The covariance matrix estimated from the ensemble data assimilation always suffers from filter collapse because of the spurious correlations induced by the finite ensemble size. The localization technique is applied to ameliorate this issue, which has been suggested to be effective. In this paper, an adaptive scheme for Schur product covariance localization is proposed, which is easy and efficient to implement in the ensemble data assimilation frameworks. A Gaussian-shaped taper function is selected as the localization taper function for the Schur product in the adaptive localization scheme, and the localization radius is obtained adaptively through a certain criterion of correlations with the background ensembles. An idealized Lorenz96 model with an ensemble Kalman filter is firstly examined, showing that the adaptive localization scheme helps to significantly reduce the spurious correlations in the small ensemble with low computational cost and provides accurate covariances that are similar to those derived from a much larger ensemble. The investigations of adaptive localization radius reveal that the optimal radius is model-parameter-dependent, vertical-level-dependent and nearly flow-dependent with weather scenarios in a realistic model; for example, the radius of model parameter zonal wind is generally larger than that of temperature. The adaptivity of the localization scheme is also illustrated in the ensemble framework and shows that the adaptive scheme has a positive effect on the assimilated analysis as the well-tuned localization.

Extreme learning machine (ELM) algorithm, which becomes more and more popular in the area of artificial intelligence for the past few years, is faster than the traditional machine learning algorithms, especially than the single hidden layer feed-forward neural networks (SLFNs). However, ELM is merely commonly used in the field of computer science or other hot areas. This paper investigates the ability of ELM to emulate the atmospheric nonlinear systems. The performance of ELM on emulating the nonlinear chaotic system-Lorenz63 is analyzed. The results show that ELM can accurately and quickly simulate the Lorenz63 forecast field at different forecast length, and thus providing a new idea for solving kinds of atmospheric nonlinear equations.

Many schemes of interpolation can be used for mapping from various coordinates of the numerical weather predictions (NWP) to the vertical coordinates of data assimilation systems. While most of the schemes do not imply the availability of a corresponding vertical mapping between the coordinates, even they are not implemented expediently. This leads to very noisy gradient with large unrealistic spikes which are simulated by the fast radiance transfer(RT) model for the first guest values from the numerical weather system and, consequently to incorrect data assimilation. The memorandum analyses a new algorithm of piecewise weighted integral interpolation applied in the Radiative Transfer for TIROS Operation Vertical Sounders (RTTOV). Experimental results show that the internal interpolation incorporated into RTTOV can map the coordinate of NWP model to the coordinate of data assimilation system flexibility, and can use the atmospheric information of the profiles on coordinate of NWP model sufficiently. Also the internal RTTOV interpolation can avoid "blind" levels derived from the nearest-neighbor log-linear interpolator and successfully retain the smooth structure of the radiance gradients.

ITM Web of Conferences, open-access proceedings in information technology, computer science and mathematics

In order to overcome the shortcomings of traditional spectral filtering method which cannot reflect the local characteristics of the spatial scales, a more efficient noise filtering technique in ensemble data assimilation is needed. In this paper, by introducing the improved threshold value and constraint parameter, a constrained wavelet threshold de-noising method (CWTD-NM) is designed to filter out the noise swamped in different scales. An ideal experiments group is implemented based on the two-dimensional barotropic vorticity equation. The best wavelet basis function (Db11) and the optimal decomposition level (five) are chosen firstly. The numerical results show that, since the wavelet coefficients are constrained by parameter α, the CWTD-NM can produce better filtering results with the smallest root mean square error (RMSE). Moreover, the filtering accuracy of 10 ensemble samples’ variances of the CWTD-NM is equivalent to the result estimated directly from 80 ensemble samples, while the running time is only about 1/7 of the latter. It also shows that Peak Signal to Noise Ratio (PSNR) performs well, and has a good corresponding relationship to the minimum RMSE.