The Establishment And Study Of A Random Velocity Field Model Of Annual Precipitation

Precipitation is one of the most important material resources in the development of human society and natural ecology.The change of precipitation directly affects the runoff of rivers and people’s lives.When the precipitation is too much,it is easy to lead to floods,landslides,and other natural disasters,which will pose a great threat to human life safety.When the precipitation is too small,it will cause a serious shortage of water for people’s life and industry,and agriculture,which can lead to drought disasters,etc.Therefore,it is of great significance to analyze and study precipitation and its changes.In this paper,a spatiotemporal model which can predict the precipitation is proposed considering the two dimensions of temporal and spatial,and a random velocity field(RVF)model with the explainable multi-step prediction for the variation of precipitation is proposed by using the gradient learning of the spatiotemporal velocity of Gaussian random field.On this basis,the predictive process is used to make a downscaling and multi-step prediction,and the Bayesian estimation of RVF model parameters can be obtained by the Markov Chain Monte Carlo(MCMC)method.The main research contents of this paper are as follows:1.Literature review and analysis on the research results of precipitation and climate change are carried out,and the necessary knowledge required in this paper is briefly introduced.Based on the existing literature,the variable of altitude(height)is introduced to establish the Spatio-temporal linear model of annual precipitation.The spatial random effect is simulated by Gaussian random field,and the temporal effect is simulated by a linear method.To further improve the prediction effect of the model,we add the predictive process to get the annual precipitation prediction model.For the parameters in the model,we derive a posteriori distribution and use the MCMC method to estimate it.Through the simulation experiment,it can be found that the effect of the model is very significant and has high precision.2.Based on the prediction model of annual precipitation,the random velocity field model is obtained by introducing the gradient of precipitation against temporal and spatial.The RVF model is used to analyze the maximum gradient direction and the minimum annual precipitation velocity in six different regions of five continents.Studies have shown that the RVF model is efficient and accurate,with a trend of annual precipitation movement observed across a variety of real-world datasets,and that most people are moving inland from the coast to maintain the same annual precipitation.Based on the direction of the maximum gradient and the rate of change of minimum annual precipitation,we can further explore future strategies for urban flood control and agricultural development.