An adaptive ensemble Kalman filter for soil moisture data assimilation

2008 ◽  
Vol 44 (3) ◽  
Author(s):  
Rolf H. Reichle ◽  
Wade T. Crow ◽  
Christian L. Keppenne
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Soil Moisture Data ◽  
Adaptive Ensemble Kalman Filter

Application and improvement of an adaptive ensemble Kalman filter for soil moisture data assimilation

2010 ◽  
Vol 53 (11) ◽  
pp. 1700-1708 ◽  
Author(s):  
XiaoKang Shi ◽  
Jun Wen ◽  
JianWen Liu ◽  
Hui Tian ◽  
Xin Wang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Soil Moisture Data ◽  
Adaptive Ensemble Kalman Filter

scholarly journals Optimal ensemble size of ensemble Kalman filter in sequential soil moisture data assimilation

2015 ◽  
Vol 42 (16) ◽  
pp. 6710-6715 ◽  
Author(s):  
Jifu Yin ◽  
Xiwu Zhan ◽  
Youfei Zheng ◽  
Christopher R. Hain ◽  
Jicheng Liu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Ensemble Size ◽  
Soil Moisture Data ◽  
Optimal Ensemble

scholarly journals Data assimilation of soil water flow via ensemble Kalman filter: Infusing soil moisture data at different scales

2017 ◽  
Vol 555 ◽  
pp. 912-925 ◽  
Author(s):  
Penghui Zhu ◽  
Liangsheng Shi ◽  
Yan Zhu ◽  
Qiuru Zhang ◽  
Kai Huang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Soil Water ◽  
Water Flow ◽  
Ensemble Kalman Filter ◽  
Soil Moisture Data ◽  
Soil Water Flow

scholarly journals Do surface lateral flows matter for data assimilation of soil moisture observations into hyperresolution land models?

2020 ◽  
Author(s):  
Yohei Sawada
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Pressure Head ◽  
Surface Flow ◽  
State Variables ◽  
Background Error ◽  
Water Flows

Abstract. It is expected that hyperresolution land modeling substantially innovates the simulation of terrestrial water, energy, and carbon cycles. The major advantage of hyperresolution land models against conventional one-dimensional land surface models is that hyperresolution land models can explicitly simulate lateral water flows. Despite many efforts on data assimilation of hydrological observations into those hyperresolution land models, how surface water flows driven by local topography matter for data assimilation of soil moisture observations has not been fully clarified. Here I perform two minimalist synthetic experiments where soil moisture observations are assimilated into an integrated surface-groundwater land model by an ensemble Kalman filter. I discuss how differently the ensemble Kalman filter works when surface lateral flows are switched on and off. A horizontal background error covariance provided by overland flows is important to adjust the unobserved state variables (pressure head and soil moisture) and parameters (saturated hydraulic conductivity). However, the non-Gaussianity of the background error provided by the nonlinearity of a topography-driven surface flow harms the performance of data assimilation. It is difficult to efficiently constrain model states at the edge of the area where the topography-driven surface flow reaches by linear-Gaussian filters. It brings the new challenge in land data assimilation for hyperresolution land models. This study highlights the importance of surface lateral flows in hydrological data assimilation.

scholarly journals Ensemble-based data assimilation of atmospheric boundary layer observations improves the soil moisture analysis

2021 ◽  
Author(s):  
Tobias Sebastian Finn ◽  
Gernot Geppert ◽  
Felix Ament
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Atmospheric Boundary Layer ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Model Systems ◽  
Surface Component ◽  
Moisture Analysis

Abstract. We revise the potential of assimilating atmospheric boundary layer observations into the soil moisture. Previous studies often stated a negative assimilation impact of boundary layer observations on the soil moisture analysis, but recent developments in physically-consistent hydrological model systems and ensemble-based data assimilation lead to an emerging potential of boundary layer observations for land surface data assimilation. To explore this potential, we perform idealized twin experiments for a seven-day period in Summer 2015 with a coupled atmosphere-land modelling platform. We use TerrSysMP for these limited-area simulations with a horizontal resolution 1.0 km in the land surface component. We assimilate sparse synthetic 2-metre-temperature observations into the land surface component and update the soil moisture with a localized Ensemble Kalman filter. We show a positive assimilation impact of these observations on the soil moisture analysis during day-time and a neutral impact during night. Furthermore, we find that hourly-filtering with a three-dimensional Ensemble Kalman filter results in smaller errors than daily-smoothing with a one-dimensional Simplified Extended Kalman filter, whereas the Ensemble Kalman filter additionally allows us to directly assimilate boundary layer observations without an intermediate optimal interpolation step. We increase the physical consistency in the analysis for the land surface and boundary by updating the atmospheric temperature together with the soil moisture, which as a consequence further reduces errors in the soil moisture analysis. Based on these results, we conclude that we can merge the decoupled data assimilation cycles for the land surface and the atmosphere into one single cycle with hourly-like update steps.

scholarly journals Do surface lateral flows matter for data assimilation of soil moisture observations into hyperresolution land models?

