A mesoscale convection dynamic tracking method based on new generation of stationary satellite data

Abstract

The invention provides a mesoscale convection dynamic tracking method based on new generation of stationary satellite data. The method comprises the following steps of 1, identifying a potential MCS;2, tracking a potential MCS; and using an area overlapping method and a Kalman filtering scheme comprehensively; for the convection with small area and high moving speed in the newborn period and theextinction period, adopting a KF method to carry out forward tracking. for convection with a large scale in a mature period or convection with a slow moving speed, adopting an area overlapping methodAOL for tracking, and in the process of tracking by using an area overlapping method, judging whether the cloud cluster is split and merged according to the overlapping rate of the pMCSs on the frontand rear frames of cloud pictures, and when the KF scheme is used for tracking, using the average motion vector of the surrounding pMCS for assigning an initial value to the KF tracking scheme; and 3,screening out the real MCS. The method has the advantages that the medium-scale convection system is accurately grabbed, and particularly the convection system which is very small, high in moving speed and incapable of being grabbed by a traditional scheme enables the tracked convection life cycle to be more complete.

Description

technical field [0001] The invention belongs to the technical field of convective dynamic tracking, and in particular relates to a mesoscale convective dynamic tracking method based on new-generation stationary satellite data. Background technique [0002] Mesoscale convective systems, namely MCS, are closely related to the occurrence and development of disastrous weather (extreme precipitation, hail, tornado, etc.). Traditional ground-based observations are difficult to effectively capture MCS due to the limitation of temporal and spatial resolution. The emergence of a new generation of geostationary satellite data makes it possible to dynamically track the occurrence and development of MCS. The traditional method of dynamically tracking MCS is often the AOL solution. This scheme is effective in tracking larger cloud clusters, but has difficulty capturing smaller, less developed MCS, and faster-moving convection. [0003] Specifically, the AOL scheme, that is, the area o...

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Problems solved by technology

[0006] Because at the initial moment, the random initialization method is usually used to assign initial values ​​to the Kalman filtering scheme in the existing schemes, so the prediction results of the Kalman filtering scheme are very unstable in the first few steps, and the algorithm needs to be iterated to a certain number of steps. will gradually converge, which leads to the fact that the small convection at the initial time cannot be accurately captured by the Kalman filtering scheme, and there may be many matching errors

[0007] In addition, in Huang et al. (2018), the area overlap rate in the AOL scheme adopts a single threshold; the splitting and merging of convection is not considered in the existing scheme, and the above factors significantly affect the integrity of the dynamic tracking of mesoscale convection. sex

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