Non-orthogonal ensemble forecasting initial value disturbance algorithm

Abstract

The invention discloses a non-orthogonal ensemble forecasting initial value disturbance algorithm, and particularly relates to the field of ensemble forecasting initial value disturbance algorithms, which comprises the following steps of: S1, for a given moment t0, superposing a small disturbance on a numerical mode initial analysis field; S2, forwards integrating the disturbed initial field and the undisturbed initial field to the t1 moment in the mode at the same time; S3, subtracting the control forecast from the disturbance forecast obtained at the moment t1; S4, scaling the output moduleobtained in the previous step at proper time to enable the output module to be consistent with the initial disturbance according to a certain norm; S5, superposing the zoomed disturbance, namely an input module, onto the t1 moment analysis value; and S6, repeating the steps from S2 to S5. The method is specially used for ensemble forecasting of a weather system with high resolution or convection scale, can fully consider the spatial locality and independence of underlying surface meteorological element non-uniformity and ensemble forecasting disturbance growth from the aspect of theoretical design, and has certain originality.

Description

technical field [0001] The present invention relates to the technical field of initial value perturbation algorithm for ensemble forecasting, and more specifically, the present invention relates to a non-orthogonal initial value perturbation algorithm for ensemble forecasting. Background technique [0002] At present, the existing ensemble forecast initial value disturbance generation technology commonly used in the industry is as follows: the European Center for Medium-Range Weather Forecast ECMWF conducts operational ensemble forecasting based on the singular vector technology (Singular Vectors, referred to as SVs) of the tangent linear model and the adjoint model. The environmental forecasting center NCEP successively adopted the Breeding of Growing Modes (BGM) and the ensemble transposition method ET as the initial perturbation technique. Since 2016, the initial perturbation technique of the NCEP global ensemble forecast system has been updated to the ensemble Kalman filt...

AI Technical Summary

Problems solved by technology

As a result, when these theories are applied to very high resolution ensemble forecasts or convective resolvable scale ensemble forecasts, the dispersion of ensemble forecasts is often low and the reliability of probability forecasts is low.

[0003] To sum up, the problems existing in the existing technology are: the existing technology uses orthogonality to solve the problem of divergence or independence among ensemble forecast members, and the effect Still not obvious, the reason may be that they did not get rid of the idea of ​​global orthogonality, that is, they did not jump out of the category of global Lyapunov exponent

[0004] Difficulty and significance of solving the above technical problems: The current initial disturbance generation method has low dispersion of ensemble forecasts, which may be related to orthogonality, and they are all based on orthogonality , it is necessary to explore a new initial perturbation generation theory for ensemble forecasting to deal with the generation of initial members for high-resolution or convectively resolvable scale ensemble forecasting. The theoretical value of the algorithm is significant, so a non-orthogonal initial value perturbation algorithm for ensemble forecasting is needed

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