Adaptive localization method of satellite data assimilation in vertical direction and ensemble Kalman filtering weather assimilation forecasting method

Abstract

The invention discloses an adaptive localization method of satellite data assimilation in the vertical direction and an ensemble Kalman filtering weather assimilation forecasting method. The adaptivelocalization method comprises the following steps: calculating correlation coefficients of observation data and mode variables according to any observation data and mode variables given in the ensemble Karman filtering assimilation system; estimating an original localization function of the observation data and the mode variable by utilizing the grouped correlation coefficients; and estimating theposition po of satellite observation according to the profile of the correlation coefficient, and fitting the original localization function with the maximum value of the GC function at the po position to obtain the influence range co of satellite observation, wherein the position po and the influence range co are the adaptive localization parameters solved by the invention. The obtained adaptivelocalization parameters are used for forecasting typhoon in a regional mode, compared with the forecasting result without the method, the forecasting result has the advantages that the error is obviously reduced compared with the observation error, Meanwhile, the forecasting of the typhoon quick enhancement stage is obviously improved by using the method.

Description

technical field [0001] The present invention relates to a weather assimilation forecast method, in particular to an ensemble Kaman filter weather assimilation forecast method based on adaptive localization technology, which adopts adaptive localization technology to correct the existing ensemble Kaman filter assimilation system, to improve weather forecast results. Background technique [0002] Data assimilation is a technique that uses observations to correct model variables to obtain the best estimate of the current state of the atmosphere. [0003] Ensemble Kalman filtering is a commonly used data assimilation method, but when it is applied to high-dimensional atmospheric models, it will be affected by sampling errors, and localization can deal with sampling errors. Localization generally assumes that correlations that are farther away from the observation are more likely to be spurious. The commonly used localization function is the Gaspari and Cohn (Gaspari and Cohn19...

AI Technical Summary

Problems solved by technology

[0004] However, for non-local observations such as satellite observations, the observation position and vertical influence range are not well defined, and localization cannot be performed directly

At the same time, for observations at different times and in different regions, the localization function of satellite observations should also be different, but the existing technology cannot adaptively estimate the required localization function

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