Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2020WR027490 |
Exploration of synthetic terrestrial snow mass estimation via assimilation of AMSR‐E brightness temperature spectral differences using the Catchment land surface model and support vector machine regression | |
Jing Wang; Barton A. Forman; Yuan Xue | |
2021-01-14 | |
发表期刊 | Water Resources Research
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出版年 | 2021 |
英文摘要 | This study explores improvements in the estimation of snow water equivalent (SWE) over snow‐covered terrain using an ensemble‐based data assimilation (DA) framework. The NASA Catchment land surface model is used as the prognostic model in the assimilation of AMSR‐E passive microwave (PMW) brightness temperature spectral differences (ΔTb) where support vector machine (SVM) regression is employed as the observation operator. A series of synthetic twin experiments are conducted using different precipitation boundary conditions. The results show, at times, DA degrades modeled SWE estimates (compared to the land surface model without assimilation) over complex terrain. To mitigate this degradation, a physically‐informed approach using different ΔTb for shallow‐to‐medium or medium‐to‐deep snow conditions along with a “data‐thinning” strategy are explored. Overall, both strategies improve the model ability to encapsulate more of the evaluation data and mitigate model ensemble collapse. The physically‐informed DA and 3‐day thinning DA strategies show marginal improvements of basin‐averaged SWE in terms of reduction of bias from 10 mm (baseline DA) to − 5.2 mm and −2.5 mm, respectively. When the estimated forcings are greater than the truth, the baseline DA, physically‐informed DA, and 3‐day thinning DA improve SWE the most with approximately 30%, 31%, and 24% reduction of RMSE (relative to OL), respectively. Overall, these results highlight the limited utility of PMW ΔTb observations in the estimation of snow in complex terrain, but do demonstrate that a physically‐based constraint approach and data thinning strategy can add more utility to the ΔTb observations in the estimation of SWE. This article is protected by copyright. All rights reserved. |
领域 | 资源环境 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/311344 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | Jing Wang,Barton A. Forman,Yuan Xue. Exploration of synthetic terrestrial snow mass estimation via assimilation of AMSR‐E brightness temperature spectral differences using the Catchment land surface model and support vector machine regression[J]. Water Resources Research,2021. |
APA | Jing Wang,Barton A. Forman,&Yuan Xue.(2021).Exploration of synthetic terrestrial snow mass estimation via assimilation of AMSR‐E brightness temperature spectral differences using the Catchment land surface model and support vector machine regression.Water Resources Research. |
MLA | Jing Wang,et al."Exploration of synthetic terrestrial snow mass estimation via assimilation of AMSR‐E brightness temperature spectral differences using the Catchment land surface model and support vector machine regression".Water Resources Research (2021). |
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