globalchange  > 气候变化与战略
DOI: 10.5194/hess-24-4291-2020
论文题名:
Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces
作者: Albergel C.; Zheng Y.; Bonan B.; Dutra E.; Rodríguez-Fernández N.; Munier S.; Draper C.; De Rosnay P.; Muñoz-Sabater J.; Balsamo G.; Fairbairn D.; Meurey C.; Calvet J.-C.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2020
卷: 24, 期:9
起始页码: 4291
结束页码: 4316
语种: 英语
Scopus关键词: Evapotranspiration ; Extreme weather ; Image resolution ; Snow ; Soil moisture ; Surface measurement ; Surface states ; European centre for medium-range weather forecasts ; Gross primary production ; Integrated forecasting systems ; Land data assimilation systems ; Murray-Darling Basin ; Satellite-derived observations ; South-eastern Australia ; Surface soil moisture ; Weather forecasting ; data assimilation ; evapotranspiration ; heat wave ; land surface ; leaf area index ; river discharge ; soil moisture ; spatial resolution ; weather forecasting ; Australia ; Murray-Darling Basin ; Lily symptomless virus
英文摘要: LDAS-Monde is a global offline land data assimilation system (LDAS) that jointly assimilates satellitederived observations of surface soil moisture (SSM) and leaf area index (LAI) into the ISBA (Interaction between Soil Biosphere and Atmosphere) land surface model (LSM). This study demonstrates that LDAS-Monde is able to detect, monitor and forecast the impact of extreme weather on land surface states. Firstly, LDAS-Monde is run globally at 0.25° spatial resolution over 2010-2018. It is forced by the state-of-the-art ERA5 reanalysis (LDAS_ERA5) from the European Centre for Medium Range Weather Forecasts (ECMWF). The behaviour of the assimilation system is evaluated by comparing the analysis with the assimilated observations. Then the land surface variables (LSVs) are validated with independent satellite datasets of evapotranspiration, gross primary production, sun-induced fluorescence and snow cover. Furthermore, in situ measurements of SSM, evapotranspiration and river discharge are employed for the validation. Secondly, the global analysis is used to (i) detect regions exposed to extreme weather such as droughts and heatwave events and (ii) address specific monitoring and forecasting requirements of LSVs for those regions. This is performed by computing anomalies of the land surface states. They display strong negative values for LAI and SSM in 2018 for two regions: north-western Europe and the Murray-Darling basin in south-eastern Australia. For those regions, LDAS-Monde is forced with the ECMWF Integrated Forecasting System (IFS) high-resolution operational analysis (LDAS_HRES, 0.10° spatial resolution) over 2017-2018. Monitoring capacities are studied by comparing openloop and analysis experiments, again against the assimilated observations. Forecasting abilities are assessed by initializing 4 and 8 d LDAS_HRES forecasts of the LSVs with the LDAS_HRES assimilation run compared to the open-loop experiment. The positive impact of initialization from an analysis in forecast mode is particularly visible for LAI that evolves at a slower pace than SSM and is more sensitive to initial conditions than to atmospheric forcing, even at an 8 d lead time. This highlights the impact of initial conditions on LSV forecasts and the value of jointly analysing soil moisture and vegetation states. © 2020 Author(s).
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被引频次[WOS]:16   [查看WOS记录]     [查看WOS中相关记录]
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/162607
Appears in Collections:气候变化与战略

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作者单位: Albergel, C., Cnrm, Université de Toulouse, Météo-France, Cnrs, Toulouse, France, European Space Agency Climate Office, Ecsat, Harwell Campus, Oxfordshire, Didcot, OX11 0FD, United Kingdom; Zheng, Y., Cnrm, Université de Toulouse, Météo-France, Cnrs, Toulouse, France; Bonan, B., Cnrm, Université de Toulouse, Météo-France, Cnrs, Toulouse, France; Dutra, E., Cnrm, Université de Toulouse, Météo-France, Cnrs, Toulouse, France; Rodríguez-Fernández, N., Cesbio, Université de Toulouse, Cnrs, Cnes, Ird, Toulouse, France; Munier, S., Cnrm, Université de Toulouse, Météo-France, Cnrs, Toulouse, France; Draper, C., CIRES/NOAA Earth System Research Laboratories, Boulder, CO, United States; De Rosnay, P., European Centre for Medium-Range Weather Forecasts, Shinfield Road, Reading, RG2 9AX, United Kingdom; Muñoz-Sabater, J., European Centre for Medium-Range Weather Forecasts, Shinfield Road, Reading, RG2 9AX, United Kingdom; Balsamo, G., European Centre for Medium-Range Weather Forecasts, Shinfield Road, Reading, RG2 9AX, United Kingdom; Fairbairn, D., European Centre for Medium-Range Weather Forecasts, Shinfield Road, Reading, RG2 9AX, United Kingdom; Meurey, C., Cnrm, Université de Toulouse, Météo-France, Cnrs, Toulouse, France; Calvet, J.-C., Cnrm, Université de Toulouse, Météo-France, Cnrs, Toulouse, France

Recommended Citation:
Albergel C.,Zheng Y.,Bonan B.,et al. Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces[J]. Hydrology and Earth System Sciences,2020-01-01,24(9)
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