Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2016WR019092 |
Improving physically based snow simulations by assimilating snow depths using the particle filter | |
Magnusson, Jan1,2; Winstral, Adam2; Stordal, Andreas S.3; Essery, Richard4; Jonas, Tobias2 | |
2017-02-01 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2017 |
卷号 | 53期号:2 |
文章类型 | Article |
语种 | 英语 |
国家 | Norway; Switzerland; Scotland |
英文摘要 | Data assimilation can help to ensure that model results remain close to observations despite potential errors in the model, parameters, and inputs. In this study, we test whether assimilation of snow depth observations using the particle filter, a generic data assimilation method, improves the results of a multilayer energy-balance snow model, and compare the results against a direct insertion method. At the field site Col de Porte in France, the particle filter reduces errors in SWE, snowpack runoff, and soil temperature when forcing the model with coarse resolution reanalysis data, which is a typical input scenario for operational simulations. For those variables, the model performance after assimilation of snow depths is similar to model performance when forcing with high-quality, locally observed input data. Using the particle filter, we could also estimate a snowfall correction factor accurately at Col de Porte. The assimilation of snow depths also improves forecasts with lead-times of, at least, 7 days. At further 40 sites in Switzerland, the assimilation of snow depths in a model forced with numerical weather prediction data reduces the root-mean-squared-error for SWE by 64% compared to the model without assimilation. The direct insertion method shows similar performance as the particle filter, but is likely to produce inconsistencies between modeled variables. The particle filter, on the other hand, avoids such limitations without loss of performance. The methods proposed in this study efficiently reduces errors in snow simulations, seems applicable for different climatic and geographic regions, and are easy to deploy. |
英文关键词 | energy-balance snowmelt model particle filter algorithm state and parameter updating snow depth |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000398568800008 |
WOS关键词 | WATER EQUIVALENT ; NUMERICAL-MODEL ; COVER PRODUCTS ; RIVER-BASIN ; ALPINE SITE ; MODIS ; TEMPERATURE ; SENSITIVITY ; SCHEMES ; FRANCE |
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/20422 |
专题 | 资源环境科学 |
作者单位 | 1.Norwegian Water Resources & Energy Directorate, Oslo, Norway; 2.WSL Inst Snow & Avalanche Res SLF, Davos, Switzerland; 3.IRIS, Stavanger, Norway; 4.Univ Edinburgh, Sch Geosci, Edinburgh, Midlothian, Scotland |
推荐引用方式 GB/T 7714 | Magnusson, Jan,Winstral, Adam,Stordal, Andreas S.,et al. Improving physically based snow simulations by assimilating snow depths using the particle filter[J]. WATER RESOURCES RESEARCH,2017,53(2). |
APA | Magnusson, Jan,Winstral, Adam,Stordal, Andreas S.,Essery, Richard,&Jonas, Tobias.(2017).Improving physically based snow simulations by assimilating snow depths using the particle filter.WATER RESOURCES RESEARCH,53(2). |
MLA | Magnusson, Jan,et al."Improving physically based snow simulations by assimilating snow depths using the particle filter".WATER RESOURCES RESEARCH 53.2(2017). |
条目包含的文件 | 条目无相关文件。 |
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