Abstract
Data assimilation (DA) has been widely used in land surface models (LSM) to improve model state estimates. Among various DA methods, the particle filter (PF) with Markov chain Monte Carlo (MCMC) has become increasingly popular for estimating the states of the nonlinear and non-Gaussian LSMs. However, the standard PF always suffers from the particle impoverishment problem, characterized by loss of particle diversity. To solve this problem, an immune evolution particle filter with MCMC simulation inspired by the biological immune system, entitled IEPFM, is proposed for DA in this paper. The merit of this approach is in imitating the antibody diversity preservation mechanism to further improve particle diversity, thus increasing the accuracy of estimates. Furthermore, the immune memory function refers to promise particle evolution process towards optimal estimates. Effectiveness of the proposed approach is demonstrated by the numerical simulation experiment using a highly nonlinear atmospheric model. Finally, IEPFM is applied to a soil moisture (SM) assimilation experiment, which assimilates in situ observations into the Variable Infiltration Capacity (VIC) model to estimate SM in the MaQu network region of the Tibetan Plateau. Both synthetic and real case experiments demonstrate that IEPFM mitigates particle impoverishment and provides more accurate assimilation results compared with other popular DA algorithms.
Highlights
Soil moisture (SM) plays a key role in the interactions between the hydrosphere, the biosphere, and the atmosphere by governing the partitioning of mass and energy fluxes between the land and the atmosphere [1]
The Lorenz96 model is used as the numerical simulation experiment to compare the performance of immune evolution particle filter with MCMC (IEPFM) with ensemble Kalman filter (EnKF) and differential evolution particle filter with MCMC (DEPFM)
IEPFM is applied for soil moisture data assimilation
Summary
Soil moisture (SM) plays a key role in the interactions between the hydrosphere, the biosphere, and the atmosphere by governing the partitioning of mass and energy fluxes between the land and the atmosphere [1]. As such, understanding SM is pivotal in various relevant fields, such as water resource management, drought warning, flood and landslide modelling and prediction, irrigation management, and even economic and policy analysis [2,3]. SM can be obtained through observation and modeling. In situ observation techniques provide accurate measurements of SM at different depths, but they are unable to characterize the SM at large spatial scales. At large spatial scales, microwave remote sensing data provide a technique of estimating only the near surface SM, limiting the microwave penetration depth.
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