沙漠与绿洲气象2024，Vol.18Issue(4)：159-166,8.DOI:10.12057/j.issn.1002-0799.2024.04.021
麦田不同高度日极端气温预测技术研究
Study on Prediction Technology of Daily Extreme Temperature at Different Heights in Wheat Field
摘要
Abstract
Using the observation data from January 2019 to June 2022 from the wheat field microclimate automatic observatory and the national basic meteorological observatory in Qihe county,the models of multiple linear regression and BP neural network are established in order to predict daily maximum and daily minimum temperature at various heights(30,60,and 150 cm)in the wheat field under different weather conditions.The accuracy and performance of these models are evaluated by comparing the predicted values with microclimate data,HRCLDAS grid temperature data,and grid temperature forecast data provided by the China Meteorological Administration.The results indicate that both models are capable of meeting the temperature forecasting requirements for the winter wheat field.The BP neural network model demonstrates higher prediction accuracy compared to the multiple linear regression model.Specifically,both models perform best in predicting the daily maximum temperature at a depth of 150 cm on sunny days,with mean absolute errors of 0.5℃,root mean square errors of 0.6℃,and 100%accuracy rate for each model.However,both models perform worst in predicting the daily maximum temperature at a depth of 30 cm on sunny days.The predicted values of daily maximum and daily minimum temperatures,varying across weather types and altitudes,align well with the HRCLDAS grid temperature data.The root mean square errors range from 2.0℃to 3.9℃and 1.9℃to 4.1℃,respectively.Conversely,there is a larger error when compared with the grid temperature forecast data,with root mean square errors ranging from 2.4℃to 5.1℃and 2.4℃to 5.3℃for these two models.The multiple linear regression model demonstrates better consistency with both the HRCLDAS grid temperature data and the grid temperature forecast data compared to the BP neural network model.关键词
麦田/多元线性回归/BP神经网络/气温预测/HRCLDAS/智能网格预报Key words
wheat fields/multiple linear regression/BP neural network/forecast of temperature/HRCLDAS/intelligent grid prediction分类
天文与地球科学引用本文复制引用
朱保美,李密,张继波,黄丙玲..麦田不同高度日极端气温预测技术研究[J].沙漠与绿洲气象,2024,18(4):159-166,8.基金项目
山东省气象局气象科研引导类项目(2021SDYD25) （2021SDYD25）
山东省气象局气象科研重点项目(2023sdqxz12) （2023sdqxz12）
科技创新2030—重大项目(2022ZD0119503) （2022ZD0119503）
德州市气象局科研项目(2023dzqxyb09) （2023dzqxyb09）
