Design Of A High-precision Surface Temperature Sensor With Solar Radiation Error Correction Functio

In the observation process of automatic weather station,in order to reduce the impact of solar radiation on the temperature sensor,the sensor probe is usually placed in the louver box,natural ventilation radiation shield or forced ventilation radiation shield.Although the louver box and natural ventilation anti radiation cover can reduce the radiation error to about 1 ℃,the reduction effect of radiation error is not ideal under the condition of low wind speed and high solar radiation intensity.Although the forced ventilation radiation shield can effectively reduce the radiation error,it is vulnerable to flying insects,rain and snow in harsh areas in the field.At the same time,it also has the limitations of high energy consumption and high price.Therefore,the development of a temperature sensor with low solar radiation error and low cost is of great significance for ground meteorological observation.In view of the above requirements,a new surface temperature sensor is designed in this paper.The piezoelectric ceramic vibrator is used to drive the sensor probe to swing,so as to speed up the air flow and promote the dissipation of radiant heat,so as to reduce the radiation error.Firstly,using computational fluid dynamics(CFD)method,aiming at many factors affecting the temperature measurement accuracy of the sensor,such as solar radiation intensity,solar altitude angle,air velocity,altitude and underlying surface reflectivity,the radiation error results of the sensor are quantified in various physical environments.The results show that the radiation error increases with the increase of solar radiation intensity,altitude,solar altitude angle and underlying surface reflectivity,and decreases significantly with the acceleration of air flow velocity.It is verified that using the vibration of piezoelectric vibrator to accelerate the air velocity around the sensor is effective to reduce the radiation error.In order to obtain a continuous radiation error correction equation,the BP neural network algorithm is used to fit 592 groups of discrete data obtained by simulation.The results show that the fitting accuracy of the algorithm is high.The average absolute error between the predicted value and the sample value is 0.0028 ℃,and the root mean square error is 0.0509 ℃.Finally,taking the actual measured value of 076 B forced ventilation temperature sensor as the reference temperature,the field comparison experiment is carried out to verify the temperature measurement accuracy of the new surface temperature sensor.The experimental results show that the average absolute error and root mean square error between the corrected sensor temperature measurement value and the 076 B standard value are 0.03 ℃ and 0.035 ℃respectively.It shows that the new surface temperature sensor can effectively reduce the radiation error.In order to facilitate the research of simulation and experimental data,this paper uses Java Script,HTML5,CSS3 and other languages to make a radiation error query and correction system.The system can query the radiation error under various conditions and correct the measured value.At the same time,the user interface is made by Echarts,which can compare and display the corrected measured value with the uncorrected measured value in real time,which is convenient for users to query and study the radiation error.