2020 ◽  
Vol 24 (8) ◽  
pp. 3881-3898
Author(s):  
Yohei Sawada
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Pressure Head ◽  
Surface Flow ◽  
State Variables ◽  
Background Error ◽  
Water Flows

Abstract. It is expected that hyperresolution land modeling substantially innovates the simulation of terrestrial water, energy, and carbon cycles. The major advantage of hyperresolution land models against conventional 1-D land surface models is that hyperresolution land models can explicitly simulate lateral water flows. Despite many efforts on data assimilation of hydrological observations into those hyperresolution land models, how surface water flows driven by local topography matter for data assimilation of soil moisture observations has not been fully clarified. Here I perform two minimalist synthetic experiments where soil moisture observations are assimilated into an integrated surface–groundwater land model by an ensemble Kalman filter. I discuss how differently the ensemble Kalman filter works when surface lateral flows are switched on and off. A horizontal background error covariance provided by overland flows is important for adjusting the unobserved state variables (pressure head and soil moisture) and parameters (saturated hydraulic conductivity). However, the non-Gaussianity of the background error provided by the nonlinearity of a topography-driven surface flow harms the performance of data assimilation. It is difficult to efficiently constrain model states at the edge of the area where the topography-driven surface flow reaches by linear-Gaussian filters. It brings the new challenge in land data assimilation for hyperresolution land models. This study highlights the importance of surface lateral flows in hydrological data assimilation.

Assimilation of soil moisture data for improving streamflow prediction: Is there a role for the hydrological model structure?

2020 ◽  
Author(s):  
Aruna Kumar Nayak ◽  
Basudev Biswal ◽  
Kulamulla Parambath Sudheer
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Hydrological Model ◽  
Assimilation Efficiency ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
The Past ◽  
Soil Moisture Data

<p>Soil moisture data assimilation has found increased applicability in hydrology due to easily available remotely sensed soil moisture data. Numerous studies in the past have explored the possibility of assimilating soil moisture information for improving streamflow forecasting. The general understanding is that if better soil moisture data can provide better streamflow forecast. However, to our knowledge no study has so far focused on understanding if the hydrological model itself has a role in assimilation of soil moisture data. In this regard, here we use three different conceptual hydrological models for soil moisture assimilation: (1) Dynamic Budyko (DB), (2) GR4J, and (3) PDM model. Assimilation of GLDAS observed soil moisture is carried out for four MOPEX basins using Ensemble Kalman Filter. DB model’s performance improved after soil moisture data assimilation for all the study basins. However, deterioration in performance was observed for GR4J and PDM for all the basins after the assimilation exercise. The performance of the assimilated models is evaluated in terms of Assimilation Efficiency (AE), which was found to be varying from 17.11 to 22.56%, from -20.98 to -41.29%, and from -8.4 to -38.23%, respectively, for DB, GR4J, and PDM. Overall, our results highlight the importance of the hydrological models structure in a soil moisture data assimilation exercise.</p>

One-Dimensional Soil Moisture Simulation Using Ensemble Kalman Filter

2012 ◽  
Vol 212-213 ◽  
pp. 177-180
Author(s):  
Xiao Lei Fu ◽  
Zhong Bo Yu ◽  
Yu Li ◽  
Hai Shen Lv ◽  
Di Liu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Soil Layer ◽  
Model Parameters ◽  
Deep Soil ◽  
One Dimensional ◽  
Initial Soil Moisture ◽  
Initial Soil

Data assimilation is a method which integrates model and observation. In hydrology, ensemble Kalman filter (EnKF) as a sequential data assimilation method is often used to correct model parameters, thus improve the simulated accuracy. In this study, we conduct one numerical experiment to predict soil moisture using the one-dimensional soil moisture system based on ensemble Kalman filter and Simple Biosphere (SiB2) Model at Meilin study area, China. The simulated period is divided into two parts: 0-60h and 60-240h. The results show that EnKF is an efficient method in assimilating the soil moisture, especially in soil surface layer and deep soil layer; in addition, the efficiency of EnKF depends on the selection of initial soil moisture profile. With different initial soil moisture profiles, the performance of EnKF is different at the first few assimilated time, but with time grows, it can improve the simulated accuracy significantly.

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