Rapid updating mixing assimilation method based on time lag set

Abstract

The invention provides a rapid updating mixing assimilation method based on a time lag set. According to a characteristic that a service value forecast system assimilates observation data in a high frequency mode and outputs a forecast field in the high frequency mode, in order to effectively introduce a flow-dependent background error covariance and simultaneously effectively reducing a calculated amount brought by ensemble forecast, the flow-dependent background error covariance obtained and calculated by the time lag set formed by a same moment forecast field obtained by initial fields of different moments based on a history sample is combined with a modeling static background error covariance of a three-dimensional variation so as to expect that assimilation and forecast effects of a current value forecast system based on a variation assimilation method are increased under the condition that calculating cost and storage cost are not increased or only increased a little.

Description

Technical field: [0001] The invention belongs to the category of atmospheric science, and relates to a fast-updating hybrid assimilation method based on time-lag sets. Background technique: [0002] Social and economic development has high requirements for numerical weather prediction, and the effect of numerical weather prediction largely depends on the accuracy of the initial field. How to obtain an appropriate initial field has always been a very important task in the research of numerical weather prediction. Operational numerical prediction centers often use data assimilation methods to estimate or optimize initial fields. For sudden strong convective weather, such as torrential rain, it is necessary to assimilate high-frequency data at short time intervals based on the latest observation data, so that the initial field can contain effective information of the weather system as much as possible (Benjamine et al. 2004). [0003] At present, the widely used data assimilat...

AI Technical Summary

Problems solved by technology

However, the hybrid assimilation method still needs a certain amount of ensemble forecast results as calculation samples at each assimilation time, which will still bring a lot of calculation pressure to some research and business units whose calculation conditions are not very sufficient, and even affect business. Forecast efficiency

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