Improving land surface soil moisture and energy flux simulations over the Tibetan plateau by the assimilation of the microwave remote sensing data and the GCM output into a land surface model

Lu, Hui; Koike, Takao; Yang, Kun; Hu, Zeyong; Xu, Xiangde; Rasmy, Mohamed; Kuria, David Ndegwa; Tamagawa, Katsunori

doi:10.1016/j.jag.2011.09.006

Cited by 32 publications

(22 citation statements)

References 50 publications

(48 reference statements)

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“…Instead, the Bowen Ratio Energy Balance (BREB) method was employed, based upon a previous validation study with eddy covariance data (van der Velde et al, 2009). Several previous studies also employed the BREB over the Tibetan Plateau in a long-term estimation (Liu et al, 2009;Lu et al, 2012). By this, it was considered that BREB is capable of estimate semi-arid climate heat flux in given temperature and vapour pressure levels, since they were previously agreeable with eddy covariance data in study above.…”

Section: Field Observation: Bowen Ratio Energy Balance (Breb)mentioning

confidence: 99%

Calibration of aerodynamic roughness over the Tibetan Plateau with Ensemble Kalman Filter analysed heat flux

Lee

Timmermans

et al. 2012

Hydrol. Earth Syst. Sci.

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Abstract. Aerodynamic roughness height (Z om ) is a key parameter required in several land surface hydrological models, since errors in heat flux estimation are largely dependent on optimization of this input. Despite its significance, it remains an uncertain parameter which is not readily determined. This is mostly because of non-linear relationship in Monin-Obukhov similarity (MOS) equations and uncertainty of vertical characteristic of vegetation in a large scale. Previous studies often determined aerodynamic roughness using a minimization of cost function over MOS relationship or linear regression over it, traditional wind profile method, or remotely sensed vegetation index. However, these are complicated procedures that require a high accuracy for several other related parameters embedded in serveral equations including MOS. In order to simplify this procedure and reduce the number of parameters in need, this study suggests a new approach to extract aerodynamic roughness parameter from single or two heat flux measurements analyzed via Ensemble Kalman Filter (EnKF) that affords non-linearity. So far, to our knowledge, no previous study has applied EnKF to aerodynamic roughness estimation, while the majority of data assimilation study have paid attention to updates of other land surface state variables such as soil moisture or land surface temperature. The approach of this study was applied to grassland in semi-arid Tibetan Plateau and maize on moderately wet condition in Italy. It was demonstrated that aerodynamic roughness parameter can be inversely tracked from heat flux EnKF final analysis. The aerodynamic roughness height estimated in this approach was consistent with eddy covariance method and literature value. Through a calibration of this parameter, this adjusted the sensible heat previously overestimated and latent heat flux previously underestimated by the original Surface Energy Balance System (SEBS) model. It was considered that this improved heat flux estimation especially during the summer Monsoon period, based upon a comparison with precipitation and soil moisture field measurement. For an advantage of this approach over other previous methodologies, this approach is useful even when eddy covariance data are absent at a large scale and is time-variant over vegetation growth, as well as is not directly affected by saturation problem of remotely sensed vegetation index.

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Section: Field Observation: Bowen Ratio Energy Balance (Breb)mentioning

confidence: 99%

Calibration of aerodynamic roughness over the Tibetan Plateau with Ensemble Kalman Filter analysed heat flux

Lee

Timmermans

et al. 2012

Hydrol. Earth Syst. Sci.

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“…With the assimilation of microwave brightness temperatures, the soil hydrological parameters related to soil moisture are calibrated first at the monthly temporal scale and the soil moisture data are subsequently updated at the daily temporal scale. The dual pass scheme was constructed with the SCE_UA algorithm (Duan et al, 1993) and has been applied by others (Lu et al, 2012;Zhao et al, 2012). The current study constructed a dual-pass data assimilation scheme with an ensemble Kalman filter (EnKF) (Evensen, 1994) to improve the surface flux predictions with assimilation of remotely sensed land surface temperature.…”

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confidence: 99%

A dual-pass data assimilation scheme for estimating surface fluxes with FY3A-VIRR land surface temperature

Liu

et al. 2014

Sci. China Earth Sci.

