A New Satellite-Based Data Assimilation Algorithm to Determine Spatial and Temporal Variations of Soil Moisture and Temperature Profiles

Pathmathevan, Mahadevan; Koike, Takao; Li, Xiaoqiong

doi:10.2151/jmsj.81.1111

Cited by 15 publications

(14 citation statements)

References 32 publications

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“…One big problem is that the data availability of these two quantities as the internal parameters of the climate system is still limited though the new satellite data are considerably improving the situation. Recently, new attempts have started on land-surface assimilation using satellite and in-situ observational data and land surface models (e.g., Pathemathevamn et al 2003;Boussetta et al 2005), which are expected to produce better data for assessing land-atmosphere interaction in the near future.…”

Section: Discussionmentioning

confidence: 99%

Role of Land-Atmosphere Interaction on Asian Monsoon Climate

Yasunari

2007

Journal of the Meteorological Society of Japan

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This paper has discussed the land-atmosphere interaction associated with the Asian monsoon climate and its variability. The snow cover and soil moisture are internal factors of the climate system particularly in the seasonal to interannual time scales, but are suggested to play some important roles in changing large-scale surface energy and water balance, which in turn affect the monsoon circulation and precipitation. From the modeling as well as observational studies it has been suggested that the snow cover is most likely to influence atmosphere essentially through the albedo effect particularly in lower latitudes and on the Tibetan Plateau. The GCM experiments have strongly suggested that soil moisture anomalies efficiently affect the atmosphere under dry or semi-arid condition. Vegetation has also been noted as an important variable for the formation of moist monsoon flow over the continent, in addition to the dynamical and thermo-dynamical effect of the Tibetan Plateau.Through the GCM and RCM experiments the anthropogenically-induced change of land cover/use including the deforestation has proved to be a great impact on regional precipitation and water cycle of the monsoon region by changing characteristics of vegetation control in energy and water cycle, which in turn affect atmospheric boundary layer (ABL) and cloud/precipitation processes in regional-scale. The discussion has also been made on how the land surface changes could induce change of precipitation through feedback processes of moisture convergence and in-situ evapo-transpiration processes. The critical role of moisture amount near surface and in the ABL is emphasized, to induce positive feedback to change of precipitation over land in the monsoon region.

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Section: Discussionmentioning

confidence: 99%

Role of Land-Atmosphere Interaction on Asian Monsoon Climate

Yasunari

2007

Journal of the Meteorological Society of Japan

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“…A land data assimilation system can assimilate a variety of data into land surface models, including surface skin temperature (van den Hurk 2001; Meng et al 2003), near-surface soil moisture (Parada and Liang 2004;Zhang et al 2006), and microwave brightness temperature (Houser et al 1998;Crosson et al 2002). Recently, mathematical methods such as variational and sequential data assimilation have been widely addressed and applied (e.g., Reichle et al 2001;Pathmathevan et al 2003;Dunne and Entekhabi 2005).…”

Section: Introductionmentioning

confidence: 99%

Auto-calibration System Developed to Assimilate AMSR-E Data into a Land Surface Model for Estimating Soil Moisture and the Surface Energy Budget

