DOI: 10.5194/hess-21-2483-2017
Scopus记录号: 2-s2.0-85019179964
论文题名: Advancing land surface model development with satellite-based Earth observations
作者: Orth R ; , Dutra E ; , Trigo I ; F ; , Balsamo G
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2017
卷: 21, 期: 5 起始页码: 2483
结束页码: 2495
语种: 英语
Scopus关键词: Atmospheric temperature
; Calibration
; Earth (planet)
; Forecasting
; Hydrology
; Observatories
; Satellites
; Surface measurement
; Surface properties
; Constrained parameters
; Land surface modeling
; Land surface temperature
; Parameter simulations
; Process representation
; Satellite based Earth observation
; Satisfactory modeling
; Weather predictability
; Weather forecasting
; array
; boundary layer
; calibration
; climate conditions
; climate feedback
; data set
; hydrology
; land surface
; model test
; parameterization
; satellite imagery
; surface temperature
; weather
; weather forecasting
英文摘要: The land surface forms an essential part of the climate system. It interacts with the atmosphere through the exchange of water and energy and hence influences weather and climate, as well as their predictability. Correspondingly, the land surface model (LSM) is an essential part of any weather forecasting system. LSMs rely on partly poorly constrained parameters, due to sparse land surface observations. With the use of newly available land surface temperature observations, we show in this study that novel satellite-derived datasets help improve LSM configuration, and hence can contribute to improved weather predictability. We use the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) and validate it comprehensively against an array of Earth observation reference datasets, including the new land surface temperature product. This reveals satisfactory model performance in terms of hydrology but poor performance in terms of land surface temperature. This is due to inconsistencies of process representations in the model as identified from an analysis of perturbed parameter simulations. We show that HTESSEL can be more robustly calibrated with multiple instead of single reference datasets as this mitigates the impact of the structural inconsistencies. Finally, performing coupled global weather forecasts, we find that a more robust calibration of HTESSEL also contributes to improved weather forecast skills. In summary, new satellite-based Earth observations are shown to enhance the multi-dataset calibration of LSMs, thereby improving the representation of insufficiently captured processes, advancing weather predictability, and understanding of climate system feedbacks. © 2017 Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79174
Appears in Collections: 气候变化事实与影响
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作者单位: European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, United Kingdom; Institute for Atmospheric and Climate Science, ETH Zürich, Universitätstrasse 16, Zürich, Switzerland; Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal; Instituto Português Do Mar e da Atmosfera, Lisbon, Portugal
Recommended Citation:
Orth R,, Dutra E,, Trigo I,et al. Advancing land surface model development with satellite-based Earth observations[J]. Hydrology and Earth System Sciences,2017-01-01,21(5)