Abstract
Soil moisture plays an important role in climate prediction and drought monitoring. Data assimilation, as a method of integrating multi-geographic spatial data, plays an increasingly important role in estimating soil moisture. Model prediction error, an important part of the background field information, occupies a position that could not be ignored in data assimilation. The model prediction error in data assimilation consists of three parts: forcing data error, initial field error, and model error. However, the influence of model error in current data assimilation methods has not been completely considered in many studies. Therefore, we proposed a theoretical framework of the ensemble Kalman filter (EnKF) data assimilation based on the breeding of growing modes (BGM) method. This framework used the BGM method to perturb the initial field error term w of EnKF, and the EnKF data assimilation to assimilate the data to obtain the soil moisture analysis value. The feasibility and superiority of the proposed framework were verified, taking into consideration breeding length and ensemble size through experiments. We conducted experiments and evaluated the accuracy of the BGM and the Monte Carlo (MC) methods. The experiment showed that the BGM method could improve the estimation accuracy of the assimilated soil moisture and solve the problem of model error which is not fully expressed in data assimilation. This study can be widely used in data assimilation and has a significant role in weather forecast and drought monitoring.
Highlights
Soil moisture is a key variable that controls major physical processes in numerical weather prediction, climate simulation, crop growth models, and flood forecasting by controlling the surface energy distribution of sensible heat and latent heat fluxes in terrestrial–atmosphere coupling and energy and water cycling [1,2,3,4,5]
The breeding length and ensemble size of the breeding of growing modes (BGM) method are discussed in Sections 4.1 and 4.2, respectively, through experiments: In Section 4.1, we first superimpose the initial mode of the soil moisture initial field of the variable infiltration capacity (VIC) model at 0:00 on 2 January 2016 and carry out the BGM method experiment with breeding lengths of 24 h, 48 h, and 72 h
For the verification metric of soil moisture data obtained by data assimilation, the root mean square error (RMSE) and the Pearson correlation coefficient R were calculated as follows: RMSE = E[(SMestimate − SMtruth)2]
Summary
Soil moisture is a key variable that controls major physical processes in numerical weather prediction, climate simulation, crop growth models, and flood forecasting by controlling the surface energy distribution of sensible heat and latent heat fluxes in terrestrial–atmosphere coupling and energy and water cycling [1,2,3,4,5]. Accurate observation of high precision soil moisture data is of great significance for crop growth, drought monitoring, and monitoring global climate change [6,7,8,9]. Soil moisture data are obtained through three main methods: (1) Simulating soil moisture using land surface process models and hydrological models [10,11,12]. (3) Using remote sensing data to retrieve soil moisture [14,15,16,17,18]. Optical and microwave technologies are widely used for monitoring soil moisture, which compensates for the space-time discontinuity in the site to some extent and can obtain large-scale soil moisture observation data by conducting large-scale and even global observations. Making the soil moisture data of each system complement each other and using the high-precision large-scale soil moisture data with a continuous time scale is a worthy research topic
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