要	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. © 2020 Elsevier Inc.
英文关键词	AMSR2; ATMS; Bias correction; Optimal interpolation; Passive microwave; Snow water equivalent; Weighted averaging
语种	英语
scopus关键词	Blending; Microwave acoustics; Orbits; Satellites; Snow melting systems; Advanced microwave scanning radiometer; Advanced technology microwave sounders; Cooperative observer programs; Data assimilation systems; Global change observation missions; High frequency channels; Hydrological monitoring; Snow water equivalent; Snow; AMSR-E; detection method; in situ measurement; interpolation; satellite mission; snow water equivalent; spatiotemporal analysis; Suomi NPP; telemetry; United States
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178979
作者单位	Department of Civil Engineering, University of Texas at Arlington, Arlington, TX, United States; Earth System Science Interdisciplinary Center, Cooperative Institute for Satellite and Earth System Studies, University of Maryland at College Park, College Park, MD, United States; Office of Water Prediction, National Weather Service, National Oceanic and Atmospheric Administration, Silver Spring, MD, United States
推荐引用方式 GB/T 7714	Gan Y.,Zhang Y.,Kongoli C.,et al. Evaluation and blending of ATMS and AMSR2 snow water equivalent retrievals over the conterminous United States[J],2021,254.
APA	Gan Y..,Zhang Y..,Kongoli C..,Grassotti C..,Liu Y..,...&Seo D.-J..(2021).Evaluation and blending of ATMS and AMSR2 snow water equivalent retrievals over the conterminous United States.Remote Sensing of Environment,254.
MLA	Gan Y.,et al."Evaluation and blending of ATMS and AMSR2 snow water equivalent retrievals over the conterminous United States".Remote Sensing of Environment 254(2021).