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In this work, a dual-pass data assimilation scheme is developed to improve predictions of surface flux. Pass 1 of the dual-pass data assimilation scheme optimizes the model vegetation parameters at the weekly temporal scale, and Pass 2 optimizes the soil moisture at the daily temporal scale. Based on ensemble Kalman filter (EnKF), the land surface temperature (LST) data derived from the new generation of Chinese meteorology satellite (FY3A-VIRR) are assimilated into common land model (CoLM) for the first time. Six sites, Daman, Guantao, Arou, BJ, Miyun and Jiyuan, are selected for the data assimilation experiments and include different climatological conditions. The results are compared with those from a dataset generated by a multi-scale surface flux observation system that includes an automatic weather station (AWS), eddy covariance (EC) and large aperture scintillometer (LAS). The results indicate that the dual-pass data assimilation scheme is able to reduce model uncertainties and improve predictions of surface flux with the assimilation of FY3A-VIRR LST data. sensible heat flux, latent heat flux, ensemble Kalman filter, common land model Citation: Xu T R, Liu S M, Xu Z W, et al. 2015. A dual-pass data assimilation scheme for estimating surface fluxes with FY3A-VIRR land surface tempera-

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“…One is the inversion of a radiative transfer model based upon observed brightness temperatures with some ground ancillary information (Das et al, 2011;Jackson et al, 1999;Kerr et al, 2012). The other is the assimilation of brightness temperatures into a land surface model driven by atmospheric forcing (Crow and Wood, 2003;Lu et al, 2012;Montzka et al, 2011;Pan and Wood, 2006;Qin et al, 2009;Reichle et al, 2002;Yang et al, 2007). No matter which approach is used, soil moisture estimates from satellites have to be evaluated against in-situ measurements before applied in practice.…”

Section: Introductionmentioning

confidence: 99%

Inter-comparison of spatial upscaling methods for evaluation of satellite-based soil moisture

Qin

Zhao

Chen

et al. 2015

Journal of Hydrology

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s u m m a r ySoil moisture is a key factor in energy and water cycles. Many satellite missions have been planned and implemented for retrieving soil moisture globally. Because the spatial representativeness of a point-scale soil moisture station is rather limited, a station network needs setting up for scale-matching validation of satellite-based soil moisture products. Even so, an upscaling procedure is needed to upscale these station soil moisture values into area-wide one. However, such a procedure itself introduces uncertainties into the upscaled soil moisture. In this study, four upscaling methods (simple average, block kriging, model-based, and apparent-thermal-inertia-based) are inter-compared according to their performance stability for evaluation of soil moisture estimated by assimilating microwave signals into a land surface model. It is found that the performance of the model-based upscaling approach is the most unstable because model simulations are full of uncertainties for representing spatial variability of soil moisture. The block kriging upscaling method performs not worse than the simple averaging approach; the former may generate more representative soil moisture if the range of the soil moisture semivariogram used in the block kriging is comparable to the extent of a satellite footprint. The apparent-thermal-inertia-based upscaling is the most stable one, which has been developed with the aid of high-resolution satellite thermal data. All analyses indicate that choosing a suitable upscaling approach is important for the effective evaluation of satellite-based soil moisture. Otherwise, uncertainties hiding in the upscaling method will be incorrectly attributed to errors in satellite products, undermining our confidence in implementing them into practice.

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Improving land surface soil moisture and energy flux simulations over the Tibetan plateau by the assimilation of the microwave remote sensing data and the GCM output into a land surface model

Cited by 32 publications

References 50 publications

Calibration of aerodynamic roughness over the Tibetan Plateau with Ensemble Kalman Filter analysed heat flux

Calibration of aerodynamic roughness over the Tibetan Plateau with Ensemble Kalman Filter analysed heat flux

A dual-pass data assimilation scheme for estimating surface fluxes with FY3A-VIRR land surface temperature

Inter-comparison of spatial upscaling methods for evaluation of satellite-based soil moisture

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