Development And Application Of Local Hourly Surface Temperature Prediction Model

In order to ascertain the impact of different properties of the underlying surface on surface temperature,to explain the physical mechanism of surface temperature change,and to provide new ideas for the prediction of surface temperature,this paper collected two types of underlying surfaces,bare soil and cement,at the Meteorological Station of Nanjing University of Information Science and Technology.We collected winter meteorological data of the mat type,analyze the characteristics of the surface temperature change of different underlying surfaces,explore the influence of the thermal characteristics of the underlying surface on the change of the surface temperature,and propose a prediction formula for the surface temperature,and give the corresponding parameter values for different underlying surfaces.On this basis,the meteorological data of highway temperature in Jiangsu Province and the meteorological data of Fenkou Station of Chun'an Tea are collected,and the surface temperature forecast equations are applied to analyze the characteristics of surface temperature changes,and the parameters are determined and the forecast effect is verified.The neural network method is used to compare and analyze the advantages and disadvantages of the two methods.The main conclusions are:?1?Through the study of the temperature characteristics of the two underlying surfaces of the test field under different weather conditions,based on the principle of surface energy balance,the equation of surface heat conduction,and the relationship equation between net radiation and temperature at night,we stablish a land surface temperature prediction equation based on the daily real-time temperature observation information.Rational surface temperature change prediction equation.Use the thermal characteristics of the underlying surface to calculate the values of parameters B and B₀ that can be used for the prediction of surface temperature,and compare them with the parameters obtained from the inversion of the measured values of meteorological observations.The parameters obtained by the two methods agree well,so they can be used.The parameters obtained from the inversion of the measured values are applied to the surface temperature prediction equation.And it is concluded that the value of B₀ is related to the conditions of the underlying surface?,c,and?,that is,when the underlying surface changes with time and space,the value of B₀ will change accordingly.The B value takes into account the impact of the local microclimate on the basis of B₀,which is related to R_n.As the temperature changes,there is a conclusion that the absolute value of the B value becomes larger as the overall climate gradually warms.?2?Applying the surface temperature prediction equation on expressways in Jiangsu Province,selecting 13 stations for pavement temperature forecasting,and giving parameters applied to pavement temperature forecasting of the expressway.The results show that the predicted values have high correlation coefficients with the simulated values,the average deviation and The standard deviation is small,and the model is efficient.The deviation between the predicted value and the actual measured value is more than 50%within 1?,and the deviation between the predicted value and the actual measured value is more than 90%within3?,which indicates that the prediction equation has good practicability and high prediction accuracy.The prediction result is reliable and can be Used to predict the temperature of highway pavement.?3?Applying the surface temperature prediction equation at the tea tea Fenkou station to make tea tree canopy temperature predictions.In the tea tree picking season from February to April,the tea tree canopy prediction equation is established and its parameters are determined in 2012,and 2013 and 2014 are used.The data is verified,and the results show that the correlation between the predicted value and the simulated value is high,the average deviation and standard deviation are small,and the model is efficient.The tea tree canopy prediction equation has good practicability and high prediction accuracy,and the prediction results are reliable.It can be used to predict the early spring tea tree canopy temperature and provide a scientific basis for the early spring tea tree frost disaster forecasting and early warning.?4?Compared with the neural network method,the surface temperature prediction model proposed in this paper is more accurate in predicting the road surface temperature and tea canopy temperature obtained by this method.The model has strong mechanism and better simulation prediction effect.