Adaptive localization method of satellite data assimilation in vertical direction and ensemble Kalman filter weather assimilation forecast method

Abstract

The invention discloses an adaptive localization method of satellite data assimilation in the vertical direction and an ensemble Kalman filter weather assimilation forecast method. The adaptive localization method calculates the correlation coefficient between the observation data and the model variable according to the arbitrary observation data and model variables given in the ensemble Kalman filter assimilation system; The original localization function; the position p of the satellite observations is estimated from the profile of the correlation coefficient o , and place the original localization function at p o The maximum value of the GC function at the location is fitted to obtain the influence range c of satellite observations o . position p o , the scope of influence c o That is, the adaptive localization parameter required by the present invention. The obtained adaptive localization parameters are used to forecast typhoons in regional models. Compared with the forecast results without the use of the present invention, the error of the forecast results relative to the observations is significantly reduced, and at the same time, the use of the present invention also significantly improves the rapidity of typhoons. Enhancement phase forecast.

Description

technical field [0001] The invention relates to a weather assimilation forecasting method, in particular to an ensemble Kalman filter weather assimilation forecasting method based on adaptive localization technology, which uses adaptive localization technology to correct the existing ensemble Kalman 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 method for data assimilation, but it is affected by sampling errors when applied to high-dimensional atmospheric models, and localization can deal with sampling errors. Localization generally assumes that correlations farther away from observations are more likely to be spurious. The commonly used localization function is Gaspari and Cohn (Gaspari and Cohn1999) function, referred to ...

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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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