Visibility forecasting model based on multi-meteorological-factor intelligent deep learning

Abstract

The invention discloses a visibility forecasting model based on multi-meteorological-factor intelligent deep learning, which mainly comprises the steps of numerical forecasting mode selection, spatial grid point multi-meteorological-factor modeling, feature extraction and visibility mapping, neural network model training, visibility forecasting, model parameter updating and the like. According to the method, the deep learning technology is combined with numerical forecasting and multi-meteorological-factor modeling, so that refined visibility forecasting of key regions can be realized.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence and meteorological forecasting, and particularly relates to the forecasting of meteorological factors, the extraction of meteorological factor features, the detection of meteorological factors and visibility, and the forecasting of refined visibility, etc. Combined refined visibility forecasting method. Background technique [0002] The current visibility forecasting methods mainly include numerical model-based visibility forecasting methods, statistical-based visibility forecasting methods, and visibility forecasting methods based on machine learning. Numerical model forecasting method is to build a meteorological numerical model system based on the current atmospheric state and mathematical and physical principles, so as to simulate and forecast various meteorological elements. In recent years, with the development of observation technology and computer technology, the accuracy ...

AI Technical Summary

Problems solved by technology

Although there are a large number of methods for forecasting visibility, there are still few fine-grained visibility forecasting methods for key areas using deep learning technology

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