Yang

Watanabe

Koike

et al. 2007

Journal of the Meteorological Society of Japan

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164

119

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Low-frequency microwave brightness temperature is strongly affected by near-surface soil moisture; therefore, it can be assimilated into a land surface model to improve modeling of soil moisture and the surface energy budget. This study presents a new variational land system used to assimilate AMSR-E brightness temperature of vertical polarization of 6.9 GHz and 18.7 GHz. The system consists of a land surface model (LSM) used to calculate surface fluxes and soil moisture, a radiative transfer model (RTM) to estimate the microwave brightness temperature, and an optimization scheme to search for optimal values of soil moisture by minimizing the difference between modeled and observed brightness temperature. The LSM is an improved simple biosphere model for sparse vegetation modeling and the RTM is a Q-h model that can account for the effects of surface roughness and vegetation. Several parameters in the LSM and RTM can significantly affect the outputs of the land data assimilation system but their values are either highly variable or unavailable. To solve this problem, we developed a dual-pass assimCorresponding author: Toshio Koike, Dr., Professor, River Lab, Department of Civil Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan. E-mail: tkoike@hydra.t.u-tokyo.ac.jp; yangk@ hydra.t.u-tokyo.ac.jp ( 2007, Meteorological Society of Japan ilation technique. Pass 1 inversely estimates the optimal values of the model parameters with long-term (@months) forcing data and brightness temperature data, while Pass 2 estimates the near-surface soil moisture in a daily assimilation cycle. This system is driven by well-established reanalysis data and global data sets of leaf area index, precipitation, and surface radiation, and was tested at a CEOP (Coordinate Enhanced Observing Period) reference site on the Tibetan Plateau. The system not only detected the effect of precipitation events that were missing in the forcing data, but also led to a significant improvement in modeling of the surface energy budget.

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“…Based on the Monte Carlo method, EnKF designs an ensemble of state variables, and the average of the ensemble can be used as the optimal estimation of state variables. Many studies have adopted EnKF for integrating remote sensing data with models in meteorological and hydrological fields [11][12][13][14]. Wang et al [15] assimilated observed soil moisture into the LPJ-DGVM by using the EnKF method to construct a data assimilation system for optimizing LPJ performance.…”

Section: Introductionmentioning

confidence: 99%

Assimilation of Remotely-Sensed Leaf Area Index into a Dynamic Vegetation Model for Gross Primary Productivity Estimation

Zhang

Tian

et al. 2017

Remote Sensing

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Quantitative estimation of the magnitude and variability of gross primary productivity (GPP) is required to study the carbon cycle of the terrestrial ecosystem. Using ecosystem models and remotely-sensed data is a practical method for accurately estimating GPP. This study presents a method for assimilating high-quality leaf area index (LAI) products retrieved from satellite data into a process-oriented Lund-Potsdam-Jena dynamic global vegetation model (LPJ-DGVM) to acquire accurate GPP. The assimilation methods, including the Ensemble Kalman Filter (EnKF) and a proper orthogonal decomposition (POD)-based ensemble four-dimensional (4D) variational assimilation method (PODEn4DVar), incorporate information provided by observations into the model to achieve a better agreement between the model-estimated and observed GPP. The LPJ-POD scheme performs better with a correlation coefficient of r = 0.923 and RMSD of 32.676 gC/m 2 /month compared with the LPJ-EnKF scheme (r = 0.887, RMSD = 38.531 gC/m 2 /month) and with no data assimilation (r = 0.840, RMSD = 45.410 gC/m 2 /month). Applying the PODEn4DVar method into LPJ-DGVM for simulating GPP in China shows that the annual amount of GPP in China varied between 5.92 PgC and 6.67 PgC during 2003-2012 with an annual mean of 6.35 PgC/yr. This study demonstrates that integrating remotely-sensed data with dynamic global vegetation models through data assimilation methods has potential in optimizing the simulation and that the LPJ-POD scheme shows better performance in improving GPP estimates, which can provide a favorable way for accurately estimating dynamics of ecosystems.

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A New Satellite-Based Data Assimilation Algorithm to Determine Spatial and Temporal Variations of Soil Moisture and Temperature Profiles

Cited by 15 publications

References 32 publications

Role of Land-Atmosphere Interaction on Asian Monsoon Climate

Role of Land-Atmosphere Interaction on Asian Monsoon Climate

Auto-calibration System Developed to Assimilate AMSR-E Data into a Land Surface Model for Estimating Soil Moisture and the Surface Energy Budget

Assimilation of Remotely-Sensed Leaf Area Index into a Dynamic Vegetation Model for Gross Primary Productivity Estimation

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