Abstract
Data assimilation has been widely applied in atmospheric and oceanic forecasting systems and particle filters (PFs) have unique advantages in dealing with nonlinear data assimilation. They have been applied to many scientific fields, but their application in geoscientific systems is limited because of their inefficiency in standard settings systems. To address these issues, this paper further refines the statistical observation and localization scheme which used in the classic localized equivalent-weights particle filter with statistical observation (LEWPF-Sobs). The improved method retains the advantages of equivalent-weights particle filter (EWPF) and the localized particle filter (LPF), while further refinements incorporate the effect of time series on the reanalyzed data into the statistical observation calculations, in addition to incorporating the statistical observation proposal density into the localization scheme to further improve the assimilation accuracy under sparse observation conditions. In order to better simulate the geoscientific system, we choose an intermediate atmosphere-ocean-land coupled model (COAL-IC) as the experimental model and divide the experiment into two parts: standard observation and sparse observation, which are analyzed by the spatial distribution results and root mean square error (RMSE) histogram. In order to better analyze the characteristics of the improved method, this method was chosen to be analyzed in comparison with the localized weighted ensemble Kalman filter (LWEnKF), the LPF and classical LEWPF-Sobs. From the experimental results, it can be seen that the improved method is better than the LWEnKF and LPF methods for various observation conditions. The improved method reduces the RMSE by about 7% under standard observation conditions compared to the traditional method, while the advantage of the improved method is even more obvious under sparse observation conditions, where the RMSE is reduced by about 85% compared to the traditional method. In particular, this improved filter not only combine the advantage of the two algorithms, but also overcome the computing resources.
Highlights
The success of data assimilation strategies in oceanography and the domains of geoscience has stimulated current efforts to exploit the Monte Carlo filter for data assimilation.These algorithms attempt to use the Monte Carlo filter calculate the probability density based on the observations, and it is usually excepted the errors for model states and observations are Gaussian [1]
The results of localized weighted ensemble Kalman filter (LWEnKF) are slightly better than the LEWPF-Sobsold. It can be seen from the figures, LEWPF-Sobs and LWEnKF have similar results, but the root mean square error (RMSE) of LWEnKF is smaller in some regions of the Ta and the assimilation results of LEWPF-Sobs are better than LWEnKF for the variables Ts
This paper describes the application of the improved LEWPF-Sobs for data assimilation in the intermediate coupled model while avoiding the limitations of traditional particle filters
Summary
The success of data assimilation strategies in oceanography and the domains of geoscience has stimulated current efforts to exploit the Monte Carlo filter for data assimilation. In difference from previous studies, this study uses an intermediate coupled model to examine the advantages of the improved LEWPF-Sobs over the classic algorithm It has been given explicitly in the previous paper [15] that in the 40-variable model of Lorenz 96, the experimental results provide that the classic LEWPF-Sobs when using fewer particles can obtain better assimilation results than the traditional LPF, and has a significant advantage when dealing with non-Gaussian observations. The descriptions of the LPF, traditional LEWPF-Sobs and the improved statistical observation calculations and localization schemes are given in Sections 2 and 3, which describe the set-up of cycling data assimilation experiments which allow different algorithms in the intermediate coupled model with different observation operators.
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