Tropical cyclone intensity forecast information generation method and system

A tropical cyclone and information generation technology, applied in forecasting, climate sustainability, instruments, etc., can solve the problems of low forecasting accuracy, improve forecasting accuracy, intelligentize and objectify forecasting processing, and improve tropical cyclone forecasting The effect of the system

Pending Publication Date: 2020-07-28
AEROSPACE INFORMATION RES INST CAS
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Problems solved by technology

The forecast error of statistical dynamic forecast is smaller than that of numerical simulation forecast. However, statistical dynamic forecast cannot solve all problems, and usually there will be large deviations
[0003] Although there is also a method for predicting the statistical dynamic i

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  • Tropical cyclone intensity forecast information generation method and system
  • Tropical cyclone intensity forecast information generation method and system
  • Tropical cyclone intensity forecast information generation method and system

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[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0046] The terms "first" and "second" in the specification and claims of the present invention and the above drawings are used to distinguish different objects, rather than to describe a specific order. Furthermore, the terms "comprising" and "having", and any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, product or apparatus comprising a series of steps or units is not defined by listed steps or u...

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Abstract

The invention discloses a tropical cyclonic strength forecast information generation method and system, and the method comprises the steps: obtaining to-be-processed data according to different data dimensions, carrying out the preprocessing of the to-be-processed data to obtain initial data, carrying out the prediction of the initial data through a preset forecast model to obtain tropical cyclonic strength forecast information. The initial data comprises a climate duration characteristic factor, an environment characteristic factor and a brainstorm characteristic factor. According to the method, influence factors on the tropical cyclone can be fully utilized, and the tropical cyclone intensity prediction information is predicted in combination with the preset prediction model, so that prediction processing is more intelligent and objective, a tropical cyclone prediction system is perfected, and the prediction accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of meteorological information processing, in particular to a method and system for generating tropical cyclone intensity forecast information. Background technique [0002] Tropical cyclone is one of the important severe weather systems, which can cause serious economic losses. For this reason, in order to solve the problem of tropical cyclone intensity forecasting, researchers have established various forecasting methods, the most common of which are continuous weather forecasting, dynamical forecasting, statistical dynamical forecasting, and ensemble forecasting. The forecast error of statistical dynamic forecast is smaller than that of numerical simulation forecast. However, statistical dynamic forecast cannot solve all problems, and usually there will be large deviations. [0003] Although there are also methods for predicting the statistical dynamic intensity of tropical cyclones based on machine learn...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/26
CPCG06Q10/04G06Q50/26Y02A90/10
Inventor 刘健靳晴文简洪登杜小平范湘涛
Owner AEROSPACE INFORMATION RES INST CAS
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