Neural network short-term and temporary rainfall forecasting method integrating foundation GNSS water vapor and meteorological elements

Abstract

The invention discloses a neural network short-term and temporary rainfall forecasting method integrating foundation GNSS water vapor and meteorological elements. The method comprises the following steps: (1) acquiring the foundation GNSS water vapor; (2) calculating an atmospheric stability index; (3) preprocessing data, including gross error data elimination, data interpolation and data normalization processing; (4) identifying rainfall forecasting factors; (5) carrying out NARX neural network design: taking the rainfall forecasting factor and the actual rainfall data determined in the step(4) as an input layer, taking the predicted rainfall data as an output layer, and adopting default values or initial parameters for the number of hidden layers, the number of hidden layer neurons, input and output delay orders and a neural network algorithm; (6) carrying out neural network training; (7) optimizing input parameters, and constructing a multi-factor short-term and temporary rainfallforecasting model; and (8) evaluating the precision of the newly constructed multi-factor short temporary rainfall forecasting model by utilizing the reserved verification data set. According to the method, a reasonable and accurate multi-factor short temporary rainfall forecasting model is established, so that the short temporary rainfall can be accurately forecasted.

Description

technical field [0001] The invention belongs to the technical field of meteorological forecasting, in particular to a neural network short-imminent precipitation forecasting method that integrates ground-based GNSS water vapor and meteorological elements. Background technique [0002] Heavy rainfall is an important weather phenomenon, especially large-scale continuous or concentrated torrential rain, which often causes flood disasters and seriously threatens the lives and properties of local people and infrastructure such as reservoirs, seawalls, embankments, and drainage pipes. , heavy precipitation is also one of the important sources of local water resources. Therefore, real-time monitoring and accurate forecasting of short-term precipitation is of great significance to reduce property losses and improve water resource utilization. Heavy precipitation is usually associated with wet convective processes, which are characterized by large short-term changes in water vapor c...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to make up for the problem in the prior art that only relies on a single factor of atmospheric water vapor to cause a high error forecast rate of short-term precipitation, and propose a neural network short-term precipitation forecast method that integrates ground-based GNSS water vapor and meteorological elements. Methods By establishing a reasonable and accurate multi-factor short-imminent precipitation forecasting model, the accurate forecast of short-imminent precipitation is realized

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