Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018WR024521 |
The Bias-Detecting Ensemble: A New and Efficient Technique for Dynamically Incorporating Observations Into Physics-Based, Multilayer Snow Models | |
Winstral, A.1; Magnusson, J.2; Schirmer, M.1; Jonas, T.1 | |
2019 | |
发表期刊 | WATER RESOURCES RESEARCH
![]() |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
卷号 | 55期号:1页码:613-631 |
文章类型 | Article |
语种 | 英语 |
国家 | Switzerland; Norway |
英文摘要 | The reliance on distributed energy- and mass-balance snow models as runoff forecasting tools has been increasing. Compared to traditional, conceptual forecasting approaches, these physics-based tools are robust to conditions that deviate from historic norms and offer improved performance in potentially dangerous rain-on-snow events. The physics-based simulations, however, depend on a large suite of accurate forcing data. Current numerical weather prediction products are capable of supplying the full range of required data, but systematic biases are often present. Data assimilation presents a means of compensating for such errors as well as potential snow model errors, yet currently available data assimilation techniques have limited usefulness in these snow models. This study introduced an alternative technique that similarly uses observations to update and improve simulations. As such, it is the first method to incorporate point snow observations in a fully distributed, physics-based, multilayer snow model while conserving mass and maintaining physically consistent layer states that are in accord with observations. At the core of this technique is an ensemble of predetermined perturbations to the model forcings termed the bias-detecting ensemble. Ensemble members were evaluated using observed snow depths to ascertain potential biases at nearly 300 sites across Switzerland. The bias assessments were distributed to 38 independent sites and incorporated into the model. Tests were conducted over three winter seasons using two numerical weather prediction-based products with varying quality. Averaged across the 38 sites and 3 seasons, the Nash-Sutcliffe efficiency score for bias-detecting ensemble-corrected snow depth was 0.98 compared to 0.81 without the bias-detecting ensemble method. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000459536500034 |
WOS关键词 | HYDROLOGICAL DATA ASSIMILATION ; LAND-SURFACE MODEL ; WATER EQUIVALENT ; CLIMATE-CHANGE ; KALMAN FILTER ; PRECIPITATION ; FORECASTS ; EVENTS ; ENERGY ; FLOOD |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
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/21970 |
专题 | 资源环境科学 |
作者单位 | 1.WSL Inst Snow & Avalanche Res SLF, Davos, Switzerland; 2.Norwegian Water Resources & Energy Directorate, Oslo, Norway |
推荐引用方式 GB/T 7714 | Winstral, A.,Magnusson, J.,Schirmer, M.,et al. The Bias-Detecting Ensemble: A New and Efficient Technique for Dynamically Incorporating Observations Into Physics-Based, Multilayer Snow Models[J]. WATER RESOURCES RESEARCH,2019,55(1):613-631. |
APA | Winstral, A.,Magnusson, J.,Schirmer, M.,&Jonas, T..(2019).The Bias-Detecting Ensemble: A New and Efficient Technique for Dynamically Incorporating Observations Into Physics-Based, Multilayer Snow Models.WATER RESOURCES RESEARCH,55(1),613-631. |
MLA | Winstral, A.,et al."The Bias-Detecting Ensemble: A New and Efficient Technique for Dynamically Incorporating Observations Into Physics-Based, Multilayer Snow Models".WATER RESOURCES RESEARCH 55.1(2019):613-631. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论