Method for predicting atmospheric visibility

Abstract

The invention proposes a method for predicting the atmospheric visibility. According to collected historical meteorological data, a new meteorological data set is established; a support vector machineis trained by using the new meteorological data set to obtain a support vector machine training model; a decision-making tree is trained by using the new meteorological data set to obtain a decision-making tree model; to-be-predicted data are normalized and the processed data are inputted to the trained support vector machine training model, the support vector machine training model carries out classification and then the processed data are inputted into the corresponding trained decision-making tree model to obtain final atmospheric visibility prediction data. According to the invention, with combination of the support vector machine and the decision-making tree, preliminary visibility classification is carried out and then the atmospheric visibility is predicted accurately. The operation is simple; the historical meteorological observation data only need to be processed slightly and the model is trained, so that the visibility is predicted.

Description

technical field [0001] The invention relates to the fields of statistical learning and meteorology, in particular to a method for forecasting atmospheric visibility. Background technique [0002] In the field of meteorology, visibility forecasting is not only used for weather analysis of daily meteorological departments, but also widely used in highways, aviation, navigation and other transportation departments, military and other fields. [0003] Daytime visibility refers to the maximum horizontal distance that a person with normal vision can see and distinguish the outline of the target object from the sky background under the current weather conditions. Visibility observation is a decisive reference for judging the phenomenon and intensity of visual range obstruction. Accurate visibility observation can effectively ensure the normal operation of the transportation industry; on the other hand, it is also an important physical quantity that characterizes the degree of low-l...

AI Technical Summary

Problems solved by technology

Obviously, this method has great limitations. On the one hand, it is related to the geographical conditions and reference objects of the weather station; on the other hand, it is affected by the subjective judgment of the testers.

Currently, instrumental methods mainly use transmissometers and forward scatter instruments. Forward scatter is highly o

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