A Comparative Study Of Two Filtering Algorithms In Term Of Soil Moisture Estimation

The ensemble Kalman filter(EnKF) calculates background error covariance matrix using Monte-Carlo method and is able to resolve the nonlinearity and discontinuity exist within model operator and observation operator.At the same,it is an easy to use, flexible, and efficient data assimilation algorithm widely used in Soil moisture Assimilation System.Although EnKF can use non-linear models to gain the background forecast field, but it based on the normality approximation of model error and observational error which is the assumption of Gaussian distribution. However, the soil moisture equation is highly nonlinear, and soil moisture is a fixed range,The sample can be highly skewed toward the wet or dry ends. In order to examine the possible impact of these negative factors,This paper gives the results from the EnKF are compared with those obtained from a sequential importance resampling (SIR) particle filter that is one of nonlinear filters.First, this thesis introduces the Sampling importance Resampling, It based on Bayesian posterior probability distribution of particles in particle filter.It needn't the normality approximation of model error and observational error which is the assumption of Gaussian distribution,and it can provide information on higher order moments.When it updates the background field of a sample weight of each particle different but the EnKF weight is the same. Secondly, in order to evaluate the soil moisture in the soil samples had dry or too wet even when the skew of dual-mode, taking into account the northwest Loess Plateau importance of land surface processes. in this thesis, dry loess soil in the process of gradually come to be observed Simulation of assimilation test. Finally, the use of soil moisture experiment in 2003 (SMEX03) the actual site observation data assimilation of atmospheric observations driven test. the conclusion are: 1,Although The EnKF estimates of soil moisture assimilation scheme does not meet the collection needs of Kalman filtering conditions, but it can quickly estimate the soil moisture profile, coupled with the required small particle samples, computation is also small; and SIR Although no such restrictions, but only under the conditions of a large sample to achieve the same estimation accuracy of soil moisture, so in terms of speed and computation of view less than EnKF.2,Statistical distribution of the two samples are quite different along the process, the EnKF edge of the sample set of probability density distribution always remains a single peak distribution, but the SIR method has experienced a sample of particles from one peak to two and then to a distribution of changes process.3,EnKF assimilation scheme to improve estimates of soil moisture profiles and also to calculate the surface sensible heat and latent heat flux is closer to EnKF observaty value.Based on the result, EnKF can basically meet the land surface data assimilation system to assimilate the requirements of algorithms can be used for assimilation of surface soil moisture observations (or microwave or temperature) to estimate the soil moisture profile.