Method for realizing error prediction by combining tangent method and tropical cyclone prediction path

Abstract

The invention relates to a method for realizing error prediction by combining a tangent method and a tropical cyclone prediction path. The method comprises the steps of providing a current position point and a plurality of prediction nodes with the quantity of an integer number, creating an error range circle for each prediction node, joining tangent lines of the error range circles of two adjacent different prediction nodes by utilizing a circular tangent method and adopting the current position point as a starting point, forming an error range circle, and effectively verifying the accuracy of the future prediction data through the error range circle. The accuracy of the future prediction data is effectively verified through the error range circle, so that the application reference added value of the prediction data can be improved, and a more-intuitive exhibition application effect can be realized. Meanwhile, the controllability of the prediction range of the future prediction data by tropical cyclone prediction personnel can be improved, the prediction of the dotted line is upgraded to the regional prediction, and the prediction amplitude and anticipation degree are more complete.

Description

technical field [0001] The invention relates to the field of flood control informatization and meteorological application, in particular to an application method for realizing a forecast error range by using a tangent method combined with a tropical cyclone forecast path. Background technique [0002] When displaying the existing tropical cyclone track and individual forecast track, the following steps are usually used to display: [0003] 1. Receive information such as longitude, latitude, central air pressure, central wind speed, moving speed, and influence wind circle at each time point released by the meteorological department; [0004] 2. Receive the forecast longitude, latitude, central pressure, central wind speed and other information for the next 24 hours, 48 ​​hours and 72 hours; [0005] 3. The historical path data and forecast path data of each time moment are formed into a point set arranged in time sequence; [0006] 4. Through the point set, the path node me...

AI Technical Summary

Problems solved by technology

However, there are usually large errors in the forecast path, which may deviate from the original forecast line, which reduces the

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